panarchy: understanding transformations in human and natural systems

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Panarchy

Panarchy Understanding Transformations in

Human and Natural Systems

Edited by Lance H. Gunderson and C. S. Holling

ISLAND PRESS Washington • Covelo • London

Copyright© 2002 Island Press

All rights reserved under International and Pan-American Copyright Conventions. No part of this book may be reproduced in any form or by any means without permission in writing from the publisher: Island Press, 1718 Connecticut Ave., N.W., Suite 300, Washington, DC 20009.

ISLAND PRESS is a trademark of the Center for Resource Economics.

Library of Congress Cataloging-in-Publication Data Panarchy : understanding transformations in human and natural systems I edited by Lance H. Gunderson, C. Holling.

p. em. Includes bibliographical references (p. ).

ISBN 1-55963-856-7 (cloth: alk. paper)-- ISBN 1-55963-857-5 (paper: alk. paper)

1. Human ecology. 2. Political ecology. 3. Social ecology. 4. Nature-Effect of human beings on. 5. Biotic communities. 6. Environmental degradation. 7. Environmental policy. 8. Environmental management. I. Gunderson, Lance H. II. Holling, C. S.

GF49 .P365 2001 304.2--dc21

The graphic editor for this book was Pille Bunnell.

British Cataloguing-in-Publication Data available.

Printed on recycled, acid-free paper. @

Manufactured in the United States of America 10 9 8 7 6 5 4 3

CoNTENTS

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

List of Boxes

Preface

Chapter 1.

Chapter 2.

Chapter 3.

Chapter 4.

Chapter 5.

Chapter 6.

XIX

XX1

Part I. Introduction

In Quest of a Theory of Adaptive Change . . . . . . . . . . . . 3 C. S. Holling, Lance H. Gunderson, and Donald Ludwig

Part II. Theories of Change

Resilience and Adaptive Cycles .................... 25 C. S. Holling and Lance H. Gunderson

Sustainability and Panarchies . . . . . . . . . . . . . . . . . . . . . 63 C. S. Holling, Lance H. Gunderson, and Garry D. Peterson

Why Systems of People and Nature Are Not Just Social and Ecological Systems ......... 103 Frances Westley, Stephen R. Carpenter, William A. Brock, C. S. Holling, and Lance H. Gunderson

Back to the Future: Ecosystem Dynamics and Local Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . 121 Fikret Berkes and Carl Folke

The Dynamics of Political Discourse in Seeking Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . 14 7 Lowell Pritchard Jr. and Steven E. Sanderson

Part ill. Myths, Models, and Metaphors

Chapter 7. Collapse, Learning, and Renewal ................. 173 Stephen R. Carpenter, William A. Brock, and Donald Ludwig

VII

VITI CONTENTS

Chapter 8. Dynamic Interaction of Societies and Ecosystems-Linking Theories from Ecology, Economy, and Sociology . . . . . . . . . . . . . . . . . . . . . . . . 195 Marten Scheffer, Frances Westley, William A. Brock, and Milena Holmgren

Chapter 9. A Future of Surprises .......................... 241 Marco A. Janssen

Chapter 10. Resilience and Sustainability: The Economic Analysis of Nonlinear Dynamic Systems ............ 261 William A. Brock, Kari-Giiran Mi:iler, and Charles Perrings

Part Iv. Linking Theory to Practice

Chapter 11. Resilient Rangelands-Adaptation in Complex Systems ............................. 293 Brian Walker and Nick Abel

Chapter 12. Surprises and Sustainability: Cycles of Renewal in the Everglades . . . . . . . . . . . . . . . 315 Lance H. Gunderson, C. S. Holling, and Garry D. Peterson

Chapter 13. The Devil in the Dynamics: Adaptive Management on the Front Lines . . . . . . . . . . 33 3 Frances Westley

Chapter 14. Planning for Resilience: Scenarios, Surprises, and Branch Points . . . . . . . . . . . . . . . . . . . . . 361 Gilberta C. Gallopin

Part V. Summary and Synthesis

Chapter 15. Discoveries for Sustainable Futures ............... 395 C. S. Holling, Stephen R. Carpenter, William A. Brock, and Lance H. Gunderson

Chapter 16. Toward an Integrative Synthesis .................. 419 Ralf Yorque, Brian Walker, C. S. Holling, Lance H. Gunderson, Carl Folke, Stephen R. Carpenter, and William A. Brock

Appendix A. A Model for Ecosystems with Alternative Stable States ........................ 439

CONTENTS IX

Appendix B. Optimizing Social Utility from Lake Use . . . . . . . . . . . 441

Appendix C. Tax as a Way to Direct Society ................... 443

Appendix D. Collective Action Problems and Their Effect on Political Power 445

References 449

List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

Index .................................................. 493

LIST OF FIGURES

Figure 1-1. Depictions of four myths of nature . . . . . . . . . . . . . . . . . . . 11

Figure 2-1. A stylized representation of the four ecosystem functions and the flow of events among them . . . . . . . . . . . . 34

Figure 2-2. Resilience is another dimension of the adaptive cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 3-1. Everglades alligator hole ........................... 64

Figure 3-2. Everglades landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Figure 3-3. South Florida physiographic and land-use patterns . . . . . . . . 64

Figure 3-4. Southern Florida ................................. 65

Figure 3-5. The Gulf of Mexico bioregion ...................... 65

Figure 3-6. The planet Earth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 3-7. Hierarchy of vegetation, landform structures, and atmospheric processes for the Everglades system . . . . . . . . . . . . . . . 67

Figure 3-8. Time and space scales of the boreal forest . . . . . . . . . . . . . . 68

Figure 3-9. A stylized panarchy ............................... 74

Figure 3-10. Panarchical connections .......................... 75

Figure 3-11. Decision hierarchies in the boreal forest .............. 87

Figure 3-12. Maladaptive systems ............................. 95

Figure 4-1. Institutional hierarchy of rule sets . . . . . . . . . . . . . . . . . . . 111

Figure 5-1. Conceptual framework for the analysis of linked social-ecological systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

XI

XII LIST OF FIGURES

Figure 5-2. Local and traditional management practices of the exploitation and conservation phases of the adaptive renewal cycle .................................. 125

Figure 5-3. Local and traditional management practices that emulate disturbances or creative destruction phases of the adaptive cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Figure 5-4. Local and traditional management or "back-loop" practices of the release and reorganization phases of the adaptive renewal cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Figure 5-5. Memory is in the panarchy ........................ 137

Figure 5-6. Three generic responses to resource and environmental crisis ....................................... 142

Figure 6-1. Alternate discourses in environmental management ............................................. 158

Figure 7-1. Rate of population growth or harvest versus population size for a fish stock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 6

Figure 7-2. Equilibria of a fisheries model with two stable states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

Figure 7-3. Flowchart of the lake fishery model ................. 179

Figure 7-4. Adaptive cycle from the lake fishery model 180

Figure 7-5. Flowchart of the agriculture and water quality model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

Figure 7-6. Typical time series for a 1,000-year simulation from the agriculture and water quality model

Figure 7-7. Adaptive cycle from the agriculture and

184

water quality model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

Figure 7-8. Management at equilibrium when ecosystem parameters are known with zero error and policies are followed perfectly by all agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

Figure 7-9. Adaptive cycle with experimentation from the agriculture and water quality model . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

LIST OF FIGURES XIII

Figure 8-1. Schematic representation of four possible responses of ecosystems to stress imposed by human use . . . . . . . . . . . 196

Figure 8-2. Graphical model for alternative stable states in shallow lakes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

Figure 8-3. Equilibrium state of ecosystem as a function of imposed stress, as in Figure 8-lC ............................. 203

Figure 8-4. "Marble-in-a-cup" representation of the stability properties of lakes at five different levels of nutrient loading .......................................... 204

Figure 8-S. Cycles can occur if stress (S) responds in a dynamic way to ecosystem state such that below a critical level of the state indicator (E'",) stress decreases slowly whereas above that level it increases ........................... 207

Figure 8-6. At a critical level of fish predation, plankton cycles hit the border of the basis of attraction of the alternative stable state with high algal biomass, in a homoclinic bifurcation point (01) ••••••••••••••••••••••••••••• 209

Figure 8-7. Graphical models showing how a theoretical society of ecosystem Enjoyers and Affectors may obtain optimal welfare from use of an ecosystem . . . . . . . . . . . . . . . . . . . . . . 215

Figure 8-8. Tax as a way to reduce stress (a) imposed on the ecosystem by the activity of Affectors to a desired level a* . . . . . . . . 218

Figure 8-9. Game theory predicts that the effort individuals invest in political pressure to reach the goal of the group depends on "noticeability" felt by the group member and on the perceived effectiveness of the pressure on changing policy in the desired direction . . . . . . . . . . . . . . 221

Figure 8-10. Differences in efficiency at mobilizing political pressure (see Figure 8-8) distort the process of social welfare optimization (Figure 8-7B) ....................... 222

Figure 8-11. An example of social network structures ............ 233

Figure 8-12. Domain transformations that usually occur after the emergence of a problem ........................ 234

XIV LIST OF FIGURES

Figure 9-1. Categories representing four different cultural perspectives ....................................... 246

Figure 9-2. Four-phase heuristic for institutional dynamics

Figure 9-3. Time course dynamics of (A) economic output, (B) C02 emissions, and (C) temperature change under

250

different climate change scenarios ............................ 255

Figure 9-4. Time course dynamics of (A) economic output, (B) C02 emissions, and (C) temperature change under different climate change scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 56

Figure 9-S. Schematic overview of the framework linking the real world, institutions, and myths of nature

Figure 9-6. The effect of learning on (A) economic output, (B) C02 emissions, and (C) temperature change compared

257

between the utopian and dystopian cases ....................... 258

Figure 9-7. The average values of economic output (top chart) and temperature change (bottom chart) in 2100 for three possible worlds where agents do learn and adapt .......... 259

Figure 10-1. Convexity of a production set with a single input (labor) ........................................ 264

Figure 10-2. Convexity of a production set with two inputs (capital and labor) ......................................... 265

Figure 10-3. The optimal allocation of resources with convex preference and production sets ......................... 266

Figure 10-4. The optimal allocation of resources with a nonconvex production set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

Figure 10-S. Hysteresis effects in the management of shallow lakes ............................................. 277

Figure 10-6. Phase diagram for the open-loop Nash equilibrium ......................................... 277

Figure 11-1. The functional responses of two landscapes to increasing levels of stress or disturbance ............. 301

LIST OF FIGURES XV

Figure 11-2. Complex adaptive system framework for national-scale processes affecting rangelands .................... 304

Figure 11-3. Complex adaptive system framework for regional-scale processes affecting rangelands .................... 305

Figure 11-4. Complex adaptive system framework for local-scale processes affecting rangelands ....................... 305

Figure 12-1. Time course of water management in the Everglades as represented by successive iterations of the adaptive cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316

Figure 12-2. Generalized adaptive cycle applied to resource management policy ................................ 327

Figure 12-3. Panarchy of linked ecological-social system

Figure 13-1. Four processes represented as four balls, which an effective manager who seeks to harness

331

complexity must juggle simultaneously ........................ 338

Figure 13-2. Use of separate adaptive cycles to depict phases of issues as interpreted in four systems-political, organizational, interorganizational, and individual . . . . . . . . . . . . . . . . 3 55

Figure 13-3. Example of how the dynamics of one problem domain can spread to those of another 358

Figure 14-1. The three pillars of decision making for sustainable development .................................... 362

Figure 14-2. Intersection between physical feasibility and decision processes ..................................... 363

Figure 14-3. Transitions among global futures .................. 373

Figure 14-4. Reference Scenario ............................. 374

Figure 14-5. Policy Reform Scenario ......................... 376

Figure 14-6. Breakdown Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 81

Figure 14-7. Fortress Scenario .............................. 382

Figure 14-8. Ecocommunalism Scenario 383

Figure 14-9. New Sustainability Paradigm Scenario .............. 384

XVI LIST OF FIGURES

Figure 14-10. Scenarios as mapped on the adaptive cycle 389

Figure 14-11. Does the path to sustainability imply adaptive cycles that are smaller, shorter, and more manageable? .............. 392

Figure 16-1. Any regional project for development or sustainability has a mixture of conservation, business development, and community empowerment objectives 420

Figure 16-2. Interdisciplinary triangles of practice and theory . . . . . . 421

Figure 16-3. General structure of the simulation models of people and nature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422

LIST OF TABLES

Table 1-1. Characteristics of alternative views or myths of nature ........................................... 12

Table 3-1. Representative key variables and speeds in seven classes of systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Table 9-1. The characteristic elements of complex ecological-economic systems ................................ 245

Table 9-2. Characteristics of cultural perspectives 248

Table 9-3. Different combination of functioning of nature and management styles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

Table 9-4. Assumptions for implementing perspectives in an economy-climate model ................................. 254

Table 11-1. Local-scale adaptations and resilience within four rangeland regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

Table 11-2. Regional-scale adaptations and resilience within four rangeland regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311

Table 11-3. National- and state-scale adaptations and resilience within four rangeland regions . . . . . . . . . . . . . . . . . . . . . . . . 311

Table 12-1. Institutional reconfigurations in response to ecological crises in the Everglades ............................ 324

Table 12-2. Attributes of human groups that dominate activities in four phases of the adaptive cycle as it applies to resource management policy ........................ 328

Table 15-1. Summary findings from the assessment of resilience in ecosystems, economies, and institutions .............. 396

Table 15-2. Some examples of the four phases of the adaptive cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400

XVII

LIST OF BOXES

Box 1-1. Fishing down the Food Web .......................... 4

Box 1-2. The Raft-A Metaphor of Stability and Resilience ......... 16

Box 2-1. Spruce-Fir Forests and Insect Outbreaks ................ 30

Box 2-2. Alternative Stable States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Box 2-3. Loss of an Alternative State? . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Box 2-4. Quasi-Alternate States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Box 2-5. The Telephone Great Fits the Figure Eight? ............. 56

Box 3-1. Malaria and Adaptive Dynamics ........................ 70

Box 3-2. An Invasive Species (Imperata cylindrica) and Human Exploitation Change a Panarchy . . . . . . . . . . . . . . . . . . . . . . . . 82

Box 3-3. Revolution and Rebellion ............................ 93

Box 3-4. The Hindu Caste System and the Hierarchy Trap . . . . . . . . . 97

Box 4-1. Mercury Contamination in Grassy Narrows, Ontario, Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

Box 4-2. Climate and Human Evolution . . . . . . . . . . . . . . . . . . . . . . . 112

Box S-1. Sacred Groves: Securing a Recruitment of Seeds and Maintaining Landscape Patchiness . . . . . . . . . . . . . . . . . . . 134

Box 5-2. Culture and Remembrance: Providing Guidance for the Milpa Renewal Cycle 136

Box 5-3. The Cree Relearn How to Treat the Caribou Properly ...... 139

XIX

XX LIST OF BoXES

Box 7-1. Maximum Sustained Yield 175

Box 10-1. Representation of Preference Functions ............... 262

Box 10-2. Social Welfare Function ........................... 269

Box 16-1. Backward- and Forward-Looking Models .............. 428

Box 16-2. Failure of Naive Backward-Expectation Models ......... 430

PREFACE

T his book resulted from a three-year project to advance the theory, policy, and practice involved in resolving issues that emerge from the interaction between people and nature. We sought to identify how

economic growth and human development depend upon joint attributes of ecosystems and institutions. We also sought ways to identify, monitor, and maintain those attributes or, if they have been eroded, to restore them. We based much of this research on the ecological notion of resilience and its corollary precepts in social sciences. Hence, we dubbed our group the Resilience Network.

The theme of resilience of ecosystems, flexibility of institutions, and incentives in economies emerged in a sequence of "Asko" meetings on an island of the same name in the Swedish archipelago, sponsored by the Beijer Institute of the Swedish Academy of Sciences. These meetings have brought together economists and natural scientists to explore similarities and differences in views and experiences of change. Their conclusions, summarized in an article in Bioscience (F olke 199 5) and in another in Science (Arrow et al. 1995), have been that economic growth is not inherently good or bad, that economic growth cannot in the long term compensate for declines in environmental quality, and that the growing scale of human activities is encountering the limits of nature to sustain that expansion.

The familiar responses to these issues are often flawed because the theories of change underlying them are inadequate. The stereotypical economist might say "get the prices right" without recognizing that price systems require a stable context where social and ecosystem processes behave

XXI

XXII PREFACE

"nicely" in a mathematical sense (i.e., are continuous and convex). The stereotypical ecologist might say "get the indicators precise and right" without recognizing the surprises that nature and people inexorably and continuously generate. The stereotypical engineer might say "get the engineering controls right, and we can eliminate those surprises" without recognizing the inherent uncertainty and unpredictability of the evolving nature of the interaction between people and nature.

Such simple prescriptions, based on bad or insufficient theory, are attractive because they seem to replace inherent uncertainty with the spurious certainty of ideology, precise numbers, or action. The theories implicit in these examples ignore multistable states. They ignore the possibility that the slow erosion of key controlling processes can abruptly flip an ecosystem or economy into a different state that might be effectively irreversible. In an ecosystem, this might be caused by the gradual loss of species in a keystone set that together determine structure and behavior over specific ranges of scale. In a resource-based economy, it might be caused by the implementation of maximum sustained-yield policies that reduce spatial diversity, that evolve ever narrower economic dependencies, and that develop more rigid organizations. The ultimate pathology of such traditional resource exploitation and management examples is to create less resilient ecosystems, more rigid institutions, and deeper social dependencies.

Three fundamental themes underly our exploration in this volume. One emerges from the mathematics and metaphors of stability, resilience, and change reviewed. These begin to identify where to seek measures of resilience, and they help define conditions for qualitatively different types of stability loss, for reversibility and irreversibility in a form that has relevance for both economies and ecosystems.

The second theme recognizes that cross-scale interactions occur in nature, ranging from centimeters and days in the dynamics of photosynthesis through kilometers and decades for disturbance processes that shape patterns on landscapes to hundreds of kilometers and millennia for geomorphological processes. Such cross-scale interactions also occur in human affairs from the individual to the community, to the nation and region, and to international patterns of relationships. When the scales of human affairs become decoupled from those of nature, signals of change are eliminated and the learning that such signals can generate begins to wither.

The third theme is one of adaptive change and learning. Cycles of slow accumulation of natural and cultural capital-in an ecosystem, an institution, or a society-are interspersed with rapid phases of reorganization where, for transient moments, novelty can emerge to become subsequently entrained. This is the least developed theme in ecology, economics, or the social sciences, but its identification and refinement are a necessary foundation for identifying the sources and sinks for novelty and renewal.

PREFACE XXITI

Stricdy on Island Time

The ideas in this volume were developed, tested, and modified in a series of workshops with the participants in the Resilience Network. Every workshop was held on an "island"-where we were in a sense isolated from the outside world and free to explore, argue, contrast, and test the concepts that are presented in this volume. The first workshop was held on the island of Ulvon in Sweden. Subsequent meetings led us to Little St. Simons Island in Georgia, USA; then to the Malilangwe Reserve in Zimbabwe; to a contentious but ultimately constructive interchange in Gozo, Malta; and finally to a coral atoll, Heron Island, in the Great Barrier Reef in Australia.

At the first workshop, we developed an unusual mode of workshop evaluation-the limerick-which was continued throughout the series of workshops. Each participant would submit limericks that contained at least two keywords-the name of the meeting venue and the word resilience. Each workshop ended with a contest whereby all submitted limericks were read and judged to determine a winning jingle. Those limericks capture the spirit of the group-where laughter, creativity, and rigor all had equal play. We include two of the revealing rhymes:

From Ulvon came work on resilience To bring the Beijer Institute millions But the economists swore, We wrote that before In streaks of neoclassical brilliance -L. Pritchard Jr.

Malilangwe has huge wildlife herds With resilience and other big words A system so stable It proved fully able To cope with a network of nerds -R. DuToit

Acknowledgments

This book is the result of a generous grant from the John D. and Catherine T. MacArthur Foundation to the University of Florida and the Beijer International Institute for Ecological Economics. Dan Martin, Caren Grown, and Priya Shyamsundar played critical roles as grantors, and we are indebted to them all for supporting the project "Resilience in Ecosystems, Economies and Institutions." Dan Martin not only approved a grant to support this synthesis, but also facilitated and advanced it because of his abiding conviction

XXIV PREFACE

that integrative theory must be the foundation for sustainable practice. Other fiscal support was provided from the University of Florida.

A marvelous group of colleagues and friends helped us argue through, expand, and illustrate these ideas. Many of them contributed to this volume and provide insights from their own perspectives and words to shape it into its present form.

It needed Carl Folke, Fikret Berkes, Frances Westley, Nick Abel, and Elinor Ostrom to educate us about property rights and the social devices that mediate relationships between people and nature;

Buz Brock, Partha Dasgupta, and Karl-Goran Maler to show us the deep roots and foundations in economics that resonate with ecological dynamics;

Steve Carpenter, Brian Walker, Bengt-Owe Jansson, Marten Scheffer, John Pastor, and Ariel Lugo to broaden and deepen our understanding of ecosystems and of ecosystem management; and John Holland, Marco Janssen, and Gilberto Gallopin to introduce us to the revelations of complex adaptive systems theory.

Pille Bunnell, as ever, understood our ideas and turned them into images that both communicated them to a larger audience and deepened our own understanding in the process.

We are particularly grateful to Garry Peterson, Don Ludwig, Lee Gass, Steve Sanderson, and Rusty Pritchard for their ever constructive suggestions, profound contributions, and sharing of discovery.

We would also like to acknowledge and thank our local hosts for memorable workshops-Kevin and Debbie Mcintyre at Little St. Simons; Jeremy Anderson, Dave Cumming, Russell Taylor, Raoul DuToit, and Ivan Bond in Zimbabwe. Karl-Goran Maler receives a special acknowledgement for his hosting and sharing of northern Swedish cuisine (fermented herring!).

Toni Carter at the University of Florida played such a pivotal role that it is likely the whole project would have failed without her cheerful and effective organizational contributions. From tracking our expenses to tracking our luggage and arranging workshops in foreign lands, her contribution was truly immense and one for which we are deeply grateful. Christina Leijonhufvud and Astrid Auraldsson at the Beijer Institute also provided hard work to ensure that the administration before and after workshops was flawless and efficient.

Of course, no thanks would be too much for RalfYorque, whose contributions ensure at least ecological beatification sometime this century.

Finally, to our spouses: To Bev, who survived bicycle crashes, flashing scientists, and

transoceanic flights to share her joy of life; And to lise for moderating, focusing, and energizing Buzz Holling's own

seven-year adaptive cycle of creative destruction and discovery.

Lance Gunderson and Buzz Holling Atlanta, Georgia, and Cedar Key, Florida

Part I

Introduction

CHAPTER 1

IN QUEST OF A THEORY

OF ADAPTIVE CHANGE

C. S. Holling, Lance H. Gunderson, and Donald Ludwig

In all things, the supreme excellence is simplicity. ·-Henry Wadsworth Longfellow

I n the last decades of the twentieth century, cascades of changes occurred on a global scale. Collapse of the former Soviet Union and its continuing struggle for stability and for ways to restructure have propagated interna

tional reverberations far beyond its borders. Increases in connectivity through the Internet are stimulating a flowering of novel experiments that are affecting commerce, science, and international community. Migrations of people, some forced by political upheaval and some initiated as a search for new opportunity, are both threatening and enriching the international order. There have been dramatic changes in global environmental systems-from climate change that is already upon us, to the thinning of the stratospheric ozone layer. Novel diseases have emerged in socially and ecologically disturbed areas of the world and have spread globally, through the increased mobility of people. The tragedy of AIDS, and its origins, transformation, and dispersion because of land-use and social changes, is a signal of deep and broad changes that will yield further surprises and crises. More and more evidence indicates that global climate change has already produced an increase in severe weather that, combined with inappropriate coastal development, has caused dramatic rises in insurance claims and human loss of life. Still other more subtle changes linking ecological, economic, and social forces are occurring on a global scale, such as the typical example described in Box 1-1, regarding the collapse of fisheries.

These examples of global environmental change signal that the stresses on the planet have achieved a new level because of the intensity and scale of human activities. Are these activities leading to a world with impoverished natural endowments, even deeper inequities among peoples, and the ulti-

3

4 HOLLING, GUNDERSON, LUDWIG

Box 1-1. Fishing down the Food Web

D. Ludwig

Although total catch levels for marine fisheries have been relatively stable in recent decades, analysis of the data shows that landings from global fisheries have shifted from large piscivorous fishes toward smaller invertebrates and planktivores (Pauly et al. 1998). This shift can be quantified through assignment of a fractional trophic level to each species, depending on the composition of the diet. The values of these trophic levels range from 1 for primary producers to over 4.6 for a few top predators such as a tuna in open water and groupers and snappers among bottom fishes. For data aggregated over all marine areas, the trend over the past forty-five years has been a decline of the mean trophic level from over 3.3 to less than 3.1. In the Northwest Atlantic, the mean trophic level is now below 2.9. There is not much room for further decreases, since most fish have trophic levels between 3 and 4. Indeed, many fisheries now rely on invertebrates, which tend to have low trophic levels.

Global trends appear to show a decline of 0.1 trophic level per decade. This is an underestimate of the actual change, since data from many areas, especially in the tropical developing countries, are lumped into categories such as "mixed fishes" that do not reflect changes in trophic level. Moreover, the analyses performed so far did not consider the decline in trophic level that occurs within species due to the increased removal of older fishes, which tend to have higher trophic levels than the young of the same species. It is likely that a continuation of present trends will lead to widespread fisheries collapses. These trends cast doubt on the idea of estimating future catches by extrapolation of present trends.

The costs of this devastation are difficult to observe since the massive exploitation of stocks is often associated with a displacement of small-scale traditional fisheries by large industrial ones. The small fishers are then jobless, and they move to cities. The costs of this conversion of members of society from being productive to being unproductive are borne by the society as a whole and are not ascribed to displacement from the fishery.

mate collapse of civil society? Or is that too easy a conclusion? Contradicting projections of collapse is the possibility that human foresight and innovation can reverse those trends and develop paths that sustain natural diversity and create opportunity.

1. IN QUEST OF A THEORY OF ADAPTIVE CHANGE 5

We do not intend to evaluate the degradation and potential for collapse of human and natural systems in this book. That has been done as well and as objectively as can be expected elsewhere (McNeill2000). Even raising the question triggers controversy that is not particularly well founded on objective fact or adequate theory.

Instead, our purpose is to develop an integrative theory to help us understand the changes occurring globally. We seek to understand the source and role of change in systems-particularly the kinds of changes that are transforming, in systems that are adaptive. Such changes are economic, ecological, social, and evolutionary. They concern rapidly unfolding processes and slowly changing ones-gradual change and episodic change, local and global changes.

The theory that we develop must of necessity transcend boundaries of scale and discipline. It must be capable of organizing our understanding of economic, ecological, and institutional systems. And it must explain situations where all three types of systems interact. The cross-scale, interdisciplinary, and dynamic nature of the theory has lead us to coin the term panarchy for it. Its essential focus is to rationalize the interplay between change and persistence, between the predictable and unpredictable. Thus, we drew upon the Greek god Pan to capture an image of unpredictable change and upon notions of hierarchies across scales to represent structures that sustain experiments, test results, and allow adaptive evolution.

We start the search for sufficient theory by turning to examples where there is adequate history-examples of interactions between people and nature at regional scales. There we see patterns of change that are similar to the more recent global ones-but examples where there has been more history of response. These include dramatic changes in the ecosystems and landscapes of ecosystems, with subsequent changes for society and economic conditions. There have been spasms of biodiversity loss as a consequence of the intersection of climate extremes, poor land use, and global economic pressures. In places, such as in some nations in southeast Africa, these exacerbate political instability. The results are not only erosion of the natural world but also erosion of trust in the institutions of governance. But in other places there has been notable learning. Degraded systems have been restored, organizations restructured, and management revitalized.

How do we begin to track down the cause of the failures and explain the occasional successes? Consider some recent resource management failures:

• Some fisheries have collapsed in spite of widespread public support for sustaining them and the existence of a highly developed theory of fisheries management.

• Moderate stocking of cattle in semiarid rangelands has increased vulnerability to drought.

• Pest control has created pest outbreaks that become chronic.

6

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HOLLING, GUNDERSON, LUDWIG

Flood control and irrigation developments have created large ecological and economic costs and increasing vulnerability.

A number of cases point to a common cause behind such examples of failure of management of renewable resources (Holling 1986; Gunderson et al. 1995a). In each case, a target variable (fish stock, meat production, pest control, or water level) is identified and successfully controlled. Uncertainty in nature is presumed to be replaced by certainty of human control. Social systems initially flourish from this ecological stabilization and resulting economic opportunity. But that success creates its own failure.

We now know that the stabilization of target variables like these leads to slow changes in other ecological, social, and cultural componentschanges that can ultimately lead to the collapse of the entire system. A pattern of events emerges: at the extreme, the ecological system fails, the economic system reconfigures, and the social structures collapse or move on. Moderate, stabilized grazing by cattle reduces the diversity of the rangeland grasses, which eventually leads to fewer drought-resistant species, less permeable soils, and poor water retention. Pest control leads to more luxuriant growth of the host plants and hence creates more favorable conditions for survival and reproduction of the pest. Effective flood control leads to higher human settlement densities in the fertile valleys and a large investment in vulnerable infrastructure. When a large flood eventually overwhelms the dams and dikes, the result is often a dramatic reconfiguration of the social and economic landscape along the river. And, as described in Box 1-1, the initial success of fisheries leads to an increase in investment and overexploitation of the resource. When the fish stock shows signs of distress, management agencies become paralyzed, the public loses trust in governance, and human institutions are unable to make the required adjustments.

The pattern common to these examples leads to the first of two paradoxes that complicate any quick and easy predictions of collapse and disaster:

• Paradox 1. The Pathology of Regional Resource and Ecosystem Management

Obseroation: New policies and development usually succeed initially, but they lead to agencies that gradually become rigid and myopic, economic sectors that become slavishly dependent, ecosystems that are more fragile, and a public that loses trust in governance.

The Paradox: If that is as common as it appears, why are we still here? Why has there not been a profound collapse of exploited renewable resources and the ecological services upon which human survival and development depend?

The observed pattern of failure can be analyzed from an economic and human behavioral standpoint. According to one view, re-


sources are appropriated by powerful minorities able to influence public policy in ways that benefit them. Hence inappropriate measures such as perverse subsidies are implemented that deplete resources and create inefficiencies (Magee, Brock, and Young 1989). A fundamental cause of the failures is the political inability to deal with the needs and desires of people and with rent seeking by powerful minorities.

But as part of the fundamental political causes of failure, there are, as well, contributing causes in the way many, including scientists and analysts, study and perceive the natural world. Their results can provide unintended ammunition for political manipulation. Some of this ammunition comes from the very disciplines that should provide deeper and more integrative understanding, primarily economics, ecology, and institutional analysis. That leads to the second paradox: the trap of the expert. So much of our expertise loses a sense of the whole in the effort to understand the parts.

• Paradox 2. The Trap of the Expert

Observation: In every example of crisis and regional development we have studied, both the natural system and the economic components can be explained by a small set of variables and critical processes. The great complexity, diversity, and opportunity in complex regional systems emerge from a handful of critical variables and processes that operate over distinctly different scales in space and time.

The Paradox: If that is the case, why does expert advice so often create crisis and contribute to political gridlock? Why, in many places, does science have a bad name?

We begin unraveling these paradoxes with an examination of the obstacles that arise not just from multiple, competing scientific perspectives but also from disciplinary hubris. The complex issues connected with the notion of sustainable development are not just ecological problems, or economic, or social ones. They are a combination of all three. Actions to integrate all three typically shortchange one or more. Sustainable designs driven by conservation interests can ignore the need for a kind of economic development that emphasizes synergy, human ingenuity, enterprise, and flexibility. Those driven by economic and industrial interests can act as if the uncertainty of nature can be replaced with human engineering and management controls, or can be ignored altogether in deference to Adam Smith's "invisible hand" of the perfect market. Those driven by social interests often presume that nature or a larger world presents no limits to the imagination and initiative of local groups.


Compromises among those viewpoints can be arrived at through the political process. However, mediation among stakeholders is irrelevant if it is based on ignorance of the integrated character of nature and people. The results may be momentarily satisfying to the participants but ultimately reveal themselves as based upon unrealistic expectations about the behavior of natural systems and the behavior of people. As investments fail, the policies of government, private foundations, international agencies, and nongovernmental organizations flop from emphasizing one kind of partial solution to another. Over the last three decades, such policies have flopped from large investment schemes to narrow conservation ones to, at present, equally narrow community development ones.

Each approach is built upon a particular worldview or theoretical abstraction, though many would deny anything but the most pragmatic and nontheoretical foundations. The conservationists depend on concepts rooted in ecology and evolution, the developers on variants of free-market models, the community activists on precepts of community and social organization. All these views are correct, in the sense of being partially tested and credible representations of one part of reality. The problem is that they are partial. They are too simple and lack an integrative framework that bridges disciplines and scales.

Partial Truths and Bad Decisions

The fields of economics, ecology, and organizational or institutional analysis have developed tested insights. Yet there is growing evidence that the partial perspectives from these disciplines generate actions that are unsustainable. One way to generate more robust foundations for sustainable decision making is to search for integrative theories that combine disciplinary strengths while filling disciplinary gaps. But before we can begin such a task, we should examine the partial constructs that characterize these fields.

Economics

Modern neoclassical economics has gone far in discovering the process whereby millions of decisions made by individuals give rise to emergent features of communities and societies (e.g., the rate of inflation, productivity gains, the level of national income, prices, stocks of various types of capital, cultural values, and social norms). Two factors make economic theory particularly difficult. First, individual decisions at any moment are themselves influenced by these emergent features and by past decisions. Learning, practice, and habit influence the moment as much as present prices do. Second, the emergent features that can be well handled by standard neoclassical economic theory and policy concern only fast-moving variables that define present conditions. The more slowly emergent properties that affect attitudes, culture, and institutional arrangements are recognized but are poorly


incorporated. The high discounting commonly employed in applications of neoclassical economic theories does not allow the possibilities beyond a decade or two in the future to influence present decisions.

Economists know that success in achieving financial return from fast dynamics leads to slowly emergent, nearly hidden, changes in deeper and slower structures, changes that ultimately trigger sudden crisis and surprise. But the complexities that arise are such that many modern economists are frustrated in their attempts to understand the interactions between fast- and slow-moving variables that create emergent dynamics (Stiglitz 1998). Chapters 7, 8 and 10 begin to expose the consequences and solutions.

Ecology

Ecosystem ecologists, on the other hand, have made it plain for a long while that some of the most telling properties of ecological systems emerge from the interactions between slow-moving and fast-moving processes and between processes that have large spatial reach and processes that are relatively localized. Those interactions are not only nonlinear; they generate alternating stable states and normal journeys of biotic and abiotic variables through those states. Those journeys-measured in decades and centuriesmaintain the diversity of species, spatial patterns, and genetic attributes. They maintain the resilience of ecological systems.

Variability in ecosystems is not merely an inconvenient characteristic of these productive, dynamic systems. It is essential for their maintenance. Ecologists are beginning to understand the way that variability and diversity are created by and sustain ecosystems because of interactions among slow and fast processes, large and small. Both Chapters 2 and 3 review and expand that understanding. Reducing variability and diversity produces conditions that cause a system to flip into an irreversible (typically degraded) state controlled by unfamiliar processes.

But ecologists limit their understanding and propose inadequate actions by largely ignoring the realities of human behavior, organizational structures, and institutional arrangements that mediate the relationships between people and nature.

Institutions and Organizations

Institutional and organizational theory and analysis do consider such features but in a largely static sense. They often stop short of the required integration of the three fields of inquiry. Institutional and organizational theory currently provides a fascinating understanding of the variety of arrangements and rules that have evolved in different societies to harmonize the relation between people and nature. Social scientists have gone far in describing the way people store, maintain, and use knowledge in stable circumstances. But they have not attended to the processes that control and


maintain these institutions dynamically, the kind of dynamic causation that is present in economics and ecology.

In order to plan for sustainability, we need to know, and we need to integrate, how information is evaluated and counterproductive information rejected. How is new "knowledge" created from competing information sources and incorporated with useful existing knowledge? Which processes create novelty, which smother innovation, which foster it? Those questions are explored in Chapters 4, 5, and 13. Neither ecology, nor economics, nor institutional theory now deals well with these fundamental questions of innovation, emergence, and opportunity. That is what evolutionary theory is about.

Evolution and Complex Systems

The emergence of novelty that creates unpredictable opportunity is at the heart of sustainable development (Holling 1994b). Biological evolutionary theory-which can be expanded to include cultural evolution-deals with just this process. The new field of complexity studies sees ecological, economic, and social systems as being similar to biological processes that generate variability and expose the patterns that result to selective forces. But, like each of the other fields, the representations are partial. They are detached from deep knowledge of the key natural and human processes, and from convincing tests of the adequacy and credibility of the results.

In this book we argue that the process of developing policies and investments for sustainability requires a worldview that integrates ecological with economic with institutional with evolutionary theory-that overcomes disconnects due to limitations of each field. But as compelling and easy as it is to criticize disciplinary gaps, they are clearly not the only reason for unsustainable practices. There are other, deeper limitations that arise from worldviews that people hold. These worldviews are also partial representations of reality: representations that are valuable because they provide temporary certitude to allow action, but whose partial nature ultimately exposes their inadequacy. They are caricatures of aspects of reality.

Caricatures of Nature

Although some of the failures of complex resource systems are due to limitations in disciplinary theories and experience, others can be traced to differences among the worldviews or myths that people hold. In this section we identify at least five such caricatures that underlie explanations of how nature works and the implications of those assumptions on subsequent policies and actions (Figure 1-1 ). Each of these caricatures, or myths, leads to different assumptions about stability, different perceptions of the processes that affect that stability, and different policies that are deemed appropriate (Table 1-1 ). We begin with the most static view: that of a nature lacking stabilizing forces-"Nature Flat."


···· .... ~

metaphor trajectory

Figure 1-1. Depictions of four myths of nature: (A) Nature Flat, (B) Nature Balanced, (C) Nature Anarchic, and (D) Nature Resilient. Each myth has three representations or metaphors: as stability landscape (left), phase diagram (center), and time-course chart or trajectory of key system variables over time (right).

Nature Flat. In this view, "flat" is used to describe a system in which there are few or no forces affecting stability. There are therefore few limitations on the ability of humans to change nature. There are no feedbacks or consequences from nature of human actions. It is much like rolling a ball around on a cookie sheet (Figure 1-1 A). The processes that affect the position of the ball-i.e., state of nature-are random or stochastic. In such a view of nature, policies and politics are random as well, often described as "garbage can" politics (March and Olsen 1989; Warglien and Masuch 1996). It is a nature that is infinitely malleable and amenable to human control and domination if only the "right" values and the "right" timing are chosen. The issues of resource use, development, and control are identified as issues that are exclusively of human action, issues that can be resolved by community activism or stakeholder control. Alternatively, it can be a view of comucopian nature where human ingenuity and knowledge surmount all obstacles to produce exponential growth. Such a "flat worlder" view is not wrong, just incomplete. There are indeed strong stochastic elements; the timing of decisions is important. Human ingenuity is a powerful force for change.


Table 1-1. Characteristics of Alternative Views or Myths of Nature

Stability Processes Policies Consequence

Nature Flat none stochastic random trial and error

Nature globally stable negative optimize or pathology of Balanced feedback return to surprise

equilibrium

Nature Anarchic globally positive precautionary status quo unstable feedback principle

Nature Resilient multiple stable exogenous maintain recovery at states input and variability local scales or

internal adaptation; feedback structural

surprise

Nature Evolving shifting multiple scales flexible and active learning stability and actively and new landscape discontinuous adaptive, institutions

structures probing

Nature Balanced. The second myth is a view of nature existing at or near an equilibrium condition (Figure 1-1 B). That equilibrium can be a static one or a dynamic one. Hence if nature is disturbed, it will return to an equilibrium through (in systems terms) negative feedback. Nature appears to be infinitely forgiving. It is the myth of maximum sustainable yield and of achieving fixed carrying capacities for animals and humanity. It imposes a static goal on a dynamic system. This view of nature underpins prescriptions for logistic growth, where the issue is how to navigate a looming and turbulent transition-demographic, economic, social, and environmental-to a sustained plateau. This is the view of several organizations with a mandate for reforming global resource and environmental policy-of the Brundtland Commission, the World Resources Institute, the International Institute of Applied Systems Analysis, and the International Institute for Sustainable Development. Many individuals in these and similar institutions are contributing skillful scholarship and policy innovation. They are among some of the most effective forces for change, but the static assumptions can create the very surprise and crisis they wish to avoid. The "balanced worlder" view is


also not wrong-just incomplete. There are indeed, forces of balance in the world, forces that can become overwhelmed.

Nature Anarchic. If the previous myth is one where the system stability could be defined as a ball at the bottom of a cup, this myth is one of a ball at the top of a hill (Figure 1-1 C). It is globally unstable. It is a view dominated by hyperbolic processes of growth and collapse, where increase is inevitably followed by decrease. It is a view of fundamental instability, where persistence is possible only in a decentralized system where there are minimal demands on nature. It is the view of Schumacher (1973) and some environmentalists. If the Nature Flat view assumes that infinitely ingenious humans do not need to learn anything different, this view assumes that humans are incapable oflearning. This is implicit in the writings ofTenner (1996), where he argues that all technology that is unleashed will eventually "bite back." This view presumes that small is beautiful, because the inevitable catastrophe of any policy must be kept localized. It is a view where the precautionary principle of policy dominates, and social activity is focused on maintenance of the status quo. The "anarchist worlder" view is also not wrong-just incomplete. There are indeed destabilizing forces, and there is a value in diversity of the small and local.

Nature Resilient. The fourth is a view of multistable states, some of which become irreversible traps, while others become natural alternating states that are experienced as part of the internal dynamics (Figure 1-1 D). Those dynamics result from cycles organized by fundamentally discontinuous events and nonlinear processes. There are periods of exponential change, periods of growing stasis and brittleness, periods of readjustment or collapse, and periods of reorganization for renewal. Instabilities organize the behaviors as much as stabilities do. That was the view of Schumpeter's (1950) economics, and it has more recently been the focus of fruitful scholarship in a wide range of fields-ecological, social, economic, and technical. These dynamics are the ones argued for ecosystems (Holling 1986). They have similarities in Harvey Brooks's view of technology (1986); recent views of the economics of innovation and competition (Arthur, Durlauf, and Lane, 1997); Mary Douglas's (1978) and Mike Thompson's (1983) view of cultures; Don Michael's view of human psychology (1984); and Barbara Tuchman's (1978) and William McNeill's (1979) view of history. It is a view of multiple stable states in ecosystems, economies, and societies and of policies and management approaches that are adaptive. But this view presumes a stationary stability landscape-stationary underlying forces that shape events. In this case, our cookie sheet has been molded and curved in three dimensions, but its basic contours are fixed over time (Figure 1-1 D). This "resilient worlder" view is also not wrong-just incomplete. There are, indeed, cycles of change that can move variables among stability domains, but those very movements contribute to the apparent fixed nature of the contours. Constrain those movements through policy actions, and the contours shift, as slow variables change. That can precipitate a more structural kind of surprise that is a con-


sequence of successful but myopic policy. Many of the examples of the pathology of resource management and regional development are just those kinds of structural surprises.

Nature Evolving. The emerging fifth view is evolutionary and adaptive. It has been given recent impetus by the paradoxes that have emerged in successfully applying the previous more limited views. Complex systems behavior, discontinuous change, chaos and order, self-organization, nonlinear system behavior, and adaptive evolving systems are all code words characterizing the more recent activities. They are leading to integrative studies that combine insights and people from developmental biology and genetics, evolutionary biology, physics, economics, ecology, and computer science. Profound innovations have been created and led by John Holland in his applications of genetic algorithms and development of complex adaptive system theory. His more recent work on a simple, highly visual model that illustrates the creation of complex structures by natural selection (Holland 1995) presents a way to explore the generation and selection of novelty in mathematical, economic, and social systems. In economics, some examples of early developments are in Anderson, Arrow, and Pines (1988). A nice review of later work is Sargent (1993), and a current collection of articles is presented in Arthur, Durlauf, and Lane (1997). Marco Janssen extends and applies those approaches to explore changing perspectives on future behavior in Chapter 9. It is a view of an actively shifting stability landscape with selforganization (the stability landscape affects behavior of the variables, and the variables, plus exogenous events, affect the stability landscape). Levin's recent book, Fragile Dominion (1999), gives an accessible and effective treatment of present adaptive, complex systems views for ecology.

Nature Evolving is a view of abrupt and transforming change. It is a view that exposes a need for understanding unpredictable dynamics in ecosystems and a corollary focus on institutional and political flexibility. We cannot, at this stage, invent a simple diagram to add this myth to those shown in Figure 1-1. In a sense, that is the purpose of the book-to develop a sufficiently deep understanding of Nature Evolving that its essential behavior and the relevant policies can be captured in a few paragraphs, a few simple models of real situations and a simple set of suggestive diagrams. Subsequent chapters provide the understanding to do just that using the theoretical framework of panarchy.

Many of the examples of successful resource exploitation followed by collapse are based on the above-mentioned myths of nature. The concepts of stability and resilience embedded in these caricatures can be given meaning in the metaphor of raft described in Box 1-2. These myths are useful underpinnings for understanding and action. Yet they reveal a paradox that goes back hundreds of years in thought. That is, if human exploitation leads to resource collapse, why haven't all ecological systems collapsed, and why are we humans still here? We discuss that paradox in the following section.


Why Has the World Not Collapsed?

Part of the answer to this paradox is that natural ecological systems have the resilience to experience wide change and still maintain the integrity of their functions. The other part of the answer lies in human behavior and creativity. People do learn, however spasmodically. Change and extreme transformations have been part of humanity's evolutionary history. People's adaptive capabilities have made it possible not only to persist passively, but to create and innovate when limits are reached.

The reason for the astonishing resilience of natural ecosystems can be found in examining the scales at which processes (including humandominated ones) operate to control the system. In most terrestrial systems, geophysical controls dominate at scales larger than tens of kilometers. At scales smaller than this, biotic processes, interacting with abiotic ones, can control structure and variability. They produce volumes and patterns of vegetation and soil, for example, that moderate external extremes of temperature, conserve moisture and nutrients, and even affect regional climate and the timing of seasons. These are also the scale ranges where human land use transformations occur so that the arena where plant- and animal-controlling interactions unfold is the same arena where human activities interact with the landscape. That is why human population growth and development are so inexorably interconnected with terrestrial ecosystem resilience.

The controls determined by each set of biotic structuring processes within terrestrial ecosystems are remarkably robust, and the behaviors resulting are remarkably resilient. That robustness comes from functional diversity and spatial heterogeneity in the species and physical variables that mediate the key processes that structure and organize patterns in ecosystems and landscapes. The stability domains that define the type of system (e.g., forest, savanna, grassland, or shrub steppe) are so large that external disturbances have to be extreme and/or persistent before the system flips irreversibly into another state. Except under extreme climatic conditions, Mother Nature is not basically in a state of delicate balance. If she were, the world would indeed have collapsed long ago.

The myths of Nature Balanced and Nature Anarchic therefore have to be expanded to include Nature Resilient. So long as we accept only the axiom that there is a balance between exponential growth and environmental/ecological limits, then we are drawn to an inexorable Malthusian determinism. The only behavior of interest is that near equilibrium and a goal to control the system to remain near that equilibrium. In contrast, when we perceive only external physical variability and passively adapting biota, then Nature Anarchic is the logical image, and spatial heterogeneity emerges as the critical ingredient for persistence in a world of locally unstable equilibria.

When, however, we perceive a structuring and controlling role for key clusters of biota at small- and fast-scale ranges; for zootic and abiotic processes like insect outbreaks, large ungulate grazing, storm and fires at in-


Box 1-2. The Raft-A Metaphor of Stability and Resilience

D. Ludwig

The concept of stability refers to the tendency of a system to return to a position of equilibrium when disturbed. For example, if a weight is added suddenly to a raft floating on water, the usual response is for the weighted raft to oscillate, but the oscillations gradually decrease in amplitude as the energy is dissipated in waves and eventually in heat. The weighted raft will come to rest in a different position than the unweighted raft would have, but we think of the new configuration as essentially the same as the old one. The system is stable.

If we gradually increase the weight on the raft, the configuration will eventually change. If the weight is hung below the raft, the raft will sink deeper and deeper into the water as more and more displacement is required to balance the higher gravitational force. Eventually, the buoyant force cannot balance the gravitational force and the whole configuration sinks: the system is no longer stable. On the other hand, if the weight is placed on top of the raft, the raft may flip over suddenly and lose the weight and its other contents long before the point at which the system as a whole would sink. This sudden loss of stability may be more dangerous than the gradual sinking because there may be little warning or opportunity to prepare for it. We may think of the raft system as losing its resilience as more weight is placed on top of it.

Is the raft likely to experience a gradual loss of stability or a sudden one? In order to decide whether a system is stable or not, we must first specify what we mean by a change in configuration or loss of integrity. If we don't care whether the raft flips over when weighted, then there is no problem of sudden loss of stability for the floating raft. We must also specify the types and quantities of disturbances that may affect the system. Suppose that a fixed weight is placed on top of an occupied raft. If the occupants of the raft move about, the raft may float at a slightly different angle, but if they move too far or all at once, the raft may tip. The range of possible movements of the occupants that do not lead to tipping is called the domain of stability or domain of attraction of the upright state. If the amount of the fixed weight is gradually increased, the balance becomes more precarious, and hence the domain of attraction will


shrink. Eventually, the weight becomes large enough so that there is no domain of stability.

The preceding example makes a distinction between the weight loading the raft and the positions of the occupants. If the amount of the weight changes very slowly or not at all, we may think of the "system" as consisting of the raft and weight. If the occupants change position relatively quickly, those changes may be thought of as disturbances of the system. On the other hand, we may more comprehensively view the raft, the weight, and the occupants as a single system. If the occupants organize themselves to anticipate and correct for external disturbances, then the system may be able to maintain its integrity long enough for them to achieve their objectives. Another possible response to disturbance might be to restructure the raft itself. If it were constructed of several loosely coupled subunits, then excessive weighting or a strong disturbance might flip one part of the system but leave the rest intact. Such a structure might not require as much vigilance to maintain as the single-system raft.

The resilience of the raft cannot be determined outside of its social and institutional context. The occupants of the raft might have differing rights and objectives. Those who stand to benefit most from heavy loading may tend to minimize the risks of tipping under load. Those who have the most to lose from a loss of stability may favor a very cautious approach. How will decisions be made about the loading and configuration of the raft? Who are the stakeholders-i.e., whose interests must be taken into account when alternative policies are considered? Does the raft have an owner? How do his rights and obligations compare with the rights and obligations of the occupants? Is there a government agency in charge of regulating rafts? Are there interest groups who would prefer that rafts not be allowed on the waterways? The eventual fate of the raft will depend on the physical characteristics of the raft, the environment in which it is deployed, and the social and political structure in which it is embedded.

termediate scale ranges; and for geophysical processes at large-scale ranges, then the image of Nature Resilient emerges. Such an image incorporates the principles of negative feedback regulation of Nature Balanced and of the stochastic physical variation of Nature Anarchic but adds the principles of biotically induced variation and self-organization. At scales from leaf to landscape, the biota can create conditions that support the very biotic processes themselves.


In the view of Nature Resilient, behaviors near equilibrium and the traditional mathematical tools for local stability analysis are irrelevant. Populations assume trajectories that are dynamically unstable. The critical focus then becomes the conditions at the boundaries of stability domains, the size of those domains, and the forces that maintain those domains. The paper that originally introduced this contrast between systems resilience and equilibrium stability (Holling 1973b) was written as an antidote to the narrow view of fixed, equilibrium behavior and of resistance of populations to local perturbation. Those narrow, essentially static notions have provided the foundations for the now discredited goals of maximum sustained yields of fish populations or of fixed carrying capacity for terrestrial animal populations. The success of achieving such goals squeezes out variability and resilience is lost. Periodic crises result.

Thus part of the answer to the question of why the world has not collapsed is that natural ecological systems have the resilience to experience wide change and still maintain the integrity of their functions.

But the other part of the answer lies in human behavior and creativity. Change and extreme transformations have been part of humanity's evolutionary history. People's adaptive capabilities have made it possible not only to persist passively, but also to create and innovate when limits are reached. At their extreme, these attributes underlie the economists' presumptions of people's unlimited capacity to substitute for scarce materials and to develop successful remedial policies incrementally once the need is apparent. The themes of human creativity and novelty are developed in subsequent chapters of this volume.

Partial Theories and Partial Explanation

We search for explanations that are simple and general. Can complex adaptive systems help us understand ecological, economic, and social systems separately and as they interact? By "understand" we mean distinguish that which is predictable (even if uncertain) from that which is emergent and inherendy unpredictable. The test of understanding is whether we can identify the processes that control the specific properties of many, qualitatively different, specific examples. Can we define adaptive responses and policies that benefit from and perhaps even create useful unpredictability? That is what adaptive policy is about.

There are not too few theories for these systems. There are too many. They are all correct or mosdy correct but incomplete. For example, in ecology the notion of Clementsian succession was a typical equilibrium theory that saw ecosystem succession proceeding from establishment of pioneer species that withstand extremes of microclimate, to climax species whose tight competitive relationships precluded other species. The theory was not wrong but incomplete, since empirical tests of that theory exposed a much more variable progression, a rich range of individual species responses


to microclimate and soils, the existence of a number of different end states, and the role of disturbance as part of ecosystem renewal.

In economics, the pure market model is an equilibrium theory in which demand and supply reach stable equilibrium prices when marginal changes just balance. It is not wrong, but we know that market imperfections occur when the simplifying assumptions are violated. Those violations become more pronounced as the scale of human impacts on the environment increase in extent and intensity (Arrow et al. 1995). That view of the market is not too different from the theory of island biogeography in ecology, in which the equilibrium number of species on islands is seen as the balance between species immigration and extinction. The theory is not wrong but incomplete, because empirical checks demonstrate that the theory can be a poor predictor. The list could go on-density-dependent regulation in population dynamics, competition in community ecology, field theory in economics, garbage-can models in decision theory.

These theories are partial truths. Once proposed, they stimulate fruitful inquiry. As a consequence, their partial nature is exposed, and extension and expansion of theory proceed. Parental affection for theory by those who form them and the psychology of adherents makes those extensions contentious. Critics become extreme; straw-man caricatures are established and roundly defeated. The best of the defenders resist throwing the baby out with the bathwater and are affronted by the often inappropriate attacks when the leading edge of theory formation has often been there earlier. That is where we see the present debates about economics from environmental perspectives. We have learned that economists have often been there before their critics. We hope that we can clarify and open fruitful inquiries through the kind cooperation of ecologists, economists, and social scientists displayed in this book.

In our quest, we would like to discover ways to integrate and extend existing theory to achieve a requisite level of simplicity, just complex enough to capture and explain the behaviors we see. Those include explanations of discontinuous patterns in space, time, and structure and explanations for how novelty emerges, is suppressed, or is entrained. For prescriptive purposes we also seek adaptive ways to deal with surprise and the unpredictable. We concentrate on adaptive approaches that do not smother opportunity, in contrast to control approaches that presume that knowledge is sufficient and that consequences of policy implementation are predictable.

So-requisite simplicity, but generality? What is the context within which the theory is functional? Generality is desired-but also to be feared. It is to be feared because once a theory is formed, once it seems to resolve paradoxes, and once it passes some empirical tests, proponents are sorely tempted to extend its application beyond its natural context. That is particularly true if the theory emerges in the natural sciences and is applied to humans. The history of science is replete with such examples-some disastrous (social Darwinism), others usefully provocative (sociobiology and


evolutionary psychology), and still others wonderfully overambitious (complexity theory?). It is not always so bad to reach beyond the theory's real grasp because the science-based efforts at least have a process, however lurching and inefficient, to test them. But caution and sharp questioning are essential.

We encountered this issue when faced with the temptation to extend a theory of adaptive cycles developed for ecosystems dynamics and renewal (Chapter 2) to other systems, particularly organizational ones (Gunderson et al. 1995a), business ones (Hurst 1995), and more generally, social and political ones (Holling and Sanderson 1996).

That led to an expansion that recognized that the adaptive cycles were nested in a hierarchy across time and space (Gunderson et al. 1995a). That expansion seemed to explain how adaptive systems can, for brief moments, generate novel recombinations that are tested during longer periods of capital accumulation and storage. These windows of experimentation open briefly, but the results do not trigger cascading instabilities of the whole because of the stabilizing nature of nested hierarchies. In essence, larger and slower components of the hierarchy provide the memory of the past and of the distant to allow recovery of smaller and faster adaptive cycles. In ecosystems, for example, seed banks in soil, biotic heritages, and distant pioneer species are all critical accumulations from the past that are available for present renewal.

That expansion did not help us avoid the pitfall of overstretched generality, however; rather, it made it worse. That was the motive that initiated this book. The expansion seemed to explain everything. It applied to theories of non-living systems, such as plate tectonics. The sequence of phases in the cycle were all there: the establishment of the plates from magma extruding at the mid-Atlantic ridge, slow movement of the plates encountering continental edges, material subducting back to be melted, and the elements resorted in new episodes of mineral formation in mountain building. In addition, too many other systems seemed equally to fit the heuristic model of change: cell development, meiotic reproduction, ecosystem formation, evolution, organizational stasis and transformation, political and social processes. If a theory explains everything, it explains nothing.

What are needed are alternative hypotheses and specific predictions that can be tested empirically. That is possible for the natural science components systems but much less so for social components. But we can continually ask where the emerging theory encounters observations that are not consistent with the theory. Why living systems are not like nonliving ones. Why ecosystems are not like organisms. Why social systems are not like ecosystems. And why linked ecological, social, and economic systems are not like any of the above.

1. lN QUEST OF A THEORY OF ADAPTIVE CHANGE 21

Seeking Simplicity in Quest of a Theory of Adaptive Change

Our goal for this book was to develop and test theories that explain transformational change in systems of humans and nature, theories that are inherently integrative.

We identified two targets for integration. One is to integrate the dynamics of change across space from local to regional to global and over time from months to millennia. Traditions of science have tended to simplify by focusing on one scale. However, growing human impacts on the planet's atmosphere and on international economic patterns have stimulated efforts over the last decade to explore cross-scale influences (Levin 1992, 1999). Examples are impacts of climate change on regional ecosystems and on local human health, or of economic globalization on regional employment and the environment, or of emergence of new diseases, like AIDS, and their spread internationally.

An economist might say that the world's local and regional ecological, economic, and social systems are increasingly influenced by externalities (Arrow et al. 1995; Levin, Barrett et al. 1998). An ecologist might say that they have become increasingly coupled, so that fast and slow processes, local and distant ones cannot be treated separately (O'Neill et al. 1998). Increasingly, local problems of the moment can have part of their cause located half a planet away and have causes whose source is from slow changes accumulated over centuries.

The processes that drive or mediate the spatial intensification range from fast processes of vegetative growth in ecosystems and of economic production in economies, to slow processes of geomorphological change and of human cultural and political development. The processes we need to understand, and in some way integrate, literally cover months to millennia, meters to tens of thousands of kilometers.

This integration builds on prior work (Gunderson et al. 199 5 a) that identified the linkages between system dynamics and scale-the roots of the term panarchy. The term was coined as an antithesis to the word hierarchy (literally, sacred rules). Our view is that panarchy is a framework of nature's rules, hinted at by the name of the Greek god of nature, Pan. Chapters 2 and 3 focus on this integration, on developing theories of cross-scale dynamics and, in Chapters 4, 5, and 6, on using it to explore specific examples of ecological, social, and organizational change.

The second target for integration was to integrate across disciplines to better understand systems of linked ecological, economic, and institutional processes. Again, the expanding influence of human activity intensifies the coupling between people and systems of nature so that neither can be understood in isolation (Vitousek 1997; Holling 1994b).


This second goal of interdisciplinary integration-of how linked systems of nature, economies, and institutions function-is a major focus of Chapters 7, 8, 9, and 10, where mathematical representations of these integrated systems are explored. Chapters 11 and 12 use the emerging theories to analyze policies and practices in two specific examples of regional systems, and Chapter 13 describes the challenges that management of resources presents to individuals. Chapter 14 raises broad questions of sustainability and equity that come from experiences in the developing world, questions that emerge when efforts are made to identify alternative paths for development. Finally, Chapter 15 summarizes our conclusions in Table 15-1, and Chapter 16 presents the synthesis we sought at the outset of the work.

We hope that our approach in the remainder of this volume embodies the major elements of a heuristic theory. It draws on theories of adaptive change in biological and ecological systems, of self-organization in complex systems, of rational actor models in economics, and of cultural evolution. We are promulgating regional tests of our approach; we have posed the test questions; we are building a network of test takers-of practitioners, scientists, and policy decision makers who wish to contribute to a sustainable future for regions and for the planet (www.resalliance.org). It is a future that encourages innovative opportunity for people to learn and prosper, that incorporates responsibility to maintain and restore the diversity of nature, and that is based on a just and civil society. We hope this volume contributes to such a future.

Part II

Theories of Change

CHAPTER 2

RESILIENCE AND ADAPTIVE CYCLES

C. S. Holling and Lance H. Gunderson

Make things as simple as possible, but no simpler. -Albert Einstein

T he purpose of this chapter, and the succeeding one, is to deepen understanding of the fifth of the worldviews described in Chapter 1-that of Nature Evolving. It is another step in the effort to

develop theories for sustainable futures. What follows in this chapter is an initial comparison of the structure

and dynamics of ecological and social systems from the perspective of ecosystem ecologists. We draw on ecological examples and theory and on lessons from examples of regional ecosystem management in order to develop new concepts to explain the organization and dynamics of complex adaptive systems. We only hint at similarities in social and economic systems-just enough that, in later chapters, they can be the source for discovering the limits of the theory.

We begin by abstracting key elements of our understanding regarding how ecosystems are organized and operate. We then use examples of different ecosystems to develop several variants of a heuristic model of change that involves four phases: exploitation, conservation, creative destruction, and renewal, which constitute an adaptive cycle. We end with questions emerging from puzzles and paradoxes not well treated by the model presented, especially in terms of cross-scale dynamics.

Key Features of Ecosystems

The accumulated body of empirical evidence concerning natural, disturbed, and managed ecosystems identifies key features of ecosystem structure and function that can be distilled into the following points:

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HOLLING, GUNDERSON

Change is neither continuous and gradual nor consistently chaotic. Rather it is episodic, with periods of slow accumulation of natural capital such as biomass, physical structures, and nutrients, punctuated by sudden releases and reorganization of those biotic legacies (Franklin and MacMahon 2000) as the result of internal or external natural disturbances or human-imposed catastrophes. Rare events, such as hurricanes or the arrival of invading species, can unpredictably shape structure at critical times or at locations of increased vulnerability. The results of these rare events represent "frozen accidents" whose influence can shape the future for long periods. Irreversible or slowly-reversible states can exist; once the system flips into such a state, only explicit management intervention can return its previous self-sustaining state, and even then recovery is not assured (D. Ludwig et al. 1997).

Critical processes function at radically different rates that span several orders of magnitude, but these rates cluster around a few dominant frequencies. Episodic behavior is caused by interactions between fast and slow variables.

• Spatial attributes are neither uniform nor scale invariant over all scales. Rather, productivity and textures are patchy and discontinuous at all scales, from the leaf to the landscape to the planet. There are several different ranges of scales, each with different attributes of architectural patchiness and texture and each controlled by a specific set of abiotic and biotic processes. They make attributes of the natural world lumpy, rather than continuous (Holling 1992), thereby concentrating resources and opportunities at particular scales.

Therefore, scaling up from small to large cannot be a process of simple aggregation: nonlinear processes organize the shift from one range of scales to another.

• Ecosystems do not have a single equilibrium with homeostatic controls to remain near it. Rather, multiple equilibria commonly define functionally different states. Normal movements of variables between states maintain structure, diversity, and resilience. Nonlinear features of processes of predation, reproduction, competition, and nutrient dynamics create the multiple equilibria. Stochastic forces and interactions between fast variables and slow ones mediate the movements of variables among those equilibria (Carpenter 2000).

On the one hand, destabilizing forces are important in maintaining diversity, resilience, and opportunity. On the other hand, stabilizing forces are important in maintaining productivity and biogeochemical cycles.

2. RESILIENCE AND ADAPTIVE CYCLES 27

• Policies and management that apply fixed rules for achieving constant yields (e.g., fixed carrying capacity of cattle or wildlife, or fixed sustainable yield of fish or wood), independent of scale, lead to systems that increasingly lose resilience-i.e., to systems that suddenly break down in the face of disturbances that previously could be absorbed (Holling 1986, 1995).

Ecosystems are moving targets, with multiple futures that are uncertain and unpredictable. Therefore, management has to be flexible, adaptive, and experimental at scales compatible with the scales of critical ecosystem functions (Walters 1986; Gunderson et al. 1995b).

Those key features provide the minimal set of strategic criteria that need to be satisfied by any theory of adaptive change appropriate for ecosystems. They lead to a view of ecosystems that can make sense only if it is compatible with some version of both Nature Resilient and Nature Evolving. We propose, moreover, that the same criteria, with several additions unique to human systems, are equally necessary for models of human institutions, organizations, and society. To set the stage we need to define what we mean by stability, variability, and resilience of a system.

Two Ways of Looking at Stability

Resilience has been defined in two very different ways in the ecological literature. These differences in definition reflect which of two different aspects of stability is emphasized. The consequences of those different aspects for ecological systems were first emphasized by Holling (1973b) in order to draw attention to the tension created between efficiency on the one hand and persistence on the other, or between constancy and change, or between predictability and unpredictability. One definition focuses on efficiency, control, constancy, and predictability-all attributes at the core of desires for fail-safe design and optimal performance. Those desires are appropriate for systems where uncertainty is low, but they can be counterproductive for dynamic, evolving systems where variability and novelty result in high uncertainty. The other definition focuses on persistence, adaptiveness, variability, and unpredictability-all attributes embraced and celebrated by those with an evolutionary or developmental perspective. The latter attributes are at the heart of understanding and designing for sustainability.

The first definition, and the more traditional, concentrates on stability near an equilibrium steady state, where resistance to disturbance and speed of return to the equilibrium are used to measure the property (Pimm 1984; Tilman and Downing 1994). We term this engineering resilience (Holling 1995; Holling and Meffe 1996).

The second definition emphasizes conditions far from any equilibrium steady state, where instabilities can flip a system into another regime of behavior-i.e., to another stability domain (Holling 1973b). In this case

28 HOLLING, GUNDERSON

resilience is measured by the magnitude of disturbance that can be absorbed before the system changes its structure by changing the variables and processes that control behavior. This we term ecosystem resilience.

These studies and examples increasingly suggest that effective and sustainable development of technology, resources, and ecosystems requires ways to deal not only with near-equilibrium efficiency but also with the reality of more than one equilibrium.

These two aspects of a system's stability have very different consequences for evaluating, understanding, and managing complexity and change. We argue here that sustainable relationships between people and nature require an emphasis on the second definition of resilience, i.e., as the amount of disturbance that can be sustained before a change in system control and structure occurs-ecosystem resilience. That shifts the management and policy emphasis from micro, command-and-control approaches to ones that set overall conditions to allow adaptive enterprises (Holling and Meffe 1996). That interplay between stabilizing and destabilizing properties is at the heart of present issues of development and the environment-global change, biodiversity loss, ecosystem restoration, and sustainable development.

Exclusive emphasis on the first definition of resilience, engineering resilience, reinforces the dangerous myth that the variability of natural systems can be effectively controlled, that the consequences are predictable, and that sustained maximum production is an attainable and sustainable goal. Gunderson, Holling, and Light (1995a) present examples showing why that leads to the pathology of resource management (Chapter 1). The very success of limiting variability of a target leads to the unperceived shrinkage of stability domains. As ecosystem resilience is lost, the system becomes more vulnerable to external shocks that previously could be absorbed.

These are two contrasting aspects of stability. One focuses on maintaining efficiency of function (engineering resilience); the other focuses on maintaining existence of function (ecosystem resilience). Those contrasts are so fundamental that they can become alternative paradigms whose devotees reflect traditions of a discipline or of an attitude more than of a reality of nature.

Those who emphasize the near-equilibrium definition of engineering resilience, for example, draw predominantly from traditions of deductive mathematical theory (Pimm 1984) where simplified, untouched ecological systems are imagined. Another example arises from experimental manipulation of organisms where the scale is limited to small enclosures or field quadrats (Tilman and Downing 1994). Yet another example is from traditions of engineering, where the motive is to design systems with a single operating objective (Waide and Webster 197 6; De Angelis et al. 1980). Such partial representations make the mathematics more tractable, the experiments more controllable, and the designs more functionally optimal. There is an implicit assumption of global stability-i.e., there is only one equilibrium steady state, or, if other operating states exist, they can be avoided with appropriate safe-


guards, so that the variables are maintained near the "best" equilibrium, well away from a dangerous break point. There are also the assumptions that it is sufficient to represent or manipulate only fast, local variables and that slowly changing, extensive variables and their interactions can be ignored.

Those who emphasize the stability domain definition of resilience (i.e., ecosystem resilience), on the other hand, come from traditions of applied mathematics and applied resource ecology at the scale of ecosystems and of landscapes. Examples are the dynamics and management of freshwater systems (Fiering 1982); of forests (Holling et al. 1976a); of fisheries (Walters 1986); of semiarid grasslands (Walker 1981); of lakes (Scheffer 1998; Carpenter, Ludwig, and Brock 1999; Janssen and Carpenter 1999); and of interacting populations in nature (Sinclair et al. 1990; Dublin et al. 1990). Because these studies are rooted in inductive rather than deductive theory formation and in experience with the impacts of management disturbances at multiple scales, the reality of flips from one operating state to another cannot be avoided. Clear lakes can tum into turbid, anoxic pools, grasslands into shrub-deserts, and forests into grasslands. D. Ludwig et al. (1997) provide a fine exploration of the mathematical underpinnings to these different views of resilience with examples from natural and managed systems. Scheffer (1999) provides a lucid and accessible example of multistable behavior in European lakes and the management strategies for dealing with them.

In ecology, the causes and conditions of multiple equilibria were challenged by Sousa and Connell (1985), who analyzed time series data of animal populations. This is an example of a laudably skeptical effort to invalidate a novel proposition. It came to an erroneous conclusion because the data systems used to test the proposition were defined too simply. They did not have the level of requisite complexity needed. They lacked the minimally essential features for answering the question. The example is instructive for other issues: of, for example, the detection and use of pattern in analyzing any long time series-ecological, paleoecological, climatic, or financial-or of spatial or geometric patterns. Causation was ignored and the relevant duration of data was defined by the assumption that fast variables alone defined multistable properties.

For example, Sousa and Connell (1985) presumed that 40 years of available data covering forty generations of the forest insect, the spruce budworm, was sufficient to test for multistable states in the budwonn/forest system. It certainly seems long enough to data-starved ecologists! However, slow variables, like the foliage accumulation of the maturing forest, set by a generation time of 80-120 years for the trees, slowly change the stability conditions for fast ones (Box 2-1). The minimal need is for a time series that covers three generations of the trees (at least 300 years). It is no wonder that moving multiple lines of evidence, understanding of causation, and recognition of requisite levels of simplicity has been the only way to establish the reality and importance of multistable states. That is what Carpenter (2000) has summarized in a masterful review of the empirical evidence. It has taken


twenty-five years to establish that multistable states are, in fact, common in ecosystems, common enough that management dare not ignore them, because of the potential high cost of doing so.

Box 2-1. Spruce-Fir Forests and Insect Outbreaks

C. S. Holling

One classic example of the adaptive cycle shown in Figure 2-1 is the dynamics of the spruce-fir forest of eastern North America. The patterns produced depend on the nonlinear processes that trigger and. organize the release and reorganization phase. One of the primary triggers for release in the eastern balsam fir forest of North America is an insect outbreak species, the spruce budworm. Two principal stability states exist. One is with low budworm populations and young, growing trees. The other is with high budworm populations and mature trees. The latter condition is associated with so much defoliation that the trees die over extensive areas. Prior to harvesting and management, up to 80 percent of the balsam fir trees in central eastern Canada and the United States would die from budworm attacks at intervals of from 40 to 130 years. It is an entirely natural phenomenon, part of forest renewal, and is an example of alternating stable states.

The release phase occurs because the maturing forest accumulates a volume of foliage that eventually dilutes the effectiveness of the search by insectivorous birds for budworm. So long as predation by birds is high, as it is in younger stands, it is sufficient, with other mortality agents, to control budworm populations at low densities. Essentially, a lower equilibrium density for budworm is set by a "predator pit" (Clark et al. 1979; Holling 1988) in a stability landscape during the phase of slow regrowth of the forest. This stability. pit eventually collapses as the trees mature, to release an insect outbreak and reveal the existence of a higher equilibrium. A more formal mathematical representation is given in Ludwig et al. (1978). A similar argument can be described for release by fire, as a consequence of the slow accumulation of fuel as a forest ages.

To summarize and generalize this example: For long periods in a regrowing forest, the slow variable (trees) controls the faster (budworm or fire) and intermediate-speed variables (foliage or fuel) until a stability domain shrinks to the point where the fast variables for a brief time can assume control of behavior and trigger a release of the accumulated capital.


Back to Myths of Nature

The features summarized in the two preceding sections suggest that the images of Nature Flat and Nature Anarchic described in Chapter 1 are wrong in their incompleteness. Both myths are wrong, because there are clearly regulatory forces that cause ecosystems to pause for longer or shorter periods in one set of relationships and one assemblage of species in one place. Some call those ecosystems. But Nature Balanced is equally wrong. There are strong destabilizing forces that introduce variability, sometimes abrupt, and that variability is the source of much of the diversity of species and the richness of nature we see. Nature Resilient would seem to provide an amalgam of both. It does that, but is it satisfactory? Is it sufficient?

Consider the consequences if a system were highly resilient. Is that entirely a desired condition? Such a system would not change in any fundamental way. In the face of large disturbances, variables would shift and move, but the system would maintain its controls and structure. If that is common, how do we explain the dramatic, changing character of landscapes over geological time? The answer might simply be that the resilience is never infinite and is eventually swamped by some external, large-scale change, and the system is replaced by something else. For example, some ten thousand years ago (very recent in geologic time frames) the treasured Everglades of southern Florida were not wedands, but a dry savanna. Had we been living then, would we, as people concerned with the conservation of nature, have sought to maintain that savanna state as desirably pristine, holding back the rising seas as glaciers melted? Placing fingers in the dikes we built? Denying the reality of climate change? Is it desirable to have a goal of preserving and protecting systems in a pristine, static state?

These tough questions are not normally addressed by conservationists or environmentalists. They are tough also because they challenge the authors' own values and desire to sustain a rich and diverse natural world. But in a complex evolving world, the function and future of linked human and natural systems evolve and are highly uncertain. Efforts to freeze or restore to a static, pristine state, or to establish a fixed condition are inadequate, irrespective of whether the motive is to conserve nature, to exploit a resource for economic gain, to sustain recreation, or to facilitate development. Shortterm successes of narrow efforts to preserve and hold constant can establish a chain of ever more cosdy surprises-versions of the pathology of resource management and development described in Chapter 1.

It helps to switch, for a moment, from thinking of ecosystems to thinking of sociopolitical ones. Clearly, locking a sociopolitical system into a fixed set of controls can transparendy create an unsustainable political system. For a time, at least, the Soviet Union was an immensely resilient "dictatorship of the bureaucracy" (Levin, Barrett et al. 1998). Its very resilience preserved a maladaptive system. What this suggests for social systems, as well as ecological ones, is that resilience is not an ideal in itself. Moreover, it is not a fixed

3 2 HOLLING, GUNDERSON

quantity that defines a system, but a dynamically varying one. Resilience can be the enemy of adaptive change. That is, the myth of Nature Resilient is too partial and static in a structural sense.

But what do we do? What is enduring and must always be so? What is sustainable? We need a transition from the structurally static view of Nature Resilient to a structurally dynamic view of Nature Evolving.

Conserving the elements we have is not the goal for a search for what is enduring. Otherwise, we would still be blacksmiths and buggy-whip makers. The challenge, rather, is to conserve the ability to adapt to change, to be able to respond in a flexible way to uncertainty and surprises. And even to create the kind of surprises that open opportunity. It is this capacity that a view of an evolving nature should be all about-i.e., maintaining options in order to buffer disturbance and to create novelty. A living system cannot be kept within some desirable state or on some desirable trajectory if adaptive capacity is continuously lost.

The purpose of theories such as panarchy is not to explain what is; it is to give sense to what might be. We cannot predict the specifics of future possibilities, but we might be able to define the conditions that limit or expand those future possibilities. As a consequence, the properties we need to choose are not those chosen to describe the existing state of a system and its behaviors, but rather ones chosen to identify the properties and processes that shape the future. This introductory exploration identifies three requirements in our quest for a theory of adaptive change:

• First, the syst~m must be productive, must acquire resources and accumulate them, not for the present, but for the potential they offer for the future.

• Second, there must also be some sort of shifting balance between stabilizing and destabilizing forces reflecting the degree and intensity of internal controls and the degree of influence of external variability.

• Third, somehow the resilience of the system must be a dynamic and changing quantity that generates and sustains both options and novelty, providing a shifting balance between vulnerability and persistence.

The Adaptive Cycle

In case examples of regional development and ecosystem management (Gunderson et al. 1995b), three properties seemed to shape the future responses of the ecosystems, agencies, and people:

• the potential available for change, since that determined the range of options possible;


• the degree of connectedness between internal controlling variables and processes, a measure that reflects the degree of flexibility or rigidity of such controls-i.e., their sensitivity or not to external variation;

• the resilience of the systems, a measure of their vulnerability to unexpected or unpredictable shocks.

Note, at this stage, we choose very general properties because our initial goal is to develop a framework of adaptive change that has generality. Such a framework is hardly a theory, therefore. Rather, it is a metaphor to help interpret events and their gross causes.

The original concept of the adaptive cycle and the review described in this section emerged from experience with productive ecosystems that exist in temperate regions of the world-places where rainfall is consistent, although seasonally variable. They specifically included the boreal coniferous forests of the Northern Hemisphere, productive grasslands on deep soils, and temperate deciduous forests. But many ecosystems have developed in very different conditions--coral reefs, nutrient-poor savannas with low and episodic rainfall, open-ocean pelagic communities, shallow and deep lakes, nutrient-poor tropical forests. In the remainder of this chapter we review the cycle as it was described for productive temperate ecosystems and possible similarities in human organizations and economies. To test its limits, we then consider more extreme types of ecosystems, hoping to discover where the metaphor breaks down. To push that exploration of limits further, we also start to explore large human organizations-bureaucratic and industrial organizations. In the next sections, we review properties of the original adaptive cycle metaphor, beginning with two of the key properties, potential and connectedness, before adding the third property, resilience.

Two Dimensions of Change: Potential and Connectedness

The traditional view of ecosystem succession has been usefully seen as being controlled by two functions: exploitation, in which rapid colonization of recently disturbed areas is emphasized; and conseroation, in which slow accumulation and storage of energy and material are emphasized. In ecology the species in the exploitive phase have been characterized as r-strategists and in the conservation phase asK-strategists. These are names drawn from the traditional designation of parameters of the logistic equation (r represents the instantaneous rate of growth of a population, and K the sustained plateau or maximum population that is attained; Pearl 1927). The r-types are characterized by extensive dispersal ability and rapid growth in an arena where scramble competition succeeds (the first to get the prize wins), while the K-strategists tend to have slower growth rates and flourish in an arena of contest competition (resources become divided and sequestered to separate


uses). To an economist or organization theorist, those functions could be seen as equivalent to the entrepreneurial market for the exploitation phase and the bureaucratic hierarchy for the conservation phase. Baron, Burton, and Hannan (1998) provide a very detailed study of the forces that determine different patterns such as path dependence in the evolution of bureaucracy, even when firms face intense competition.

But subsequent ecological understanding indicates that two additional functions are needed, as summarized in Figure 2-1. The first revision is that of release, or "creative destruction," a term borrowed from the economist Schumpeter (1950, and as reviewed in Elliott 1980). The tightly bound accumulation of biomass and nutrients becomes increasingly fragile (overconnected, in systems terms) until suddenly released by agents such as forest fires, drought, insect pests, or intense pulses of grazing. We designate that as the omega (.Q) phase.

The second additional function is one of reorganization, in which soil processes minimize nutrient loss and reorganize nutrients so that they

a K

r Q

connectedness _____..

Figure 2-1. A stylized representation of the four ecosystem functions (r, K, n, a) and the flow of events among them. The arrows show the speed of that flow in the cycle, where short, closely spaced arrows indicate a slowly changing situation and long arrows indicate a rapidly changing situation. The cycle reflects changes in two properties: (1) Y axis-the potential that is inherent in the accumulated resources of biomass and nutrients; (2) X axis-the degree of connectedness among controlling variables. Low connectedness is associated with diffuse elements loosely connected to each other whose behavior is dominated by outward relations and affected by outside variability. High connectedness is associated with aggregated elements whose behavior is dominated by inward relations among elements of the aggregates, relations that control or mediate the influence of external variability. The exit from the cycle indicated at the left of the figure suggests, in a stylized way, the stage where the potential can leak away and where a flip into a less productive and organized system is most likely.


become available for the next phase of exploitation. Part of this reorganization involves the transient appearance or expansion of organisms that begin to capture opportunity-the pioneer species. Their source is from growth of previously suppressed vegetation, from germinating seeds stored in seed banks accumulated from the past, and from dispersal of both endemic and exotic propagules from distant places. The reorganization phase is essentially equivalent to one of innovation and restructuring in an industry or in a society-the kinds of economic processes and policies that come to practical attention at times of economic recession or social transformation. We designate that as the alpha (a) phase.

If the omega phase represents the end, then it is immediately followed by the alpha phase, the beginning-a progression at least as interesting philosophically as it is ecologically.

During this cycle, biological time flows unevenly. The progression in the ecosystem cycle proceeds from the exploitation phase (r phase, Figure 2-1) slowly to conservation (K phase), very rapidly to release (Q phase), rapidly to reorganization (a phase), and rapidly back to exploitation. During the slow sequence from exploitation to conservation, connectedness and stability increase and a "capital" of nutrients and biomass is slowly accumulated and sequestered. Competitive processes lead to a few species becoming dominant, with diversity retained in residual pockets preserved in a patchy landscape. While the accumulated capital is sequestered for the growing, maturing ecosystem, it also represents a gradual increase in the potential for other kinds of ecosystems and futures. For an economic or social system, the accumulating potential could as well be from the skills, networks of human relationships, and mutual trust that are incrementally developed and tested during the progression from r to K. Those also represent a potential developed and used in one setting that could be available in transformed ones.

As the progression to the K phase proceeds, the accumulating nutrient and biomass resources become more and more tightly bound within existing vegetation, preventing other competitors from utilizing them. The potential for other use is high, but it is expropriated and controlled by the biota and processes of the ecosystem in place. That is, the system's connectedness increases, eventually to become overconnected and increasingly rigid in its control. The actual change is triggered by agents of disturbance such as wind, fire, disease, insect outbreak, and drought or a combination of these. The resources sequestered in vegetation and soil are then suddenly released and the tight organization is lost. Its potential for other uses drops until the released resources that remain are reorganized so that the potential for other uses reemerges in the a phase.

A number of such patterns have been discovered in several terrestrial and near terrestrial ecosystems at landscape scales (Boxes 2-2 and 2-3). In all instances, periodic flips from one stable state to another are mediated by changes in slow variables that suddenly trigger a fast-variable response, or escape.


Box 2-2. Alternative Stable States

G. Peterson

Alternative stable states have been described for a diverse variety of terrestrial and near terrestrial ecosystems. In each of these cases, periodic flips from one state to another are mediated by changes in slow processes that suddenly trigger a fast-process response, or escape from a state. The following cases provide examples:

Meta-population dynamics. A connected set of populations can exist at either a high-density connected state or a low-density fragmented state. In a landscape composed of potential habitats, the population of a particular habitat depends on its neighboring sites. If the population at a site becomes extinct, the probability of recolonization increases with the aggregate size of the surrounding populations. This effect produces a positive feedback between the density of a region's population and the likelihood that that region's population can maintain itself. Consequently, a regional population can rapidly decline if its population begins to fail to recolonize potential sites, because this further reduces the probability of recolonizing sites (Hanski et al. 1995).

Shallow lakes. In shallow lakes the interactions among turbidity, nutrients loading, vegetation, and fish produce two alternative stable states (Scheffer et al. 1993). Lakes can exist either in a state in which water is clear and dominated by rooted aquatic vegetation, or in a state in which water is turbid and dominated by phytoplankton. The large, rooted plants stabilize the substrate sediment, reduce turbidity, encourage the stabilization of nutrients, and provide refugia for phytoplankton-consuming fish. If rooted plants are eliminated, the resulting turbidity blocks light for plants, and resuspended sediment makes nutrients available to phytoplankton. Lakes usually switch between states due to a combination of changes. For example, a clear lake can lose rooted plants and become turbid due to an increase in nutrient loading, a decrease in algae-eating fish, an inflow of sediment, or the removal of vegetation (Blindow et al. 1993). Similarly, a turbid lake can be made clear by reducing the population of bottom-foraging, turbidity-increasing fish, or by decreasing the number of fish that eat algae eating fish.

Reefs. Corals, surface algae, and macro-algae are all components of coral reef communities. Changes in the extent of predation on algae by fish and sea urchins, changes in nutrient concentrations, and the presence of new areas to grow control

2. RESILIENCE AND ADAPTIVE CYCLES 3 7

switches between states (Knowlton 1992). Consequently, shifts between stable states can be influenced by disturbance events that provide new areas for recruitment, resuspend sediments, and cause variations in the population of algae eaters (Hughes 1994). Fishing and variation in recruitment can strongly influence fish populations, while the interaction of density-dependent recruitment and circulation patterns allows sea urchins to exist at self-maintaining high- or low-density states (McClanahan et al. 1996). These interactions suggest that reefs can exist in three selfmaintaining states: coral-fish, turf algae-urchins, and macro-algae (Done 1992; Knowlton 1992).

Sea otters, sea urchins, and kelp forests. Along the coast of the northern Pacific, rocky near-shore communities can be dominated by either dense stands of kelp or few kelp and large concentrations of sea urchins. The presence of these states is controlled by the presence of sea otters that prey upon sea urchins. In the absence of sea otters, urchin populations can increase to a density that prevents kelp forests from establishing. On the other hand, when sea otters are present, their predation on sea urchins allows key kelp forests to become established (Estes and Duggins 1995).

Fire in North Flurida. Oak trees and pine trees dominate sandhill communities in northern Florida. Fire mediates the competitive relationships between the abundance of these two species. Longleaf pine (Pinus palustris) is a particularly fire-tolerant pine species. Mature longleaf pines shed needles that provide good fuel for ground fires, and young longleaf pines can survive ground fires. Young hardwoods are intolerant of fire, and mature hardwoods shed leaves that suppress the buildup of fuel for ground fires. This lack of fuel tends to suppress fire in hardwood stands, encouraging the growth of more hardwoods, while fuel accumulation in stands of pine tends to encourage fire, suppressing hardwoods and encouraging the growth of pine (Glitzenstein et al. 1995; Rebertus et al. 1989).

Fire spreads itself from burning sites into combustible sites. A fire that is surrounded by noncombustible sites will be unable to spread and will extinguish itself. The mutual reinforcement between fire and longleaf pine will occur only if the fires are started frequently and are able to spread across a large area. Otherwise, sites will bum infrequently, and fire-susceptible vegetation will be replaced by fire-suppressing vegetation. The ability of fire to spread, and consequently the rate at which patches of hardwood or pine either grow or shrink, is determined by the distribution of hardwoods and pine across the landscape. The relative proportion of

continues


hardwood and pine in the area surrounding a site will determine the succession of a forest site.

Elephants, fire, and savanna. Dublin et al. (1990) propose that the elephants and fire interact with competition between grasses and trees to produce two alternative stable states in the Serengeti-Mara. Fire shifts from a woodland to a grassland state. Grassland is maintained by herbivores, particularly elephants, consuming young seedlings. However, this consumption is not sufficient to shift woodland to grassland, as it is significant only at low-seedling densities. Low-herbivore density and infrequent fire allow woodland regeneration to occur (Dobson 1995; Dublin 1995). For example, when rinderpest eliminated a huge number of grazers, woodlands experienced a pulse of regeneration (Prins and Jeud 1993).

As the system shifts from a. to r, some of the potential leaks away because of the collapse of organization; some of the accumulated resources literally leave the system. In addition, new entrants, those that survived to the a. phase, and the "biotic legacies" of past cycles (Franklin and MacMahon 2000) begin to sequester and organize resources in a process that leads to the r species establishing "founding rights" over the remaining capital. The result of both processes lowers the potential from a. to r.

Note that in a sustainable ecosystem, the accumulated resources that determine ecological potential might be eroded, might partially leak away, but are only partially reduced. If they were completely or largely eliminated, recovery would be impossible, and the system would slip into a different, degraded state. Such a condition would occur, for example, if species critical in maintaining structure and function became extinct. That has certainly happened in geological history with extinctions of large herbivores in North America at the end of the Pleistocene some ten thousand years ago. It has also occurred in Australia with the consequence of loss of a stable state (Box 2-3).

But in most swings of the cycle, there is sufficient carryover from cycle to cycle to sustain an ecosystem's possible states. Typically, the actual aggregate resources accumulated would take a different path than the trajectory of potential shown in the figure, modestly fluctuating in amount through one cycle. Or, as in the case of wetlands, like the Everglades, those resources could continually accumulate, cycle by cycle, stored in the immobilized accumulation of peat. The basic cycle of vegetation in the Everglades from ponds to sawgrass to fire takes in the order of decades. However, the accretion of five meters of peat in the Everglades occurs over multiple cycles on the order of a five-thousand-year period (Gleason 1984). What does change dramatically during a cycle in all such systems is the potential. It alternates between high potential in the a. and K phases, lower potential in the r phase, and still lower potential in the n phase.


Box 2-3. Loss of an Alternative State?

G. Peterson

Occasionally, due to the loss of an important system component, transition between multiple states results in the elimination of a former stable state. The extinction of species that perform a critical ecological function can cause such irreversible transitions. Pleistocene extinctions may provide an example of such a transition.

Sediment cores from Australia show that about 100,000 years ago pollen from fire-tolerant plants and mangroves increased while other species declined. These increases were likely due to the increases in burning that are also documented by an increase in charcoal in the sediment cores. Increases in fire frequency would have allowed firetolerant plants to spread, while at the same time leaving more bare soil to be eroded and deposited as coastal sediment and providing increased habitat for mangroves. Similar climatic conditions had existed previously without increases of fire, which suggests that the arrival of humans may have been responsible (Kershaw 1988).

Flannery (1994) proposes that it was overhunting of Australia's large marsupial herbivores that caused this change, rather than anthropogenic modification of fire regimes. During the time in which humanity is thought to have been in Australia, fifty large and medium-sized marsupial herbivores became extinct, along with several large herbivorous birds and turtles. If these herbivores lived similarly to existing large herbivores (Dublin et al. 1990; OwenSmith 1989), then their extinction also likely eliminated their maintenance, through grazing, physical disturbance, and nutrient cycling, of a variety of vegetative patterns across the landscape. The removal of this small-scale patterning, and a buildup of fuel, may have facilitated the occurrence of larger and more intense fires. Such fires reduce local nutrient cycling by causing larger-scale erosion. Flannery suggests that this process caused the expansion of heathlands of fire-tolerant species at the expense of fire-intolerant vegetation adapted to herbivory. Without large herbivores to prevent and fragment vegetation, an ecosystem of fire and fire-dominated plants could expand at the expense of a system of large herbivores and herbivore-adapted plants. Flannery argues that hunting and use of fire removed large herbivores and volatilized accumulated nutrients, irreversibly switching the system from a more productive state, dependent on rapid nutrient cycling, to a less productive state, with slower nutrient cycling, maintained by fire.


Human enterprises can have similar behavior, as, for example, when corporations such as IBM and General Motors accumulate rigidities to the point of crisis, followed by efforts to restructure (Hurst and Zimmerman 1994; Hurst 1995). The key test of the limits of the metaphor is not whether resources and potential increase from r to K, but whether rigidities inevitably do so as well. Are there designs and actions that allow growth without increasing rigidities to the point of collapse? That kind of test is what is needed to adapt and expand the metaphor.

But before we can start comparing and contrasting different systems in order to discover where the scheme breaks down, it is necessary to add the resilience dimension to those of connectedness and potential. That addition disentangles some of the inconsistencies that emerge when the adaptive cycle is applied to specific situations. It is necessary to add vulnerability to change in addition to the other two properties of limits of change (potential) and degree of internal control over variability (connectedness). That property of vulnerability is determined by the resilience of the system.

Adding Another Dimension: Resilience

Figure 2-2 adds the third dimension, resilience. The appearance of a figure 8 in the path of the adaptive cycle (as in Figure 2-1) is shown to be the consequence of a projection of a three-dimensional object onto a two-dimensional plane. We can view that three-dimensional object from different perspectives, in order to emphasize one property or another. Figure 2-2 revolves the object to expose the resilience axis.

As the phases of the adaptive cycle proceed, a system's ecological resilience expands and contracts as suggested in Figure 2-2. Note that the myth of Nature Resilient described in Chapter 1, in contrast, sees resilience of a system as a fixed quantity for the whole system. In that view, a system is resilient or not in various fixed degrees. But here we see resilience expanding and contracting within a cycle as slow variables change. We had to recognize that feature as an essential attribute for the myth of Nature Evolving and for resolving paradoxes encountered in examining specific examples of sustainable change.

The essential requirement is to recognize that conditions are needed that occasionally foster novelty and experiment. Those become possible during periods when connectedness is low and resilience is high. The low connectedness permits novel reassortments of elements that previously were tightly connected to one another. The high resilience allows tests of those novel combinations because system-wide costs of failure are low. Those are the conditions needed for creative experimentation. This recognition of resilience varying within a cycle is the first element added that provides a way to reconcile the delicious paradoxes of conservative nature versus creative nature, of sustainability versus creative change. Other additions concerning the nature of hierarchies will be explored in the next chapter.


~[g] i r

connectedness

rotation reveals resilience

Figure 2-2. Resilience is another dimension of the adaptive cycle. A third dimension, resilience, is added to the two-dimensional box of Figure 2-1, showing that resilience expands and contracts throughout the cycle. Resilience shrinks as the cycle moves toward K, where the system becomes more brittle. It expands as the cycle shifts rapidly into a "back loop" to reorganize accumulated resources for a new initiation of the cycle. The appearance of a figure 8 in Figure 2-1 is shown to be the consequence of viewing a three-dimensional object on a two-dimensional plane.

The a phase begins a process of reorganization to provide the potential for subsequent growth, resource accumulation, and storage. At this stage, the ecological resilience is high, as is the potential. But connectedness is low, and internal regulation is weak. There is a wide stability region with weak regulation around equilibria, low connectivity among variables, and a substantial amount of potential available for future development. Because of those features, it is a welcoming environment for experiments, for the appearance and initial establishment of entities that otherwise would be out-competed. As in good experiments, many will fail, but in the process, the survivors will accumulate the fruits of change.

But the same condition of low connectedness results in the system becoming "leaky." This leaky-ness is a signal of the a phase. It was first demonstrated empirically by Bormann and Likens (1981) in the famous Hubbard Brook experiment. Various treatments (e.g., tree removal, herbicide) of a small, forested watershed in New England mimicked a K to Q event. The water flow from the watershed was monitored and showed a pulse of nutrient loss that, within weeks, was slowed and stabilized as the ecosystem processes became reorganized. The same leaky phase has been described for semiarid savannas subject to the persistent disturbance of sheep grazing. If that continues, as it can when ranchers have no viable economic alternative, the rangelands progressively and irreversibly erode into a shrub-


Box 2-4. Quasi-Alternate States

G. Peterson

The dynamics of a system with a single stable state may approximate a system with multiple stable states if a perturbation can cause the system to persist in a slowly changing unstable state. While such a system does not have true alternative states, its dynamics and management may be similar. Semiarid grazing systems provide an example.

Competition between grasses and woody vegetation is mediated by stocking rates of cattle and sheep that graze grass but not woody vegetation. At low grazer densities, grass dominates; however, as stocking density increases, grazing may shift the competitive balance in favor of woody vegetation. If high stocking densities persist, the grass will be unable to persist and the system will be dominated by woody vegetation. This state is relatively self-maintaining, and a reduction of stocking densities does not allow grass to replace woody vegetation. However, in some conditions of relatively good soils, the woody vegetation--dominated state is not stable, because rainfall variation and the death of shrubs allow grasses to re-invade woody sites.

Woody vegetation dies back very quickly in dry years but recovers only slowly in wet years. Grass can recover much more quickly. Grass biomass can expand up to tenfold during a season by utilizing water not used by the slow-growing woody vegetation. In addition, as woody vegetation gradually dies, patches are opened that can be colonized by grasses. Over time, these patches allow fire to invade a woody patch. The grass state of this rangeland is the only stable equilibrium of such a system, but when this state is perturbed by overgrazing, the system will make a slow transition through a woody-dominated period before it returns to a grass-dominated state. High stocking levels over a time period of five to twenty years allow woody plants to replace grasses. However, during the following thirty years, the death of woody vegetation allows fire to invade, replacing woody vegetation with grasses. This type of slowly changing unstable state is not a true alternative stable state, but to a rancher who is making decisions about stocking levels, it may as well be a. Ludwig et al. 1997; Walker 1988).

If we chose to redefine the system to include ranchers as a dynamic part of it, then the slowly changing state could, however, be converted to a true stable state. In such a case, economic reality could so lock the rancher into continued stocking of sheep that recovery would be impossible.


dominated semidesert that is sustained by low-level grazing G. Ludwig et al. 1997; Chapter 11; Box 2-4).

Note that the a phase is the condition for the greatest uncertainty-the greatest chance of unexpected forms of renewal as well as unexpected crises. As we emphasize later, this is one of the key elements in Nature Evolvingthe condition where, momentarily, novel reassortments of species in ecosystems (or recombinations of genes in cell division) generate new possibilities that are later tested. That is precisely what happens in meiosis, where novel reassortmen:ts and recombinations of genes contained within the sex cells launch novel experiments that are tested by natural selection. It is the basis of the modeling use of genetic algorithms invented by John Holland, to generate and explore novelty in economic, social, and mathematical systems (Holland 1995; Chapter 9).

rto K

In both the a and r phases, surviving residual vegetation and physical structures represent biotic legacies from the previous cycle (Franklin and MacMahon 2000). They provide a template on which the seeds from the past or from distant sources germinate. The r phase becomes rapidly dominated by a thriving biota that is adapted to high variability of microclimate and extremes of soil conditions and can further occupy unexploited territory through effective dispersal. Because of these adaptations, resilience remains high. Similarly, it is a condition in which, in the economy, the innovator sees unlimited opportunity. Or in which producers of new products can aggressively capture shares in newly opened markets. Because connectedness is low, the entities are very much influenced by external variability-both as opportunities to exploit and as constraints to bear. As a consequence, they have evolved or are selected from a pool that includes species and individuals adapted to dealing with the stresses and opportunities of a variable environment-the risk takers, the pioneers, the opportunists.

A period of contest competition among entrepreneurial pioneers and surviving species from previous cycles ensues. The ones fastest off the mark and most aggressive are the ones likely to persist. Many fail. Aggressive invasive species start to sequester ecological space. Start-up organizations, whether in businesses, research, or policy, initiate intense activity energized by a pioneer spirit and opened opportunity. Markets start to become controlled by products once they exceed about 5 percent of the potential.

This starts a progression from r to K as the winners expand, grow, and accumulate potential from resources acquired. We use the term resources in the broadest sense, including, for example, carbon and nutrients for the biota, production and managerial skills for the entrepreneur, marketing skills and financial capital for the producer, and physical, architectural structure for all systems. Connectedness between interrelated entities begins to increase because facilitation and contest competition between species


inexorably increases as expansion continues. A subset of species begins to develop close interrelations that are mutually supportive-i.e., they form self-organized clusters of relationships. The future starts to be more predictable and less driven by uncertain forces outside the control of the system. Microclimatic variability becomes moderated by vegetation, soils improve, the quality and quantity of supplies become more certain, the trust needed for effective cooperation increases and becomes more dependable. In short, the actors, whether species or people, develop systems of relationships that control external variability and, by so doing, reinforce their own expansion. That is, connectedness increases.

Diversity of species peaks just as intense competition and control begin to squeeze out those less able to adapt to the changing circumstances. It is during the intermediate stages of ecosystem succession, for example, that the greatest variety of species is found (Bormann and Likens 1981; Connell 1978). As the system evolves toward the conservation phase, K, connectivity among the flourishing survivors intensifies, and new entrants find it increasingly difficult to enter existing markets. The future seems ever more certain and determined.

Since the competitive edge shifts from those that adapt to external variability and uncertainty (r-selected entities), to those that control variability (K-selected), more return is achieved by increasing efficiency for utilizing energy, minimizing costs, and streamlining operations. At the extreme, this can result in increasing returns to scale, as Arthur (1990) suggests for some corporations and products, so much so that new entrants, new innovations, might have reduced opportunity to enter despite their potential superiority. Note, however, that the dynamics of competition in many industries where increasing returns would appear to loom large, and would appear to block potentially superior products, are extremely subtle (Shapiro and Varian 1999).

Not only do potential and connectivity change in th·e progression to the conservation, K, phase, but ecological resilience also changes. It decreases as stability domains contract. The system becomes more vulnerable to surprise. In the forest, fuel for fires and food for insect defoliators reach critical levels as processes that inhibit fire propagation (e.g., fire "breaks") and insect population growth (e.g., avian predation) are homogenized and diluted (Box 2-1). Markets for products can become saturated and profit margins can narrow, with little flexibility for further efficiency increases. Wages might become a target for cost cutting, and the trust accumulated during growth could thereby be weakened. Organizations can become bureaucratized, rigid, and internally focused, losing sight of the world outside the organization. Those, of course, are tendencies, whose inevitability depends on management and design. The exceptions to these tendencies identify the limits to the metaphor presented to this point, and the possible features of human systems that can react and adapt to future events. More on that in Chapter 4.


Kton

In the cases of extreme and growing rigidity, all systems become accidents waiting to happen. The trigger might be entirely random and external-a transient drying spell for the forest, a new critic appointed to the board of directors of the company, an election of a new minister of government responsible for the agency. We have seen all of these in earlier case examples (Gunderson et al. 1995a). Such events previously would cause scarcely a ripple, but now the structural vulnerability provokes crisis and transformation because ecological resilience is so low.

As a consequence, in Schumpeter's (1950) words, a gale of creative destruction can be released in the resulting n phase. Accumulated resources are released from their bound, sequestered, and controlled state, connections are broken, and feedback regulatory controls weaken.

In the shift from K to n, strong destabilizing positive feedbacks develop between the revolting elements (the insect defoliator, the aroused stockholder) and the established aggregates (the trees in the mature forest, the bureaucracy of the firm). But that process is transient and persists only until the resources are exhausted. Insect pests run out of food, and fire runs out of fuel. Workers are fired in efforts to reduce costs, and CEOs are fired to set the stage for restructuring. Temporarily, potential plummets.

ntoa

If the progress from r to K represents a prolonged period during which short-term predictability increases, the shift from n to a represents a sudden explosive increase in uncertainty. It is the phase where conditions might arise for formal chaotic behavior. This alternation between long periods of somewhat predictable behavior and short ones of chaotic behavior might result in systems periodically probing and testing limits. The process generates and maintains diversity--of, for example, species in ecosystems or functions in an organization. And that diversity "lies in waiting" to allow the system to respond adaptively to unexpected future external changes.

The potential left over is from the resources that were accumulated in the mature forest or mature firm. Those resources exist in a variety of forms as legacies of past cycles (Franklin and MacMahon 2000)-in the dead branches and tree trunks not consumed by fire or insects; in the nutrients released by decomposing organic material; in the seed banks established in soil; in the animals and propagules that move over small and large distances; in the physical, architectural structure that had been earlier created. The high potential in K shifts, momentarily, to a low potential where the residual resources are unavailable to or not actively involved in ecosystem growth or maintenance. Nutrients released in the soil begin to leak away until processes of immobilization slow the loss and processes of mobilization


begin to make the soil available for reestablishment. The ecosystem is going through a reorganization, with weak interactions between elements.

The result is that the variables and actors have few resources, and there is, momentarily, lower potential until the reorganization is consolidated and exploited. Species and individuals have loose connections to others and function in a wide, loosely regulated domain of stability as they progress to the phase of reorganization, a. Resilience is high. The released capital begins to leak away, but the wide latitude and flexibility allowed variables and actors means that unpredictable associations can form, some of which have the possibility of nucleating a novel reorganization and renewal. This is the time when exotic species of plants and animals can invade and dominate future states, or when two or three entrepreneurs can meet and have the time and opportunity to turn a novel idea into action. It is the time when accidental events can freeze the direction for the future.

Moreover, the totally unexpected associations and recombinations that are possible in the a phase make it impossible to predict which events in this phase will survive to control subsequent renewal. The phase becomes inherently unpredictable.

Similarly, some of the skills, experience, and expertise lost by the individual firm remain in the region. They are not lost, but they exist only as a potential for future utilization in new or old enterprise. It takes time for the reorganizations to expose the potential in surviving resources.

The a phase turns what might otherwise be a fixed, predictable progression or cycle into wonderfully unpredictable, uncertain options for the future. Controls over external variability are weak. Because of the weakness of connections, the potential in resources now becomes more freely available, and the high resilience and low connectedness makes for random assortments among elements, some of which can nucleate unexpected processes of growth. It is what John Holland captures in his use of geneticalgorithms to model novelty and change in economic and other systems (Holland 1995).

As an ecological example, when there was a massive planetary transformation during the retreat of the ice sheets fifteen thousand years ago, a protracted phase of a conditions gradually shifted northward. Paleoecological reconstructions (Webb 1981; Davis 1986) demonstrate that whole ecosystems did not move as integrated entities. Rather, individual species moved at their own rates to establish themselves where climatic and edaphic conditions made survival possible. Once established, novel associations became possible among previously separated species. Where chance compatibility existed, sustaining relationships then could develop among key species to form and reinforce relationships that were mutually reinforcing. A self-organized system became possible.

In summary, the major ecosystems we know now were nucleated as a mixture of independent species established in an a phase of the adaptive cycle and consolidated during the r and following phases. Subsequent se-


quences of adaptive cycles then could establish stronger interactions among mutually supporting species in a process of competitive and synergistic sorting. That led to the development of self-organizing processes--of a mix of biotic interactions like competition, facilitation, predation, and herbivory, and abiotic ones like fire and storm-processes that reinforce their own function (Levin 1999). The result is the ecosystems we now know as boreal coniferous forests, temperate deciduous forests, grasslands, and the like.

Front Loop/Back Loop: Embracing Opposites

The adaptive cycle illustrated in Figures 2-1 and 2-2 shows two very different stages. The front-loop stage, from r to K, is the slow, incremental phase of growth and accumulation. The back-loop stage, from Q to a., is the rapid phase of reorganization leading to renewal. The first stage is predictable with higher degrees of certainty. The outcomes following destruction and reorganization in the back loop can be highly unpredictable and uncertain.

It is as if two separate objectives are functioning, but in sequence. The first maximizes production and accumulation; the second maximizes invention and reassortment. We have no theorem to prove it, but our intuition suggests that any complex system, if it is adaptive, must generate these two phases in sequence, at some scale. The two objectives cannot be maximized simultaneously; they can occur only sequentially. And the success in achieving one tends to set the stage for its opposite. The adaptive cycle therefore embraces the opposites of growth and stability on the one hand, change and variety on the other. This metaphor suggests that attempting to optimize around a single objective is fundamentally impossible for adaptive cycles, although optimizing the context that allows such a dynamic might be possible. In that case, the nested cycles themselves become part of the machinery to probe and explore an adaptive landscape. That concerns the subject of the next chapter.

The economics literature is noted for its search for optimal solutionseconomic and social. Standard notions of competitive equilibrium, for example, generate allocations that approximately maximize a weighted sum of objectives for some fixed set of weights. Theory shows that these allocations end up converging to a generically unique optimal steady state (McKenzie 1986). However, the assumptions needed for this kind of behavior in general equilibrium economics are severe. Although some effects of relaxation of these assumptions have been studied by Brock (1988) and Grandmont (1998), it is difficult to sort out which predictions of relaxation of these assumptions are consistent with the adaptive cycle metaphor and which ones are not. In any event, the adaptive cycle metaphor might suggest an interesting future research agenda for economics.

Very similar patterns of interactions, at landscape scales, have been discovered in a number of terrestrial and near terrestrial ecosystems-but not all ecosystems, as we will shortly note for pelagic and semiarid grasslands.


Where the full adaptive cycle does operate, periodic flips from one state to another are mediated by changes in slow variables that suddenly trigger a fast-variable response or escape (Boxes 2-1, 2-2; Carpenter 2000).

In real situations of ecosystem management, no manager actually knows the ecosystem model. One must simultaneously estimate it and update it while managing the system. It appears that discounting might be an important force in causing recurrent phases of behavior that could, depending upon the detailed properties of the ecosystem being managed, lead to dynamic trajectories that look rather like an adaptive cycle pattern. Carpenter, Brock, and Hanson (1999) offer an example in which the support of the shock distribution is wide enough and there is a slow variable (phosphate in mud) that recurrently builds up vulnerability, which locates an alternative stable state inside that support. Hence, a manager who discounts the future lightly has a difficult time avoiding an occasional "flip" because of the occurrence of rare but large shocks. We suspect that when learning of model parameters is coupled onto this management problem, even more interesting dynamic interactions will appear. It will be interesting to try to identify the conditions for these patterns to look like adaptive cycles. Are they such as to characterize traditional management of complex ecosystems and thereby explain the paradox of regional resource management introduced in Chapter 1?

This is an example in which consideration of the adaptive cycle metaphor steers the investigator toward asking precise questions about the relationship among the location of potential alternative stable states, the rate of buildup of slow variables, the impact of the slow variable upon construction of alternative stable states, and the size of the support of the shock distribution as a function of current stock and stock of the slow variable.

We do have a growing number of specific mathematical models that expose the specific nonlinear processes that produce this behavior. Carpenter, Brock, and Ludwig (Chapter 7) describe one such set for lake systems. Some more analytically tractable models have also been developed that allow more formal exploration of stability properties. These include ecosystem examples of the dynamics of budworm and forest (Ludwig et al. 1978); of grassland grazing systems (Walker 1981); and oflake eutrophication (Scheffer et al. 1993; Scheffer 1999; Carpenter, Ludwig, and Brock 1999).

In economics, Brock and Hommes's (1997) model of information in an economy has the same features of flipping from one phase to another, as an interaction between fast and cheap learning and slow and expensive learning. In that model, agents have a choice between using last period's price to predict next period's price and base their production plans on that or purchase an accurate prediction of next period's price for a fee and base their production plans on that. For high enough values of a parameter that measures how responsive agents are to economic incentives, this system generates patterns that look rather like an adaptive cycle. This is so because instabilities gradually build up during "normal times" until fluctuations caused by


those instabilities exceed a threshold (which depends upon the size of the fee for more accurate predictive information). This phase looks very much like an r to K phase in the adaptive cycle. When the threshold is exceeded, many agents switch to buying the accurate predictor, which abruptly stabilizes the system. This abrupt change from naive prediction to costly but more accurate prediction resembles a K to .Q phase in the adaptive cycle. At that point the system reorganizes itself after a few periods of stabilization into a new "normal times phase." This looks rather like a compressed version of an .Q to a, a to r phase in the adaptive cycle.

Testing the Limits of the Adaptive Cycle Metaphor

The adaptive cycle is one part of a heuristic theory of change. The other parts concern hierarchies that are formed by nested sets of such cycles at progressively larger scales. Those will be considered in the next chapter. But even at this stage we begin to explore the limits to the adaptive cycle. In itself, the cycle is too general to be viewed as a testable hypothesis. Its value is as a metaphor to classify systems, order events, and suggest specific questions and testable hypotheses that are relevant for our theme of understanding transformations in linked systems of people and nature.

To do that, we examine specific forms of the three properties defining the cycle-potential, connectivity, and resilience-in order to test the limits to this metaphor.

Potential for Change

The potential for ecological, social, or economic change can be expressed and measured in ways specific to specific situations or systems. Ecosystem potential, for example, could be represented by potential productivity-the potential provided by the amount of biomass, physical structure, and nutrients accumulated as a consequence of ecosystem successional dynamics. That is the use Carpenter, Brock, and Hanson (1999) chose when they developed a model and analysis of a prototype watershed where water quality, agricultural productivity, and management decisions interact (Chapter 7).

Social or cultural potential could be represented by the character of the accumulated networks of relationships-friendships, mutual respect, and trust among people and between people and institutions of governance. Falke and Berkes (Chapter 5) and Westley et al. (Chapter 4) use the term cultural capital to describe this potential.

In the economy, potential could be represented by the economic potential provided by accumulated usable knowledge, inventions, and skills that are available and accessible. A particularly important version of that is foresight potential, possible because of the unique self-awareness and cognitive abilities of people. We will dwell on that in more detail later (Chapter 4) because it adds a role for future expectations and the influence of future con-


ditions on the present. This capacity is one of the features that distinguishes human systems from strictly biological and physical ones. It answers, in part, the question of why human systems are not like ecosystems (Brock 2000; Chapter 4). An early model of a process by which humans build expectational models of the system they cocreate and revise is in Brock (1972). An excellent treatment is in Sargent (1999).

Connectedness

The second property is connectedness. It reflects the strength of internal connections that mediate and regulate the influences between inside processes and the outside world-essentially the degree of internal control that a system can exert over external variability. An organism, ecosystem, organization, or economic sector with high connectedness is little influenced by external variability; its operation and fate are controlled by internal regulatory processes that mediate variability. It could be assessed by a measure of equilibrium stability-of speed of return after a small disturbance, for example. Or, less theoretically, it could be measured by the intensity of control by direct human activity as Carpenter, Ludwig, and Brock (1999) did in a model representing a watershed with a linked ecosystem and agricultural economy.

A particularly clear biological example of strong connectedness of this kind is temperature regulation in endothermic or "warm-blooded" animals. Five different physiological mechanisms (such as evaporative cooling and metabolic heat generation) operate to keep internal temperature of the organisms within a narrow range, independent of external variation. The benefit is to open opportunity for the organisms to exist and exploit habitats and conditions forbidden to an exotherm or "cold-blooded" animal. The cost is the cost of maintenance of the regulation-in this example a metabolic cost ten times greater in endotherms that exotherms.

Ecosystem Resilience

The third property is ecosystem resilience, or its opposite, vulnerability. As described in an earlier section, we use resilience in its ecosystem sense (Holling 1973a, 1996; Holling and Meffe 1996) to represent the capacity of a system to experience disturbance and still maintain its ongoing functions and controls. Resilience of this sort depends on the existence of multistable states, for it concerns the likelihood of flipping from one to another. A measure of resilience is the magnitude of disturbance that can be experienced without the system flipping into another state or stability domain.

Carpenter, Ludwig, and Brock (1999) measured resilience in just that way. And that is the way it is treated in Chapters 6, 7, 8, 9, and 10 for linked ecological and economic systems and Chapter 5 for the approaches of traditional societies to sustainability.

2. REsiLIENCE AND ADAPTNE CYCLES 51

These three properties shape a dynamic of change. Potential sets limits to what is possible-it determines the number of alternative options for the future. Connectedness determines the degree to which a system can control its own destiny, as distinct from being caught by the whims of external variability. Resilience determines how vulnerable the system is to unexpected disturbances and surprises that can exceed or break that control. When these properties are used to analyze a model of a linked economic, ecological decision system, the trajectory indeed has the complex "figure 8" form of Figure 2-2 (Carpenter, Brock, and Hanson 1999; Figure 7-4).

Four key features characterize an adaptive cycle and its properties of growth and accumulation on the one hand and novelty and renewal on the other. All are measurable in specific situations and can be used to test the limits of the adaptive cycle representation:

• Potential (e.g., ecosystem structure, productivity, relationships, inventions, and mutations) increases incrementally, in conjunction with increased efficiency but also in conjunction with increased rigidity.

• As potential increases, slow changes gradually expose increasing vulnerability-to fire, insect outbreak, competitors, opposition groups, stockholder revolts.

• Innovation occurs in pulses, in surges of innovation when uncertainty is great and controls are weak so that novel combinations can form.

• Those innovations are then tested; some fail, but some survive and adapt in a succeeding phase of growth.

The adaptive cycle in its most general form is a metaphor and should not be read as a rigid, predetermined path and trajectory-for ecosystems at least, let alone economies and organizations. It suggests periods of waxing and waning tendencies, with various degrees of predictability at different stages. All actors and species can be present throughout-pioneers, consolidators, mavericks, revolutionaries, and leaders. It is their role and significance that change as their actions create the cycle. Phases of the cycle can overlap, but the most distinct separation is between K and n. That is the shift that occurs as a stability region collapses, or as a disturbance moves variables into another stability domain. But even the most predictable sequence from r to K can be diverted by extreme or episodic events.

Even though the adaptive cycle heuristic is general, limits to its applicability need to be identified. As described earlier, the model is too general, even as a metaphor. It even seems to apply, superficially, to non-living systems. There is a dose parallel, for example, between some phases of the adaptive cycle and the sandpile models inspired by Per Bak (1996). At this level of abstraction, the Bak sandpile process looks rather similar to part of the adaptive cycle. First, as sand is added to the pile, it reaches criticality (the


difference between pile size at the beginning and pile size at criticality is like a "potential" at a very slow time scale); and second, the pile, continually fed by sand falling onto it, recurrently relaxes and releases an avalanche.

In these physical cases, potential is accumulated during the r to K phase and dissipated from K to Q in the way described for the adaptive cycle. But unlike such physical systems, living systems transform, invent new forms (mutations, mistakes, and inventions), and endogenously control the potential as it accumulates. When released, it provides the stage for novel reassortments and rearrangements of new elements accumulated from r to K. And these experiments are tested in subsequent phases of growth. Sandpiles do not evolve into new forms; living systems do.

But even restricting the cycle to living systems suggests that too many of those systems seem equally to fit the heuristic model of change: cell development, meiotic reproduction, ecosystem formation, evolution, human organizational stasis and transformation, political and social change and transformation. What is different about these very different systems?

Although there are many examples that match the cycle, we need to explore extreme examples that are likely to be exceptions. Four will be briefly discussed here, to set the stage in later chapters for deeper analysis. The criterion to select extreme examples concerns the way external variability is treated by the system.

Broadly, there are three strategies for dealing with external variability. One is to live passively with external variability by evolving appropriate adaptations; one is to control variability actively, minimizing its internal influences; and one is to anticipate, create, and manipulate variability.

The empirical studies that led to the development of the adaptive cycle were all examples of the second strategy-of at least partial regulation of variability. The ecological examples we used were from temperate, productive terrestrial systems where considerable resources of biomass, structure, and nutrients are accumulated and where processes self-organize physical structures and patterns that regulate external variability. An ecosystem is not, in any rigorous sense, homologous to an individual organism, and the regulation is considerably looser (Levin 1999). But the regulation is sufficient to partially moderate external variability. The temperature within the closed canopy of a forest, for example, fluctuates over a narrower range than that outside the forest. And the nutrients from variable rain and erosion are "managed" by the biota to be sustained in soil or biomass. Even at a regional scale, for example, it has been shown, through simulation models, that the landscape-scale attributes of the Amazonian forest can affect regional climate in a way that maintains that forest (Lean and Warrilow 1989). In northern forests, snow melt and initiation of the growing season occur earlier in the spring because of greater heat input associated with low albedo spruce forests (Hare and Ritchie 1972).


Four Extreme Examples

If we are to find exceptions, therefore, the first place to look is for systems that might represent examples of the other two strategies-living passively with variability or creatively manipulating it. We initially focus on two examples of the first: pelagic, open-water communities and semiarid savanna. Each is strongly influenced by external variability, and the species in each evolve adaptations to live passively with that external variability.

We follow with two possible examples of the second: examples of forward expectations viewed through the lens of the economists' market model and examples of large bureaucracies such as AT&T and resource agencies of government. It is in such human systems that we might identify ways to anticipate and manipulate variability creatively, and escape the apparent inevitability of the adaptive cycle and its prediction of rigidity leading to crisis.

Aquatic Systems

Some aquatic communities are built around species that can attach to or build substrate. As a consequence, the physical attributes of the plants or structures can moderate influences of external variability, and the biota can accumulate substantial biomass in individual organisms, much as terrestrial forests can. For example, kelp forests and coral reefs show the existence of multistable states and adaptive cycles like those already described (see Box 2-2). And both kelp and coral moderate the variability of currents and waves. The same is true of shallow lakes and lagoons where rooted aquatic plants become part of the determinants of the state of the ecosystem (Scheffer 1999; Box 2-2; Chapter 10). Scheffer (1999; Chapter 10) shows multistable states and the possibility of boom-and-bust cycles organized by nonlinear relationships like the adaptive cycle.

In contrast, open-ocean or pelagic biotic communities remote from land or substrate exist at the whim of ambient currents and nutrients. They therefore become organized largely by the external physical variability of turbulence, waves, upwelling, and gyres in the ocean and by trophic relationships among the species. Pelagic communities have no way to develop the fixed physical structures that can moderate external environmental variability by establishing self-organized architectural patterns on their landscape or in their waterscape. Ramon Margalef, the Spanish ecologist, noted that such communities are organized into classes defined by two properties-one of extant nutrient level and one of turbulence, similar to two of the axes of the adaptive cycle (Margalef 1981). In these cases, external physical processes at any point in the ocean fix the level of those properties and define the biotic classes. Each class has evolved adaptations to deal passively with the external variability it is exposed to.


In these pelagic examples, the communities are fixed in their condition, developing remarkable adaptations to do that. As communities or ecosystems, they do not cycle through the full suite of phases of the adaptive cycle. Each community finds itself in one of the phases of the adaptive cycle, oscillating because of trophic dynamics. But they stay there because they cannot exert dynamic control over external turbulence or nutrient levels. At best, they experience only part of the cycle as, in the case of highly eutrophic, lowturbulence situations, the communities (like red tides) flip into anoxic states and are dispersed. It is only the individual cells that go through the full cycle as described, in a classic process of individual variation and natural selection, thereby developing the adaptations to deal with the variability they experience but cannot control.

Semiarid Savanna Ecosystems

Arid grassland systems "are simply waiting for the big event, the trigger of rainfall. Using an amazing array of adaptive mechanisms they remain relatively quiet and inactive during dry times waiting for favorable conditions" G. Ludwig et al. 1997). Hence the potential in biomass and nutrients (r to K) does not accumulate in as regular and continuous a way as in the temperate ecosystem examples. Rather, biomass and nutrients accumulate potential episodically, triggered by external events like a rare pulse of rainfall. After the pulse, there is a slow decline of potential and accumulated resources. Growth along the trajectory from r to K is therefore sporadic, ratchet-like rather than continuous. Marvelous adaptations have evolved to keep the potential for spurts of growth in waiting for the rare but large rainfall event and to slow its loss in succeeding periods of drought. Physical topographic patterns at micro, meso, and landscape scales provide a heterogeneous template for sustaining nodes of potential for increase.

If enough growth does accumulate, the larger amounts of biomass can begin to control the variability of exogenous resources. For example, there is evidence for regulation of nutrient variability and soil moisture by patchy distribution of biotic material acting as traps for water and nutrients (Tongway and Ludwig 1997a). Moreover, prior to European settlement, there is evidence in these savannas of cumulative sequences of vegetative growth that were ultimately released in a K to Q break by an interaction between fire and grazing by mid-sized marsupial herbivores. The result was similar to the adaptive cycle described earlier, and, as in such cycles, the cycle maintained a balanced set of species, serving different ecological functionsin this case, annual and perennial grasses, shrubs, and trees. A changed fire regime after European settlement, combined with the extinction of midsized mammals, establishment of the European rabbit, and sheep grazing, led to a simplified system much more driven by external episodic events, with less accumulation of biomass.

We conclude that these arid grassland systems tend to stay in the lower quadrants of the adaptive cycle (Figure 2-1). That is where potential is low,


connectivity is low, and resilience is high. It is where novel adaptations of species to external variability are continually generated and tested through natural selection. It is the condition in which external variability controls the system's development. Although these grasslands are not very productive for use in grazing, they are astonishingly resilient to the effects of overgrazing. Remove grazing pressure and they recover-slowly, but they do recover (see Box 2-3). They have evolved adaptations to persist through extremes. \Vhen the productivity is so low that insufficient biomass can accumulate to trigger a K to n shift, they are therefore dominated by properties of the a. and r phases, where there are continual adaptations to external variability being developed. This therefore represents a variant of the adaptive cycle seen in more productive systems, where variation is more predictable and is controlled.

Large Organizations: Bureaucracies and an Industry

Alfred Marshall, the dean of British economics, has stressed life-cycle theories of firms and industries since his Principles of Economics was published in 1890. Indeed, Marshall thought much more like a biologist than an economist but was constrained by the types of mathematics available at his time. A reread of Marshall with modern mathematical equipment from mathematical biology and pattern generation and recognition might be a useful way to develop the adaptive cycle idea for serious use in economics. That is beyond this chapter and this book, but perhaps we can set the stage by reviewing patterns of change in human-dominated systems, structuring events with the help of Figures 2-1 and 2-2, and seeking to identify the kind of empirical evidence needed to discover exceptions.

We start with a bias. Not that the adaptive cycle applies in all details to human organizations, but that it does not. Human cognitive abilities provide the ability for developing forward expectations that should allow humandominated systems to respond not just to the present and the past, but to the future as well. In theory, at least, that is what happens in true marketsfuture risks and opportunities are identified by a myriad of entrepreneurs, and specific solutions are given present value through a futures market. Such forward expectations, together with an effective market mechanism, would stabilize the boom-and-bust cycles of the adaptive cycle. In fact, that is what has happened over the past decades as societies have encountered potential scarcity of resources (Solow 1973; Chapter 4). More accurately, that would transfer those cycles from the economy as a whole to smaller elements within it-to the gamblers who bet on the future. It suggests a hierarchical structure of cycles, a construct that will be discussed in the next chapter.

We have barely started this effort to rationalize such theoretical features of market economics with the adaptive cycle. Chapter 10 faces the issue directly, as does Chapter 7. Both encounter serious analytical problems when the natural parts of the linked economic/ecological system have nonlinearities


Box 2-5. The Telephone Great Fits the Figure Eight?

W. A. Brock

In the following paragraphs, I explore the use of the adaptive cycle diagram in the history of telephony in the United States. This box makes a feeble attempt to subject the adaptive cycle diagram to a weak type of Popperian falsification test using the history of the Bell System. The terms AT&T and Bell System are used synonymously. The story is based on work by Bornholz and Evans, in Evans 1983.

The industrial organization of telephony in the United States has gone through several growth, reorganization, and renewal eras: (1) Open competition at the birth of the industry led to temporary dominant monopoly of the Bell System due to patent and other head-start advantages. (2) A serious threat to the Bell System and partial breakdown of its temporary dominant monopoly due to patent expirations in 1893 and 1894 caused a reorganization, in order to face another period of open competition from independent telephone companies (called telcos). (3) After finding (around 1907) a workable strategy to fight the competition unleashed by patent expiration, the Bell System evolved into a dominant monopoly, which led to a crisis (circa 1915-19) resulting from antitrust action and possible government nationalization of the telephone industry. (4) Resolution of this crisis led to a regulated monopoly of the Bell System, which prevailed essentially until the early 1980s when the settlement of a lawsuit restructured the entire industry. The U.S. government filed the suit against AT&T in 1974. The case was settled in 1982 with an ordered breakup of the company.

I'll organize the telling of the history of the above phases using the adaptive cycle diagram (Figure 2-2). One could ask whether the historical sequence is consistent with the adaptive cycle diagram and, in a falsification sense, what it means to be consistent or inconsistent with that diagram. In other words, can one use the Bell System history to hint at what it would take to identify a sequence of events that we would rule as being in agreement with the adaptive cycle or not?

The adaptive cycle diagram suggests a certain inevitability to the occurrence of the following sequence of phases: r to K, K to n, n to a, (l to r (with a possible flip between n and (l enroute to r);


repeat. Furthermore, resilience to shocks supposedly decreases toward the end of the r to K phase as capital gets bound up more and more tightly. That is, the internal dynamics of the industry and AT&T are predicted during the initial r to K phase of the cycle to push the company toward an edge of precariousness, due to this binding and rigidification, where the company would be "an accident waiting to happen."

But the first crisis it faced at the end of phase 1 was due simply to the expiration of its two most basic patents in 1893 and 1894. This was a mammoth shock since the whole business was based on exploiting the temporary monopoly granted by those patents. Testing the predictive power of the validity of an r to K phase in era 1 would involve a detailed historical reading of the record of response to see if the Bell System had rigidified. This examination would reveal whether the natural accumulation of habits, protocols, and other efficiency-enhancing procedures when one optimizes in a stable, recurrent, setting had occurred during the period when the company was protected by the two basic patents. That is, before the expiration of the patents had the company's resilience to shocks lessened? Since the management knew when the patents would expire, this kind of analysis could reveal whether management created more resilience in anticipation of the shock it should have known was coming. If the historical record showed an increase (rather than a decrease) in a usable measure of resilience before the patent expirations in 1894, that might be viewed as contradictory to the r to K to Q part of the diagram. It is beyond the scope of this box to answer the question, but it appears to be well posed.

The record does show that a type of reorganization occurred following the expiration of the patents, in the form of a vigorous counterthrust by the Bell System toward new entrants in the market. Bell faced the competition head-on by prohibiting interconnection, prohibiting supply to independents by its manufacturer, rapidly expanding its own network, filing patent suits against the independents, and cutting its own prices when independents appeared (Evans 1983).

The second crisis occurred around 1907, when Bell System management had to create a new style appropriate to dealing with the surge of new entrants into the business after the strategy described above had failed. In 1907 a changing of the guard took place along with an abrupt change in policy to "financial competition through absorption and purchase of independents" (Evans 1983).

continues


The third crisis was the reaction of the independent telcos and the U.S. government to the monopolization of the business. Acquisition of independent telcos in the early 1910s led to the emergence of the structure of a regulated monopoly with a fringe of independents, which characterized the industry until the early 1980s.

The phase that lasted from the early 1920s to 1982 might fit the adaptive cycle diagram quite well. During that phase, over a sixtyyear period, the Bell System evolved an elaborate, routinized way of doing business. Furthermore, almost all of the top positions were ingrown and the top officers were hired from within the organization. This structure would appear to be rigidified by any measure.

The crisis that led to the court-ordered breakup of AT&T in 1982 may have occurred because technological change had made the old cost allocation across the set of users unsustainable. The longdistance calling portion of the business was heavily subsidized by local calls. A common sound bite was "6 percent of the users generate 60 percent of the revenue, and these users now have the technology to bypass Bell's network." Hence, when these highdensity users put pressure on the regulatory framework, AT&T reacted in a rather routinized, knee-jerk fashion by using the regulatory process to bar entry while claiming to act in the public interest. The reaction happened even though any definition of the public interest, using available, standard economic science, would have suggested something like an analogue of taxation on revenue diversion with tax rate based on estimated depth of AT&T scale economies and with surtax on sales of the whole industry to fund public interest services such as lifeline service for the poor. The seeming inability of AT&T to react creatively may have been due to an r-to-K-type phase of rigidification from the narrow-based type of optimization ideal for the sixty-year-old industrial structure in which they operated.

The forced breakup (a K to Q phase) led to a powerful reorganization of the component parts of the Bell System as it struggled to adapt to a brand-new competitive environment after operating in much the same way for almost sixty years (ann to a. phase followed by a. tor). The computer revolution and AT&T's role in that revolution could be viewed as the latest r to K phase, which is still ongoing.

and multistable states, and when there are interactions among nested sets of fast and slow variables. At a minimum we conclude that, in those circumstances, anticipating and creating useful surprises needs an actively adaptive approach, not a predictive, optimizing one.


We hoped to discover useful exceptions in a deeper examination of change in specific large human organizations. But we failed. The book that motivated the Resilience Project, Barriers and Bridges to the Renewal of Ecosystems and Institutions (Gunderson et al. 1995a), offers a number of case examples of bureaucracies dealing with natural resources in ecosystems and with people's needs and desires. All cases seem slavishly to follow the adaptive cycle, with the bureaucracy attempting to reinvent itself in a series of crises and responses to crises but having difficulty doing so because of a lack of external competitors (Light et al. 1995; Chapter 12).

The history of telephony in the United States has a rather similar shape to that of the case studies discussed in Gunderson et al. (1995a) and in this volume. That history is summarized in Box 2-5. In the adaptive cycle storytelling framework, one can label the year 1894 as the point at which AT&T ended the first r to K phase, swept through the release of the "old ways of doing business" accumulated during the period of patent protection, and reorganized itself to deal with the new influx of entrants to initiate a second r to K phase. Much like the initial stage of r-selected species in ecosystems, young, brash, fast-growing, aggressive entrepreneurial companies sprang into existence and raced each other across the landscape to lay out telephone wire and poles ahead of rivals. It looked like a race to build networks since each realized the competitive advantage of the largest interconnecting network, and each realized that the first to lay the largest network would ultimately lock in most of the market. Thereafter, two additional waves of growth, collapse, restructuring, and innovation have occurred.

The empirical evidence suggested in Box 2-5 to test the reality of elements of the cycle has not been collected and analyzed for the telephone industry. But there is at least the suggestion that early in development, the early telephone companies did show enterprise and sensitivity to outside variability (a to r). There is even the suggestion that they structure themselves with sufficient flexibility (low connectedness) so they are poised to take quick advantage of episodic opportunities. But then gradually resources accumulate and rigidification sets in. Baron et al. (1998) provide measures of bureaucracy and time histories of the development of those measures that document parts of the phase of rigidification of an adaptive cycle. As hard as we try, we cannot see these specific examples of bureaucracies and industries as exceptions to the adaptive cycle pattern.

We argue that a formal effort is needed to disprove the patterns of the adaptive cycle, using other examples of companies that have apparently solved the challenge of adapting to external variability and internal rigidities by developing foresight capabilities and a market for them within the company. Some claim that that is what Jack Welch, CEO of General Electric, was able to design in the reinvention of that company (Hurst 1995).

Where does the extraordinarily important argument of economists regarding the role of foresight potential exert its stabilizing role? There certainly are some examples of the exercise of foresight potential and the exis-


tence of a futures market that turn future conditions into present decisions and actions. In theory and in practice this can reduce variability, establishing these examples as cases of the third strategy: to anticipate and manipulate the variability creatively. When it works, does this keep the system/sector in the lower quadrant of the adaptive cycle, cycling largely between a and r, perpetually inventing and innovating and adapting? If so, this is another cycle that is qualitatively distinct because of the strategy of creatively manipulating variability. But is its very success transient, creating the resources that launch the other phases of the adaptive cycle? All we can do at this stage is to pose questions in forms that have broad relevance for sustainability and development:

• Under what conditions does increasing accumulation of potential not lead to increasing rigidity?

• Are there patterns of evolutionary change that do not experience an alpha phase of reorganization and reassortment?

• How is a loosely structured set of relationships maintained in order to be alert to unexpected opportunity?

• When does foresight potential or forward expectations not reduce variability?

Adaptive Cycles, Maladaptive Consequences

Management and resource exploitation can overload waters with nutrients, turn forests into grasslands, trigger collapses in fisheries, and transform savannas into shrub-dominated semi-deserts.

There are many examples of managed ecosystems where loss of resilience is followed by a shift into an irreversible state or a very slowly recovering state-e.g., in agriculture, forest, fish, and grasslands management, as summarized in Holling (1986) and Box 2-3. In each of these cases the goal of management was to stabilize production of food or fiber or to moderate extremes of drought or flood for economic or employment reasons. In each case the goal was successfully achieved by reducing natural variability of a critical structuring variable such as insect pests, forest fires, fish populations, water flow, or grazing pressure. The result was that the ecosystem evolved to become more spatially uniform, less functionally diverse, and thereby more sensitive to disturbances that otherwise could have been absorbed. That is, ecological resilience shrank even though engineering resilience might have been great. Short-term success in stabilizing production reduces natural variability, so that the stability landscape shifts and evolves to reduce adaptive capacity. Short-term success in optimizing production leads to long-term surprise.

Moreover, such changes can flip the system into an essentially irreversible state because of accompanying changes in soils, hydrology, disturbance processes, and species complexes involved in the regulation or


control of ecological structure and dynamics. In those situations, control of ecosystem function shifts from one set of interacting physical and biological processes to a different set (Holling 1995).

But at the same time that the natural systems become less resilientmore vulnerable-changes occur in three other connected entities: the management agencies, the associated industries, and society at large. Specifically, the management agencies, in their drive for efficiency, become progressively more myopic and rigid; the relevant industries become more dependent and inflexible; and the public loses trust. This seems to define an ultimate pathology that typically can lead to a crisis triggered by unexpected external events, sometimes followed by a reformation of policy (Gunderson et al. 1995b).

Examples of this pathology were first described in systems of forest development, of fisheries exploitation, of semiarid grazing systems, and of disease management in crops and people (Holling 1986). These examples have been greatly expanded and the analysis deepened (Gunderson et al. 1995b), adding examples of development, exploitation, and management of wetlands (e.g., the Everglades, Light et al. 1995); rivers (Columbia River, Lee 1995); marine bays (Chesapeake Bay, Costanza and Greer 1995); and large enclosed bodies of water (Great Lakes, Francis and Regier 1995; Baltic Sea, Jansson and Velner 1995).

That is what led us to define a pathology of regional development and renewable resource management (Gunderson et al. 1995).

Policies and development initially succeed, leading to agencies that become rigid and myopic, economic sectors that become slavishly dependent, ecosystems that are more fragile, and a public that loses trust in governance.

This occurs as a consequence of efforts to constrain the adaptive cycle in the ecosystem and in the management agency. Adaptive capacity is lost, and each swing of the cycle demands larger and more expensive solutions. At the moment, for example, critical processes of the Everglades of Florida are being restored in what is the largest and most expensive effort of restoration ever attempted.

The examples of adaptive systems suggest a remarkable persistence, in roughly similar form. What explains such persistence not always, certainly, but frequently? Systems do change if external conditions change sufficiently, or if internal accumulation of capital passes critical thresholds. But such conditions occur rarely, relative to the speed of the basic adaptive cycle. There is another paradox. On the one hand, experiment and novelty are essential for an adaptive system; but on the other, experiments can destroy the experimenter, and novelty can be maladaptive. Something is missing in the story, something that speaks to the sustainability part of the phrase sustainable development. That missing part concerns dynamic cross-scale interactions-the panarchy. That is the subject of the next chapter.


Summary and Conclusions

Abrupt shifts among a multiplicity of very different stable domains have been observed in a number of regional ecosystems (lakes, marine fisheries, benthic systems, wetlands, forests, savannas, and rangelands), some economic systems, and some political systems.

A fundamental unit for understanding complex systems from cells to ecosystems to societies to cultures is an adaptive cycle. Three properties shape the pattern of dynamic change in the cycle: Potential sets limits to what is possible-it determines the number of options for the future. Connectedness determines the degree to which a system can control its own destiny, as distinct from being caught by the whims of external variability. Resilience determines how vulnerable a system is to unexpected disturbances and surprises that can exceed or break that control.

Different classes of systems represent variants of or departures from the adaptive cycle. Some examples of exceptions are:

• Physical systems in which a lack of invention and mutation limits the potential for evolutionary change (examples: tectonic plate dynamics, Per Bak's sandpiles (1996)).

• Ecosystems strongly influenced by unpredictable episodic external inputs, with little internal regulation and with highly adaptive responses to opportunity (examples: exploited arid rangelands, pelagic biotic communities); they can remain largely in the lower quadrant of the cycle, oscillating in the a and r phases, dominated by trophic dynamics.

• Ecosystems and organizations with predictable inputs and some significant internal regulation of external variability over certain scale ranges (examples: productive temperate forests and grasslands, large bureaucracies); they represent the full cycle of boom-and-bust dynamics.

• Biological entities with strong and effective homeostatic internal regulation of external variability (examples: cells and ionic regulation, "warm-blooded" organisms with endothermic control of temperature). System variables remain near an equilibrium, and the individual is freed to exploit a wider range of opportunities within a community or ecosystem. It is an example of local control that can release external opportunity and variability at a different scale-a transfer of the adaptive cycle to a larger arena.

• Human systems with foresight and adaptive methods that stabilize variability and exploit opportunity (examples: entrepreneurial business, futures markets and resource scarcity, some traditional cultures). The high variability of the adaptive cycle is transferred from the society to the individual entrepreneur.

CHAPTER3

SUSTAINABILITY AND PANARCIDES

C. S. Holling, Lance H. Gunderson, and Garry D. Peterson

Goat-legged, enthusiastic, lover of ecstasy, dancing among stars, Weaving the harmony of the cosmos into playfUl song.

-Description of Pan from The Orphic Hymns

I n the late 1960s the first photographs of Earth from space provided an evocative perspective of the planet. The planet appeared as an integrated entity made up of a membrane of life intermixed with atmosphere,

oceans, and land. To many, the image suggested that humans were part of that entity, nurtured and challenged by it and responsible for its protection. To others, it suggested the possibility that humans could control planetary development for human opportunity. An advertisement of the development arm of a bank, for example, published the photograph with the caption: "Businessmen, Devour This Planet!" What seemed to be a delicate jewel to some was a digestible morsel to others. But it was the image itself that suggested the integrated nature of the planet. The photograph showed that scale of observation shapes both explanations of patterns in nature and actions conceived.

What is the appropriate scale of observation in our search for theories and actions for sustainable futures? Our focus here is local, regional, and global; so there can scarcely be any single appropriate scale. Moreover, we are concerned with interactions across scales from the very small and fast to the very big and slow. A sense of the patterns and processes across those scales is provided by a marvelous set of images in the book Powers of Ten (Morrison and Morrison 1982). These images range in scale from microscopic to the universe, each photograph covering a size that is one order of magnitude larger than the preceding. Hence the evocative image of Earth from space is only one of a sequence of thought-provoking images. And that sequence suggests another kind of integration that emerges from small things affecting larger ones, and large ones influencing small things.

63

64 HOLLING, GUNDERSON, PETERSON

A disrupted society and an expanding transportation system can transform a local infection of chimpanzees into a global epidemic. AIDS is an example.

Our interest is in a subset of those scales shown in Powers of Ten, where life, including human activities, interacts strongly with physical processes. To help communicate the significance of those scales for issues of sustainability, we assembled two series of powers of ten images for one of the case studies that informs this book-the Florida Everglades. One set started with a sugar cane plant in the extensive agricultural area south of Lake Okeechobee, and one set started with a sawgrass plant in the very heart of the Everglades. Both ended with the image of the planet from space. Some selections from the latter set are shown in Figures 3-1 through 3-6.

Over fifteen orders of magnitude separate a plant in the Everglades from the planet in space. Distinct regions of scale appear with unique objects and distinct processes in each. At the smaller scales, individual plants suggest the physiological processes of plant growth, nutrient exchange, and decomposition (Figure 3-1). At coarser scales, microtopography and small-scale disturbances establish plant associations of sawgrass, pond, and wet prairie (Figure 3-2). Still coarser scales show how the slowly moving water in the "river of grass" (Douglas 1947) establishes tree islands whose elongate patterns reflect the direction of the movement of water (Figure 3-3). Coarser yet, and landforms emerge, representing human and natural land-use patterns and conflicts between wilderness areas of Everglades National Park, water conservation areas, large-scale industrial agriculture, and urban development (Figure 3-4). A network of canals defines each, developed as responses to one or more of the crises of the past caused by interactions among those land uses (Chapter 12, Figure 12-1). Still larger scales suggest geomorphological structures and land-ocean-atmosphere interactions that mediate climate warming and sea level rise (Figure 3-5). At that same large scale, geopolitical and international trade policies have set indirect subsidies for sugar (Figure 3 -6). They establish dependencies and trigger conflicts that affect life and the environment in places as far flung from the Everglades as sugar-growing regions in Louisiana, Cuba, Zimbabwe, and eastern Australia.

This examination of the Everglades from the perspective of a plant to that of the planet provides a starting point for a discussion of the relationship between sustainability and scale. Four points launch this chapter from this impressionistic journey.

First, as scale increases, distinct objects appear and persist over distinct scale ranges and disappear, to be replaced by others that are aggregates of those objects. At each such range of scales, the objects have geometric properties of size measured as extent and grain. They also have temporal qualities of duration measured as generation time and turnover time. They are dynamic, not static, entities. This is summarized in Figure 3-7, where each object is shown in axes of space and time.

Second, there are abrupt breaks in patterns, across scales. We cannot simplify by assuming fractal constancy across scales. We might expect such self-similarity if the only processes were physical processes like those in air

m

4

2

0

-2 ·· ······ ·•··· .

10 m

3. SUSTAINABILITY AND PANARCHIES 67

100 m

. .. ··· ·i ···

km 10 km

100 1,000 km km

'fronts, land/sea tropical

processes storms ,

thunderstorms :

10,000

century

year

months

days hours

-4 .... ... <· ...... ;.

0 2 3 4 5 6

log space (meters)

Figure 3-7. Hierarchy of vegetation, landform structures, and the atmospheric processes for the Everglades system. This plot depicts in scales of space and time the structures apparent from the shifting "powers often" windows of Figures 3-1 through 3-6.

or water. But biological processes, interacting with abiotic ones, add scaledependent patterns on the physical templates. Vegetation affects hydrological processes, creating depositional rates for sediments and decomposed material to form structures that reinforce the vegetation processes. For example, once a tree island begins to form on depositions that rise above some water level threshold, the islands expand, stabilize, and persist (Figure 3 -7). Meso-scale disturbances of fire and storm establish successional patterns that shift from ponds to wet prairie to sawgrass and back in a multidecadal dynamic. They create the conditions for their own existence. They represent processes of biotic self-organization over specific scale ranges on a physical template.

Third, human impacts depend on the scale and on the medium affected-land, atmosphere, or water. Human influence on atmosphere occurs at all scales and has become planetary, as indicated by atmospheric C02 accumulation and the greenhouse gas effect. Human influences on water are largely up to the scales of regions through construction of dams, dikes, and canals that allow water storage and transfers. Human influence on land, however, does not have that sweep; it is more local. For example, industrial agriculture homogenizes patterns at scales of fields within agricultural areas, but at coarser scales, human land-use patterns (agricultural area, park, and urban) largely reflect the existing landscape topography, formed by slow and extensive geomorphological processes. People farmed where soils exist; cities formed above flood-prone areas on the Atlantic ridge. And those utilization patterns change slowly. Humanity has yet to become the terraformers at the planetary scales suggested in science fiction.


Finally, issues, problems, and opportunities are not just local; they can have integrated causes from processes at several scales. Some of those are local and are perceived locally. Some can originate half a world away, formed by geopolitical hemispheric policies, world trade, and climate change.

In the remainder of this chapter we seek to understand how these crossscale processes shape ecological and social dynamics. We first discuss the nested nature of temporal dynamics and spatial structures in both human and social systems. We then develop an alternative theoretical construct (dubbed panarchy) to capture these relationships. We then discuss the structure and dynamics produced by panarchical constructs and end with a brief description of what a panarchical perspective suggests about inherent differences between human-dominated systems and ecosystems.

Nested Cycles

Three decades of studies of regional ecosystems from northern forest, southern wetlands, dry grasslands, lakes, and seas show that the interaction between fast and slow processes establishes the key features of ecosystems described in Chapter 2. The entities created by those interactions form hierarchies, such as those illustrated for the Everglades in Figure 3-7 or for northern boreal forests in Figure 3-8.

A growing body of empirical evidence, theory, and models suggests that these hierarchical ecological structures are primarily regulated by a small set

100 10 1,000 em m m km km

4 ...... , ............. "[ landscape r · .......

2 climate

~ change

~ El Nif'lo 0 year

<I> long months E :;::; . waves C'l -2

fronts days !2

-4 thunderstorms hours

breeze ·6 minutes

-2 0 2 4 6

log space (meters)

Figure 3-8. Time and space scales of the boreal forest (Holling 1986), of the atmosphere (Clark 1985), and of their relationship to some of the processes that structure the forest. Contagious meso-scale processes such as insect outbreaks and fire mediate the interaction between faster atmospheric processes and slower vegetation processes.


of plant, animal, and abiotic processes (Carpenter and Leavitt 1991; Holling 1992; Levin 1992). Each of these key processes operates at characteristic periodicities and spatial scales (Holling 1992; Figure 3-8). Small and fast scales are dominated by biophysical processes that control plant physiology and morphology. At the larger and slower scale of patch dynamics, interspecific plant competition for nutrients, light, and water influences local species composition and regeneration. At a still larger scale of stands in a forest, meso-scale processes of fire, storm, insect outbreak, and large-mammal herbivory determine structure and successional dynamics from tens of meters to kilometers, and from years to decades. At the largest landscape scales, climate, geomorphological, and biogeographical processes alter ecological structure and dynamics across hundreds of kilometers and over millennia (Figure 3-8). These processes produce patterns and are in tum reinforced by those patterns; that is, they are self-organized (Kauffman 1993).

In over thirty examples, the complexity of the behaviors and the challenges to policy can be traced to interactions among three to five sets of variables, each operating at a qualitatively distinct speed (Holling 1986; Table 3-1). We conclude that some small number of variables is important

Table 3-1. Representative Key Variables and Speeds in Seven Classes of Systems

The Variables

The System Fastest Slower Slowest References

Forest-pest insect foliage tree Clark et al. 1979; dynamics Ludwig et al.

1978

Forest-fire intensity fuel trees Holling 1986 dynamics

Savanna annual grasses perennial shrubs and Walker 1981; grasses grazers Chapter 11

Shallow lakes phytoplankton sea grasses grazers Scheffer et al. and seas and turbidity 1993; Chapter 8

Deep lakes phytoplankton zooplankton fish and habitat; Carpenter, Brock, phosphate in and Hanson mud 1999; Carpenter,

Ludwig, and Brock 1999

Wetlands periphyton saw grass tree island; Gunderson peat accreation 1994, 1999a

Human disease disease vector and human MacDonald organism susceptibles population 1973; May 1977


Box 3-1. Malaria and Adaptive Dynamics

M. Janssen and G. Peterson

Malaria is one of the world's most important vector-borne diseases, and its impact is expected to become more severe in the coming decades. It is caused by several species of parasites (Plasmodium vivax, P. falciparium, P. ovate, and P. malarie). The primary vector is the mosquito. Every year about 6 million people become sick with malaria, and of that number 1-1.5 million die. Many of those who die are children, and chronic nonsymptomatic infections usually persist in surviving children.

After World War II the effective use of DDT and other insecticides led to the eradication or near eradication of malaria in temperate zones and in some tropical areas. The rate of decrease has now slowed considerably, and a resurgence of malaria has occurred in several countries (Krogstad 1996; World Health Organization 1996).

The resurgence of malaria is partially due to the success of previous control efforts. The malaria parasites have become increasingly resistant to antimalarial drugs, and mosquitoes have become more resistant to insecticides. The evolution of resistance in the parasite and in mosquitoes can reduce the resilience of malaria control and may lead to higher levels of malaria than before the control strategy was introduced.

When a person survives malaria infection, he or she develops some immunity to malaria. When insecticides or drugs reduce the number of people who are exposed to malaria, fewer people build up immunity and more people become susceptible. The greatest increases in susceptibility are among older people. A combination of increased resistance of the malaria parasite or malaria mosquito and an increase in the number of susceptible people can produce a higher incidence for malaria 0 anssen and Martens 1997).

These dynamics can convert an endemic disease to a potentially epidemic disease. Disease control leads to a loss of disease resistance in people and an increase in control resistance in the disease system. These changes can increase the difficulty of controlling the disease, as the risk of a disease outbreak increases and the ability to control it decreases. Consequently, the management of malaria must manage not only the fast dynamics of malaria and mosquito populations, but also the slower dynamics of malaria susceptibility, drug resistance, and pesticide resistance and the still slower dynamics of human populations and development.


because a minimum number of interactions must be represented for any particular problem or policy. A dynamic of one or two variables, while convenient for analysis, misses critical properties of stability and instability for adequate understanding of predictability and uncertainty for effective policy and action. Simple graphical stability analyses explain how nonlinear attributes can generate novel patterns in ecosystems (see Chapter 8; Scheffer 1998). Such graphical techniques also explain, in an accessible way, how unique properties and behavior of ecosystems emerge as interactions go from one to two to three variables (Holling 1986). These case studies suggest that a handful of critical variables-more than two, certainly, and probably fewer than six-can capture key behavior.

We particularly emphasize that the speeds of each set are distinctly different from those of their neighbors. Needles, for example, cycle with a generation time of one year, foliage cycles with a generation time of ten years, and trees cycle with a generation time of one hundred years and more. In the cases noted in Table 3-1, there is typically at least an order of magnitude difference between speeds. Thus frequency plots of variables show a small number of peaks, each reflecting the influence of one of the set of critical variables. The three to five fast/slow sets of variables, the nonlinear relationships between them, and stochastic processes generate the multistable behavior and the kinds of policy surprises discussed in Chapter 2. An example for malaria is described in Box 3-1.

A beautiful example of the consequences of such attributes for understanding and for policy has been shown by Carpenter, Brock, and Hanson (1999) in a model of a prototypical watershed where a lake ecosystem with three speeds of environmental variables interacts with phosphate from agriculture and decisions of managers. That model and others with similar attributes are summarized in Chapter 7. These models suggest that a minimal set of attributes needs to be incorporated into a modeling framework to deal with the issues of scale. Among the ingredients needed for such policy-relevant tools are a small set (three to five) of key variables that operate at at least three different speeds, nonlinear interactions among the variables, relationships that create shifting controls, and changing vulnerability that tracks the slowly moving variables. The results from these models present a major challenge to traditional optimization and traditional policy assumptions, as described in later chapters.

Chapter 2 focused on resilience and the adaptive cycle of growth, reorganization, and renewal as it might apply to a landscape scale. But each element in the hierarchy-from plant to patch, to stand, to ecosystem, to landscape-has its own adaptive cycle. There are nested sets of such cycles. The rate of cycling and the size of the element establish its position in the space-time hierarchy. But how do those elements interact with each other? The answer reveals that hierarchies are dynamic structures whose features retain both the creative and the conservative properties that define sustainability.


Hierarchies and Panarchies

The adaptive cycles described in Chapter 2 represent one of the two features that distinguish the scheme presented here. The second feature concerns the manner in which elements of complex adaptive systems nest in one another in a hierarchy. Simon (1974) was one of the first to argue the adaptive significance of such structures. He called them hierarchies but not in the sense of a top-down sequence of authoritative control. Rather, semi-autonomous levels are formed from the interactions among a set of variables that share similar speeds (and, we would add, geometric attributes). Each level communicates a small set of information or quantity of material to the next higher (slower and coarser) level. An example for a forested landscape was presented earlier as Figure 3-7. Another example comes from social scientists who argue that social action is predicated on a hierarchy of three structures: slowly developed myths (structures of signification), faster rules and norms (structures of legitimation), and still faster processes to allocate resources (structures of domination) (Westley 1995; Chapter 4). And the attributes of the slower levels emerge from experience of the faster.

As long as the transfer from one level to the other is maintained, the interactions within the levels themselves can be transformed or the variables changed without the whole system losing its integrity. As a consequence, this structure allows wide latitude for experimentation within levels, thereby greatly increasing the speed of evolution.

Ecologists were inspired by this seminal article of Simon's to transfer the term hierarchy to ecological systems and develop its significance for a variety of ecological relationships and structures. In particular, Allen and Starr (1982) and O'Neill et al. (1986) launched a major expansion of theoretical understanding by shifting attention from the small-scale view that characterized much of biological ecology to a multiscale and landscape view that recognized that biotic and abiotic processes could develop mutually reinforcing relationships.

These hierarchies are not static structures; rather, the hierarchical levels are transitory structures maintained by the interaction of changing processes across scales. A critical feature of such hierarchies is the asymmetric interactions between levels (Allen and Starr 1982; O'Neill et al. 1986). In particular, the larger, slower levels constrain the behavior of faster levels. In that sense, therefore, slower levels control faster ones. If that was the only asymmetry, however, then hierarchies would be static structures, and it would be impossible for organisms to exert control over slower environmental variables.

However, it is not broadly recognized that the adaptive cycle, shown in Chapter 2 (Figure 2-1), transforms hierarchies from fixed static structures to dynamic, adaptive entities whose levels are sensitive to small disturbances at the transition from growth to collapse (the Q phase) and the transition from reorganization to rapid growth (the a phase). During other times, the


processes are stable and robust, constraining the lower levels and immune to the buzz of noise from small and faster processes. It is at the two phase transitions between gradual and rapid change and vice versa that the large and slow entities become sensitive to change from the small and fast ones.

The structural, top-down aspect has tended to dominate theory and application, however, reinforced by the proper, everyday dictionary definition of hierarchy that is vertical authority and control. The dynamic and adaptive nature of such nested structures has tended to be lost.

It certainly is true that slower and larger levels set the conditions within which faster and slower ones function. Thus a forest stand moderates the climate within the stand to narrow the range of temperature variation that the individuals within it experience. But missing in this representation is the dynamic of each level that is organized in the four-phase cycle of birth, growth and maturation, death, and renewal. That adaptive cycle is the engine that periodically generates the variability and novelty upon which experimentation depends. As a consequence of the periodic but transient phases of creative destruction (Q stage) and renewal (a stage), each level of a system's structure and processes can be reorganized. This reshuffling allows the possibility of new system configurations and opportunities from the incorporation of exotic and entirely novel entrants that had accumulated in earlier phases.

For organisms, those novel entrants are mutated genes or, for some bacteria, exotic genes transferred occasionally between species. For ecosystems, the novel entrants are exotic species or species "in the wings" waiting for more appropriate conditions. For economic systems, those novel entrants are inventions, creative ideas, and people that emerge in the earlier phase of growth where they were constrained from further realization of their potential. The adaptive cycle explicitly introduces a slow period of growth where mutations, invasions, and inventions can accumulate, followed by a brief period of rearrangements of those. It is a periodic process that can occur within each hierarchical level, in a way that partially isolates the resulting experiments, reducing the risk to the integrity of the whole structure.

In many ways the hierarchy and its nested adaptive cycles could as well represent biological evolution. For example, for a cell, the a phase represents the stage at meiosis when translocations and rearrangements generate a variety of experimental genetic recombinations that natural selection operates on at the level of the individual organism. Hence species attributes can periodically be reshuffled and invented to explore the consequences of novel associations that are then tested in the longer phase of organismal growth from r to K.

The organization and functions we now see embracing biological, ecological, and human systems are therefore ones that contain a nested set of the four-phase adaptive cycles, in which opportunities for periodic reshuffling within levels maintain adaptive opportunity, and the simple interactions across levels maintain integrity. What distinguishes the biological, ecologi-


cal, and human systems from one another is the way inventions are accumulated and transferred over time. More on that later.

Since the word hierarchy is so burdened by the rigid, top-down nature of its common meaning, we prefer to invent another term that captures the adaptive and evolutionary nature of adaptive cycles that are nested one within the other across space and time scales. We call them panarchies, drawing on the image of the Greek god Pan-the universal god of nature. This "hoofed, horned, hairy and horny deity" (Hughes 1986) represents the all-pervasive, spiritual power of nature. In addition to a creative role, Pan could have a destabilizing, creatively destructive role that is reflected in the word panic, derived from one facet of his paradoxical personality. His attributes are described in ways that resonate with the attributes of the four-phase adaptive cycle: as the creative and motive power of universal nature, the controller and arranger of the four elements-earth, water, air, and fire (or perhaps, of K, r, a., and Q!). He therefore represents the inherent features of the synthesis that has emerged in this quest for a theory of change.

Two features distinguish this panarchy representation from traditional hierarchical ones. The first, as discussed earlier, is the importance of the adaptive cycle and, in particular, the a. phase as the engine of variety and the generator of new experiments within each level. The levels of a panarchy could therefore be drawn as a nested set of adaptive cycles, as suggested in Figure 3-9.

100 10 1,000 em m m km km

4 10,000 years

3 century (j) .... as Q)

2 10 years .b Q)

E ":+::: year Ol .Q

0 months

-2 0 2 4 6 log space (meters)

Figure 3-9. A stylized panarchy. A panarchy is a cross-scale, nested set of adaptive cycles, indicating the dynamic nature of structures depicted in the previous plots.


The second is the connections between levels. There are potentially multiple connections between phases at one level and phases at another level. But two are most significant in our search for the meaning of sustainability. Those are the connections labeled "Revolt" and "Remember" in Figure 3-10, where three levels of a panarchy are represented. The Revolt and Remember connections become important at times of change in the adaptive cycles.

When a level in the panarchy enters its n phase of creative destruction and experiences a collapse, that collapse can cascade up to the next larger and slower level by triggering a crisis, particularly if that level is at the K phase, where resilience is low. The "Revolt" arrow suggests this effect-where fast and small events overwhelm slow and large ones. And that effect could

large and slow

K

intermediate size and speed

Figure 3-10. Panarchical connections. Three selected levels of a panarchy are illustrated, to emphasize the two connections that are critical in creating and sustaining adaptive capability. One is the "revolt" connection, which can cause a critical change in one cycle to cascade up to a vulnerable stage in a larger and slower one. The other is the "remember" connection, which facilitates renewal by drawing on the potential that has been accumulated and stored in a larger, slower cycle. Examples of the sequence from small and fast, through larger and slower, to largest and slowest for ecosystems are shown in Table 3-1. For institutions, those three speeds might be operational rules, collective choice rules, and constitutional rules (Ostrom 1990; Chapter 5); for economies, individual preferences, markets, and social institutions (Whitaker 1987); for developing nations, markets, infrastructure, and governance (Barro 1997); for societies, allocation mechanisms, norms, and myths (Westley 1995, Chapter 4); for knowledge systems, local knowledge, management practice, and worldview (Gadgil et al. 1993; Berkes 1999; Chapter 5).


cascade to still higher slower levels if those levels had accumulated vulnerabilities and rigidities.

An ecological example of this situation occurs when conditions in a forest allow for a local ignition to create a small ground fire that spreads to the crown of a tree, then to a patch in the forest, and then to a whole stand of trees. Each step in that cascade moves the transformation to a larger and slower level. A societal example occurs when local activist groups succeed in efforts to transform regional organizations and institutions because they had become broadly vulnerable. Such a change occurred in New Brunswick when small groups opposed to spraying insecticide over the forest succeeded in transforming increasingly vulnerable regional forest management policies and practices (Baskerville 199 5), as part of a slowly unrolling saga of lurching understanding-both scientific and political.

The downward arrow labeled "Remember" in Figure 3-10 indicates the second type of cross-scale interaction that is important at times of change and renewal. Once a catastrophe is triggered at a level, the opportunities and constraints for the renewal of the cycle are strongly organized by the Kphase of the next slower and larger level. After a fire in an ecosystem, for example, processes and resources accumulated at a larger level slow the leakage of nutrients that have been mobilized and released into the soil. And the options for renewal draw upon the seed bank, physical structures, and surviving species that form biotic legacies (Franklin and MacMahon 2000) that have accumulated during the growth of the forest. It is as if this connection draws upon the accumulated wisdom and experiences of maturity-hence the choice of the word remember.

It is what Stewart Brand (1994) describes in his marvelous treatment of buildings as adaptive, hierarchical entities. The mature evolved buildings of lasting character are a reflection of seasoned maturity-an accumulation of idiosyncratic, wise, sustaining, and thought-provoking experiments accumulated in the form and content of the evolved structure. In The Clock of the Long Now, Brand (1999) goes further and generalizes the role of remembrance and revolt for society as a whole. In a healthy society, each level is allowed to operate at its own pace, protected from above by slower, larger levels but invigorated from below by faster, smaller cycles of innovation.

That summarizes succinctly the heart of what we define as sustainability. The fast levels invent, experiment, and test; the slower levels stabilize and conserve accumulated memory of past successful, surviving experiments. The whole panarchy is both creative and conserving. The interactions between cycles in a panarchy combine learning with continuity. That clarifies the meaning of sustainable development. Sustainability is the capacity to create, test, and maintain adaptive capability. Development is the process of creating, testing, and maintaining opportunity. The phrase that combines the two, sustainable development, is therefore not an oxymoron but represents a logical partnership.

3. SUSTAINABILITY AND PANARCIDES 77

Panarchies and Lumps

The concept of the adaptive cycle and the observation that scales among key variables are separated came from a synthesis of empirical studies (Holling 1986). But were that concept and observation the consequence of the way analysts and modelers make convenient modeling decisions, or are they the way real ecosystems, industry, and management actually organize and function?

It does help that the regional models were based on extensive knowledge and analysis of actual ecological processes, and the parameters were usually independently estimated in the field. Moreover, predictions of some of the critically informing studies, such as the budworm-forest one (Clark et al. 1979; Holling 1986), were extensively tested by comparing them to observed behavior from different regions of eastern North America having radically different climatic conditions and forest dynamics. The models consistently had strong predictive powers even in such extreme, limiting conditions. Ecosystems do grow, collapse, reassemble, and renew. Small sets of critical structuring variables are separated in scale-both speed and size-in example after example (Table 3-1). This was not deductive theory derived from first principles dictating what should be observed, but observations in nature and practice dictating theory. The panarchy is such an inductive representation.

Evidence for Panarchies

Nevertheless, it was skeptical students, with newly refined ways of critical thought and historical awareness of the hubris of those who generalize, who asked the critical question: "How do you really know?" Deductive economic theorists, themselves vulnerable to this challenge, agreed. We needed to move the metaphor of the panarchy into sets of competing and testable hypotheses.

There has turned out to be lots of those. Fruitful metaphors generate useful and relevant hypotheses. As the hypotheses and tests evolved, the metaphor of the panarchy was deepened and extended to take the form described in the previous section.

All the hypotheses and tests so far have come from an overall proposition that panarchies of living systems, social as well as ecological, provide a discontinuous template in space and time that entrains attributes of variables into a number of distinct lumps. By lumps we mean not only the discrete aggregates that Krugman (1996) explains and describes for human settlements-dries, towns, villages, and the like. He isolates centripetal and centrifugal forces that cause instabilities, which produce agglomerative patterns and discrete aggregates. There are such discrete aggregates in ecosystems-some obvious like individual organisms, some more amorphous like plant associations and ecosystems themselves. But in addition, we mean that attributes of size, speed, and function of each of those discrete aggregates should themselves be distributed in a lumpy manner. Those attributes


could be periodicities of fluctuations, size of objects at different scales on a landscape, the scales of decision processes of animals and humans, or the morphological and functional attributes of animals and plants.

There are two reasons an ecosystem/landscape panarchy as described would create a lumpy template. One is the gappy, discontinuous nature of the processes that form elements of the panarchy. Those are the ones that create a disjunct separation of scales among key, structuring variables. The other is the nature of the adaptive cycle itself. The phases of the cycle are distinct and the shift in controls from one to another is abrupt, because the processes controlling the shifts are nonlinear and the behavior multistable. Each phase creates its own distinct conditions that in tum define distinct attributes of size and speed of aggregates that control the phase or are adapted to its conditions. K-species and firms tend to be big and slow; r-species and firms tend to be small and fast. We are not saying that the four phases of a cycle entrain four lumps, though it would be fun to further develop and test that hypothesis. We are saying that the combination of panarchy-level discontinuities and adaptive cycle ones will generate a number of lumps, the number defined by the resolution of the observations and the range of scales tested. Panarchies form a lumpy template that entrains the same lumpy attributes in organisms that create or are part of them.

Distributions, the proposition states, will not be continuous or unimodal. Rather, they should be discontinuous (gaps in a distribution) and/or multimodal. Similarly, scaling relations should produce clusters of attributes along regression lines (lumps) or indicate breaks between scaling regimes.

In contrast to that proposition, much of modern science, including ecology, seeks simplifying, universal laws by searching for continuous, unimodal properties. For example, the scaling of physical, biological, ecological, and social phenomena has become a major focus of efforts to develop simple and universal representations of complex systems (GellMann 1994). From that has come the identification, explanation, and testing of scaling laws for systems as wide ranging as biophysical (Bak 1996; West et al. 1999); ecological (Keitt and Stanley 1998); firms and countries (Brock and Evans 1986; Stanley et al. 1996); and human aggregations (Krugman 1996). But there has been little focus on the pattern and dynamics of departures from those scaling relationships-either as clustering of attributes (lumps) or as breaks between two scaling regimes. Brock (1999b) reviews and discusses the perils and pitfalls of the application and interpretation of scaling laws in economics.

There is empirical evidence that biological and ecological attributes of specific landscapes exhibit multiple scale regimes-there are breaks between scale levels as processes controlling structure shift from one set to another, and there is clustering of attributes at distinct scales. That was suggested impressionistically in Figures 3-1 through 3-6, but, in addition, formal analysis of vegetation pattern on landscapes has shown that different scaling regimes exist, each with its own fractal dimension (Krummel et al. 1987).


Analyses of animal communities on specific landscapes also have revealed cross-scale, multimodal, or gappy patterns in animal attributes such as body mass (Holling 1992). Architecturally simple landscapes have few lumps in body mass of animals living in them; complex ones have many. For example, Schwinghammer (1981) and Raffaelli et al. (2000) show that architecturally simple marine sediments have communities living within them with three, and perhaps four, lumps in the size of their inhabitants. Boreal forest landscapes (Holling 1992) are somewhat more complex; their mammal and bird communities show about eight lumps in body mass. Tropical forests systems are still more complex, and their bird inhabitants show a still larger number of lumps (Restrepo et al. 1997). We suspect a strong correlation between complexity of lump structure and productivity or other correlates of net energy flux through terrestrial ecosystems.

In addition, plant as well as animal attributes show the phenomenon. For example, Walker et al. (1999) show that morphological attributes of plants, as well as of animals, have lumpy distributions and that each lump corresponds to a functional role plants play in an ecosystem. They demonstrate that functionally significant morphological attributes of grass and forb species show three to five lump clusters in savanna ecosystems.

There is skepticism that such lumps are real. Part of that skepticism is because so many apparent patterns in nature proposed in the past have subsequently been shown to be artifacts. Manly (1996) applied an elegant but conservative statistical test to the original data sets presented by Holling (1992) and concluded that only two lumps or aggregations of body mass were significant, rather than the eight or more that Holling identified. Conservative tests, of course, reduce the chance of being wrong (Type I error)-but they also reduce the chance of being able to detect real patterns (Type II error). Siemann and Brown (1999) argue that no lumps at all exist in body mass data of animal communities. But they asked a different question than one that was relevant for testing the proposition discussed here. Their test concerned the sizes of individual gaps, not the existence of a pattern of lumps and gaps.

But more convincing tests come from proposing and invalidating alternative hypotheses of causation. It is those tests, together with appropriate statistical ones of the kind suggested by Manly (1996), that can lead to multiple lines of evidence that converge on a credible line of argument. It took over three decades to confirm the existence and management significance of multistable states in ecosystems (Chapter 2; Carpenter 2000). It might take as long for establishing the reality, cause, and significance of lumps.

Causes of Lumps

There are at least six proximate causal mechanisms that could directly produce lumpy distribution of body masses. Some represent slow processes, some fast.


As an example of a generic slow process, panarchies form patterns on the landscape that result in a mosaic of different-sized resource aggregations at different scales. Each reflects the influence of one of a few dominant ecosystem processes. The resource aggregations across scales and well-known allometric relationships can explain aggregations of body sizes. There are well-established allometric relationships between the body size of an animal and its energy needs, speed, distance of movement, and life span (Peters 1983). As a consequence, not all sizes could survive-only those whose scaled physiological, behavioral, and life cycle features matched the lumpy resource availability. Morton (1990) used that possibility to explain the total extinction of all middle-sized mammals after European settlement in Australia. He proposed that changed fire regimes, the vegetative impacts of introduced rabbits, and predation by introduced fox reduced the resource in patches at intermediate scales and increased mortality of the mammals exploiting them. The significance for land management is obvious.

Phylogeny and organizational constraints also reflect the operation of slow processes that might explain the lumps, because organisms might have evolved a limited number of body sizes that can function efficiently. That is, evolution may produce a lumpy universe of species from which assemblages are drawn. Any one assemblage from an area might show lumpy attributes because assemblages are drawn from a lumpy universe of species created through evolution. Or there could be founder effects-the luck of the draw might mean that only a limited number of sizes established themselves and their sizes thereafter constrained the sizes of those that followed.

Competitive and trophic relationships are faster processes that could also produce lumps. Roughgarden (1997), for example, showed that lumpy distributions can be produced in an elegant model that combined the fixed carrying capacity of an animal with growth and size-dependent competition. Such lumpy distributions result for much the same reason that Krugman's agglomeration of products does (Krugman 1996). Trophic relationships could also result in lumpy distributions as size resonances form in communities because big beasts eat little ones (Carpenter and Kitchell1993).

Evidence to test these alternatives is accumulating. It demonstrates that body masses are distributed in a lumpy manner both on land and in water, and that the cause must be associated with slow, conservative properties of landscapes and waterscapes.

The most extensive test has been performed by Havlicek and Carpenter (2000). They analyzed data on species, populations, and species sizes of phytoplankton, zooplankton, and fish collected over years from eleven lakes in Wisconsin. All lakes showed body mass distributions of species with an extensive lump and gap structure. Moreover, that structure was very similar in all lakes, even though the lakes differed widely in area, depth, nutrient status, food web structure, species composition, and productivity. That was even the case after experimental additions of phosphate and removal of fish produced massive differences in community structure, primary production, nutrients,


chlorophyll, and bacterial production. Despite substantial differences in species composition, community structure, and physical/chemical characteristics of the lakes, many of those lumps and gaps persisted at similar size ranges across all lakes and treatments.

The same conservative nature of the body mass lump structure was demonstrated on a smaller scale by Raffaelli et al. (2000). They perturbed enclosures of marine littoral sediments in a way that changed trophic structure, species composition, and sizes of communities. The lump structure remained little affected. It is a highly conservative feature, reflecting, therefore, slow processes that structure panarchies at all levels of scale.

It takes the kind of extreme disturbances seen over paleoecological time and space scales to change the body mass lump structure in a major way. Eleven thousand years ago, for example, all the very large herbivores, such as giant ground sloths and the shovel tusked elephant, became extinct in North and South America in less than one thousand years (Martin 1967). Lambert and Holling (1998) analyzed two reconstructed fossil data sets from either side of the continent to identify the body mass lump structure before and after that massive extinction pulse. The data demonstrate a significant lump structure that remained entirely unchanged for animals of less than 41 kilograms, even though extinction occurred among those species. Replacement by new species of similar sizes maintained the structure. But above 41 kilograms, the lump structure was entirely transformed, and the largest lump of animals with masses greater than 1,000 kilograms was eliminated entirely. Climate change associated with global deglaciation, changed fire regimes, and. hunting by a new, efficient hunting culture conspired to completely change the template at coarse scales, but only at coarse scales.

It is likely, moreover, that the large herbivores created and maintained that coarse pattern of grasslands and forest in the manner proposed by Zimov et al. (1995) for the megaherbivores of northern Russia and Alaska during the same period. Grazing by the large herbivores likely created and maintained vegetative patterns appropriate for their own existence, as is still true for large herbivores in Africa (Owen-Smith 1998). These herbivores were therefore likely to have been part of one set of critical, ecosystem selforganizing processes that created a slow, large adaptive cycle at coarse scales in the panarchy. As indicated in Chapter 2, such self-organizing processes and the adaptive cycle they create are very resilient, but once they collapse, they unravel precipitously in a positive feedback chain of collapse. Thus one slow, large level of the panarchy collapsed, explaining the sudden and continental scale of the transformation. But the collapse did not cascade to smaller scales, so that the body sizes appropriate for them remained unchanged.

On a shorter time scale, parts of panarchies and the lumps they form can change because of the occupation of some scales by an external invader. A particularly clear example of the effects of interaction between an invasive grass and human exploitation of new opportunity is described in Box 3-2.


Box 3-2. An Invasive Species (Imperata cylindrica) and Human Exploitation Change a Panarchy

G. Peterson

Imperata cylindrica (also known as cogon grass, or alang alang) has colonized and established large grassland areas in Southeast Asia's uplands (Terry 1994; Whitten et al. 1987). It is a common weed throughout the world and rapidly invades lands following clearing. Cogon grass has a number of characteristics that make it successful as an invader of landscape disturbed by massive human modification.

Cogon grass is a perennial that spreads vegetatively through creeping roots. It also produces copious seed that is able to disperse long distances along roads and trails (Sauer 1988). Cogon grass frequently flowers following environmental stress, such as fire, cutting, or drought. These attributes allow it to rapidly invade areas in which vegetation has been disturbed.

Cogon grass is maintained by fire. It bums readily, but because its roots are protected below ground, it can rapidly resprout. It is shade intolerant, so food crops, trees, and legumes out compete it in the absence of fire, but fire kills tree seedlings and other potential competitors.

As human population density has increased, the increased burning of agricultural sites and accidental fires have encouraged the expansion of Cogon grass. Furthermore, the difficulty of removing Cogon grass has encouraged it to be used and purposefully burned for grazing; Cogon grass grasslands are difficult to farm. The grass's rapid regrowth and the strength of its roots make farming difficult. However, such grasslands can be used for cattle forage.

The features of rapid growth, fire adaptation, and complementarity with human action have enabled Cogon grass to spread across large areas of the tropics. However, it is particularly Cogon grass's relationship with fire that provides it with its resilience. Unlike many ecological processes, fire experiences increasing returns to scale. That is, larger connected areas of combustible Cogon grass are more likely to bum than smaller areas, because larger areas are more likely to be ignited than smaller areas. Consequently, as areas covered by Cogon grass become larger, they become more resilient. By regulating their own disturbance to exclude potential competitors, Cogon grasslands are able to maintain a high biomass and


remain tightly connected and resilient. Furthermore, by being useful to people, they are able to coexist with, and even benefit from, anthropogenic ecological transformation.

The conservative, persistent structure of lumpy body mass distributions reflects the robust, sustaining features of the panarchy described earlier that are formed by slow ecological and evolutionary processes. The distribution of lumps and gaps is a kind of bioassay of the structure of a panarchy. Although lumps themselves are stable, populations of species within them are not-they are highly labile and reflect the effect of stochastic processes, competition, and dynamic changes that structure adaptive cycles. Recently, Allen et al. (1999) have shown that such turbulence is particularly evident at the edge of gaps in body mass distributions.

They showed that endangered and invasive species in a community have body masses that occur at the edges of body mass clumps two to four times as often as expected by chance. That correlation is consistent in all eight data sets examined in that study. Those comparisons now have been expanded, with exactly the same result, to include four different taxa (birds, mammals, herpetofauna, and bats) in examples of two different ecosystem types (Mediterranean and wet savanna) on three continents (Australia, North America, and Europe). It is suggestive that the most invasive species of all, humans, had a body size on the plains of Africa also at the edge of a body mass lump (Holling 1992). Humans' generalist morphology, combined with gradually developed technologies, allowed actions and influence at wider and wider scales-from home territories to, ultimately, the planet as a whole.

Moreover, a set of poorly understood biological phenomena that seem to mix contrasting attributes correlates with those same edges of body mass lumps/gaps. These phenomena include endangerment, extinction, and nomadism on one hand, with invasiveness, high variability, and migratory behavior on the other. All these phenomena that cluster at the edges of body mass lumps, or at the edge of gaps, are opposite faces of rapid, turbulent change-of both success and failure. Generalists are able to exploit opportunity created by the uncertainty and turbulence. Specialists are vulnerable to that same uncertainty and turbulence.

That suggests that the potential for crisis or opportunity is greatest at the scales exploited by these "lump/gap edge species." In Chapter 2, we described why opportunity and crisis are greatest at the edge of a stability shift in time (from creative destruction to reorganization, or from 0 to a. in the adaptive cycle). It seems that the same conditions occur in space as well, and that the edge of a body mass lump/gap represents a scale of landscape transition equally turbulent and rich in potential. It gives specific content to Kauffman's intuition that life flourishes at the edge of chaos (Kauffman 1993).


Significance of Lumps

Once the pattern of lumps and gaps is formed in a distribution, it entrains a complex set of related variables. The consequences determine, in part, how resilient the pattern is and how robust to modification by policy or by exogenous change. For example, understanding the scaled nature of animal communities and the scale breaks intrinsic within them has led to a better understanding of the manner in which ecological resilience and sustainability are generated from biological diversity.

There are two types of such diversity, one concerning how diversity affects biological function within a range of self-similar scales-within a lump (Walker et al. 1999); and one concerning the way it affects biological function across scales-between lumps (Peterson et al. 1998). Both types of diversity contribute to the resilience and sustainability of the system.

For example, the properties and patterns of the boreal forest described in Box 2-1, Chapter 2, are maintained by a set of processes involving an insect defoliator (the spruce budworrn), two species of trees, and avian predators of the budworrn. The thirty-five species of bird predators are critical. They are distributed over five body mass lump categories (Holling 1988). Species in the same lump compete with one another because they forage at similar scales. But they have different responses to climatic and other environmental changes. The result is that there are at least some species present from a particular size cluster, over a large range of fluctuating external conditions.

But species in different lumps forage at different scales, initiating their foraging responses to different-sized aggregations of budworm. Small warblers, for example, respond to aggregations on branches, larger ground sparrows to aggregations on trees, and still larger grosbeaks to aggregations in forest patches. Hence, as budworm populations start to jump from one level of the panarchy to influence larger ones, a strong counteraction develops that brings more and larger avian predator species into play, with larger appetites from larger areas. When the regulation eventually breaks, it does so suddenly and over large spatial scales of hundreds of kilometers. The creative destruction phase of the forest's adaptive cycle is released.

Diversity of functional types of plants in different morphological lump categories contributes to resilience and persistence of functions in a similar way, as Walker et al. (1999) demonstrated when they compared savannas exposed to different intensities of grazing. We suppose that the variety of grazer and browser species in African savannas also provides a wide range of both within- and between-scale sustainability and resilience.

This effect of diversity is not redundancy in the replicated sense that an engineer might apply it to achieve engineering reliability. Rather, each species in the same size lump has a similar scale of function but has different responses to unanticipated environmental change. If the ecosystem were a theater, the species within a lump would be like stand-in actors who are prepared to replace each other in the event of unexpected external surprises and


crises. Species in different lumps can also engage in similar or related ecosystem functions, but, because of their different sizes, they differ in the scale and degree of their influence. In our ecosystem theater, species in different lumps are like actors waiting in the wings to facilitate a change in pace or plot when needed. The within-scale and between-scale diversity produces an overlapping reinforcement of function that is remarkably robust. We call it imbricated redundancy.

The same kind of imbricated redundancy is a common property of many biological phenomena. For example, physiological regulation of body temperature in homeotherms (warm-blooded animals) is regulated by five different mechanisms ranging from metabolic heat generation to evaporative cooling. Each operates over different ranges of temperature with different efficiencies and speed of feedback control. The result is remarkably robust regulation of temperature around a narrow range. As a behavioral example, migratory birds navigate with great success between summer and winter feeding areas over enormous distances, by using at least four different signals for direction-magnetic, topographic, sound, sidereal-each of which has different levels of precision and accuracy. It is the overlapping, reinforcing nature of those separate mechanisms that makes the total effect so robust.

Decision Panarchies

The objects encountered by animals are either edible, frightful, lovable, ignorable, or novel. The first three define the resources on the landscape needed to provide food, protection, and opportunity for survival and reproduction. The latter two are items that should simply be forgotten or should be investigated for the potential they might represent. That is, forgetting, curiosity, and memory are essential in order to develop rules that are flexible and adaptive enough so that a species can persist in a fluctuating, changing world.

All five kinds of objects are created or sustained by the template formed by the ecosystemflandscape panarchy (e.g., such as those illustrated in Figures 3-7 for the Everglades and 3-8 for the boreal forest) and by external introductions, events, and variability. Because the template formed by the panarchy is so remarkably conservative and persistent, animals can develop rules for actions that take advantage of that persistence while retaining enough flexibility to adjust to variability and the unexpected. That is, those decision rules have the features of the adaptive cycle-both conservative and changeable.

The rules become rules of thumb or schemas that minimize information needs and processing. The ones that persist are those with the least demand on information, while contributing to survival and reproduction over long periods. They are not detailed, accurate, and precise, but they are economical, just sufficient, and adaptive. And if some decisions do not encounter or generate variability, they can gradually become more and more stereotyped and automatic. A simple example is the entrained rules a person learns in


driving to and from work along the same route. And among insects and birds, there are many examples of rules that become genetically encoded and guide instinctive behaviors. In humans such rules can become encoded in the myths and rituals of the culture. A beautiful example is that of the Milpa, the maize culture of Mexico, that is so remarkably integrated within the natural ecosystem panarchy while providing opportunities for experimentation within that context (Chapter 5, Box 5-2).

Holland (1995) and Holland et al. (1989) describe these rules as schemas or scripts in which information stored in clusters serves to generate plausible inferences and problem solutions. When unexpected events occur that provide a poor match with experience, then new rules can form out of the stored bits and pieces that become recombined in novel ways, much as described for the adaptive cycle. Bricolage (Levi-Strauss 1962) and self-organization are as central to the formation of rules for decision making as they are for forming biological or ecological structures.

Such sets of rules are also organized as a hierarchical sequence, each set operating over a particular range of scales. Holling (1992), for example, described a typical sequence for a large wading bird of the Florida peninsula and Cuba. At very coarse scales, tagging records indicate that the decisions for an area in which to locate are made over several hundred to one or two thousand kilometers from a bird's birthplace. Once an area is found and accepted, a home range or foraging area is established within an area covering tens of kilometers. \Vi thin that, smaller habitats are identified and exploited among a set of ponds of various sizes; within those, still smaller patches of food aggregation are selected; and within those, specific types and sizes of food items. Each of those elements also has a turnover time that correlates with its geographic size. There are sufficient data from enough species that general equations have been developed that fix the spatial and temporal position of choices for food, home range, and area of animals of different sizes (Holling 1992). An example is provided in Figure 3-11 for animals in a boreal forest landscape.

The figure shows that the spatial range for decisions covers the same range as the ecosystem/landscape hierarchy. That is, there is a tight spatial coupling between these two hierarchies. That is precisely what one expects if spatial discontinuities are the primary source of body mass discontinuities. The specific position in the hierarchy of each of three species representing three very different body mass lump categories is also shown. A deer mouse, for example, establishes a home range over tens of meters; a moose, over tens of kilometers. The differences in the size of choice areas of the smallest and largest animals cover some three orders of magnitude.

Finally, the two hierarchies do not overlap completely in time. The overall decision hierarchy operates at a speed three to four orders of magnitude faster than that of the overall ecosystem hierarchy. That means that the slower dynamics of the ecosystem and the landscape largely constrain and control the variability experienced for animal decisions. And hence it is those

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slower ecological, evolutionary, and geological dynamics that determine the lumpy distribution of animal body sizes.

These panarchies of landscape provide a template that clusters opportunity and choices over a wide range of scales. They therefore provide a template for species diversity and restrict competition largely to those species within a cluster or lump size.

Lumps in Human Systems

Is this lumpy structure arising from panarchies likely to occur in other systems? If there are such lumps in the size of firms, are the ones on the edge of lumps similarly functionally unique as a growing firm shifts from the edge of one scale of operation to that of a larger one? In the size of cities? In the size of the GNP of nations? Might that be true of nations as they shift into a different development path?

For nations, Barro (1997) reviews his own influential work as well as that of some others, with the purpose of uncovering and measuring causal forces behind differential cross-country economic performance. He groups countries into economic lumps called "convergence clubs." Countries within a given club have economic growth performances that tend to converge. These patterns of growth performance across countries appear to be structured by movement toward a long-term target rate of growth for each country, where


the long-term target is determined by slow and medium time scale variables. Slow processes of governance establish the degree of flexibility, trust, and freedom of institutionaVpolitical structures. Medium-speed processes set the general level of public physical infrastructure and education.

This explanation and the nonlinear functions that support it (Durlauf and Quah 1999) seem very similar to those of the ecological panarchies. The great difficulty in moving nations from one lump or from one development pathway to another suggests the same conservative features of lumpy patterns in ecosystems. Both seem to be sustained by conservative, slow sets of variables forming the panarchy. Both the management of ecosystems and the development of nations require that attention be focused on the slow variables while encouraging experiments that engage fast ones. A critical number of levels of the panarchy need to be involved in order to satisfy minimal needs for understanding and action

The attraction of scaling laws is that they emerge from simple physical and statistical processes and have astonishingly wide application (Brock 1999b). However, in this chapter, we argue that there are regular patterns of departures from or clustering along those scaling laws, and these lumps of attributes might have more ecological, economic, and social interest, and practical use, than the single laws or distributions themselves.

Specifically, these lumps seem to demonstrate how living systems of animals, plants, and human organizations develop self-organized interactions with physical processes over distinct ranges of scale. Just as pulses of resource acquisition over time by organisms increase efficiency of energy utilization, perhaps these "lumps" in the morphological, geometric, and behavioral variables of animals, plants, and people emerge from self-organizing properties that affect evolutionary change and development. They represent attractors, created by key biological and social processes, along a more continuous, physically defined template. Thus the measurable attributes of lumps and gaps, like body mass gaps in a distribution, are a transform of the potential that is discontinuously sustained across a panarchy.

In brief, physics sets the constraints around which life structures opportunity.

Cascading Change

The panarchy represents the dynamic interplay between processes and structures that sustain relationships on the one hand, and create and accumulate potential on the other. Some of the specifics are developed in more detail in subsequent chapters. We will close this chapter with a section on how whole panarchies can be transformed, either because productive novelty cascades up the levels, or because destructive catastrophes cascade down.

Novelty

Biological evolution is the one field of science where questions of how novelty is generated, selected, and spread have been most deeply and broadly


explored. It is a science that covers scales from the language of genes on chromosomes, to interactions of individual organisms in changing environments, to isolation and mixing of whole fauna as continents join, separate, and drift apart over geological time, to spasms of planet-wide extinction caused by asteroid impacts.

Simon Levin (1999) says it well in Fragile Dominion:

The combined weight of multiple small scale processes can accumulate to help shape other patterns of interaction, and hence the structure and function of ecosystems, from small scale to the biosphere. Natural selection, together with other drivers of evolutionary change such as mutation, recombination, environmental factors, and simple chance events, provides the central organizing principle for understanding how the biosphere came to be, and how it continues to change. No teleological principles are at work at the level of the whole system, or even at the local level. The biosphere is a complex adaptive system in which the never ending generation of local variation creates an environment of continual exploration, selection, and replacement.

But, despite the marvelous complexity and diversity of life, evolution is astonishingly conservative. In 1998, the nematode Caenorhabditis elegans became the first organized multicellular animal whose genes were completely unraveled and described (Hodgkin et al. 1998). An astonishing 19 percent of those genes and their 97 million bases in this multicellular animal correspond to those in yeast, a single-cell organism. Despite the billion years of evolution from some common ancestor, and the enormous transformations required to produce an organized multicellular organism, a remarkable number of the genes of the single-cell yeast and the multicellular nematode are shared. Similarly, despite the differences between chimpanzee and human, some 98.4 percent of their DNA is shared (Diamond 1992).

This suggests that the source of novelty lies not in single mutations alone, but also in novel, unpredictable combinations with existing genes that can suddenly establish new genetic domains of influence, opening an entirely new set of adaptive paths for selection. Similarly, the great sixty-year wave of technological innovation initiated in the nineteenth century was triggered not by the single invention of the steam engine, but by the context of a whole economy and society that had accumulated a set of rigidities and invented novelties that precipitated, synergized, and directed the transformation (Fischer 1996). That is what is happening with the Internet now.

Levi-Strauss (1962) used the word bricolage to describe this process of recombining existing elements and new mutations and inventions to form something novel that solves a newly emerged problem or creates new opportunity. It is the adaptive cycle that accumulates those elements as potential and then, for transient moments, rearranges them for subsequent testing in changing circumstances. Consequential rearrangements can nucleate new opportunity and accumulate further potential. If that accumulated potential


exceeds a threshold, it can cascade upward in the panarchy and create new panarchical levels. Think of the way the inventive circus Cirque du Soleil evolved in steps from individual street performers to a self-sustaining group, to a multitalented company in Montreal, to an international enterprise, accumulating capital, experience, organizational processes, and new skills in steps along the way.

Such transformations are qualitatively different from the incremental changes that occur during the growth phase of the adaptive cycle described in Chapter 2. They are also qualitatively different from the potentially more extreme changes and frozen accidents that can occur during the more revolutionary shift from creative destruction (Q) to renewal (a). They are transformations that cascade and transform the whole panarchy and its constituent adaptive cycles.

Major transformations are rare and extreme because a unique combination of separate developments has to conspire together simultaneously. Some developments emerge within adaptive cycles during the back loop of the cycle, when recombinations and external influences can generate unexpected new seeds of opportunity that can nucleate and modify the subsequent phase of growth. So long as connections with other levels are maintained, those innovations are contained and do not propagate to other levels. But as such recombinations and inventions independently accumulate in a number of adjacent levels, a time will come when the phases of several neighboring cycles become coincident, when each becomes poised as an accident waiting to happen in a shift from Q to a. Windows open that can then allow those independent inventions and adaptations to interact to produce a cascade of novel self-organized patterns across a panarchy, creating fundamental new opportunity. There is an "alignment of the stars."

In ecosystems, the period of those cycles differs between neighbors in the panarchy typically by an order of magnitude. Thus the frequency with which several cycles come simultaneously into the vulnerable phase decreases as the power of the number of cycles involved. Therefore, phases of vulnerability at multiple scales can be quite rare.

But what of human organizations and institutions, which operate on faster scales than biologicaVevolutionary ones? As a signal of that structure, studies of regional resource management and development show that policy and organizational changes also occur in spasmodic lurches of learning driven by crises precipitated by earlier myopic policy successes leading to larger failures (Chapter 12, Figure 12-1; Light et al. 1995).

That is what so often frustrates those of us who have been part of efforts to transform research, policies, and structures in rigid government agencies, universities, and research institutes. We learn that change in resource management agencies and policies, for example, requires much more than integrative scientific understanding of the uncertain and unpredictable features of linked natural and economic systems over different scales. While that understanding is often missing, it can usually be achieved by strategic analy-


sis and modeling by groups of scientists and scholars from different agencies, universities, and science-based NGOs. But such groups are effective only for short periods, and only if they act informally as a transient group that functions outside the constraints of its own organization and constituency. That is the assessment phase of the approach termed adaptive ecosystem management (Holling 1978; Gunderson et al. 1995a; Walters 1986, 1997).

It is the rest of the process, the implementation of adaptive policies, that frustrates because it encounters the reality of politics and power in societies where entrenched interests manipulate information for narrow purpose. Carl Walters beautifully summarizes his decades of such frustrations in a review (Walters 1997) that has triggered a series of responses and a special feature on adaptive management in the electronic journal Conservation Ecology (www.consecol.org).

In these situations, panarchical change can occur only when a triggering event unlocks the social and political gridlock of larger levels in the panarchy. In the case of the transformation of New Brunswick regional forest policy (Baskerville 1995), for example, the cycle of political elections allowed a new politician to emerge and become minister of the Department of Natural Resources at a time of unambiguous failure of earlier forest policies. Willing to admit the mistakes of predecessors, and wanting to place his own mark, he encouraged development of an integrative regional policy that could exploit the understanding that had accumulated in previous cycles of scientific experience, analysis, and communication. The person who created that opportunity, designed and implemented it was a "wise person"-a mix of scientist, politician, and manager, in this case Gordon Baskerville. Such a person is another critical ingredient for fundamental transformation. Transformation of forest fire policy in the U.S. national parks followed a similar history of frustrating resistance to accumulated integrative understanding, followed by a sudden lurch of policy transformation (Christensen et al. 1989). Frances Westley provides another example of resource management and intimate details of the events in Chapter 13. The reality of those situations is captured in the title of that chapter, "The Devil in the Dynamics." Truly transforming changes are panarchical ones that can cascade up a panarchy as a conscious act of wise, purposive design and implementation. Westley's example of regional policy change illustrates that cascade of decisions in Figure 13-2.

From a more distant perspective, the two great creative transformations in human progress were the agricultural revolution ten thousand years ago and the industrial revolution that began about 1750. Such panarchical, creative cascades are rare, "coming in great storms rather than occasional showers" of the kind that occur within adaptive cycles (Anon. 1999).

Collapsing Panarchies

Stochastic events external to a cycle can trigger spasmodic collapses, particularly if they encounter vulnerabilities within an adaptive cycle. Extremely


large events can overwhelm any sustaining properties of panarchies, destroying levels and triggering destructive cascades down levels of a panarchy. The great loss of biological diversity 65 million years ago (about 70 percent of Earth's species; Jablonski 1995), for example, is likely to have been caused by the impact of an asteroid (Alvarez et al. 1980). That event, perhaps associated with massive volcanic eruptions around the same time, unraveled the web of interactions within and between panarchicallevels over scales from biomes to species. There have been five major spasms of biodiversity loss during Earth's history Gablonski 1995), each probably precipitated by different causes (Donovan 1989). Each required at least 10 million years of evolutionary change to reestablish the lost diversity (Kirchner and Weil 2000).

Since recovery from these events is so delayed, it is likely that mass extinction events not only eliminate species, but also by doing so, eliminate ecological niches. That is, species depend upon an environment that is created by life. By eliminating most species, mass extinction events eliminate many ecological niches. The recovery of biodiversity from mass extinction events requires the reconstruction of these niches, before species can evolve to fill them.

Notably, different families, orders, and species dominated the new assemblages after recovery; new inventions and ways of living emerged. The dinosaurs became extinct during the collapse 65 million years ago; the mammals, inconspicuous before that, exploded in a diversification that created new opportunity. The conservative nature of established panarchies certainly slows change, while at the same time accumulating potential that can be released periodically if the "decks are cleared" of constraining influences, by large extreme events.

Similarly, human history has been one not of regular change but of spasmodic, catastrophic disruptions followed by long periods of reinvention and development. Unlike the sudden collapses of biological panarchies, there can be long periods of ruinous reversal, followed by slow recovery and restoration of lost potential. Robert Adams's magnificent reconstruction of Mesopotamian societies (1966, 1978) and his review of other archaeological sequences at regional or larger scales (Adams 2000) led him to identify two trends in human society since the Pleistocene. One is an overall increase in hierarchical differentiation and complexity of societies. That is, levels in the panarchy are added over time. If enough potential accumulates at one level, it can pass a threshold and establish another slower and larger level. The other trend Adams identifies is of discontinuous rapid shifts, interspersed by much longer periods of relative stability. Such irregularities, he remarks, "provide the framework for most archeological theory and synthesis, paralleling the long dun~e outlook with which Femand Braude! has enriched the study of history."

Several scholars have focused on such societal dynamics in more recent history. Goldstone (1991) has attempted to understand why periods of revo-

3. SusTAINABILITY AND PANARCmEs 93

lution appear across broad regions. In his book Revolution and Rebellion in the Early Modern World, he discusses how Eurasia experienced a wave of revolutions after a period of calm in the seventeenth century. He proposes that state breakdown occurs when there are simultaneous crises at several different organizational levels in society-i.e., adaptive cycles at different levels in a panarchy become aligned at the same phase of vulnerability (Box 3-3). That is, he explicitly posits a cascading, panarchical collapse.

Box 3-3. Revolution and Rebellion

G. Peterson

The breakdown of states in the seventeenth century (Goldstone 1991) provides an example of panarchical revolt. Revolutions occurred when a high potential for mass mobilization and conflict among elite groups intersected with a state in fiscal distress. Population growth, driven by increased agricultural productivity, produced stresses that intersected with rigid social institutions. These stresses came from a growing proportion of socially and economically marginalized people and produced a crisis when other social changes reduced the ability of the state to cope. That loss of social resilience occurred as inflation eroded the real value of taxes collected to support the state.

In the seventeenth century, population growth increased the demand for food without proportionally increasing food yields, leading to inflation in food and other prices. Institutional inflexibility prevented states from adjusting taxes to account for inflation. This loss of income reduced the ability of the state to respond to changes in society and increased the vulnerability of social organization to unusual events, such as a war, a bad harvest, or new policies. A less vulnerable society could cope with such events, but in these more vulnerable societies the events triggered larger crises.

As the state crises began, elite groups struggled for power and attempted to mobilize the general population for revolution. During a revolution, a new type of dynamic developed, as new ideas and ideologies of social reorganization were developed and spread. The period of state breakdown can be compared to a landslide, as the collapse of the state releases accumulated stresses, which then cascade, knocking down whatever lies in its path. The struggle for power that follows a state collapse is in many ways like an epidemic that becomes more virulent the faster it spreads.

cuntinues


During the period of revolutionary mobilization, ideological varieties struggled for support, and in this intellectually competitive environment, moderate positions became radicalized. Radicalization was driven both by competition between ideas and by the underlying slow processes that brought about state breakdown in the first place. During this period of revolution, different groups desired different degrees of reform. Consequently, moderate policies were likely to be unsatisfying to most groups, as each sought more radical policies that suited their goals. The slow changes that brought about crises were not eliminated by state collapse, because the implementation of policies developed by moderates who initially gained power did not bring about social reform. Therefore, ideas calling for more radical change were able to proliferate.

This type of radical mobilization led to periods of revolutionary terror and mass political violence. In terms of the adaptive cycle, societal collapse had become so severe that it exceeded the society's capacity for renewal, moving a society into an alternative configuration. Terror was usually short term, as people left or were killed. However, this type of terror allowed an authoritarian group to seize power and establish order through force. Goldstone (1991) argues that once such a state was reached, it often took at least a political generation, or decades, before a more civil society began to emerge.

In The Great Wave, David Fischer (1996) presents a somewhat similar model of state breakdown that focuses much less on analysis of social stratification and revolutionary dynamics, and much more on analysis of empirical price data and inflation. He demonstrates that at least three waves of social unrest swept Eurasia in the fourteenth, seventeenth, and late eighteenth centuries. He demonstrates how currency mismanagement and diseases amplified inflation driven by population growth.

What unites these two models of societal change are their proposals that slow dynamics drove social organization. Periods of success brought about their own downfall, because stresses and rigidities slowly accumulated. Organizations and institutions failed to cope with these slow changes because either the changes were invisible to them, or they were so complex and contested that no action could be agreed upon. It is a view that Weber (1999) developed in the 1920s, when he argued that disintegration propagates among several levels of a monolithic culture into anarchic systems of competing ideologies. Those pave the way for a new synthesis by visionary or charismatic authority, which in tum becomes routinized into hierarchically complex and increasingly monolithic cultures.


Modem democratic societies are clearly vulnerable to the same process, but they have invented ways to diffuse large episodes of creative destruction by creating smaller cycles of renewal and change through periodic political elections. So long as there is a literate and attentive citizenry, that invention demonstrates that the painful lessons from episodic collapses of whole societal panarchies might be transferred to faster learning at smaller scales. Various designs in business make the same attempt-from creation of "skunk-works" to total quality management.

Such examples of collapsing panarchies start their collapse within individual adaptive cycles that have become maladaptive. We argued in Chapter 2 that the path of an adaptive cycle oscillates between conditions of low connectedness, low potential, and high resilience to their opposites. We argued that such an oscillation is inevitable in a system that persists and adapts in a changing environment. Its consequence is to probe the ever changing context of threat and opportunity, while accumulating and sustaining potential in the process.

Could we imagine systems in other combinations of those three attributes where variability is sharply constrained and opportunity is limited? We suggest two possibilities in Figure 3-12. If an adaptive cycle collapses because the potential and diversity have been eradicated by misuse or an ex-

Figure 3-12. Maladaptive systems. A poverty trap and a rigidity trap are suggested as departures from an adaptive cycle. If an adaptive cycle collapses because the potential and diversity have been eradicated by misuse or an external force, an impoverished state can result, with low connectedness, low potential, and low resilience, creating a poverty trap. A system with high potential, connectedness, and resilience is represented by the rigidity trap, suggestive of maladaptive conditions present in hierocracies, such as large bureaucracies.


ternal force, an impoverished state can result with low connectedness, low potential, and low resilience, creating a poverty trap. That condition can then propagate downward through levels of the panarchy, collapsing levels as it goes. An ecological example is the productive savanna that, through human overuse and misuse, flips into an irreversible, eroding state with sparse vegetation, where subsequent drought precipitates further erosion, and economic disincentives maintain sheep production (Box 2-4, Chapter 2). An example of such a collapse occurs when a society is traumatized by social disruption or conflict, where cultural cohesion and adaptive abilities have been lost. Individuals can depend only on themselves and perhaps family members. In a sweeping analysis of poverty, Dasgupta (1993), for example, resolves the paradox of population growth at times of increasing impoverishment by explaining that children become needed for their work and minimum demands.

We could imagine that some such societies might exist in this degraded state of bare subsistence, barely able to persist but unable to accumulate enough potential to form the larger structures and sustaining properties of a panarchy. Still others might collapse in anarchy. That, in many ways, has been the history of both ecological and economic imperialism (Crosby 1986), following waves of human migration and expansion, initially from the Middle East and subsequently from Europe over the last seven centuries. If we have difficulties defining the conditions for sustainable, adaptive systems, we certainly have no difficulties in identifYing the conditions for unsustainable, maladaptive ones.

The question raised in Chapter 5 by Berkes and Folke is how far such erosion can occur before recovery is impossible. When recovery is possible, what critical attributes need to be reinvented and reestablished from the residual memory stored in slowly fading traditions and myths in order to recreate a new, sustaining, panarchy? A specific example is described in Box 5-3 for the Cree Indians of northern Quebec and Labrador.

Figure 3-10 also suggests that it might be possible to have a sustainable but maladaptive system. Imagine a situation where potential is high, connectedness great, and, unlike the phase where those conditions exist in an adaptive cycle, resilience is high. The high resilience would mean a great ability for a system to resist external disturbances and persist, even beyond the point where it is adaptive and creative. The high potential would be measured in accumulated wealth. The high connectedness would come from efficient methods of social control whereby any novelty is either smothered or sees its inventor ejected. It would represent a rigidity trap.

We see signs of such sustained but maladaptive conditions in great "hierocracies," such as those that include rigid and apparently immutable caste systems. An example is described in Box 3-4 for the Hindu caste system. We are tempted to suggest, from our own frustrating experiences, that other examples might be found in present universities controlled by unchangeable, disciplinary departmental structures, or in agro-industry, where command


Box 3-4. The Hindu Caste System and the Hierarchy Trap

F. Berkes and C. F olke

The caste system in India has always fascinated students of human society. Indians marry according to their caste, and many professions are in the control of certain castes that have traditionally specialized in those tasks. Although discrimination by caste is against the law in contemporary India, many traces of the caste system are still visible. How did the caste system come about, and how did it become a "hierarchy trap"?

Gadgil and Malhotra (1983) hold the view that Indian society is analogous to a biological community made up of a number of cultural species or endogamous caste groups. They argue that the destruction of the ecological resource base and the effects of modernization have eventually rendered the caste system maladaptive.

Gadgil and Thapar (1990) trace the origins of the caste system to the breakdown of city-states and waves of migrations to the countryside in the Ganges plain after the fifth century, resulting in severe pressures on the rural land base and resources:

Indian society seems to have responded to the crisis through an elaboration of the caste system. The caste system divided society into innumerable endogamous groups within which most marriages and much social intercourse were restricted. The endogamous groups of caste society traditionally resembled tribal groups, from which they might in large part have been derived, in having a restricted geographical distribution, and in being self-governing. Each group tended to follow the customary pursuits of the group, a hereditary, rather well-defined mode of subsistence, and ... several endogamous groups lived together ....

The modes of subsistence of such co-occurring groups tend to be diversified in ways that serve to minimize the competition between them. Thus in the Sirsi Taluka county of Karnataka State, for example, nine different endogamous groups use plant material to fabricate a variety of implements and structures. This resource use is highly diversified: for instance, only Christians employ cane to produce furni-

continues


ture, and only Chamagars use Phoenix palm to produce mats and brooms. While both Badigars and Acharis use Careya arborea, they fabricate different articles out of it .... These endogamous groups lived together in multi-caste village communities knit together in a web of reciprocity. It was, of course, an inegalitarian system with lower-status castes providing services far in excess of returns from higher-status castes.

Nevertheless, resource partitioning may have contributed to sustainable resource use and to the persistence of the caste system itself for some fifteen hundred years. However, according to Gadgil and Malhotra (1983):

The advent of British rule heralded the disorganization of this system. The British imposed high levels of demands on natural resources. They took over as government property vast resources which, until then, had been owned communally .... This led to considerable impoverishment [loss of social and ecological resilience] and often the complete collapse of the natural resource base .... The persistence of the caste, with loss of its traditional complementarity has, therefore, led to an increasing level of conflicts amongst different castes ... rendering the once adaptive organization of caste society largely maladaptive. In fact, it has now become an impediment in coming to terms with new modes of resource utilization to which our society must adapt. But nurtured as it is by a long history ... it is a very difficult task indeed to break out of the hold of this maladaptive system.

and control have squeezed out diversity and power, politics, and profit have reinforced one another. But all such systems might well have the seeds of their own destruction built in, much as in the case of the dictatorship of the bureaucracy in the now defunct Soviet Union. The speculation is interesting, maybe even useful, but we are now way beyond our own knowledge and conviction. We need enlightenment from political scientists and historians as described by Pritchard and Sanderson in Chapter 6.

Panarchy in Human and Ecological Systems

This effort of synthesis suggests that biological, ecological, and social systems exhibit properties of the four-phase adaptive cycle and of panarchi-

3. SusTAINABILITY AND PANARCHIES 99

cal relationships across scales. These properties characterize all complex, adaptive systems. The adaptive cycle metaphor distinguishes the opposing forces operating between periods of gradual change and periods of rapid change, where long periods of accumulating potential alternate with briefer periods of creative opportunity. The panarchy distinguishes the influence of those cycles across scales in space and time. The interactions between cycles within a panarchy combine learning with continuity. The panarchy conserves the capacity to create, test, and maintain adaptive capability. The panarchy also preserves, accumulates, and transforms the potential created by that opportunity.

But this representation was largely formed from analyses of ecosystems and landscapes and the management agencies and activities developed to exploit those systems. The social science, economic, and ecological experience of authors of other chapters has helped challenge and deepen the concepts and their application. The resulting abstraction seems to identify events and sequences in human organizations and societies and to indicate the forces that might shape those sequences. But in the process, it becomes clear that human systems are different from ecological ones. Human systems show at least three features that are unique, features that change the character and location of variability within the panarchy, and that can dramatically enhance the potential of the panarchies themselves. Those three features are foresight, communication, and technology.

Foresight and Intentionality

As noted in Chapter 2 and further developed in Chapters 4, 5, 6, and 7, human foresight and intentionality can dramatically reduce or eliminate the boom-and-bust character of some cycles. Predictions of looming economic crises and collapses caused by resource scarcity, for example, are an important part of the debates about sustainability. The economist Solow (1973) provides a withering critique of such doomsday scenarios, pointing out that they ignore the forward-looking behaviors of people. These behaviors play a role in transmitting future scarcities into current prices, thereby inducing conservation behaviors seen today in the real economic world. This forwardlooking process functions through futures markets and strategic purchase and holding of commodities. These provide very large incentives for some to forecast the coming scarcity better than the rest of the market, and to take a position to profit from it. But what one market participant can do, all can do, and this process transmits information to the market as a whole.

But there are limits to this process as described by Carpenter and Brock (Chapter 7) and Carpenter, Brock, and Hanson (1999). These are illustrated in specific examples of models that combine ecosystem models with economic optimization and decision processes. Both models suggest that even when knowledge is total, a minimally complex ecosystem model, together with stochastic events, can thwart the forward-looking economic and deci-


sion capacity to eliminate booms and busts. Those minimal requirements for the ecosystem characterize the ecosystem panarchy-at least three speeds of variables; separation among those speeds (lumpy, fast/slow dynamics, therefore); and nonlinear, multistable behavior. Such minimal models can create the reality of wide variability of an adaptive cycle and allow for exploration of actively adaptive approaches that minimize the consequence of transformational changes. An example is shown in Figure 7-9.

Finally, how can we explain the common tendency for large organizations to develop rigidities that precipitate major crises that initiate restructuring in a larger social, ecological, economic setting? Or of the long history of ruinous reversals in the development of societies? Such reversals seem to be more extreme and require much longer recovery than internally generated cycles of ecosystem panarchies. Certainly in management agencies, the exercise of foresight and intentionality is often brilliantly directed to protect the positions of individuals, not to further larger societal goals. The foresight that constructively maintains creativity and change when connected to an appropriate economic market can lead to rigid organizations that are maintained when there is no market with the same attributes. The market in these cases is a market for political power of the few, not a free market for the many (Chapter 6).

Communication: Transfer and Storage of Experience

Organisms transfer, test, and store experience in a changing world genetically. Ecosystems transfer, test, and store experience through forming self-organized patterns that repeat themselves. These are formed and refined by a set of interacting variables that function over specific scale ranges and form a mutually reinforcing core of relationships. In fact an ecosystem is developed from a few such sets, establishing a reproducing, discontinuous template that provides niches for species diversification and individual organism adaptation.

In human systems the same self-organized patterns are strongly developed, but humans uniquely add the power to communicate ideas and experience, which, as they are tested, can become incorporated into slower parts of the panarchy-from cultural myths (Chapter 5) to legal constitutions and laws (Chapters 4 and 13). Multiple sources of media, from television and movies to the Internet, are global in their connectedness and influence. These are contributing to a transformation of culture, beliefs, and politics at global scales. At smaller scales, the role of media is critical in the process of creating and disseminating the types of ecological crises described in Chapters 1 and 2. Subsequent chapters (6, 12, and 13) expand on the role that media and mass communication can play-from perpetuating myths to aggravating differences, to conducting forums that help resolve the crises.

3. SUSTAINABILITY AND PANARCIDES 101

Technology

The scale and influence of every animal but humans are restricted by its size. Such relationships were discussed in earlier sections, with regard to the identification of lumpy characteristics of body mass distributions and impacts on decisions made by animals. But technology transforms the actions of humans to influence an astonishing range of scales, from submicroscopic to planetary and, modestly at the moment, even a little beyond Earth itself.

This has evolved over a hundred thousand years, accelerating and changing the rules and context of the panarchies in the process. The specialized tools, habitation, and defense of hunters and gatherers, for example, together with the domestication of canines as hunting companions, opened opportunity over wide scales. The use of fire by early humans placed them as part of a structuring process capable, in temperate North America and Australia, for example, of transforming mosaics of grasslands and woods into extensive regions of contiguous grasslands or forests (Flannery 1994).

Progressively, the horse, train, automobile, and aircraft extended the ambit for human choices from local to regional to planetary scale, while the time for each of the sets of choices changed little, or decreased. Trips between home and work, for example, have always been largely limited to less than an hour or so, although the spatial scale has expanded from a maximum of a few kilometers by foot to potentially a few hundreds of kilometers by commuter aircraft. The slope of the decision panarchy of people, if plotted in that same space, as in Figure 3-11, now angles sharply upward, intersecting and dominating other panarchies of nature.

The characteristics that distinguish the self-organized patterns of ecological systems from those seen in social systems are developed in the next chapter and in Chapter 6. Chapter 4 addresses the question of why there is more than just disciplinary disunity between theories developed in social and ecological systems, and Chapter 6 addresses the particular dynamics of political systems linked to ecological dynamics.


Developing theory for sustainable futures requires a model of how human and ecological processes interact across space and time. The concept of panarchy provides an organizing framework for discussing these complex dynamics. Viewing sustainability from the perspective of panarchy yields five propositions:

1. Attributes of biological and human entities form clumped structures that reflect panarchical organization, create diversity, and contribute to resilience and sustainability.

2. Sustainability is maintained by relationships among a nested set of adaptive cycles arranged as a dynamic hierarchy in space and time-the panarchy. The panarchy represents the dynamic inter-


play between processes and structures that sustains relationships on the one hand and accumulates potential on the other. The concept is sufficiently new that precise insights and prescriptions are just beginning to be made. Many of the alternative stable states mentioned above are situations in which panarchies are transformed, either because productive novelty cascades up the levels, or because destructive catastrophes cascade down.

3. Panarchies identify three types of change, each of which can generate a different kind of learning: incremental change and learning, abrupt change and spasmodic learning, and transformational learning.

4. Being as simple as possible, but no simpler than necessary, leads to the minimal complexity needed to understand a panarchy and its adaptive cycles. We propose that minimal complexity requires:

• three to five key interacting components,

• three qualitatively different speeds,

• nonlinear causation and multistable behavior,

• vulnerability and resilience that change with the slow variables,

• biota that create structure that reinforces biota, and

• spatial contagion and biotic legacies that self-organize over space and time.

5. Self-organization of ecological systems by interaction between the biota and physical variables establishes the arena for evolutionary change. Self-organization of human institutional patterns, by adding human activity to the set of interactions, establishes the arena for future sustainable opportunity.

The ideas summarized in the previous paragraphs are developed and tested in the second and third parts of this book. Part 2 develops quantitative representations of these dynamical systems, while Part 3 develops an integrated, more qualitative representation in applying these concepts to managing large complex systems. But before these tests appear, the next three chapters develop more theoretical underpinning, beginning with a chapter that explores ideas presented above on why ecological and social systems may not be similar.

CHAPTER4

WHY SYSTEMS OF PEOPLE AND NATURE

ARE NoT JusT SociAL AND

ECOLOGICAL SYSTEMS

Frances Westley, Steven R. Carpenter, William A. Brock, C. S. Holling, and Lance H. Gunderson

There are in nature no rewards or punishments, just consequences. -Anon.

As we seek sustainable futures, we grapple with understanding complex systems of people and nature. Both the social and ecologic components of these systems have long histories of discipline-based

scientific inquiries-replete with theories, methods, and findings. One way of understanding how these components interact is to link them in a common framework. This is a "systems" approach, in which a universal or common framework can be used to unite different components in the system. The previous chapters use this approach to address similarities in dynamics, properties, and structures between ecological and social systems by using the adaptive cycle heuristic. Other chapters (7, 8, and 9) expand on that approach and create mathematical models of linked systems that include economic, ecologic, and social components. We attempt something different in this chapter.

People in Ecosystems or Ecology of Social Systems

We would like to address at least two audiences, joined by a common interest in sustainability of people and their environment. The first group comprises those natural scientists interested in ecology who want to include in their world model a box called "people." The second group comprises those social scientists interested in resource issues who wish to include in their model a box called "natural environment." Our intention is to open up those two boxes to indicate the possible differences-i.e., the extent to which they deserve to be treated as two separate systems-and the possible similarities and relationships-i.e., the extent to which we can use conceptual

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104 WESTLEY, CARPENTER, BROCK, HOLLING, GUNDERSON

frameworks to bridge the differences and explore interlocking theoretical and action frameworks.

We are exploring two alternative questions, each arising from a disciplinary perspective. The first question is a challenge from system ecologists: Why are systems of people and nature not just ecosystems? This question follows from the previous chapter and seeks to put at risk the proposition that complex systems (including those of people and nature) have similar properties, patterns, and behaviors to those observed in "ecological" ones. The second question is generated from social scientists: Why are systems of people and nature not just a type of social system? These extreme queries suggest that both are plausible.

We begin with a null hypothesis that would propose that the combination of people and nature is indeed an ecosystem (or in shorthand: people+ nature = ecosystem). Sociobiologists argue that human beings are just a dominant species and therefore like other kinds of animal. They are dependent on the natural environment but can also change or destroy it-but so can other species. This perspective sees the nature-culture split as arbitrary, a distinction that masks a continuum of lower- to higher-order processes, an artifact of the human brain, that is itself natural.

An alternative hypothesis is that the combination of people and nature generates a social system (people + nature = social system). This camp is represented by people who argue that in fact we have changed nature to such an extent that it is effectively lost (McKibbon 1989). There is no such thing as nature separated from human social processes. Other authors (e.g., Roe 1998) argue that there is a spectrum of "human domination" on which these two hypotheses form the endpoints. Nature is therefore a construct, as is the notion of ecosystem, and is largely used as a form of political discourse to secure use of resources by different social groups.

To argue against these hypotheses we would need to find ways in which human systems and natural systems (or social systems and ecosystems) differ from one another either in quantity or in quality. Differences in quality should be determined by identifying a pattern in one system that cannot be matched in the other system. Differences in quantity should be determined by sheer orders of magnitude-that is, matching occurs between the two systems, but there are more examples of a particular behavior in one system than in the other. In addition, there is the issue of hierarchy, suggested by the two null hypotheses. Are the two systems equal, or is one subordinate to the other?

While some biologists, sociobiologists, and evolutionary psychologists are inclined to define people as "just" a more highly evolved mammal (Pinker 1997), it can nonetheless be argued that due to the dominance of this species, its system has come close to engulfing and subsuming the natural system and should be seen as not merely a variable but a context for determining ecological processes. Another way of envisioning this is that the adaptive landscape in which species interact and compete has been transformed in its basic struc-

4. SYSTEMS OF PEOPLE AND NATURE 105

ture. The dominance of the human species establishes a Mt. Olympus on which the other species play out their bids for mountains and basins, with the humans "managing" the process to some extent, creating hills for preferred species and basins for those less preferred. Yet another way to frame this relationship is to argue that self-organizing properties of human systems are overtaking the self-organizing properties of ecological systems. This chapter aims in part to help elaborate just what these self-organizing properties are in human systems, how they operate, how they fail, and what kinds of new complexities they introduce to ecosystems.

Four themes help guide our comparisons of social and ecological systems. These themes are not structured to evaluate the questions and hypotheses posed above, but are propositions in themselves-suggestions of where to place the next tentative step for exploration. All four of these themes explore the relationship between ecosystems and social systems by means of describing different dimensions around which structures and processes of these systems are organized (or self-organized). We argue that the temporaVspatial dimensions key to ecosystem dynamics contrast with the temporaVspatiallsymbolic dimensions key to social systems. We first discuss how the symbolic dimension, or the "structures of signification" (the interpretive schemes that give meaning to our activities, sometimes described as myths, paradigms, or ideologies), allows social systems to abstract from local environments. The second theme is related to the first: The symbolic dimension of social systems means that social systems are characterized by reflexivity, which provides a heightened degree of flexibility and cross-scale interactions to social systems. The third theme describes the capacity for forward-looking behavior, which abstraction and reflexivity make possible and which provides purposefulness and consequences, as suggested by the epigraph to this chapter. The last theme describes in brief the ability of humans to expand their capacity to externalize their logic in technology and the implications this has for resource use and misuse.

Dimensions of Organization and Behavior

Dimensions are defined as any measurable extent; hence they form the basis for scientific inquiry. We use a slightly broader definition, one that allows for organization of inquiry around key components or factors. Some of the dimensions (space and time) around which theories are developed and tested are similar for both ecological and social systems. Other dimensions can be very different. Contrasts of these key organizing concepts are developed in the following sections, beginning with dimensions of ecosystems.

Ecosystems

Ecosystems are defined as places on earth that consist of biotic components (life) and abiotic or physical components (Carpenter 1998). Those components interact in such a way that a dynamic set of processes produces a


complex and diverse set of structures. The interaction is described as selforganizing-that is, structure and processes mutually reinforce each other (Levin 1999).

Space and time scales have been fundamental constructs for the development of understanding and interpretation of self-organized ecosystem dynamics (Levin 1992). Space and time are critical dimensions because they provide the basis from which theory can be generated and hypothesis can be derived and tested.

Ecosystems occur over a wide range of scales; hence it is necessary to develop a framework that covers equally broad scales in space and time. Ecosystems can be contained in a few centimeters (such as an abandoned beer can or a petri dish), and they can cover thousands of kilometers (such as the Pacific Ocean or the boreal forest). One framework that usefully depicts broad-scale ranges is a Stommel plot, named for a twentieth-century oceanographer. Stommel plots have been used to capture structural and process features of ecosystems such as oceans (Powell and Steele 1995) and terrestrial systems such as the boreal forest (Holling 1992) and wetlands (Gunderson et al. 1995a). In terrestrial systems, the hierarchy of elements covers six to ten orders of magnitude in space-from leaves to plants to crowns to patches to landscapes to watersheds to biomes (Figure 3-8). Another key feature of these elements is the discontinuous arrangement across spatial scales. The discontinuous textural pattern is attributed to selforganizing processes operating at distinctly different scale ranges (Holling 1992). Stommel plots are useful for capturing the structural features of ecosystems but not as good at capturing the temporal dynamics for which other frameworks have been developed.

Ecosystems are frequently described through their characteristic temporal structures. They have dominant rhythms, as expressed in the turnover times of the major components or as frequencies that explain most of the variance in time series (Carpenter and Leavitt 1991; Powell and Steele 1995). Not all cycling times or frequencies are represented in ecosystem dynamics. Certain cycling times or frequencies are more common than we would expect by chance (for example, diel or seasonal cycles and the life cycles of keystone species), while others are underrepresented. The dominant frequencies are the temporal lumps of ecosystem dynamics. Spectral analysis of ecological time series shows that variance is aggregated at certain frequencies that correspond to the temporal lumps.

The repertoire of characteristic time scales is critical in ecosystem responses to disturbance. Time scales of response depend on the rarity, severity, duration, and spatial scale of the disturbance. Disturbances that have been routine in the evolution of the ecosystem's species are endogenized to become a part of the normal successional processes of the ecosystem. Exotic or rare disturbances may have more catastrophic effects and longer recovery times. For example, consider forest ecosystem responses to climate warming. An unusually warm day evokes response mechanisms


with fast cycles. Plants cool leaves by transpiring more water; animals modify behavior and physiology to maintain body temperature within tolerable limits. An unusually warm century evokes changes in species composition with much slower dynamics controlled by colonization rates and tree life cycles. But exceptionally rapid warming beyond the tolerance of the available species pool may lead to wholesale change in ecosystem structure. The rate of recovery is scaled by slow processes such as long-distance dispersal, evolution of new varieties, and pedogenesis.

Ecosystem resilience depends on an imbricate series of mechanisms, partly distinctive yet partially overlapping in return time and function. Imbrication is characteristic of evolved homeostatic processes at all levels of organismal biology. It also emerges in systems that self-organize from evolving components, like ecosystems (Gunderson et al. 1997; Peterson et al. 1998; Levin 1999).

Social Systems

A social system is defined as any group of people who interact long enough to create a shared set of understandings, norms, or routines to integrate action, and established patterns of dominance and resource allocation. Like any system it is dynamic, meaning that it is difficult to change any one part of it without considerable effects on other parts. Depending on how boundaries are drawn, social systems can be as small as a family or as large as a nation. Like natural systems, social systems must fulfill key functions. They must be oriented toward certain goals or objectives, they must create mechanisms for integration and adaptation, and they must create mechanisms for self-reproduction (Parsons 1951).

Time and space are important dimensions of social system organization. However, in contrast to ecological systems, social systems are structured by the human ability to construct and manipulate symbols, the most obvious of these being words. These "structures of signification" along with "structures of domination" (the flow of power and resources and patterns of authority in a particular system) and "structures of legitimation" (norms, rules, routines, and procedures) provide the building blocks of social systems (Giddens 1987).

We will look in some detail at the role of such structures of signification in distinguishing social systems from ecosystems. We will first consider their potential for creating a hierarchy of abstraction equal in shaping power to temporal and spatial hierarchies. Second, we will look at the capacity for reflexivity inherent in structures of signification. Third, we will look at the capacity for forward looking that is made possible by such meaning structures. Last, we will look at the externalization capacities that technical logic (dependent on all three of the foregoing capacities) makes possible. It is these elements, we will argue, that set social systems most clearly apart from ecosystems.


Abstraction

Human beings are "sense-making" animals. Through the use of communication, language, and symbols they collectively invent and reinvent a meaningful order around them and then act in accordance with that invented world, as if it were real. Sense making is important as a way to "place our feet firmly in midair" (Michael 1984). This ability has obvious consequences for the environment, as "when people take their interpretations seriously and act on them, the material world may cohere in a different way than it did before" (Weick 1995). An excellent example is the notion of the garden (whence came the original European term for "culture"), where physical space is reshaped along cultural lines.

But sense making and signification not only provide a powerful shaping force, they also provide a third hierarchy, equal to time and space, for structuring social system dynamics. Our meaning systems have the ability to insulate us and separate us from the physical ground of our being (hence the aptness of the idea of feet planted firmly in midair), meaning that systems absorb large amounts of uncertainty. This ability of social systems to create structures of signification that provide a "virtual reality" is key to understanding resilience in social systems. Routines and even resources may suffer a loss of resilience (D. Ludwig et al. 1997), but as long as the structures of signification stay in place, the whole system will not transform radically, but rather will return to a previous equilibrium (Frankl 1985). The opposite is also true, as studies of communities in crisis indicate (Box 4-1). If meaning is lost, human systems seem unable to recover (Erikson 1995).

Such an ability to find and construct meaning through processes of symbolic communication permits a higher level of self-organization than that found in ecosystems. It also allows human systems to apparently flip from one kind of organization to another and back again, in a much quicker time frame. For example, an organization can switch relatively effortlessly from formal, rigid behavior to free-flowing improvisation as the task demands. Due to the ability to play roles (an ability made possible by consciousness), humans can, on cue, switch fundamental properties of their organizational system. This switch is done not by adapting to change in one aspect of the system but by shifting the system configuration, in the same way that a soccer team will flip between offensive and defensive alignments. An example of this is Holland's emergency services, which flip from rigid hierarchy (command and control) to self-organizing adhocracy depending on the task at hand. The concept of "practice" or "rehearsal," which is closely allied to this kind of flip, requires a qualitatively different capacity for selforganizing. So does the idea of improvisation, which is deliberate, continual recreation and experimentation, demanding the capacity for reflexivity (discussed further below) (Weick and Westley 1995; Crosson 1998).

Most important for purposes of comparison to ecosystems, however, is that the social systems' ability to shape and then be shaped by structures of


Box 4-1. Mercury Contamination in Grassy Narrows, Ontario, Canada

F. Westley

When the Grassy Narrows watershed became contaminated with mercury, a deep sense of betrayal infiltrated the community, making it difficult to relate not only to the natural world but also to each other. A kind of chronic hopelessness set in, as it appeared to the community that the very system that had sustained them had been poisoned. The mercury came from a paper mill in the beginning, but it had been absorbed into the natural world by the time it reached Grassy Narrows. So the environment itself had been darkened and contaminated and had become less reliable. An elder tried to explain: "We call it pijiblYWin. This is the Ojibwa word for poison. You can't see it or smell it, you can't taste it or feel it, but you know it's there. You know it can hurt you, make your limbs go numb, make your spirit sick. But I don't understand it. I don't know how the land can turn against us." So the problem was not only a medical one and an economic one but also a psychological or even spiritual one, for the apprehensions and uncertainties that followed the discovery of mercury poisoned the mind in a way that clinical tests could not even begin to trace. The fear of poison is a pervasive one that the world of nature and the world of human beings can no longer be relied on in the old way. The fish are full of poison, the waters are contaminated, the land itself is diseased, and the social world is in disarray (Erikson 1995).

signification allows human systems to divorce themselves to some degree from space and time, the critical organizing dimensions of ecosystems. Initially, time and place had meaning only in relation to each other: time was different from place to place or was determined by physical, local phenomena. But as time was systematized and rationalized, it became possible to keep track of it, irrespective of place. Similarly, place and space became separated. If place is "best conceptualized by means of the idea of locale," then the "fostering of relations between 'absent' others" means that locales are "thoroughly penetrated by and shaped in terms of social influences quite distant from them" (Giddens 1990). In place of the face-to-face monitoring and interpersonal relations that characterized social interactions in time and place, trust in rational symbolic systems (such as money) and expert systems allows for social institutions to exist on a global scale, deterritorialized or disembedded from geographical location.


Paradoxically, this makes social systems more resilient to environmental disturbances at the local level and less able to respond to surprises and uncertainties at that level. On the other hand, it at least opens the potential for anticipating surprises at the global level (for example, climate change), perhaps for the first time in human history. Resource flows follow symbolic organization, allowing individuals or subsystems to self-organize either temporarily or continuously to solve problems that transcend individual systems. This obviously has great implications for natural resource management at both local and global scales.

Reflexivity

A major aspect of social systems that concerns us here is the human tendency to create a social or virtual reality that is externalized, objectified, and then experienced or internalized as "real" (Berger and Luckman 1967). Human beings create the systems that constrain and motivate them but then lose their sense of these as social productions.

This does not mean that social systems cannot be changed. For if social structures are to be maintained, they must in fact be continually reproduced, in social action, by the members of that society. Social laws are constructed and mutable, unlike some laws that govern biophysical systems, such as the laws of gravity, thermodynamics, biogeochemistry, or evolution (Ludwig 2000). For example, a society could change its laws to better represent environmental externalities in its marketplaces. A society cannot change the rules that govern gravitational acceleration, the creation of entropy, the cycling of carbon, or the extinction of maladapted species.

This ability for self-organization, tied as it is to the human ability for symbolic communication of a fairly high order, also allows human beings to easily and quickly transcend the boundaries of the system that they have created and in which they are involved. Human beings play roles in a wide variety of systems and are able to move between them with great skill. This is in part what gives each individual system the rich repertoire of possibilities for combinations and recombinations to deal with crises (Whittington 1992; Rubinstein and Woodman 1984).

It would appear that human systems are able to self-organize in qualitatively different ways. While nature has the capacity for remembrance (e.g., in the form of biotic legacies), humans and human systems have the capacity of consciousness and reflexivity. This allows them to consciously maintain the notion of integrity and identity while becoming disorganized at lower levels of self (memory, stimulus response, reflex arcs). Hence, while structures of signification have a hierarchy, built from numbers of people involved, as well as along a temporal scale (Lee 1993; Gunderson et al. 1995a; Figure 4-1), reflexivity means that processes at lower scales have the potential to disturb processes and structures at higher scales.

At one level, human's ability to expand its effective energetic reach is thought to be deeply rooted and reinforced by culture. Popular (Quinn

3

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-1

-2


one ten one hundred one hundred thousand million million

millennium

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...... small group or individual decisions ··u -- ·

· ... .. ...... ; ............... , ... · .. ....... ,.... ) months

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Figure 4-1. Institutional hierarchy of rule sets. In contrast to ecological hierarchies, this one is structured along dimensions of the number of people involved in rule set and approximate turnover times (Gunderson et al. 1995b).

1994) and technical works argue that indeed many of the environmental challenges we face today-loss of biodiversity, global climate change, depletion of renewable resources, and others-are rooted and maintained through culture. But that culture is not static; indeed, its change can be abrupt and dramatic. Biological evolution exhibits similar patterns (Eldredge 1998), but the mechanisms are different between the cultural development and biological evolution.

Organic evolution is irreversible proliferation, at a specific level of organization in a hierarchy of structures. Once a new species splits off, it is distinct forever. Biotic community and ecosystem organization offer some higher-order opportunities for innovation, but ecosystems are not coherent reproductive units. Cultural change, in contrast, can be powerfully accelerated by amalgamation of different traditions (Sober and Wilson 1998). A concept can be imported into a culture and change that culture rapidly and forever. Gould (1997) refers to technology, but social structures (and cuisine!) also exhibit these patterns of change (Box 4-2).

Organic evolution works at the rates of mutation, crossover, recombination, and selection of the fittest. Cultural change is potentially far faster. Any good ideas acquired in one generation can spread rapidly and be passed to the next generation directly. It is essentially a Lamarckian process-an inheritance of acquired characteristics-that cannot occur in organic evolution.

Again, under conditions of modernity and postmodernity this reflexivity is increasing (Giddens 1990), intensifying the potential for cross-scale distur-


Box 4-2. Climate and Human Evolution

S. R. Carpenter

In Children of the Ice Age, Stanley (1996) correlates climate shifts and human evolution. Approximately 2.4 million years ago, our ancestors lived in trees, and every time they came down to the ground they became food for sabertoothed cats, lions, and hyenas. (This perhaps contributed to abstractions such as the "cat-like" attributes in most demonic personifications of evil [Chatwin 1987]-witness the features of the character Darth Maul in the most recent "Star Wars" movie.) The isthmus of Panama rose from the sea, blocking exchange between the Pacific and Atlantic oceans and ultimately causing both oceans to become colder, because heat circulation got less efficient and there was more upwelling of cold water near the tropical continents. This cooling caused central Africa to get both cooler and drier (there is less evaporation from a colder ocean). The tropical rain forest shrank at least tenfold in area, to three relatively small refuges. Our ancestors had few trees to climb, and the mayhem must have been awful. A small number of our largerbrained ancestors emerged from the crisis. This little band had figured out two important things: (1) they had learned to chip handheld tools out of rocks, and (2) they had learned to kill cooperatively. Tool making and communication changed selective pressures in such a way that brain size doubled in the blink of a geological eyelash. The ancestral smaller-brained folk had lived in the trees unchanged for more than 2 million years and then almost instantly were replaced by larger-brained animals that changed very little between then and now. Within a couple of hundred thousand years, the larger-brained hominids were indistinguishable from our species and had colonized all of Asia and Europe. From that point on, human evolution was more cultural than physical.

bances, whether revolt, remembrances, renewal, or reconstitution. Whereas previously traditions and laws held an immutable and impenetrable quality from the point of view of average citizens, today the socially constructed aspect of such human systems makes tradition the object of exploration and questioning, once again loosening the hold of locale and increasing the power of disembedded institutions (global economy, expert systems). If new and different structures of signification can find an outlet, particularly through the consolidating power of charismatic or visionary leaders, humans


can simply refuse to reconstruct an old order and put a new one in its place. This happens rarely, as it provides a threat to identity and sense of self, but it may happen very quickly as we see in religious conversions, where a series of interactions at the level of meaning can result in a state change in individuals, groups of individuals, and whole social systems.

In sum, the human capacity for representation, for communication, and for making meaning seems to drive the processes of both maintaining system integrity and dealing with change. Yet that abstraction and reflexivity have limits when applied to complex problems of the environment.

People have great difficulty solving problems that involve multiple time scales (Domer 1996). For example, we have very poor abilities to infer functional relationships from time series data, and poor judgment about the trajectories of dynamic systems. In a series of experimental studies, Domer found that inability to handle slowly changing variables and time horizons of years was a common cause of human failures in decision making. People have similar difficulties with probability, an essential concept in forecasting and making decisions (Lindley 1985; Anderson 1998). Experienced decision makers know that time and probabilities are problematic. Individuals who have skills in evaluating dynamic systems and probabilities are highly valued. We expend considerable effort in developing mathematical models, technical abstractions that help us cope with the stochastic properties and temporal evolution of systems we seek to man~ge. We do not, however, spend sufficient attention in educating our children to develop capacities to deal with this level of abstraction (Ornstein and Ehrlich 1989).

Time scales of problem solving seem distinctly different in human systems and ecosystems. If we view disturbance as a "problem" solved by the self-organizing features of ecosystems, we see an imbricate array of potential time scales and mechanisms of response. Collectively, these mechanisms create ecological resilience more like a tree in the wind than like a girder in concrete. In contrast, human systems tend to solve problems one time scale at a time. The result is systems that are successful in a certain domain, but that have a rigidity that limits their resilience. Moreover, the solutions tend to create spin-off problems that may appear remote in time and space. At best these engage us in a recursive loop of endless problem solving. At worst, they commit us to courses of action that turn out to be disastrous.

Modeling Backward- and Forward-Looking Behaviors

As evident in this volume (Chapters 7, 8, 9, 10, and 11), scientists attempt to understand complex dynamics of human and social systems through the development of simulation models. Experienced ecological modelers aggregate components to represent a few focal cycling rates or return times of ecosystems (Chapters 3 and 15). Ecological modelers are less familiar with the challenge of modeling the forward-looking behavior of people. Some of the


first models to integrate realistic ecological dynamics with forward-looking human behavior are found in this book.

One way to differentiate human systems from ecosystems is in the degree of foresight potential. The presence of forward-looking purposive behavior has significant implications for the mathematics needed to solve for equilibria (Sargent 1993).

A famous example of how one type of systems modeling was discredited by economists and other critics was the Club of Rome debate of the 1970s. Solow (1973) wrote a withering critique of environmental modeling that ignored forward-looking behaviors such as futures markets and strategic buying and holding of commodities. Such mechanisms play a major role in transmitting future scarcities into current prices that help to induce conservation behaviors today in the real economic world. Ignoring these forward-looking features of human systems radically biases model results. Promotion of these models harmed the reputation of ecology in economics and created a legacy that still impedes the bridging of these disciplines.

In relatively frictionless systems like financial markets, there is almost zero evidence for exploitable patterns in stock prices over time and across securities. This is an equilibrium type of property that would not be generated by typical ecological models. But it is consistent with forward-looking rational expectation models. The foresight of the agents enables them to exploit any under- or overvaluations of commodities and immediately squash any fluctuations of this type. In ecosystems, the rate of response to opportunity would be limited by dispersal rate, numerical response of predators, or rate of adaptation. In social systems, the responses are instantaneous.

Even if there are slow-moving variables in the real world that might lead to a collapse in economic modeling, one must find the force that blunts the very large incentive for people to find this variable, measure it, forecast the coming collapse better than the rest of the market, and take a position to profit from it. But what one market participant can do, all can do; and this process transmits information to the market as a whole. This type of selfinterested behavior in pursuit of profit is an example of linking information from the future to the present and is a major difference in dynamics between ecosystems and human systems.

The modeling discussed by Solow (1973) stressed the emergence of overshoots and crashes as humans were projected to run down natural resource stocks. Some critics have stressed the poor forecasting record of similar-looking doomsday modelers from the past and (in some cases) the extremely damaging policies that have been implemented based on such work.

Indeed, critics of ecological modeling might argue that focus on potential instability of ecosystems draws attention away from how some ecological economists would describe the real problem in environmental management. They argue that it is more important to correct the accounting system to reflect the "full costing" of production and consumption and get the incentive system corrected so that all who create costs bear their own costs. For


many economists this "incentive gap" is the real environmental tragedy. For them more good can be done by convincing governments to implement reforms such as green accounting and imposing green taxes to gain revenue for the treasury, as well as realigning incentives to stop private interests and government departments from exploiting the environment and offloading the costs onto everyone else.

Thus the main complaint of critics against purely ecological policy models is that such models ignore essential human behaviors such as forward-looking expectational behavior, forward-looking institutions, incentives such as futures markets, and incentives for human agents to store the cheap resources now and sell them in the future when they are scarce-making themselves wealthy in the process. Such strong incentives generate signals (high prices today) to conserve and, perhaps, steer the system away from instability.

Indeed, basic results from economic theory suggest that such forces are powerful in removing instabilities in systems. Brock (1988) discusses possible "frictions" in the real world that may blunt this usual result.

Given the complexity of real world ecosystems, the cost to each agent of building workable predictive models must surely rise with the level of complexity of the system being modeled by that agent. We can imagine agents in a model who ignore forward-looking behavior (because it is cheaper to do so) and thereby have a simpler, cheaper, but less predictive model.

Assume that it is more expensive for each agent to build a forwardlooking model than to simply fit equations to past experience. There may be periods of time where the net benefit (cost of model building compared to benefits from an accurate model) to the "backward-looking" modelers is higher than the net benefit to the forward-looking modelers. Hence, if the cost to each agent of obtaining accurate information about the system is high enough, the forward-looking motives in the real world will suppress instabilities caused by backward-looking behavior, if those instabilities develop slowly enough. However, once instabilities grow to the point where it pays agents to purchase better models, then the instabilities will tend to be dampened. Brock and Hommes (1997) present a system in which instabilities caused by backward-looking behavior could be crushed by forward-looking behavior. When forward-looking behavior was more costly than backwardlooking behavior, backward-looking behavior could be more competitive. This tension generated another layer of dynamics that could be unstable if agents had high enough choice across net profits generated by different viewpoints or predictors. The instability of this "meta layer" of dynamics becomes smaller as the cost of obtaining information to implement forwardlooking behavior drops. Similar complexities occur when social models of this type are combined with multistable ecological models (Carpenter, Brock, and Ludwig 1999).

It is a delicate matter to uncover the determinants of these complex dynamics. One thing is clear, however. The duplication of costs of gathering information among agents to build better and more forward-looking models


points toward a role for government in delivering information to individual agents. A tradeoff emerges between the cost of involving government in the information gathering process and the savings to individual agents of duplication in information collection.

Even though forward-looking behavior is a definitive feature of human systems, history contains many examples of failures of foresight. One case is the collapse of Easter Island's civilization (Diamond 1995). The Polynesians who settled Easter Island found extensive forests of large trees suitable for building seagoing canoes. The canoes were used to harvest porpoises, which became a staple food for the Easter Islanders. The trees were also used for buildings and in the construction and transport of the enormous stone statues for which Easter Island is famous. Within a few hundred years, the island was deforested and some of the tree species had become extinct. Exposed soils eroded rapidly, and soil fertility declined. About that time, all work on the statues stopped. It was no longer possible to build canoes to harvest porpoises, so the people turned to seabirds and nearshore marine and terrestrial animals for food, and depleted those over the next two to three hundred years. During that period there is evidence of cannibalism. Houses were abandoned, and people moved into caves. Rival bands began destroying each other's statues. The population dwindled in famine and war. The Easter Islanders were caught in a total environmental and social breakdown. How could they have failed to foresee the trends and take corrective action?

The archaeologist Charles Redman (1999) shows that the Easter Island case is not an isolated one. He classifies diverse case studies of human interaction with the environment in four categories: exploitation and loss of species, impacts of agriculture, impacts of urbanization, and synergistic effects associated with high human population densities. He concludes, "If my reading of the archaeological record is correct, this seemingly selfdestructive situation occurs repeatedly-individuals, groups, and entire societies make decisions that initially are productive and logical, but over time have negative and sometimes disastrous environmental implications. \Vhat are the cultural filters and institutional frameworks that again and again appear to have inhibited otherwise highly successful societies and people of great creativity and intelligence from accurately perceiving the problems that beset them and acting to remedy them in a timely fashion?" In part, the present volume is our attempt to address Redman's question.

Externalization and Technologic Development

Our last theme explores the power of structures of signification to result in technical logic (means-ends logic designed to solve a particular problem) and technologies. There is a discontinuity between human's ability to exploit a variety of scales and niches and the explorative/exploitative ability of other species. This in part reflects human species' success as a tool maker. Not


only are human beings able to construct social and symbolic systems that become objectified "virtual realities," they can externalize those in the construction of technical systems, which in turn become objectified and appear at times to be outside human control.

Organic evolution does not include any principle of progress toward greater complexity (although greater complexity can arise incidentally during evolutionary change). Gould (1997) refers to complexity at the level of the organism being selected, not complexity of ecosystems. But cultural change is potentially self-complexifying, in technology and social structures. Gould mentions some of the obvious caveats-technology is a mixed blessing; societies do not always choose to avail themselves of technological solutions; technological complexification is not the same as "good" in a moral sense and may even lead to destruction.

Even with the ability to create novel futures through technology, it has proved to be a double-edged sword. Humans often fail to build selforganizing or adaptive capacities into their technologies. The tendency is to make single-variable interventions or to create inventions without regard for their impact on other parts of the systems, to ignore internal mechanisms that facilitate adjustments, or to fail to balance objectives. Industries are often allowed to purchase technology on an independent basis without regard for large-scale impacts or without acknowledgement of the failure of technology to achieve sustainable futures (Commoner 1992; Gouldner 1976).

Much has been written of the way in which technological and ecological systems work differently and at times antagonistically (Commoner 1992; Westley and Vredenburg 1996). Human technology has a tendency to be built on linear logic, as opposed to cyclical process; it often represents singlevariable interventions in complex or imbricate systems. Technological solutions focus on the limited scales of a particular problem. In consequence, they often create new problems at other time scales. For example, using industrial nitrogen fixation to make fertilizers solved an important problem in agricultural production, but unexpected side effects included toxic levels of nitrate in ground water, widespread acidification of ecosystems, increased flux of greenhouse gases to the atmosphere, and eutrophication and increasingly frequent toxic algal blooms in coastal oceans (Vitousek 1997; Carpenter et al. 1998). Furthermore, the human population growth supported by increased food production creates dependency on industrially produced fertilizers. Positive feedbacks of increased dependency on technologies with increasingly severe side effects are common in environmental management (Gunderson et al. 1995).

During most of the twentieth century, the goal of technologically based resource management has been to control the external sources of variability in order to seek a singular goal, such as maximization of yield (trees, fish) or controlling levels of pollution. This approach, also called "command and control," focuses on controlling a target variable, and may be successful at first but then slowly changes other parts of the system. Thus, isolating and

118 WESTI.EY, CARPENTER, BROCK, HOLLING, GUNDERSON

controlling the variables of interest (assuming the uncertainty of nature is subject to the certainty of control) result in the erosion of resilience. The manifestation of that erosion is the pattern of policy crisis and reformation mentioned in Chapter 1 and elsewhere (Gunderson et al. 1995a). Much of subsidized agriculture, where incentives are set up to deal with changes in markets and costs, as well as surprises from nature, falls into this category.

People involved in the practice of resource management are all linked by the need for understanding. During the twentieth century, scientists and engineers (the heart and soul of technology and technologic solutions) became the key arbiters of understanding complex resource systems. But there has been a growing sense that traditional scientific approaches are not working, and indeed make the problem worse (Ludwig et al. 1993). Two reasons rigid technological approaches eventually fail are that they tend to focus on the wrong types of uncertainty and on narrow types of scientific practice. Many formal techniques of assessment and policy analysis presume a system near equilibrium, with a constancy of relationships, and uncertainties that arise not from errors in tools or models, but from lack of appropriate information.

Colored light provides a metaphor to illustrate the technological approach common during the past century: Light can be monochromatic (one frequency) or a mix of frequencies like sunlight. The multiple frequencies of ecosystem dynamics are like a complex polychromatic light, giving a diversity of hues. Human technological solutions tend to be monochromatic, a single stark color.

In contrast to the narrow targeting of some environmental management practices, human attitudes toward the environment are ever changing. The geographer Yi-Fu Tuan (1974) attributes this variation to pursuit of an ideal that intersects a shifting mix of values: "Human beings have persistently searched for the ideal environment. How it looks varies from one culture to another but in essence it seems to draw on two antipodal images: the garden of innocence and the cosmos. The fruits of the earth provide security as also does the harmony of the stars which offers, in addition, grandeur. So we move from one to the other; from the shade under the baobab to the magic circle under heaven; from home to public square, from suburb to city; from a seaside holiday to the enjoyment of the sophisticated arts, seeking for a point of equilibrium that is not of this world."

Together, the ever changing environment and changing human aspirations create an intricate dynamic that is difficult to foretell. Any credible vision of the future must be highly uncertain. A unique property of human systems in response to uncertainty is the generation of novelty. Novelty is key to dealing with surprises or crises. Humans are unique in that they create novelty that transforms the future over multiple decades to centuries. Natural evolutionary processes cause the same magnitude of transformation over time spans of millennia. Examples are the creation of new types and arrangements of management institutions after resource crises in the


Everglades (Light et al. 1995), the Columbia River Basin (Lee 1993), and the Baltic Sea Qansson and Velner 1995). In technologies it is invention and adaptation that transform the future (Arthur eta!. 1997; Kauffman 1995).


In this chapter we have attempted a cursory comparison between ecological systems (as perceived, characterized, and understood by ecologists) and social systems (with all of the similar processes applied by social scientists). We argue that some differences can be traced to disciplinary disunityrelated to perspectives and paradigms embedded in the disciplines. Moving past a postmodern perspective, however, some real differences pertain to sustainability and sustainable use of resources. The key to understanding those differences lies in understanding the dimensions around which patterns of structures and processes are identified and studied. In ecosystems, the key dimensions are space and time. For social systems, we need to add a third dimension, which is symbolic construction or meaning. Four elements of this third dimension are particularly helpful in understanding differences. The first is the creation of a hierarchy of abstraction, which loosens the power of time and space to explain social systems. The second is the inherent capacity of such meaning structures for reflexivity. The third is the ability to generate expectations and look forward rather than to react and look backwards in time. The final element is the ability of humans to externalize these symbolic constructions in technology, which is the equivalent of extending their energetic footprint beyond that of the typical 100-kg biped. Each of these elements, we have shown, not only helps to illustrate the difference between social and ecological systems but also helps to explain the fundamental lack of responsiveness or adaptability to environmental signals that characterize much of natural resource management. Much of this book is devoted to reconceptualizing that relationship. This chapter has merely outlined the nature of these challenges. The next chapter draws on examples to suggest how societies with a long history of interaction with nature have sustained over long terms, in spite of the limitations presented in this chapter.

CHAPTER 5

BACK TO THE FuTuRE: ECOSYSTEM

DYNAMICS AND LOCAL KNOWLEDGE

Fikret Berkes and Carl F olke

There is no question that there is an unseen world. The question is, how far is it from midtown and how late is it open?

-Woody Allen

Our starting point in this chapter is that resource management has been and continues to be problematic. Some of the problems arise from the perception that there is only one world of resource man

agement. But is there only one world? Certainly, there is the world of the rationalist, Newtonian clockwork, conventional Western resource management. However, other worlds of resource management also exist. For Woody Allen and many of us, even an unseen world tends to be within the sphere with which we are intimately familiar. Perhaps we need to take a chance and venture further afield than Woody Allen's midtown New York.

This chapter examines local knowledge and management systems to look for some of those unseen worlds. The task is to try to broaden the range of the resilience inquiry. In particular, we are looking for insights on how institutions respond to feedback from the environment, and how they use ecological knowledge to learn and become more resilient. "Every natural system is subject to regular disturbance; those that have survived, indeed must have built up some degree of resilience" (Levin et al. 1998). When we probe traditional management systems, our aim is not anthropological erudition but a practical inquiry on resilience. In celebrating the holistic analysis of linked ecosystems and institutions, we are not turning our back to science to celebrate the noble savage-rather, we are acknowledging the existence of a "people's science" as an antidote to excessively centralized and bureaucratized resource management science (Chapter 6).

121

122 BERKES,FOLKE

Holistic approaches are obviously not limited to local communities and traditional societies; all good resource managers are adept at handling complexity, thinking holistically, and responding adaptively (Chapter 13). We are using examples of scientific management systems as well as traditional systems to get at some of the issues of adaptiveness raised by the renewal cycle. We are focusing on institutions and linked social-ecological systems that understand complex adaptations of ecosystems (Levin 1999). The examples are eclectic and cross-cultural. The adaptive renewal cycle is very suitable for such cross-cultural thinking because it breaks through linear time, a thoroughly Western superstition, as one finds, for example, in the older ecological notion of climax with an end point.

Introduction

Resource management and sustainability problems are typically systems problems in which it is rarely possible to consider social systems and ecological systems as separate entities. Yet it is usually the case that scientists examine either social systems or natural systems, rather than the linkages and feedbacks between the two. It is a daunting task to deal with both societies and ecosystems-the scope needs to be narrowed. One possible approach to linking the two systems is to focus on ecological knowledge, its creation, accumulation, and transmission. Much ecological knowledge is created by professional ecologists. However, groups of resource users, such as indigenous peoples who live off wildlife, fish, and forests, also create knowledge from their own observations and ecological understanding, based on the accumulation of generations of trial-and-error experience. Such folk ecology is well recognized as a body of knowledge, traditional ecological knowledge, paralleling indigenous knowledge in other areas-for example, in medicine and agriculture (Folke, Berkes, and Colding 1998; Berkes 1999).

This chapter focuses on ecological knowledge, of both the scientific and the traditional kind (including local knowledge that has less time depth than indigenous knowledge), to link the dynamics of social systems with those of ecological systems. It explores insights and possible principles regarding resilience and the adaptive renewal cycle. It examines the creation of ecological knowledge from observation and understanding, its incorporation into resource use practices, its transmission and transformation, and its re-creation through cycles of resource crises and social crises.

First, some definitions and clarification of concepts are needed. We hold the view that the delineation between social and natural systems is artificial and arbitrary. Thus, we use the terms social-ecological system and socialecological linkages to emphasize the integrated concept of humans and nature. Local knowledge is used as a generic term referring to knowledge generated through observations of the local environment and held by a specific group of people. Indigenous knowledge is used to mean local knowledge held by indigenous peoples, or local knowledge unique to a given culture or society

5. ECOSYSTEM DYNAMICS AND LOCAL KNOWLEDGE 123

(Warren et al. 1995). Traditional ecological knowledge is used more specifically to refer to "a cumulative body of knowledge and beliefs, evolving by adaptive processes and handed down through generations by cultural transmission, about the relationship of living beings (including humans) with one another and with their environment" (Berkes 1999). The word traditional is used to refer to historical and cultural continuity, but at the same time recognizing that societies are in a dynamic process of change, constantly redefining what is considered traditional.

Local knowledge or traditional ecological knowledge is part of the cultural capital by which societies convert natural capital-that is, resources and ecological services-into human-made capital or the produced means of production (Berkes and Folke 1994). Knowledge as capital is analogous to natural capital, with the difference that there are different "levels" of knowledge. They can be considered a qualitative series: information, knowledge, understanding, wisdom.

Traditional and local management is considered different from Western (or conventional) resource management, defined here as resource management based on Newtonian science and on the expertise of government resource managers. We recognize that all societies have their own science (Feyerabend 1987). We identify conventional resource management science and method to represent a particular kind of science-based approach that is used predominantly as the basis of resource management by centralized bureaucracies in all parts of the world (Gunderson et al. 1995a).

Institutions are defined as "humanly devised constraints that structure human interaction. They are made up of formal constraints (rules, laws, constitutions), informal constraints (norms of behavior, conventions, and self-imposed codes of conduct), and their enforcement characteristics" (North 1994). Institutions are "the set of rules actually used (the working rules or rules-in-use) by a set of individuals to organize repetitive activities that produce outcomes affecting those individuals and potentially affecting others" (Ostrom 1992). But it is also important to note that institutions are socially constructed; they have normative and cognitive, as well as regulative, dimensions (Scott 1995; J en toft et al. 1998). The cognitive dimension is particularly relevant to this chapter, as it has to do with questions of the nature of knowledge and the legitimacy of different kinds of knowledge.

Institutional learning is defined as learning that takes place at the level of institutions, as opposed to individuals (Lee 1993). Institutional memory (related to resource use) is memory of experience, which provides context for modification of resource-use rules and regimes, and typically refers to a decadal scale of time, as opposed to a time scale of months or a year. Institutional memory incorporates local or traditional knowledge. The existence of ecological knowledge and an understanding of how to respond to environmental change are prerequisites for the management and sustainable use of resources, biological diversity, and ecosystems (Figure 5-l).

124 BERKES,FOLKE

bioregion

watershed

local ecosystem

institutional setting

managing institutions

local management

Figure 5-l. Conceptual framework for the analysis of linked social-ecological systems. On the left-hand side is the ecological system, which may consist of nested ecosystems (e.g., a regional ecosystem containing the drainage basin of a river, which in turn consists of a number of watershed ecosystems, and so on). On the right-hand side is a set of management practices in use. These practices are embedded in institutions, and the institutions themselves may be a nested set. The linkage between the ecosystem and management practice is provided by ecological knowledge and understanding. This linkage is critical. If there is no ecological knowledge and understanding of the dynamics of the resource and the ecosystem in which it operates, the likelihood for sustainable use is severely reduced. Management practices and institutions have to recognize, interpret, and relate to ecosystem dynamics in a fashion that secures the flow of natural resources and ecosystem services.

This chapter follows from and extends the findings of Gunderson et al. (1995a) on institutional learning and resource management, Hanna et al. (1996) on property rights, and Berkes and Folke (1998) on management practices and social mechanisms for building resilience. A major finding of our earlier work on traditional ecological knowledge in relation to resilience was that traditional practices have certain similarities and parallels to the theory of complex systems, with emphasis on nonlinear relationships, threshold effects, multiple equilibria, the existence of several stability domains, cross-scale linkages in time and space, disturbance, and surprise.

To explore these parallels further, this chapter analyzes some local and traditional resource-use practices using the framework of Holling's (1986) adaptive renewal cycle. The chapter starts with the exploitation and conservation phases. These two phases of the adaptive renewal cycle describe phenomena that are well known in both Western ecological science and in traditional practice. The section explores a potentially complementary relationship between resource and ecosystem management science, which is predominantly concerned with quantitative data, and local and traditional knowledge, which is mainly concerned with a qualitative understanding of resource and ecosystem processes.

5. EcOSYSTEM DYNAMICS AND LOCJ\L KNOWLEDGE 12 5

We then turn to the next two phases of the adaptive renewal cycle, release and reorganization, which together may be termed the "backloop." These two phases are gaining attention from resource and ecosystem management science focusing on complex systems (D. Ludwig et al. 1997; Levin 1999). They have received less emphasis from conventional resource management science but, as we will illustrate, relatively more attention from traditional practice. In the section on the release phase, we analyze the evidence for what appears to be a very distinctive pattern: traditional knowledge-based practices often seem to focus on creating small-scale disturbances that act to "put the brakes" on release, thus building resilience. The section on the reorganization phase focuses on certain social practices, such as sacred groves, that enhance regeneration, and the idea that "memory is in the panarchy." A section follows on institutional learning and institutional memory, concentrating on the significance of ecological understanding and wisdom. That section explores how institutions deal with infrequent disturbances, and the role of elders, as carriers of knowledge necessary for the backloop phases. Finally, based on insights from traditional and contemporary societies and how they deal with crisis in the resource system, we propose a general model of the evolution of adaptive responses.

Exploitation and Conservation Phases of the Adaptive Renewal Cycle

If the adaptive renewal cycle (Holling 1986; Holling et al. 1995; Chapter 2) can be decomposed into its constituent parts, the exploitation and conservation phases form an S-shaped curve or a logistic curve (Figure 5-2). This curve may be thought to depict a succession sequence-from the initial few pioneers in the exploitation phase to the mature and complex community, such as a climax forest, in the conservation phase. In the classical succession

• r-strategy r . pioneer

K · K-strategy ·climax • consolidation

.___ __ ·_o_p_po_rt_u_n_is_t __ _J connectedness --

Figure 5-2. Local and traditional management practices of the exploitation and conservation phases of the adaptive renewal cycle.

126 BERKES, FaLKE

theory, the climax was often thought among terrestrial ecologists to be a state the system would reach if not disturbed, rather than merely a transition phase in a continuous cycle. Alternatively, the logistic curve spanning the two phases may be thought to depict the population growth and stabilization of an individual species. It starts with slow growth and proceeds to rapid growth, followed by a change in the rate of growth at the inflection point of the curve (which occurs at the very center of the adaptive cycle; see Figure 2-1). It continues with decelerating growth up to a peak, which usually depicts, in conventional resource management, the carrying capacity.

The exploitation and conservation phases are the parts of the adaptive cycle with which conventional resource management has largely concerned itself. Much of living resources management is about situating the population level of a given species along the sigmoid curve. If the curve could be determined, it was believed that the harvestable surplus population could then be estimated. In effect, the sigmoid curve reflects the single-equilibrium model predominantly used in conventional resource management (D. Ludwig et al. 1993, 1997).

Several kinds of local and traditional practices may be found in the exploitation and conservation phases of the adaptive cycle. These practices may overlap with scientific management but tend to have a fundamentally different emphasis with respect to the importance accorded to quantitative information. Three case studies, covering a fisheries example, a wildlife example, and an example of locally constructed sustainability indicators, illustrate the similarities and differences.

Scientific stock assessment of salmon populations in the Pacific Northwest is based on counting the salmon ascending the river and establishing levels of total allowable catch based on the results of this count and on an estimate of the number of salmon that should reach the spawning ground (the "escapement"). By contrast, traditional "stock assessment" of salmon, as practiced by the indigenous people of the Pacific Northwest, was not based on counting the salmon and establishing quantitative catch limits. It was based on the ritual regulation of fishing activities. Swezey and Heizer (1977) describe traditional salmon fishing as practiced in Northern California up to about 1850.

When the spring run first began, catching and eating of salmon was strictly forbidden. The first salmon was caught by a ritual leader and elaborately prepared for consumption, during which time no one else could fish. The ceremonial period lasted for a variable number of days before the fishing season was actually opened. For example, among the Hupa people fishing on the Klamath, this ritual closure lasted about ten days. Among the Shasta, the first fish had to pass unmolested. As soon as it passed, fish could be caught, but the first one taken had to be split and dried. No salmon could be eaten by anyone until this first fish was completely dry, and a portion of it was eaten by all.

The details of the practice varied from group to group, but there is evidence that ritual leaders observed the salmon run and its intensity and could


tailor the taboo period to the strength of the run. One can speculate that the ritual leader acted as a resource manager, combining the results of the current year's observations with the experience of many previous years (Swezey and Heizer 1977). Hence, monitoring is qualitative as opposed to quantitative; it can potentially lead to good management if the traditional leader is experienced and holds a memory of ecological knowledge and understanding, and if the tribal group is respectful of rituals and rules.

A second example comes from the area of wildlife management. In the eastern subarctic region of Canada, the traditional Cree Indian management system for caribou monitors much the same information base as does Western science - geographic distributions, migration patterns and their change, individual behavior, sex and age composition of the herd, fat deposits in caribou, and the presence or absence and effect of predators. Of these indicators, the fat content of the caribou seems to receive relatively more attention by the Cree than by biologists (Berkes 1999).

This finding may be significant because of evidence that other traditional peoples in their management systems also monitor fat content. Examples include the Inuit of northern Quebec and Labrador, the Innu of Labrador, the Dogrib Dene of the Northwest Territories, and the Dene groups who hunt the Porcupine caribou herd of the Yukon-Alaska border. The Porcupine example is the most fully documented case; Kofinas (1998) has documented the use of no less than nine indicators of the health of the caribou population, the top three of which were fat related (back fat, stomach fat, and marrow).

As a management rule of thumb (Gadgil et al. 1993), the monitoring of fat content for caribou management makes a great deal of sense because it provides an index of health of both the individual animal and the herd as a whole. As a time-tested indicator with enormous information content, fat content embodies an accumulation of trial-and-error social learning. The use of fat content as an indicator of population health is indicative of systems-level understanding, since it integrates the combined effects of a number of environmental factors, such as predation and the condition of feeding range, acting on the caribou population. It is therefore not surprising that the monitoring of caribou fat content is not merely an area-specific bit of local knowledge but rather a principle of traditional ecological knowledge widely applicable across the full range of caribou distributions from Labrador to Alaska.

The Cree system has many similarities to the Western science of caribou management. But at the same time, it is fundamentally different from conventional resource management science, which gives priority to quantitative population models for management decision making. The Cree system, by contrast, neither produces nor uses estimates of population size. Rather, it uses a qualitative mental model that provides hunters with an indication of the population trend over time. This qualitative model reveals the direction (increasing or decreasing) in which the population is headed; it does not

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require quantitative estimation of the population size itself for making management decisions.

Such traditional knowledge may be thought to be complementary to scientific knowledge and not a replacement for it. Monitoring fat content alone will not necessarily lead to good management decisions, for example, in the case of predator-limited (as opposed to range-limited) caribou populations, or in the case of a caribou population affected by two or three successive bad winters. On the other hand, exclusive reliance on biological population survey data will not lead to good management decisions either. There are several cases in the Canadian North and Alaska, with caribou and other wildlife, in which the results of biological censuses misled management decisions and were subsequently corrected by the use of other biological perspectives and the traditional knowledge of indigenous groups (Berkes 1999).

A third example comes from a mountain ecosystem in the western Himalayas, Manali area, Himachal Pradesh, India. Mountain villagers in this part of India have a very intensive land-use system. Livelihoods are based in part on the gathering of a number of forest products, such as fodder and animal bedding material, which become inputs into the agricultural system, supplementing the productive capacity of a limited land base. Ecological knowledge of the mountain forest is gendered. Village men are experts of the forest environment because they use a network of grazing areas for their animals; women are experts because they do most of the fodder, bedding, and firewood gathering. Duffield et al. (1998) sought to tap into the detailed knowledge of villagers of the mountain ecosystem by asking them to generate a set of sustainability indicators. The question posed to the villagers was, "What signs and/or signals should be watched to predict a good or bad future for you, for your children, and your grandchildren?"

The villagers generated a highly discriminated set of indicators that may be clustered into five groups. The first is a set of forest condition indicators that assess size of forested area, tree density, and species diversity. The second set includes forest-linked indicators (including forest product availability, consistent water flow, avalanches, mud slides); forest management indicators (reforestation, conservation, enforcement of rules, village control of resources); agricultural livelihood indicators; and socioeconomic indicators. When the same question was posed to local professionals in the field of natural resources, the same groups of indicators were elicited, but the professionals placed greater emphasis on the presence or absence of forest management practices as indicators of sustainability. They also accorded more weight to agroforestry and agricultural diversification and stressed a somewhat different mix of socioeconomic indicators. The villagers produced a higher frequency of responses in the forest-linked category, emphasizing their intimacy with forest products but also with the hazards of mountain environments. They did not possess the maps and statistical information held by forestry officials but nevertheless displayed a detailed qualitative understanding of the linkages within the mountain ecosystem (Duffield et al. 1998).

5. ECOSYSTEM DYNAMICS AND LoCAL KNOWLEDGE 129

Each of the three cases illustrates the complementarity of traditional and Western knowledge at the practical level and highlights the need for conceptual pluralism in resource and ecosystem management (Norgaard 1994). The salmon and caribou examples help illustrate how scientific knowledge and traditional knowledge focus on different aspects of a species management problem. The two examples can also be used to make the argument that the two kinds of knowledge, although different, can be considered complementary in the way each can be used to add to the strengths of the other, helping reflect the dynamics of the ecosystem/landscape context, of which the harvested population is a part.

The conventional scientific approach tends to focus on quantitative measures for the management of a population by investigating and estimating the number of individuals by sex and age-class. By contrast, the local and traditional approach tends to focus on qualitative information such as the strength of the spawning run (for salmon) and fat content (for caribou). Such knowledge informs resource users on trends or the direction of change, whether the population is increasing or decreasing, is more healthy or less.

The third example, mountain ecosystem sustainability, goes beyond a single-species approach and shows the feasibility of constructing indices of ecosystem health using local knowledge. Sustainability indicators of the mountain ecosystem, as seen by rural villagers, reveal a great deal of ecological understanding. The villagers recognize as important not only tree cover, density, and species composition, but also water flow (hydrology), avalanches, and mud slides, disturbances that are related to the renewal cycle of the mountain forest. The large overlap between the sustainability indices offered by villagers and those offered by government environmental experts indicates agreement on many basic concepts. But there are also differences between the two groups that highlight differences in environmental perception and in social and economic priorities.

Release Phase of the Adaptive Renewal Cycle

The adaptive renewal cycle stresses that the sequence of gradual change of the S-curve (exploitation through conservation phases) is followed by a sequence of rapid transformation, triggered by disturbance (Figure 5-3). This view emphasizes that disturbance is endogenous to ecosystem development, and that periods of gradual change and periods of rapid transformation coexist and complement one another. We have argued elsewhere that there are local and traditional practices that behave like a disturbance and that nurture sources of renewal (Berkes et al. 1995; Berkes and Folke 1998). Among those are aboriginal uses of fire (Lewis and Ferguson 1988), smallscale patch clearing in traditional agroforestry (Alcorn and Toledo 1998), and pulse grazing by migratory cattle as practiced by African herders (NiamirFuller 1998).

There is an important link between practices of disturbance management and ecological knowledge accumulated during the exploitation-conservation

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Practices acting as disturbance

t

• patch clearing • fire management • pulse fishing • pulse hunting • pulse grazing

• disturbance agents (fire, storm, pests)

connectedness -

Figure 5-3. Local and traditional management practices that emulate disturbances or creative destruction phases of the adaptive cycle.

phases. This knowledge helps in monitoring and deciding when to initiate disturbance practices-when the system is "ripe" for triggering a disturbance. Having the ability, or the ecological knowledge, to read the local ecosystem in this fashion helps management mimic the frequencies and magnitudes of natural disturbances such as fire outbreaks and grazing pulses by wild ungulates.

Examples can be offered from traditional practices in several different kinds of ecosystems, focusing on the practices of herding, fire management, and shifting cultivation systems. African herders behave like a pulse disturbance by following the migratory cycles of the herbivores from one area to another (Niamir-Fuller 1998). Pulses of herbivore grazing contribute to the capacity of the semiarid grasslands to function under a wide range of climatic conditions. If this capacity of the ecosystem to deal with pulses is reduced, an event that previously could be absorbed can flip the grassland ecosystem into a relatively unproductive state, dominated and controlled by woody plants for several decades (\Valker 1993).

Our appreciation of the role of fire management in cultural landscapes is relatively recent. For example, the early explorers of the U. S. Pacific Northwest encountered a varied landscape of open woods, spacious meadows, and patches of prairies, instead of a land covered by dense forests. Far from being a pristine wilderness, much of the Pacific Northwest was actively managed by its aboriginal inhabitants, who used fire as a primary tool (Boyd 1999). Among Australian aborigines, California Amerindians, and northern Canadian Amerindians, fire management was practiced widely to open up clearings (meadows and swales), corridors (trails, traplines, ridges, grass fringes of streams and lakes), and windfall areas. These clearings provided improved habitats for ungulates and waterfowl, thus increasing hunting success; clearing corridors and windfall areas improved accessibility


(Lewis and Ferguson 1988). Similarly, patch clearing through swidden-fallow management and associated agroforestry systems among Amazon area tribes, with patch burning or clearing, in cycles of up to thirty or forty years, provided a diversity of resources and ecosystem services over the long term (Denevan et al. 1984; Posey 1985; Irvine 1989).

By mimicking fine-scale natural perturbations, these practices help avoid the accumulation of disturbance that moves across scales and further up in the panarchy (a nested set of adaptive renewal cycles over temporal and spatial scales). In contrast, practices of conventional resource management tend to support the phases of gradual change, that is, exploitation and conservation, but strive toward avoiding rapid transformation, that is, release and reorganization (Holling and Meffe 1996). Such management creates ecosystems that are less variable and diverse over space and time (Peterson et al. 1998). Social and economic resilience may be created in the short term, but at the expense of loss of ecological resilience. This strategy leads to more brittle systems, and eventually to a resource crisis (Gunderson et al. 1995a). Local resource users with experience and ecological knowledge let disturbance enter at lower levels in the panarchy of adaptive renewal cycles, and may thereby reduce the risk of creating unexpected coarse-scale surprises and crises, simply by being in tune with ecosystem processes and functions (Holling et al. 1998). As will be discussed in the following sections, this behavior is one of a sequence of practices that generates social-ecological resilience (Figure 5-4).

During the release phase, which is the stage that follows disturbance, there are practices that aim at reducing the effects of disturbance and surviving the effects of disturbance. In contrast to conventional resource management that aims at removing disturbance (Holling and Meffe 1996),

Practices nurturing renewal • recruitment areas • seasonal harvest closures • protection of age classes

protection of species protection of habitat

connectedness -

Practices slowing release • savings banks • emergency crops • fire breaks • landscape-level management

Figure 5-4. Local and traditional management or "back-loop" practices of the release and reorganization phases of the adaptive renewal cycle. Some practices work to slow down the rate of release, while others nurture sources of renewal.

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these practices accept disturbance as part of ecosystem dynamics and focus instead on putting the brakes on release by affecting the magnitude and frequency of disturbance.

The implementation of practices in the rapid-release phase seems to be based more on experience than on monitoring. We believe that these practices have developed as a result of actual experience of having to deal with the effects of disturbance in nature, as a result of a trial-and-error process of social-ecological response and adaptation. This experience, based on ecological understanding of the role of disturbance, has been stored in the institutional memory of the group and is reflected in management practices that build resilience. Examples of management practices that aim at reducing the effects of disturbance include using sacred groves as firebreaks (Gadgil et al. 1998) and cutting tree branches to place in paddy fields for reducing pest outbreaks (Pereira 1992).

Social practices designed to improve survival of disturbance include practices that manage biodiversity through redundancy at several levels, from the species to the landscape level. For example, certain groups of species are used as emergency food among the tribes of the Pacific Northwest. Turner and Davis (1993) identified over one hundred species of plants that were not normally eaten but saved as special foods, alternative foods, and hunger and thirst suppressants. Many traditional agricultural groups conserve lowproduction crop varieties as insurance for climate and pest events that impact high-yield crop varieties (Altieri 1994; Oldfield and Alcorn 1991).

Conserving patches in the landscape to serve as emergency resource supply is a common practice. One example is the establishment of range reserves within the annual grazing areas of African herders. These reserves provide an emergency supply of forage, which functions to maintain the resilience of both the ecosystem and the social system of the herders. They serve as "savings banks" when drought challenges the process and function of the grassland ecosystem (Niamir-Fuller 1998). Access to those areas is prohibited unless there is a crisis. Sacred groves in parts of south-central India serve a similar function, allowing the use of dead branches and of livestock grazing under the trees during periods of drought (F. Berkes, unpublished field observations in Karnataka and Tamil Nadu, India).

Such release-related management practices may be analogous to the functional role of biodiversity as insurance in ecosystems (Folke et al. 1996). In ecosystems there is a great deal of biodiversity during ecological succession that may seem redundant. But redundant species and their overlapping functions within and across scales may become of critical importance for generating and maintaining resilience after disturbance and disruption (Peterson et al. 1998). Overlapping functional diversity increases the variety of possible alternative reorganization patterns following disturbance and disruption. Just as redundancy of biodiversity may help buffer disturbance and maintain opportunity for innovation in ecosystems (Holling et al. 1995), release-related practices that may seem redundant from a conventional re-

5. ECOSYSTEM DYNAMICS AND LOCAL KNOWLEDGE 13 3

source management perspective may also function to buffer disturbance (e.g., famines) and maintain opportunity for innovation in social systems.

For example, suppression of forest fires locally will cause an accumulation of fuel on the forest floor and an accumulation of tree biomass. When a fire event finally occurs, it will be hot and intensive, burning deep into the soil and affecting seed viability, microorganisms, organic content, and nutrients. Soil formation is considered to be a "slow variable" in ecosystem structuring processes. An ecosystem that can withstand a small, low-intensity fire may be severely affected by a large, hot fire that can change soil conditions, affect water-holding capacity, and destroy old, seed-bearing trees that are important for the reorganization phase. Hence, suppression of disturbance can modify the essential preconditions for ecosystem redevelopment.

Several studies have illustrated that suppression of disturbance will diminish the ability of the ecosystem to renew itself. Diversity within functional groups may be reduced, and there may even be loss of whole functional groups, with an overall loss of resilience (Holling et al. 1995). This implies loss of ecological memory and capacity for self-organization and evolution, thereby constraining the capacity and potential for reorganization. Developing management practices that "put the brakes on the release phase" help build insurance and maintain the ability of the system to reorganize later on. Such backloop practices are of great importance for building socialecological resilience. As we will illustrate in the following section, these practices are strongly interconnected with the reorganization phase, the last phase of the backloop.

Reorganization Phase of the Adaptive Renewal Cycle

A number of common practices contribute to ecosystem recovery, or the reorganization phase of the renewal cycle. Gadgil et al. (1993) pointed out that five rules of thumb found in indigenous systems had parallels to scientific systems of conservation: total protection of certain species; protection of vulnerable life history stages; protection of specific habitats; temporal restrictions of harvest; and monitoring ecosystem change. Each of these practices contributes to the reorganization phase by nurturing sources of renewal-that is, facilitating ecosystem reorganization and recovery (Figure 5-4). They maintain and enhance ecological memory and its dynamics.

Also important for renewal, and found in some traditional systems, are the protection of keystone species (Gadgil et al. 1993; Colding and Folke 1997) and management of landscape patchiness (Niamir-Fuller 1998; Alcorn and Toledo 1998). Among practices used by traditional societies, habitat protection through sacred groves or taboos may be especially important to help secure recruitment of seeds and larvae into an area affected by a disturbance (Box 5-1). The reorganization of a coral reef damaged by a hurricane or a forest ecosystem affected by fire is dependent on such "spatial resilience" as maintained by practices in the release phase. It is therefore of interest to note

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Box 5-1. Sacred Groves: Securing a Recruitment of Seeds and Maintaining Landscape Patchiness (Gadgil 1989)

Fikret Berkes

Indian ecologist Madhav Gadgil writes about how he first discovered sacred groves in west central India:

The hill ranges of the Western Ghats are close to the heart of every Maharishtrian. So my thoughts naturally turned to fieldwork on the forests of these hills when I returned from six years of theorizing at Harvard. After three months of wanderings on the Western Ghats I received a remarkable letter. It was from a tiny village, Gani, located in a remote area of Konkan. The villagers had learned, the letter said, of my interest in sacred groves. Their particular village had one of the best, and it had recently been marked for felling by the Forest Department. Could I come over and help them save it from this fate? Intrigued, I promptly took a bus to Srivardhan and then trekked over 8 kilometers of barren hills to Gani, a hamlet of 40 huts.

Above the settlement was a beautiful patch of rain forest, some 25 hectares in extent, in the catchment of the stream that ran past the village. The villagers had witnessed other streams drying up as tree cover had been lost over the last half century and were determined to save their catchment forest. Fortunately, I was able to persuade the Forest Department to abandon plans to fell this sacred grove. In the process I discovered that many foresters thought of it as a stand of overmature timber. For the villagers, though, it evidently was something more. In fact, they were aware of its value not only for water conservation, but also as a gene bank. For they showed me a specimen of the magnificent leguminous climber Entada pursaetha in another grove and explained that its seeds were of great use in treating snakebite among cattle. People came from as far as 40 kilometers away to collect seeds from that grove.

The sacred grove is undoubtedly an ancient tradition in India. For example, we learn from the story of Buddha's life


that he was born in a sacred grove in the sixth century B.C.

These groves have been preserved over time not because of any economic or practical arguments but rather on the basis of religious beliefs. The benefits of sacred groves accrue to the social group on a long-term basis; the individuals often would be better off in the short run by violating the grove. It seems probable that cultures have cast prescriptions that lay in the long-term interest of the group and against the short-term interest of individuals, in the form of religious sanctions.

that sacred areas or sacred groves used to be common in the terrestrial ecosystems in all parts of the world, from the Americas to Africa. They were less common in the marine environment but did exist in such areas as East Africa until the 1950s (McClanahan et al. 1997). Historic and current examples of protected habitats can also be found in the South Pacific region, with customary taboos imposed on areas of land, reefs, and lagoons. On Tahiti, for example, a chief could place a taboo on a portion of the coast that he ruled Oohannes 1978), or on sections of reefs, often near the lagoon entrance, which tends to be rich in schooling fish (Ruddle 1988).

Plain taboos such as the prohibition on picking coconuts that fell during the night (as practiced in some Pacific Islands, T. Elmqvist, pers. comm.) have far-reaching consequences. In the coconut example, they not only guarantee the regrowth of palm trees and a continuing food supply, but also provide other ecosystem services such as a windbreak near the coastline to protect habitats of other species and help general ecosystem functioning and resilience.

Adaptive practices of the reorganization phase serve not only ecological but also social objectives. Some help conserve sufficient ecological structure and function for making reorganization possible, while at the same time creating room for innovation and novelty. They conserve ecological memory to restart the adaptive renewal cycle. A good example is milpa agriculture in Mesoamerica (Box 5-2). A regional version of shifting cultivation, milpa is based on succession management, and the cycle is based on a culturally internalized plan, marked by festivals and rituals (Alcorn and Toledo 1998). Sense making (Chapter 14) at the social and organizational level facilitates the reorganization phase in the natural environment. Beliefs and meanings are important, as it is the meaning system (that is, the cultural centrality of milpa to indigenous Mesoamerican life) that allows reorganization at the ecological level.

Thus, in agroecological systems such as shifting cultivation, ecological reorganization requires institutional memory as well as ecological memory, and the two kinds of memory make a linked system. Note that this is a fun-

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Box 5-2. Culture and Remembrance: Providing Guidance for the Milpa Renewal Cycle (Alcorn and Tredo 1998)

Fikret Berkes

Shifting cultivation, or swidden, systems are common in all tropical forest areas of the world, from the Amazon to New Guinea. Swiddening involves the clearing, planting, harvesting, and fallowing of small forest areas over a multiyear cycle. Milpa is the Mesoamerican version of shifting cultivation; it is based on the culture of maize. A culturally encoded procedure for the proper use of shifting cultivation enables people to manipulate the renewal cycle of a forest ecosystem to produce a crop of maize without disrupting ecological processes of the forest. Milpa manipulates natural regrowth to manage the regeneration processes, in a sequential cropping of food and nonfood species. Farmers know that in an ideal milpa cycle, new fields are cleared in high forest. Milpa can also be made in a secondary forest with 3- to 4-meter-tall regrowth, provided that only one maize crop is taken. If more than one successive crop of maize is taken in a short fallow milpa, weedy species come to dominate the plot and forest regeneration may not occur. Land may be dominated by grasses and shrubs, as has occurred in some mestizo pasturelands.

Many of those familiar with Mesoamerican agriculture think of milpa as meaning "cornfield." But milpa is not primarily a spatial concept; it is an institution and a process. It is a "script," that is, an internalized plan used by people to carry out and interpret a routine of activity. Its basic structure is a series of steps with alternative subroutines and decision nodes, with room for experimentation. Ecological knowledge, derived from the experiences of farmers who have adapted to the local environment for generations, is encoded in the local variation of the milpa script. The script is passed on to children and supported by cultural beliefs, mythologies, and the yearly festivals that mark the key events of the milpa cycle.

Culture plays a significant role in milpa cycles, and in turn milpa shapes culture. Various researchers have commented on the integral role of milpa in Mesoamerican cultural life: "They do not raise maize to live, they live to raise maize." For the Maya, "the making of milpa is the central, most sacred act, one which binds together the family, the community, the universe. Milpa forms the


core institution of Indian society in Mesoamerica, and its religious and social importance often appears to exceed its nutritional and economic importance." Each stage of the milpa cycle is named and marked by ritual activities. Tales of a maize culture hero are associated with all stages of the milpa.

damentally different view from the usual one in conservation biology, in which institutional memory is considered irrelevant or is taken for granted, and biodiversity is thought to be related only to ecological memory and other biological processes. Figure 5-5 illustrates the idea that "memory is in the panarchy." Landscape-level ecological memory is maintained in the system through the presence of different patches in different stages of succession. The use of the patches, in tum, is governed by social practices such as milpa rules and rituals. Thus, spatial resilience is carried through cultural practice.

coast

Figure 5-5. Memory is in the panarchy. In such systems as shifting cultivation, landscape-level ecological memory is maintained in the system through the presence of different patches in different stages of succession. The use of the patches, in turn, is governed by social practices.

Institutional Memory

Even though they help reorganization in practice, traditional systems provide no recipes for the backloop phases. Indeed, they provide no fixed recipes at all. Instead, they tend to emphasize the importance of "allowing for great local flexibility in adapting to new situations and circumstances, while still keeping within certain generally defined bounds of acceptable social behavior and political procedure" (Hviding 1998). Hviding was not

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writing about the adaptive renewal cycle in the preceding quotation; he was referring to the way Melanesian customary practices help make sense of new situations. For example, Melanesians tended to interpret the Europeans not as unique examples of a type of spirit or god, but rather as yet another type of human arrival from afar. Hence, new encounters were structured by the Melanesians according to precedents set by "old" events not involving Europeans (Hviding 1998).

Customary laws in Oceania include many that deal with the conservation of marine resources, illustrating the application of the principle of institutional flexibility and diversity. For example, Johannes (1998) surveyed twenty-six villages in Vanuatu and found that all but one had village-based marine resource management measures, and no village had exactly the same set as any other. It is this flexible nature of customary marine tenure, leading to rich diversity of practice, that compelled Hviding (1998) to suggest that customary laws should not be written down and codified. Such codification would run the risk of making marine tenure rules brittle. It makes more sense to keep customary rules flexible, and to pass enabling legislation to safeguard and legitimize their use, which is the direction taken by several island nations in Oceania.

Customary practice allows for local flexibility of rules, and experience provides the context. But how is the experience held and transmitted? The lesson of traditional knowledge systems, not only from Oceania but from many parts of the world, is that elders and other wise persons play a key role. They act as keepers of ecological knowledge; they help transmit knowledge by direct teaching and through rituals and oral history; and they provide the wisdom to interpret novel observations. In most known cases of traditional societies, elders' wisdom combines both ecological and social knowledge; there is no artificial split between nature and culture (Berkes 1999).

As described by Lees and Bates (1990), the work of the anthropologist Raymond Firth provides some insights regarding the mechanisms by which elders and chiefs in a traditional society use experience to deal with disasters. When Firth returned to the Pacific island of Tikopia, he found an island devastated by a hurricane, which had destroyed houses and gardens and caused acute food shortage. He inquired whether, and to what extent, the disaster was "abnormal." He found out that hurricanes of such intensity were not unknown but apparently occurred on the average of once every twenty years, or about once a generation. Firth viewed the disaster as a test of the strengths and weaknesses of the social system, the extent to which the system could withstand the strains of the disturbance (essentially, the resilience of local institutions).

Firth described a variety of responses to the disaster: Chiefs directed facility repairs, took measures to reduce opportunities for theft, directed labor to planting rather than fishing, and sent workers abroad for wage work. Household-level responses included changing diet, reducing hospitality, restricting kinship obligations, reducing ceremonies, and using unripe crops.


Resource management strategies included shorter fallows, restriction of planting and collecting rights, and stricter demarcation of land boundaries (Lees and Bates 1990). Tikopia's response to the hurricane can only be interpreted as "response with experience," showing that a disaster of once-a-generation frequency is well within the response capability of the local social system.

The Tikopia example does not address how a local social institution could deal with environmental variability of a lesser frequency, or perturbation never before experienced. Such a case is the growth and decline cycle of North American caribou. Caribou cycles are poorly documented by biologists; no one has a data set of even one full cycle, from peak to peak. No one, that is, except indigenous hunters of the caribou, the Inuit, Dene, and Cree peoples of northern Canada and Alaska. Some Inuit groups apparently think that caribou have cycles of about one hundred years. The Cree have no figures on periodicity; the Cree belief system holds that caribou will decline but will eventually return, provided they are treated with respect. In one documented case, Cree elders were able to invoke an event that occurred in the 1910s to help redesign the rules and ethics of the caribou hunt in the 1980s, when young hunters with powerful guns threatened the recovery of the population (Box 5-3).

Box 5-3. The Cree Releam How to Treat the Caribou Properly (Berkes 1999)

Fikret Berkes

Cree Indian hunters of Chisasibi in subarctic Canada saw their first large caribou hunts of this century in the winter of 1982-83. The following year, large numbers of caribou were taken along the road. Hunters brought back to Chisasibi literally truckloads of caribou. However, some community leaders were unhappy, not because of the large numbers killed, but because many hunters had been shooting wildly, killing more than they could carry, and not disposing of waste properly. The leaders were worried that hunters' attitude and behavior signaled a lack of respect for the caribou, a serious transgression of the traditional code in which it is believed that ritual respect ensures that animals will make themselves available. The following winter, there were almost no caribou on the road, and hunters in trucks left empty handed. People were concerned: Had the caribou decided not to come to the Chisasibi hunting grounds after all?

continues

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Meetings were called. Two of the most respected elders stepped forward and retold the story of the disappearance of the caribou shortly after the tum of the century. Caribou had been declining on the James Bay coast since the 1880s but continued to be plentiful near the center of the Quebec-Labrador Peninsula. In the 1910s a disaster occurred. Equipped with repeating rifles, which had just become available, previously respectful hunters lost all self-control and slaughtered the caribou at the crossing points on the Caniapiscau River at Limestone Falls. After that, the caribou disappeared for generations. But the Cree believe that all changes occur in cycles, and not all was lost. The caribou would once again be plentiful, Cree wise men predicted at the time, but the hunters had better take good care of them if the caribou were to stay. Now the caribou had indeed come back, and oral history was validated. However, by violating traditional ethics, were they about to lose the caribou once again? The elders' words had a profound effect on the younger hunters.

The following winter, the hunt was carried out very differently. It was productive, and hunters took about two caribou per household. Monitored by elders and other leaders, the hunt was carried out in a controlled and responsible manner, in accordance with traditional standards. There was little waste and no wild shooting. The harvest was transported efficiently, and wastes were cleaned up promptly. After that, the caribou kept coming. By 1990, hunters' observations of tracks, consistent with the results of biological surveys, showed that caribou had reached the sea all along the James Bay coast, reestablishing their former range.

The elders, who are the holders of the knowledge and the values, play a key role in this culture and in this remembrance story. Cree society relies on oral history, and the elders span the generations to provide information feedback. What makes elders wise? Wisdom in the present case may be in the elders' timing (they waited for a whole year after the transgression until people were likely to be receptive to their message), their choice of message (the well-known story of the caribou overkill at Limestone Falls), and their effective use of myth (the ancient prophecy that the caribou will return).

From that event and from other sources, oral history seems to be an effective mechanism that reaches back at least one hundred years among some Cree groups. The information is carried by the elders and transmitted in the form of stories and myths; conservation practices themselves are encoded in


rituals-in this case, rituals of respect for the animal (Berkes 1999). Elders span the generations to provide information feedback and are able to reinterpret current events in the light of ancient myths to help guide their society. Their skill as wise and effective leaders, in this particular caribou case, seemed to be related to three things: their timing (they waited for a year before the younger hunters were judged to be receptive to their message), their choice of message (a well-known event in oral history), and their effective use of myth (the prophecy that the caribou will return) (Berkes 1999).

Many traditional societies have myths and rituals, but contemporary Western societies do not hold myths and rituals in high esteem, although they definitely exist in resource management. Yet, as Anderson (1996) points out, "All traditional societies that have succeeded in managing resources well, over time, have done it in part through religious and ritual representation of resource management." Rituals help people remember rules and interpret environmental signals appropriately, as in the case of milpa (shifting cultivation) cycles in tropical Mexico (Alcorn and Toledo 1998). In our previous work, we identified rituals and ceremonies as a primary mechanism by which practices leading to resilience and sustainability can be culturally internalized (Folke, Berkes, and Colding 1998).

In many non-Western societies, ecological knowledge, resource management systems, and worldviews are inseparable. Although myths and rituals in the service of environmental management have lost much of their power and have even become obstacles to sound management in the contemporary world, there are examples of the revival and explicit recognition of the role of rituals and ceremonies for effective management function. For example, Johannes (1998) observes that ceremonies can still be crucial for the success of modern community-based marine resource management in Vanuatu:

Two village elders told of experiences that have caused them to modify the way in which a fishing taboo is formally declared. When fishing taboos were merely announced without fanfare, observance was unsatisfactory. Now, in these villages, closures are announced with substantial traditional ceremony. Pigs are killed, a feast is held and church leaders are asked to bless the taboo. By thus impressing villagers with the seriousness of the taboos, their observance, according to these leaders, is now much improved.

Adaptive Responses to Ecosystem Change

Such stories as the role of wisdom holders in traditional societies and the significance of modern-day adaptations of rituals provide insights regarding institutional learning. However, we lack theories linking the creation of ecological knowledge from observation and understanding to its incorporation into resource use. Figure 5-6 provides a conceptual model of possible responses to a crisis situation. For our purposes, the crisis may be broadly defined as a large perturbation; it may be human-made (e.g., a resource col-

142 BERKES,FOLKE

no effective response without response experience

v;,1~Fr\_

adaptive change of rules

• response with experience

• institutional learning

Figure 5-6. Three generic responses to resource and environmental crisis. Most responses fall into categories of (1) ignoring a crisis, which can lead to larger-scale surprises; (2) reacting with no memory or experience; or (3) responding through learning.

lapse) or natural (e.g., a hurricane). Three generic responses are possible when a crisis occurs. "No effective response" is one possibility. A second possibility is "response without experience," in which the institution (a government agency or an informal local management institution, for example) responds to a crisis but does not have previously tested policies, with accumulated ecological knowledge, at its disposal. A third possibility is "response with experience," in which the institution has previous experience with a crisis of that kind and management policy that was used on previous occasions.

In centralized and bureaucratized management systems, the "no effective response" is the management reaction that often characterizes "brittle" (as opposed to flexible) institutions (Holling 1986; Gunderson et al. 1995). Such a response allows the disturbance to accumulate up the panarchy; that is, it tends to create the conditions for a larger-scale crisis later on (Holling and Meffe 1996). The crisis can be both ecological and political; preserving the status quo politically often leads to organizational and political brittleness, as well as to ecological brittleness (Gunderson et al. 1995a).

"Response without experience" is a frequently seen reaction to crisis. It may result in a series of policy responses, including that of no effective response. Alternatively, it can lead to institutional learning or learning of the transformational type (Chapter 3). If the crisis is a true "surprise" (Holling 1986), then the institution will have no previous experience with it. Or the crisis may have been predictable but be of a magnitude that had never been experienced in that area. An example might be the cod resource collapse in Newfoundland, which had been predicted by inshore fishers and some field biologists (Finlayson and McCay 1998). The problem was exacerbated by "an over-reliance on the science and culture of quantitative stock assessment" (National Research Council 1998) by central government agency


population modelers, who (in retrospect) misused or misjudged their data and precipitated a stock collapse unprecedented in its magnitude in the North Atlantic.

Would the Newfoundland cod collapse help the management agency "respond with experience" the next time a similar crisis looms? There is no clear answer to the question because responding with experience, as we formulate it in Figure 5-6, depends on institutional learning based on previous crises. If the memory of the experience provides a context for the modification of management policy and rules, the institution can act adaptively to deal with the crisis. The preferred response is to deal with the crisis while it is still a small disturbance at a lower level in the panarchy, and not a fullblown, higher-level crisis (Gunderson et al. 1995, 1997; Holling et al. 1998).

The mechanism for institutional learning, as for any learning, is trial and error. If this trial-and-error learning takes place as active learning (Hilborn 1992) and deliberately uses management policies as experiments from which to learn, then we have the basics of adaptive management (Holling 1978; Walters 1986; Lee 1993). International experience with large-scale environmental management agencies shows that there often is institutional learning following

·a crisis, although much of the behavior of these agencies hardly fits the model of an ideal adaptive management approach (Gunderson et al. 1995a).

There are several reasons large institutions may be slow and sporadic learners. The essential steps in learning from experience include documenting decisions, evaluating results, and responding to evaluation (Hilborn 1992). But even if these are done, management agencies have few mechanisms of institutional memory to retain the lessons learned. Publications, data records, and computer databases are often not adequate to serve the institutional memory. As Hilborn (1992) puts it in relation to fisheries management:

The richest form of memory is stored in the cerebrum of the staff of fisheries agencies. We sometimes forget how much an individual may have learned in 20, 30, or even 40 years of work in an agency. For each documented experience, there are probably ten that are left unwritten. Those that are documented may be a biased sample. Journals do not often publish negative results; managers don't like to hear bad news-we don't document our failures. When someone retires, much information walks out of the door along with the gold watch.

Is Hilborn's analysis applicable to other management systems as well, including those based on local and traditional ecological knowledge? Examining Figure 5-6 with local-level management institutions in mind, and moving through the three response options, it is easy to see that the "no effective response" option likely led to the extinction of a group of people dependent for their survival on a local resource. If the group responded in various ways to the crisis, but without previous experience of a comparable situation, they may have developed an adaptive response by trial and error, or they may have

144 BERKES, FOLKE

responded in inappropriate ways, likely with disastrous consequences for the group. Response without experience is probably typical of incipient local management systems, such as those among the users of coastal and forest resources in the Caribbean, systems that have only a few generations of experience with resources and their behavior over time (Berkes 1999).

By contrast, response with experience is characteristic of fully developed traditional ecological knowledge systems, such as that of the Cree Indians of James Bay (Berkes 1999) or the milpa agriculturalists of tropical Mexico (Alcorn and Toledo 1998), in which there is multigenerational, culturally transmitted knowledge about local and regional resources. The examination of such traditional systems provides insights about the mechanisms applicable for the reorganization phase of the adaptive renewal cycle, and supports Hilborn's (1992) view that developing mechanisms for the retention of institutional memory may be the key to arriving at the option of response with experience. Hilborn and others emphasize the difference between active and passive learning. In traditional management systems, there are no known examples of systematic, purposeful, active learning, or active adaptations through probing, that are comparable to adaptive management (Holling 1978; Walters 1986). However, active adaptive management, as developed by scientists, is a fairly recent approach (Holling 1978). Thus, the institutional memory of large, infrequent disturbances, for example, does not exist in scientific management; it has to be imported from "passive" adaptive management systems, including those that rely on the cultural capital of non-Western societies.

Does modern society need to create elders? Being an elder is an earned social role, not an occupation, and not all old people have wisdom. Elders in a traditional society do not retire; to paraphrase Hilborn (1992), information does not walk out of the door along with a gold watch. One can speculate that accumulating wisdom requires experiencing several disturbance cycles or crises of medium-term nature. Alternatively, wisdom may require the ability to hold on to oral history information for events that exceed the human life cycle. Steele (1998) has emphasized the prevalence of decadalscale (apparently ten to thirty years) regime shifts in marine ecosystems. Other resource systems-for example, the rangelands of New South Wales, Australia-also have decadal-scale regime shifts under certain circumstances (B. Walker, pers. comm.). These shifts are within the periodicity that elders can span.

However, in the case of large, infrequent disturbances (Turner and Dale 1998), whether elders or oral history can help is much less certain. Dale et al. (1998) stress the need for an institutional memory of these type of disturbances as a part of ecosystem management, in order to reduce the risk of management responses that are not in tune with ecosystem dynamics and development. Berkes's (1999) example of the management of Cree caribou events extends human generations and illustrates that such institutional memory may exist among local resource users, particularly in traditional so-

5. ECOSYSTEM DYNAMICS AND LoCAL KNOWLEDGE 145

cieties. As reflected in Figure 5-6 on adaptive responses, such ecological knowledge is to a large extent absent in the "no response" and "response without experience" pathways.


In sum, we propose that local and traditional resource-use practices are valuable in resource management. They complement conventional resource management in at least three ways, and we discuss each in tum:

• qualitative monitoring and management during the exploitation and conservation phases of the adaptive renewal cycle,

• building resilience during the release and reorganization phases,

• providing long time-series of local observation and institutional memory for understanding ecosystem change.

The exploitation and conservation phases are the two stages that are the main concern of conventional resource management science, where the emphasis is overwhelmingly on the collection and use of quantitative data. Many traditional systems, however, collect and use qualitative data, as the examples of Pacific salmon and Arctic caribou indicate. The two approaches, and the kinds of information they collect, are complementary in that they may be used to add to the strengths of one another.

The release phase of the adaptive renewal cycle seems to be largely ignored by conventional resource management. The reorganization phase has been recognized in conventional resource management science, for example, in seasonal closures of harvest or protection of species and habitats. But these management practices have generally been implemented without recognition of ecosystem dynamics, including disturbance regimes (Holling et al. 1998). By contrast, many local and traditional systems seem to accord a great deal of emphasis to these phases, judging by the rich variety of practices that exist in a variety of cultures and geographic areas. These practices interpret and respond to feedback from complex adaptive ecosystems. They include practices that mimic disturbance at lower scales of the panarchy and those that nurture sources of renewal. Instead of removing or eliminating disturbance altogether, local and traditional adaptations seem to accept perturbations as an intrinsic part of ecosystem dynamics, and focus instead on "putting the brakes on release" by focusing on the magnitude and frequency of release.

The strength of conventional science and management is in the collection of synchronic (simultaneously observed) data, whereas the strength of many local and traditional management systems is in diachronic information, or long time-series of local observations. Traditional knowledge, by definition, is a cumulative, culturally transmitted body of knowledge that evolves by adaptive processes. Knowledge carriers, such as elders, play a crucial role

146 BERKES,FOLKE

in the institutional memory of ecosystem change. So do myths and rituals, by helping people remember the rules and interpret environmental feedbacks appropriately.

Insights are available from local management systems for how to adaptively deal with environmental crises. This is particularly true for the management of disturbance at various scales. Experience with local institutions shows that creating small-scale disturbances can build social-ecological resilience, thereby increasing the adaptive capacity of a system to deal with larger-scale disturbances. Some of these local knowledge systems thus anticipate large, infrequent disturbances, recognizing their existence as a natural feature of ecosystems. Recent scientific understanding of complex adaptive systems and their management could be enriched by insights from local management systems as exemplified in this chapter. Combining complex systems science with useful insights and attributes of local and traditional systems dealing with complex ecosystem dynamics may enhance the adaptive capacity for coping with disturbance and building social-ecological resilience.

CHAPTER6

THE DYNAMICS OF POLITICAL

DISCOURSE IN SEEKING SUSTAINABILITY

Lowell Pritchard Jr. and Steven E. Sanderson

The world is the house of the strong, I shall not know until the end what I have lost or won in this place, In this vast gambling den where I have spent more than sixty years,

dicebox in hand, shaking the dice. -Denis Diderot

Politics is without question an important consideration in the quest for sustainability and enlightened environmental management. The social world, after all, is organized politically from the globe to the village.

Every element of every ecosystem, and every natural resource user, falls under multiple political jurisdictions that cover many scales, some nested and some overlapping. These include not just the state and its subjurisdictions (nation, states, regions, counties, cities, etc.) but also a range of other institutions from formal and global (the UN) to informal and local (some common property regimes).

The political processes that relate people to ecosystems are diverse. Rules about property, access to and allocation of resources, sovereignty, regulation of environmental externalities, and restrictions on land use (zoning) are all manifestations of political processes. Moreover, political theory, in some great measure, has sought to define humans and nature in specific landscapes, biomes, latitudes, life zones, and cultural traditions.

Consideration of human management of and response to natural systems would be incomplete without a consideration of politics. Power, legitimacy, authority, taxation, subsidy, and welfare are political terms as important as optimality, efficiency, and other economic constructs. Given the increasing dominance of human activity in the biosphere, political constructs vie with ecosystem dynamics as ways of understanding variety and variability in the natural world. Consideration of the symbols and narratives by which humans understand their relationship with natural systems is incomplete without an understanding of politics: models, history, the contestation of public space, and "cultural learning" are politically suffused.

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Many discussions about natural resource management that invoke "politics" as a causal factor use it as a residual category for human behaviors exhibiting corruption, greed, and arrogance. Such ideas anthropomorphize political systems, as if they were human in the individual sense. Other conceptions of politics see it as merely derivative of economic, cultural, or even ecosystemic forces. Holling, Westley, and Gunderson in this volume have argued that life is more than a pile of sand, that ecosystems are more than just life, and that social systems are more than ecosystems-that there are emergent properties that characterize these systems. In the same way, political systems are more than just markets for power or non-price (i.e., subsidyand tax-determined) rationing devices. While politics is not without reference to economic exchange, neither is it merely derivative.

Still other descriptions of politics view only dynamics within single arenas of action, a view that might be characterized as infrapolitical. A proper description of politics will include notions of constraint, opportunity, driving force, and even irrelevance. An even richer conception of politics will serve to enlighten debate and to facilitate novel approaches to thorny environmental problems.

There are several ways to proceed with that study, corresponding roughly to traditions in political economy) One emphasizes agency and choice: politics is seen as a set of actions, rooted in the (primarily economic) motivations of individuals or groups as they interact in the public sphere.2 In this view, institutions and organizations are not the mere sum of their parts, but they are some sort of complex resultant of the economic motivations of their components-whether expressed in an economic or "political" marketplace. The study of politics in this tradition is to consider the behavior of agents in a given political institutional regime, and to look for patterns that emerge from their interaction-not unlike looking at the behavior of consumers in a market. Analysis is focused on individuals, their preferences, choices, and ways of aggregating them.

A second tradition emphasizes structure and institutions as the nodes where broad political forces of power and class find expression. This tradition focuses more on the hierarchical structure of political institutions, and on finding patterns of domination, suppression, and the entrainment of fast variables (political behaviors, such as voting or regulation or protest) by the slow ("captured" organizations, social classes).

Much social science is thus focused either on fast variables or on slow variables, and little on their interaction (a complaint that can be made of natural sciences as well). A more evolutionary approach is suggested here, one that asks where fundamental structures come from and how they might be expected to change over time-both of themselves and in response to pressures from below and above.

Besides focusing on single scales, much writing about natural resource management tends to focus on single issues, in single arenas of activity, without considering how issues change scales in political systems, and how

6. DYNAMICS OF POLillCAL DISCOURSE IN SEEKING SUSTAINABILITY 149

they move between arenas. For example, consideration of resource issues will look very different viewed from the perspective of an agency trying to calculate maximum sustainable fisheries yield (MSY) (with a limited budget, while simultaneously pursuing other objectives), of a political system trying to reconcile the views of a diverse group of fisheries stakeholders, or of a community fighting to guard its marine resources against encroachment.

To understand the differences, it will be useful to characterize the different discourses-the competing conversations and languages-that form the context for political conflict over resources, and that include assigning cultural and political meaning to concepts such as MSY, stakeholder, community, encroachment, and even fishery and resource. Understanding the different competing discourses will allow us to frame a set of questions for future research, which is the goal of this chapter. First, however, we set the stage by outlining some of the implications that the ecological insights of this volume hold for political systems.

Political Implications of (Eco )Systems away from Equilibrium

The advent of the "new ecology" is often heralded in recent social science and legal literature (Behnke et al. 1993; Leach and Mearns 1996a; Leach et al. 1997; Scoones 1996), regardless of the fact that many of the insights into the behavior of systems away from equilibrium are not very new (Holling 1973b; Walker et al. 1969) or, for that matter, always very ecological. The "old ecology," as it is often styled, is diagnosed as preoccupied with systems at or headed for equilibrium. Climax was determined by the signature of environmental driving forces, including insolation, biotemperature, rainfall, soil type, water and nutrient availability, etc. Thermodynamics and predictable organizational features of biotic communities set a limit on the size and structure of ecosystems and their components. Ecological problems generally involved a disturbance from steady state (often human induced) and an asymptotic return afterwards.

Management of such systems would be straightforward if they behaved that way. Renewable resource systems ostensibly would have well-defined and well-behaved properties (like sigmoid growth curves); one could generate a sort of short-cycle production function for ecosystem goods and services; and they could be managed for (generally maximum) sustained yield. Managing for optimality would be uncontroversial. Ecological and political issues, like people and nature, would be essentially separable and manageable (Pritchard 1999). A progressivist ideology of management-basically, an engineering approach-would be recommended.

New insights in ecology challenged this perspective. The new ecology appeared in a couple of variants-the first proclaiming chaos, contingency, and disorganization, and the second suggesting a more subtle, organized complexity in natural systems. One version of the new ecology has three


tenets (Profeta 1996; Wiener 1996): Ecosystems are dynamic (change is the only constant), ecosystems are spatially heterogeneous (all ecology is local), and humans are part of ecosystems (we are everywhere). Importantly, this affects the way new ecology separates or integrates humans and nature itself. Implications can be summed up as: "There is no baseline," "You can't generalize," and "There is no nature 'out there.'" Current states of nature are seen as extremely path dependent (i.e., historically contingent) (Pimm 1991); surprise is frequent and recurrent; and humans are endogenous. 3 One could conclude that many models of nature could not be falsified; the resulting view tends toward extreme relativity. Small wonder, then, that this view is compatible with the postmodem notion that all models of nature (especially those with resource management implications) are products of social forces (expressions of economic or political self-interest). Curiously, it bends around far enough to lend itself to a certain separability from nature, since nature's secrets are fundamentally unknowable or not objectifiable.

If there is any political power to be gained from this view, it comes when agents prove themselves to be historical (having long experience with an ecosystem, and perhaps some knowledge of complex dynamics); to be local (or traditional or indigenous, giving a particularist perspective on a system); and to believe they are a part of nature (that they do not subscribe to the Western human-nature dichotomy). If natural systems are utterly historically contingent, political power comes from proving one's culture to have been an authentic part of that history (Brosius 1999).

The second new ecology reconciles and contextualizes some of the perspectives of the first two views, and in doing so it claims that nature is knowable, at least to an extent (Carpenter 1998; Levin 1999). Quoting from Holling (Holling and Sanderson 1996):

The environment is not constant. Environmental change is not continuous or gradual, but episodic .... [The] critical processes that structure ecosystems take place at radically different rates covering several orders of magnitude, and these rates cluster around a few dominant frequencies.

The spatial organization of natural systems is not uniform either. Nor is it the same at different scales. Ecological systems are "patchy" at all scales-from the leaf, to the landscape, to the planet. . . . Therefore, scaling up from small to large cannot be a process of simple aggregation: nonlinear processes organize a shift from one range of scale to another.

Ecosystems do not have a single equilibrium. Rather, multiple equilibria define functionally different states, and movement between these states is a natural part of maintaining structure and diversity. On one hand, destabilizing forces are important in maintaining diversity, resilience, and opportunity. On the other hand, stabilizing forces are important in maintaining productivity and biogeochemical cycles.

6. DYNAMICS OF POLITICAL DISCOURSE IN SEEKING SUSTAINABILITY 151

The concern, therefore, focuses on changing but identifiable temporal and spatial dynamics, multiple but not infinite equilibria, and identifiable but interacting scales (often expressed in hierarchies of fast-and-fine-scale to slow-and-broad-scale phenomena).4 The consequences of these insights for human systems are profound. Finding a right scale for management is called into question, the role of environmental surprise in driving institutional change is magnified, and ecological complexity makes room for competing models of ecosystem dynamics, with political power becoming a more potent selector than scientific evidence.

The Problem of Fit Becomes Salient but Slippery

One of the fundamental problems of social science in environmental issues has been to find the "right scale" of adaptation to and management of nature (Falke, Pritchard et al. 1998).5 Little in the literature or in policy questioned whether a right scale exists, much less suggesting a consistent approach to finding the right scale. The importance of this problem has been reinforced by attention to scale in natural systems. Some decisions are best devolved to local authorities, or communities, or even individuals, depending on who has the best information, on the scale and extent of externalities (for example, if a watershed approach is indicated), and on the capacity for collective action (or the lack thereof). That is, ecological scale and social scale are both important. Social systems have hierarchies of their own, but they are not necessarily congruent with those of ecosystems, or with each other. For example, in a federal system, redistributive policies are frequently difficult to implement on the local level because of business mobility. Hence the problem referred to as competitive federalism-municipalities that compete for investment simply cannot do certain things that federal governments can, regardless of the fact that localities have an information advantage. Further, there is no guarantee that an institution or political entity even exists at the scale for which the need to manage is most acute (witness the problem of urban sprawl across multiple jurisdictions in Atlanta and Miami). Sadly, such limits of devolution or of federalism have rarely led to a critical examination of single-scale or scale-determined policy initiatives. Thus, from the Great Lakes to the Western Waters debate, and from river system management on the Mississippi to hydrological engineering of the Everglades, different organizational entities have been imbued with political power at a given scale, in order to manage ecosystems and peoples. The fragmentation of dynamic indicators, political constituencies, and organizational vocations has resulted in a bureaucratically leaden and ineffectual resource management system, which seems to work best when it works against its own formal purposes.

If ecological change occurs in the patchy and cross-scale manner suggested in this volume, then there is no single right scale for management (Folke, Pritchard et al. 1998). Some indicate a comanagement approach, where bureaucracies collaborate with local user groups across scales. Others recommend a nested approach that provides a "tenurial shell" for local use


groups, buffering them from the vagaries of international and national markets and policies and providing space for traditional management practices (Alcorn and Toledo 1995). The real challenge becomes dealing with systems that are not only cross-scale but dynamic, where the nature of crossscale influences in the linked ecological/economic/social system changes over time, creating fundamental problems for division of responsibility between centralized and decentralized agents (Pritchard 2000).

Environmental Crises Become Critical, but Ambiguous, Drivers of Change

If the episodic view of change is true, then knowing "where you are" in episodes of change becomes important. The adaptive cycle heuristic for understanding change is discussed elsewhere in this volume, but one can see that if change is episodic (that is, patchy in time the way ecosystems are patchy in space), one's only hope for leverage is to understand where in the episodic cycle the system is, and to act accordingly. If, in fact, the tendency of social systems is to lock in to a given set of goals, outputs, and working processes, can it be said that the stable system is locked into a trajectory of development that can't be altered until it generates a crisis (Holling and Sanderson 1996)? If crises (environmental or social) are opportunities for change, then how are those crises constructed or denied? Is the claim of crisis, in fact, double edged, provoking alarmed responses to symptoms that underestimate more durable subsurface system dynamics (Roe 1998) and ultimately loading the system with a new set of pathologies that will be expressed in the future? We take for granted that at least some crises are socially constructed rather than preexistent, and that there is room for some strategic maneuvering. We also acknowledge the cultural and political manipulation of such "crises" for reasons other than ecosystem management.

We suggest that organizing around or even constructing environmental crises can cut politically in two directions-more or less autonomy for communities; that is, more or less control for centralized bureaucracies. Both are seen in environmental resource conflicts. Some crises bring into question long-standing management practices of local people and can be used to dispossess them of their resources. It has been argued that this is the case for soil erosion and deforestation in Africa (Leach and Mearns 1996b)-that colonial regimes, by more or less intentionally misunderstanding local ecological dynamics, generated environmental crises that "justified" colonial control of indigenous lands. On the other hand, in the United States, grassroots activists tend to surf on waves of environmental crisis to unsettle and challenge bureaucratic management (Szasz 1994). The question to answer is, Can disequilibrium be a cover for legitimating environmental destructionthe worry of Worster (1994)--or for wresting resources from the control of less powerful groups (Richards 1983) or for reallocating power to the powerless but vocal?

6. DYNAMICS OF POLillCAL DISCOURSE IN SEEKING SUSTAINABILI'JY 153

Regardless of whether they lead to more centralization or more decentralization, crises either can create more opportunities for novelty, or can rigidify and entrench the status quo. Much depends on the actual history of how, and in what arenas, crises are constructed (see below). When do crises create space for institutional change, and, as important, when do they notwhen do they cement existing relationships and power structures? We suggest that both can occur, and that discerning between them is an important topic of future research. What are the preconditions for institutional change? Can institutional change be sufficient for improving management, when the environmental conditions of a management institution are hostile to the change? So, if a section of the Everglades were given over entirely to the Miccosukee nation, would they, from their limited enclave, be able to govern the greater Everglades ecosystem? The obvious answer to this is no, but the illustration suffices to pose a fundamental critique of the local mobilization school of resource management.

How do political structures influence possibilities for monitoring, learning, and adapting? How is it decided which experiments are off limits, and by what agents? When and how do political organizations try to control learning and information? What political processes govern the legitimation and institutionalization of knowledge?

Alternative Models and the Burden of Proof

Another lesson from the complexity of natural systems is that many models are right sometimes and all models are wrong sometimes. A variety of social constructions of nature are able to coexist. This creates a tremendous problem for achieving legitimacy in ecological management. But if there are great stakes in having one's model of natural systems accepted, then it is natural that political and economic conflict will find its way into science. Political structures will influence the possibilities for monitoring, learning, and adapting. Certain experiments will be declared off limits, and the control of learning and information will be contested. A fundamental lesson from hazardous waste management is that standards of proof are very different for communities and bureaucracies, and the basis for "scientific" inference radically different in local communities vs. detached scientific bureaucracies (and they matter very little for pluralistic discourse-see below). In regional issues of transportation or water management, not only do interests differ substantially over fundamentals (wealthy and poor communities over public transport, or recreational boaters, sport fishers, and urban water users over reservoir storage), but they also fight over the venues in which those differences are revealed. And often, overarching and generally irrelevant ideological positions add confusion to the discourse (e.g., local hostility to federal government "protection" from real estate development and "job creation").

In particular, local experience can conflict with bureaucratic understanding, and it is unclear which should be privileged or how they should be


reconciled. The joke among communities affected by hazardous waste dumps is that "an ecological disaster is an effect so large that even an epidemiological study could detect it" (Dryzek 1997).6 Much of the rigidity in systems comes from the burden of proof-the complexity of ecological systems means that activists must convince people to act without "sufficient" evidence. Evidence can always be declared insufficient (witness the obduracy of those refusing to acknowledge anthropogenic global change), and acting without sufficient evidence will always putatively threaten those who benefit from the status quo. Even the simplest of multiple equilibrium models can confuse rather than inform the public.

Political Power and Ecological Complexity

The fact that multiple models of nature and management can be sustained without being falsified means that other, more social, factors can play a profound role in management choices and conflicts. That is, multiple and conflicting models aren't compared and contrasted in a political vacuum. In particular, if there are bounds on information and cognition, then the choice of models may depend partly on the power relations between agents. Power is a difficult concept since it can mean so many different things, but it is useful to offer a simple typology to organize our thoughts. Steven Lukes (1974) has written elegandy about three faces of power in political systems, and we have borrowed (and extended) the billiards metaphor from Johr. Dryzek (1997).

Billiard Ball Model

The first face of power, for Lukes (1974), is the one most democratic theorists write about, and it is closest to the popular view: a pure quantitative view of power. The expression of political power is defined in terms of the result of a set of forces converging on public policy-a billiard ball model of political power. The vectors of desire are weighted by the number of claimants or voters espousing a position. All political interaction is mediated at the collective level-the results of an election or the actions of a responsive governing board. Preferences, beliefs, and organizing capacity are not endogenous. There is a simple calculus of political power.

The quantitative view of power is the least interesting to systems with uncertain and complex ecological dynamics. If issues were raised one at a time, and if their resolution were not linked to t..'le order in which they were addressed, and if agents really evolved their preferences and gathered their information independendy, this might be a useful simplified model of democratic processes. But such is not the case; this led Lukes to describe two more faces of power.

6. DYNAMICS OF POLITICAL DISCOURSE IN SEEKING SUSTAIN ABILITY 15 5

Adding or Moving Bumpers and Pockets

The second face of power can be understood as agenda framing--efforts to control what gets discussed in politics and what doesn't. The ability to keep certain issues off the table, to constrain the motion of the billiard balls on the table, is an expression of the second face of power. If the possibility of social regulation of externalities is successfully characterized as "Soviet-style command-and-control," with all the attendant negative connotation, then it is likely that only market-oriented solutions will be considered. In the regulation of U.S. hazardous wastes, for example, industry lobbyists were successful in preventing consideration of process standards or a permitting system, limiting debate to disposal regulation only (Szasz 1994). Consideration of agenda framing means that political explanation must account for decisions not made and options not considered. The methodological problem this raises-studying events that didn't actually occur-does not negate the importance of the phenomenon (Crenson 1971).

This view of power is particularly important for the management of complex ecosystems if, as was suggested above, multiple models can coexist.

· Political power may then determine which models are even considered, by controlling the agenda of scientific and political inquiry. But in this view, although it does not emerge in the public arena, conflict is still recognized by agents in the system. A third view of power considers a more subtle exercise of control.

Tilting and Warping the Table

The third face of power is along the lines of ideological domination-what Marx thought of as false consciousness. The expression of this form of power is much more insidious, occurring through the process of advertising, socialization, and even public debate. Leaders not only respond to constituent preferences, they help to shape those preferences. By manipulating preferences and perceptions, actual conflict is submerged, and real interests may be suppressed. In fact, the Marxist idea of the "hegemony of civil society" involves the sublimation of politics in favor of an internalized ideology (of the consumer, for example) in which the politically important and mobilizable differences among people are buried in a consciousness that makes all people common. In so doing, hegemonic ideologies alter the surface of the table on which the billiard balls roll, inclining it or warping it, even if imperceptibly. Think of the number of Americans who believe that their jobs are threatened by environmental regulation, or that the economy will be destroyed if the Senate ratifies the Kyoto Protocol (Goodstein 1999), or that evolution reduces to our "dropping out of the trees."7

15 6 PRITCHARD, SANDERSON

Political Dynamics

Where alternative viable models of resource dynamics exist, and where economic and political interests are at play, the use and abuse of power (in all its forms) are possible. Such cases can quickly tum into "wicked problems." As Don Ludwig describes them:

• they involve a host of traditional academic disciplines or social perspectives (that is, alternative viable models exist); and

• they cannot be separated from issues of values, equity, and social justice (economic and political interests exist, with the potential for the use of power).

To which we have added:

• They refuse to stay put in single arenas of action.

Even political processes that appear to hold promise for resolving wicked problems tend to lose their grip easily. Wicked problems are, by definition, slippery, subject to redefinition and reconceptualization, evading final answers. Further; they are subject to the expression of each of the faces of political power described above. It is useful to consider the arenas of action, the forms of political and regulatory discourse that prevail, and the ways in which environmental issues move between them.

Alternate Discourses

Opinions are innumerable on what institutions, approaches, and methods are necessary to solve environmental problems. A handful of these are archetypal in the sense that they are seen by their most ardent advocates as cure-ails, or at least templates on which to model solutions to a very wide range of problems. These self-reinforcing ideas form recognizable "discourses," or languages, about environmental management, which have been described recently (Dryzek 1997; Williams and Matheny 1995). Just as we can fail to perceive the accents with which we speak, we can fail to recognize the distinctives and the limitations of the discourse we use. For example, in a characteristically succinct statement, Elinor Ostrom lamented the singleminded efforts of economists and policy analysts to get prices and policies right, even as she proposed getting institutions right, instead (Ostrom 1990).

Administrative Rationalism

Administrative rationalism is the New Deal or welfare state perspective on the power of bureaucracies not only to know the public interest but to act on it with all the power of the state. The modality of action is "problem solving," and it is, properly speaking, hierarchical. The era of the welfare state is over, though, and it is fashionable to hate bureaucracies; they have been successfully stigmatized as embodying command-and-control strategies

6. DYNAMICS OF PoLmCAL DISCOURSE IN SEEKING SUSTAINABILITY 157

for managing the environment. Nevertheless, for historical as well as political reasons this is often the starting point for arguments about environmental management.

Market Rationality

This set of solutions is well known and easily characterized. For better or worse most agents are pretty polarized about the promise of the market to cure environmental ills, and there is little in the way of balanced consideration. Proponents and opponents tend to be ideological, which serves as further evidence that market rationality is a fundamental and self-reinforcing discourse. Elinor Ostrom has compared an economist talking about "the market" to an ecologist talking about "the plants" or "the animals" (pers. comm.). Every market has different properties and is governed by different rules or institutions. But in a mythic sense, "the market" is as close to a panacea as is ever prescribed.

Markets versus hierarchies is about as far as many analysts get-all political phenomena are viewed on a spectrum between these two alternatives (the free market v. command and control v. a "mixed economy"). However, two other discourses are important to consider.

Pluralist Politics

Pluralist politics is the political equivalent of market rationality-in this view, all that is needed to come to grips with a problem is the legitimacy that comes from voting on it. A vote will reveal the will of the people-and especially a secret ballot will reveal what they really think. 8 In representative democracies, the will of the people is also heard through lobbyists and through civic organizations, which organize and reflect the interests of various constituencies. A key rhetorical issue is the freedom of political expression and the resulting balance of competing interests. Another key feature of pluralist systems is a built-in inattention to politics on the part of the populace during normal times, and their representation by special interest groups during other times.

· This aversion to politics-the private citizen versus the political citizen-has spawned a new generation of political analysis that focuses on civil society, rather than political society, as the locus of power in a free world.

Communitarian Discourse

Finally, a communitarian discourse has been identified (Williams and Matheny 1995) and overlaps with what some analysts call deliberative democracy (to distinguish it from pluralist democracy described above), of which the exemplar institution is the town meeting (rather than the ballot box or voting booth)-harking back to the idea of the polis (but for recent renditions see Elster 1998). The communitarian discourse has certain strengths: discussion, reasoning, and deliberation are the attractive features of community decision making, the ideal being that face-to-face arguments


lead to better decisions, and moreover to consensus. Further, communities are connected to their resources in ways that bureaucracies or national polities aren't. When given responsibility and authority over those resources, a presumption is that they will nurture and steward them. Most of the impetus for community-based natural resource management draws on some combination of these key features of communities. Communities can be either continuously organized, as in the systems discussed in this volume by Berkes and Folke in Chapter 5, or only ephemerally active, as in the system discussed by Gunderson et al. in Chapter 12.

There are hybrids· and mutants of these fundamental discourses: adaptive management, citizen science (both of which are discussed below), sustainable development, green modernism.

Shared Features of the Discourses

These discourses roughly correspond to the ways of life of cultural theory (Chapter 9). They are not strictly mutually exclusive, although they are, in the end, ideologically self-reinforcing and typical of different organizational styles. They share some common features, and we have attempted to portray them in Figure 6-1.

relativist view of science

objective view of science

inclusivity, process

unresolved or wary view of science

Figure 6-1. Alternate discourses in environmental management, as discussed in Williams and Matheny (1995). Labels inside boxes describe the strength of each discourse; labels above and below boxes indicate attitudes toward science; and labels between boxes indicated shared values and attributes. Market rationality is related loosely to both expert decision making and pluralist democracy, but was not an explicit part of Williams and Matheny's typology.

Endogeneity of Preferences

Endogenous preferences appear most strongly in the communitarian discourse. The alternative is to assume that values are "out there"


(predetermined in individuals), just as the optimal policy is out there (discoverable by experts). For both administrative rationality and pluralist democracy (as well as market rationality), preferences are out there. They are preexistent and valid in themselves. The goal (for an agency under administrative rationality, or for a political system, in the pluralist discourse) is to uncover and serve these interests. The community, on the other hand, has less concern for balancing plural public interests-it may be more oriented around consensus and maintaining a moral meaning in that consensus. In the communitarian discourse, individual preferences may be preexistent, but those are not considered good preferences. Only those preferences formed "in community" are worth upholding (Sagoff 1982). Both pluralist and expert discourses assume that values are "out there" and that they are commensurable-for voting individuals who determine a political preference from their individual preferences and for bureaucracies who determine a best policy from a progressive sense of the common good. The shared belief in commensurability also means that optimization looms large as a method for deciding what to do-there is a preoccupation with problems that can be solved by finding a minimum or a maximum, perhaps at the expense of finding the right problem to solve (Pritchard et al. 2000).

Source of Legitimacy

The power of bureaucracies comes from doing what they claim to do wellthey solve problems. When they fail to perform, they are most vulnerable to loss of substantive responsibility or, worse, budget. The power of the other discourses (the two forms of democracy) is that they are inclusive and process oriented. Fairness is an issue for both the democratic discourses; efficiency is the issue for bureaucracies. A similar tension means that progressive approaches are centered on particular problems to be solved, often in isolation from other problems-a strategy not possible in democratic systems.

Role of Consensus

Pluralist democracy's goal is to balance competing interests in such a way that they are willing to come back and play the game again next time. Bureaucracies and communities, however, are more oriented to handling differences by reaching consensus-either an emergent or an enforced consensus, to be sure. But they share in common a sense of mission and a (presumed) commitment to deliberation and the legitimacy of arguing. The difference is that bureaucratic consensus may be organized around either expert science or an agency mission, whereas community consensus may revolve around moral issues; both result from and add to shared cultures.

Role of Science

Where the discourses differ the most, and why the politics of complex ecosystems are so contentious, is in the role played by science in the dis-


courses. The strongest advocates of objective science are found engaged in expert discourses. Pluralists reserve only a relative or instrumental role for science (this is where campaigns of pseudoscientific disinformation are likely to emerge and to be granted credibility, for example). And a criticism of community discourse has been the near absence of a role for science: Technical feasibility and rigorous statistical proof are not typically core values for communitarians. Nor, to be fair, has science spoken to such communities in ways that make scientifically generated knowledge or scientific approaches to knowledge a core part of community culture anywhere.

Pathologies

Each of these discourses is vulnerable to particular pathologies. The bureaucratic management pathology is often discussed: the development of a trap of competency proceeds quite naturally from expert management. Bureaucrats get better and better at what they do, when they need to be finding novel solutions and confronting uncertainties. A parallel trap is the trap of political bureaucracy-loss of legitimacy when a regulatory agency is trying to manage across competing interests. Yet another is the trap of defending the bureaucracy for its own sake, or a bureaucratic process because it is identified .as beneficial, whether or not evidence shows it to be reaching its targets. An egregious, but perhaps benign, example is the USDA effort to set national nutrition standards and to spend handsomely to tell people to eat their greens. Despite vocal and highly organized opposition from the food industry, USDA insists on this policy. Yet the results in American dietary behavior suggest that the program is driven more by internal imperative than evidence of efficacy. Finally, and perhaps most important, is the trap of regulatory capture of bureaucracies by stakeholder groups.

Much analysis is content with diagnosing the problems of bureaucratic rationality, without considering that alternate discourses have their own peculiar traps; Pluralism or communitarianism become panaceas for what ails failing bureaucracies. Understanding the other traps can help us understand how issues move between discourses-how the failures of a particular approach result in an issue moving into another arena. For example, for pluralist discourse, the perennial trap is that of stagnation or gridlock, the trap of competing interests each with virtual veto power. When economies are organized to the point of Pareto-efficiency, to borrow economic jargon, no one can be made better off without someone else being made worse off. If the agents have veto power, they can stifle innovation, pushing politics to the point of a zero-sum game purely about distribution. This is where issues are likely to stall in the pluralist discourse and be taken up only by an alternate discourse.

For the communitarian discourse there is the trap of deliberation-faceto-face communication may lead to preference falsification in the-case of goals (through intimidation or manipulation) and social proof in the case of models (where information is limiting, agents tend to follow a leader). A par-


allel trap is loss of attention; when issues become complex, when they have little opportunity for hands-on action, and particularly when they involve only slight threats to the community, attention tends to wander and citizens become disengaged. Finally, a communitarian trap is innocence of politics. A common criticism of community-based decision making is that it demeans the formal political process and seeks to depoliticize local discourse, as if it were not political at all. To the extent that this makes community groups either hostile to politics (the "compromise is treason" approach) or alienated from its processes (why bother testifying before the Corps of Engineers; they'll do what they want, anyway), it undermines community power.

Given the complexity of ecosystem dynamics and the multiple discourses of environmental resource use, it is easy to see that there are multiple equilibria in politics-both in terms of outcome (voting may yield discontinuities in management) and multiple equilibria in terms of organizational frameworks (important community issues may break onto a management agenda). Understanding the dynamics of political activity within single discourses is interesting but well studied. Community organization is addressed well by the social movements literature; interest group politics and its effect on voting are addressed by democratic theorists; and agency dynamics are captured by organizational theorists. Less attention has been paid to the unfolding of issues across the discourses or to the emergence and collision of issues in multiple arenas, jurisdictions, and discourses. Such an understanding explains why mere prescriptions to "get the prices right" or to "get the science right" or even to "get the institutions right" are in vain. There is a historical dimension to all the resource conflicts described in this volume that isn't captured by such synoptic approaches.

Much of politics is competition over processes, or over power, rather than over outcomes. It is easiest to describe when it coincides with interests defined narrowly and economically, but that is not the limit of politics. And much of politics is an argument over what is political and what is not, since each discourse has a different characterization of what is political and what is legitimate to argue about. The point here is that there is no natural forum for political or economic conflict. The choice of the forum is itself a political issue, one often submerged under Lukes's (1974) second face of power (agenda setting). Even the choice of jurisdictions can be a point of political strategizing, as various agents will prefer to operate at the scale for which they have comparative advantage. So, for example, a logging company might support a campaign for indigenous rights to forests, in the hope that local institutions will be more inexpensively corruptible.

Common Trajectories

The garbage-can model of politics (March and Heath 1994) says that issues are attacked, associated with other issues, or ignored, depending on the prevailing political environment when they arise. At the extreme, this describes


a system with extreme dependence on initial conditions. Political questions move through various organizational arenas and spatial/political scales over their life cycle. Are there a small number of trajectories that political issues take as they weave their way through the discourses and arenas of politics? To answer yes will be to say that there are either a limited number of environments (ways of organizing) or a limited set of possibilities for evolution.

For example, normal political organization is a shifting balance between expert bureaucracies and the special interests that patronize them. Bureaucracies may try to manage objectively and expertly, but they run afoul and are frequently charged indirectly with political management, because their policies have redistributive consequences. An environmental crisis may emerge that generates a community-style response. Community leaders, looking for a point of leverage, join in pluralist politics, playing their politics of outrage. Policies are worked out and handed to bureaucracies, but during this time, communities lose interest. Their leaders are left on the national stage, with no public outcry to rely on for legitimacy. Since policy generated in crisis is lousy, the pluralist system kicks in, ultimately characterizing the community response as another special interest. Leaders either disappear or remain and try to generate continual crises. Another trajectory may be out of the political system. Forward-looking agents choose how to exercise power while always maintaining the option of exit from the system, into another discourse, or even into private, nonpolitical arrangement-witness the recent wave of abandonment of the Global Climate Coalition (an industrysponsored think tank) by companies who realize that challenging the scientific consensus on climate change is fruitless.

For example, a typical issue cycle described by Williams and Matheny in Democracy, Dialogue and Environmental Disputes (1995) is for community activists to generate alarm about a toxic threat, perceived locally. There is a mismatch of scales as they interact with a bureaucracy (as well as a misunderstanding about how establishment science relates to their folk epidemiology), and they find their local government unresponsive (since it can't deal with redistributive issues without frightening potential investors). As they take their case to the legislature, they quickly learn that they need a full-time lobbyist to represent their situation in the capitol. By this time, people mobilized early on are beginning to lose interest-a few leaders are radicalized by the experience and hook up with a national NGO (exchanging their local community for a virtual one). Their increasing professionalization (and radicalization) further alienates their former community, so that by the time the issue is on the government agenda, it is easy to characterize the NGO as a special interest group, although it thinks of itself as operating in the public interest. If legislation is passed (if the crisis was large enough), it is done in a hurry, and done poorly, so that the regulatory agency receiving a mandate is left with a vague and poorly conceived mandate. In the rule-making phase, the regulatory agency invites in a range of stakeholders, some from industry, some from NGOs, some from local governments, and the potency of the


legislation is further watered down. The resulting rules are so poorly crafted that within a few years the industry is able to petition for regulatory relief from their economic impact, and eventually they are withdrawn.

Linking Discourses

Most of the attention of bureaucratic management, as currently practiced, is still on problem solving-how to reduce uncertainty in managing systems. If management is to be adaptive, it should be focused on how to handle irreducible uncertainty, how to test hypotheses about system function and resilience, how to maintain the adaptive capacity of the ecosystem. But in the face of wicked problems, what are managers to do? How are they to manage better? Despite the manner in which they are constantly maligned, many of the attempts to bridge discourses have been on the part of management agencies, which realize that for a number of reasons, both intrinsic and instrumental, they need to incorporate public participation into the decisions they make.

The dynamics of self-legitimizing behavior pull bureaucracies in at least three directions. One is obvious-the panacea of the market whether it is simply unleashed or has to be repaired (if it has failed) or created (if it is missing). The point is not whether this is right or wrong in any absolute sense, only to recognize that there is a powerful attractor in that direction. But as agencies are called to be all things to all people, they are pulled in a democratic, participatory direction as well. There are a number of advantages for management to be more participatory.

From a strictly instrumental perspective, participatory approaches may yield better information: using people as monitors and modelers helps to overcome a couple of problems. Information about the system is distributed-both "on the ground" and in understandings and models held by stakeholders. Participatory approaches treat people as the computers who synthesize information and anticipate outcomes. Moreover, participatory approaches deal with the problem of incommensurability-that decision rules can't be reduced to optimization problems because people are unwilling to make certain formal tradeoffs-because they are willing to make those tradeoffs implicitly.

Participatory approaches hold out the possibility of enhancing the legitimacy of bureaucratic decisions (recognizing that process matters as well as outcome). This comes partly by being more effective (as in the paragraph above) and partly by making some attempt at balancing competing interests by incorporating multiple stakeholders. Adaptive management approaches, for example, have long used basic science models for creating an otherworldly environment where resource users can put aside their differences and learn other perspectives on a problem. Inherent also is the possibility of using the power of deliberation for uncovering or generating novelty.

Adaptive management in practice has leaned more in the direction of progressivism than its proponents realize. The goal, laudably so, is better environmental outcomes. Democratic approaches are the handmaiden of that


process. But what kind of democratic approaches, given the large differences between pluralist and communitarian discourses? Along which side of the triangle of discourse do agencies travel?

The main axis of attraction as adaptive management tries to be more participatory is from expert bureaucracy toward pluralist democracy, or some variant of it. This is, after all, quite familiar to most management agencies. Legislatures frequently give vague mandates to bureaucracies about what to manage, what to control, and leave the bureaucracy to write the specific rules. At that point, the attention of all the actors of pluralist democracynotably special interest groups-turns from lobbying the legislature to lobbying the bureaucracy. With little direction about handling such attention, bureaucratic effort becomes absorbed with balancing competing interests. With or without legislative mandate, bureaucracies organize public consultations, alternative dispute resolutions, policy dialogues, and public inquiries and publish data (with some help from right-to-know legislation). At the extreme, they could determine public opinion by organizing a poll or a straw ballot. In practice, they organize public participation by involving "stakeholder groups."

Linking bureaucracies with pluralist systems is thus still fairly progressivist-after all, this perspective is the engineering one-"Give me the objectives, and I'll give you the most efficient design." The progressivist view certainly delinks policy and implementation and concentrates on means rather than ends. This explains part of its affinity with economics-"You give me an objective function (utility, profit, whatever), and I'll maximize it for you."

Much of the process of adaptive management is thus moving along the axis from expert management/bureaucratic rationalism to some sort of pluralist democracy, informed by contingent political coalition formation and experimental approaches to behavior. It may suggest a "floating crap game" model of politics, in which players and places (and scales and degrees of organization) vary according to a rapidly refreshing feedback loop, as well as the dynamic response of other entities in the game. This recognizes that collective interests are involved, that stakeholder groups will have to be addressed, and that organizing those groups will lead to more information about the system so that it can be managed better. Yet the approach is highly portable and flexible in its constituency; costs of participation are low, inclusion is straightforward. Liberal democracy and bureaucratic rationalism share the view that the information you need to get at to manage correctly is "out there" in terms of the public interest, and "in there" in terms of individual preferences. The floating crap game trivializes the "out there" and the "in there" according to some simple democratic values: high inclusion and easy participation.

Participatory adaptive management is a kind of pragmatic approach to managing systems. Dryzek (1997) characterizes the relationship of citizen participation:


For problems of any degree of complexity the relevant knowledge cannot be centralized in the hands of any individual or any administrative state structure. Problem solving should be a flexible process involving many voices in cooperation across a plurality of perspectives. As long as this plurality is achieved, there is no need for more widespread public participation in problem solving, so the degree of democratic participation with which pragmatists are happy corresponds roughly to the limited amount found in existing liberal democracy.

Moving along the alternate track, toward communities and deliberative democracy, is more of a challenge, and is less often tried. The emphasis would be to try to involve communities more and more in a real sense, and to privilege those communities. But communities are more of a challenge for bureaucracies. If communities are really organized only in times of crisis (or perceived crisis), it is unlikely that they will want to cooperate with a bureaucracy at that time (after all, it may be thought that bureaucratic mismanagement is what got them into trouble). Further, there is a moral dimension to community organization and mobilization-decision making is not simply about maximizing utility, it is about right and wrong. The pragmatists in the pluralist box, on the other hand, are unhappy with any attempts to propose moral absolutes to govern environmental affairs. Finally, different discourses are just that-different languages. Reconciling the technical, formal, globalizing language of management agencies with the place-specific knowledge and perspectives of communities is difficult at best (Scott 1998).

Open Questions

Most of the interesting questions about the politics of resilience remain open. Future research, when framed to include political considerations, may shed light on these questions.

What Does a Resilient Political/Ecological System Look Like?

There is a corollary to the Holling (1995) frustration: resilience is maintained through disturbance. Maintaining (or extending) the boundaries of the stability domain means exploring and experiencing those boundariesif you withdraw, the boundary follows you in. But what political system manages for variability? Political systems evolve to maintain the status quo or to replace it. It is not clear that there is any political selector for oscillations-nor that oscillating systems are more resilient. But the Jeffersonian idea that unrest, whether intellectual or physical, is functional to the system suggests that his thoughts favoring frequent revolution were adaptive in style.


But issues do bounce around from arena to arena, and probably not at random. Is there a useful typology of life cycles for environmental issues? Are there affinities between certain kinds of issues and certain kinds of life cycles? We need to do for environmental issues and governance what March and Olsen (1989) did for organizational science. In the face of the disorganized complexity of what organizations actually do, they proposed not that organizations did things at random, but that they operated according to a garbage-can model. Issues get associated, and alliances get built, based on what is happening at the time they begin to garner attention. Individuals and units within the organization pick up or lose touch with issues according to their own dynamics rather than according to any issueoriented rationality.

"Where Does Adaptive Capacity Reside?

One of the key questions coming out of the political economy work is "Where does adaptive capacity reside for social-ecological systems?" So rather than ask yet another round of questions about "people and politics and the environment," future work should address the issues of roadblocks and opportunities for adaptive capacity and innovation, and how the question is addressed in different discourses and by different disciplines. Where does flexibility or innovation emerge? What are windows of opportunity for innovation and corruption? And the Big Question: Do some life cycles for environmental issues seem to lead to novel or adaptive solutions, and do others maintain or reinforce the status quo? Can issues change trajectories?

One lesson is clear: resolving wicked problems by true consensus will require that people be willing to compromise not just on means but on ends. Only when preferences, economic interests, and models of change are topics for deliberation can novelty arise in certain locked-in systems. Some would describe this as looking for win-win solutions, but a more subtle characterization would lean less on pluralist perspectives.

"Why, in Practice, Is Adaptive Management Not Adaptive?

Why do good people do bad things ... where do best practices go wrong? What happens when adaptive ecosystem management is "operationalized"? How is the transition away from a technocratic approach so easily ignored? Part of the puzzle of adaptive management is how to build a nonbureaucratic bureaucracy. Is it possible to have a legitimate, capable, and responsible management organization that is constantly reforming and reinventing itself, undergoing revolt? This is the pluralist analogue of the discourse of "workplace democracy," in which the importance of attacking a hierarchical division of labor superseded the purpose of the workplace-to produce.


How Can Science Be Made to Serve Citizens?

What can keep citizens engaged in a process of adaptive management without becoming labeled as special interests? How is it possible to uncover the political and economic interests of those on the margins of conflicts, whose interests are most likely to be ignored, submerged, or manipulated by any system of governance? How are we to conceptualize and model political power in its various forms, including coercion, force, manipulation, authority, and even persuasion? And what arenas and processes are best able to sustain attention to the resource problems that must be faced?

Over the course of the research described in this volume, various informal approaches to politics were tried and found wanting. One approach was to ignore or circumvent political phenomena-suggesting that citizen science is all that is needed to repair bureaucratic science, or that the goal of management is to avoid political messiness by focusing on "win-win" mythologies. Other approaches leading to important insights were simple renditions of politics-infrapolitical models of politics within a single arena, similar in a way to economic models with their emphasis on choice and agency. At the end, however, the common realization is that political phenomena are worth studying in their own right, that understanding the evolution of political structure and the contest over how issues are to be resolved is key to breaking through the barriers to better environmental management. In what follows we suggest some important methodological reorientations and substantive hypotheses:

• Organizational resilience resides in "invisible colleges," epistemic communities, or the like, that rise to address organizational pathologies or inefficiencies. These are likely, when successful, to span both organizations and discourses. Their ephemeral nature makes it difficult or impossible to institutionalize resilient behaviors.

• Discussing multiple equilibria in historical, social, and political systems means addressing head-on the issue of paths not taken, lessons not learned, and decisions not made. This is methodologically difficult, but no more so in politics than in ecology.

• The relationship between institutional stability and institutional resilience varies according to the four-box heuristic. There is a limited set of trajectories over which environmental problems will travel. Much of politics is about "deciding" whether to debate, how to frame a debate, in which jurisdiction to address an issue, and at what scale to organize. These need to be understood in dynamic rather than static terms.

• In a similar way, there are elective affinities between kinds of politics and kinds of resilience, which in tum affect ecosystem


dynamics. Different political systems will provide a capacity to respond to different kinds of environmental variability, and there is no single optimum or climax form of political organization. Scale is important, since individuals and institutions have different adaptive capacity and ability to learn. Moreover, there are pathologies in every form of organization, not just the bureaucratic. The struggle is to understand which of various institutional forms of organization is favored at particular times, and when each is considered inappropriate, even dangerous.

• Simplifying (or uncomplicating) the relationship between organization (bureaucratic and political) and community affords greater resilience but is far subtler than declaring processes "open and participatory."

• The organization of community is a function of power-no communities are prepolitical, and their internal structures and the details of their articulation with other institutions and actors influence ecological outcomes.

• A general paradox is that innovation undermines bureaucratic rule making and stability but induces greater resilience. An elaboration of the paradox is that innovators are put at risk when they undermine agencies' stability.

• Politics is not scale-invariant; the expression of power changes as one crosses scales. A fundamental question is how power is channeled across levels of political organization. Openness, violence, knowledge, and the social construction of authority are all media.

Testing these hypotheses and applying these lessons to the thorny puzzles of environmental management and governance are the goals of future work. The greatest promise lies in addressing political issues directly, rather than in avoiding or submerging them. The fondest hope might be that individuals, communities, and formal organizations engage the spirit of adaptation and experimentation, by allowing a set of contingent ideas to shape "the gamble" of democratic resource management, and citizen experts to report on the results. Of course, for such a profoundly disorganized and multiscale approach to thrive, government, market, and citizen must share a common vision-that all must address these puzzles in order that they might be engaged and worked on-not solved forever; that "expertise," popular voice, and power are separable, and none holds the dice for more than a pass.

Notes

1. The following paragraphs summarize the arguments of Cerny (1990).


2. Marxist and neoclassical economics are bedfellows in the assumption that only economic relationships matter, although they differ as to whether those relations lead to equilibrium.

3. The U.S. government has enshrined the idea that ecosystems always have people in them, in its notion of "ecosystem management" (Frampton 1996).

4. The archetypal story about cross-scale dynamics is about contagious processes in forests (fire or pest outbreak, for example): fragmented forests with small-scale patches may be relatively immune to disastrous fire or disease outbreak; however, as they age, they may converge in flammability because of accumulating litter loads. As the characteristic spatial scale of the system increases, so does the potential for catastrophic fire.

5. As noted by Arun Agrawal (pers. comm.), problems of cross-scale dynamics only compound the difficulties that already inhere in reconciling social, political, economic, and ecological processes at the same scales-cupidity, unintended and unforeseen consequences, and disarticulation.

6. The seeming uselessness of science is highlighted by the belief that "for every expert there is an equal and opposite expert." The conclusive evidence for this proposition can be found in the history of "expert testimony" on the relationship between smoking and cancer.

7. Nick Abel has suggested a "fourth face of power" -the constraint of history, in which current choices are limited by the institutional framework and pattern of resource use established by previous generations (pers. comm.).

8. We leave aside for the moment various paradoxes associated with voting (Arrow 1951).

Acknowledgments

The authors wish to thank Arun Agrawal, Nick Abel, Lance Gunderson, and other participants in the Resilience Network and Resilience Alliance activities for their comments and suggestions for this chapter.

Part III

Myths, Models,

and Metaphors

CHAPTER 7

COLLAPSE, LEARNING, AND RENEWAL

Stephen R. Carpenter, William A. Brock, and Donald Ludwig

You can't depend on your judgement when your imagination is out of focus. -Mark Twain

D iverse case studies of environmental management suggest that crisis and collapse are common, or even predictable (Gunderson et al. 1995a; Hilborn et al. 1995; Levin 1999; Redman 1999). If collapses

are so predictable, why are they so common? And if collapses are so common, why is humanity still here? A pessimistic answer is that humanity is on a transient downward spiral. We take a more optimistic view that sustainability is possible, contingent on the resilience of nature, flexibility of societies, and creativity of people. Here we discuss relatively simple models motivated by these ideas. Such models may be useful for building common understanding during periods of innovation and reorganization that follow a collapse or crisis. This part of the adaptive cycle has been called the "backloop," or more specifically the transitions from omega to alpha, and from alpha tor (Chapter 2; Gunderson et al. 1995a). Rationalizations, perceptions, and decisions made at this phase of the cycle may have a large influence on subsequent human behavior, and may even establish the template for future collapses. Gaming with simple models can help evoke effective collaboration, creativity, insight, and hope. Such models are computerized metaphors designed to illustrate general patterns of system behavior, rather than to make exact spatial and temporal predictions (Domer 1996; Holling et al. 1979; Janssen 1998; Scheffer and Beets 1994; Walters 1994). They are designed to spark imagination, focus discussion, clarify communication, and thereby contribute to collective understanding of problems and their potential solutions.

Environmental scientists have produced a rich diversity of models useful in the exploitation (r) through conservation (K) phases of the adaptive cycle

173

174 CARPENTER, BROCK, LUDWIG

(Chapter 2), when the underlying physical and biological systems are thought to be well understood. Such models are used, for example, to design engineering structures, forecast ecosystem changes, estimate statistical parameters, and solve optimization problems. They are designed to perform well on specific, narrowly defined tasks. They are often mechanistically detailed and data intensive. Such models are valuable and an important line of work yet quite separate from our goals in this chapter. The more innovative backloop phase requires models that are simple, flexible, easily modified to accommodate unforeseen situations and new ideas, programmable on the fly, and understandable by diverse participants. The goals are qualitative understanding and accessible heuristics. The models must be frugal in structure in order to be fast and flexible (Gigerenzer et al. 1999). These simple models are appropriate in situations where uncertainty is high and exploration is paramount.

We begin with a sketch of harvest models for living resources, where the concept of maximum sustainable yield (MSY) provides a prototype of nonlinear dynamics and optimal control during the exploitation and conservation phases. The history of this area provides well-documented examples of the types of collapses from which we wish to learn. Then we introduce a more complete-yet still as simple as possible-framework for interacting social and natural systems. We describe the cycles that occur in the models, discuss realistic complications that make them more severe, and suggest some steps that may ameliorate the cycles. We discuss the use of models to focus collective learning and action during the backloop.

MSY and Collapse of Fisheries

Because the idea of maximum sustained yield is a familiar one in management of living resources, it provides a useful portal to models for teaching about the adaptive cycle (Box 7-1 and Figure 7-1). The theory of optimal harvesting includes much more than the simple concept of MSY. Clark (1990) shows that economic optimization leads to a policy of maintaining the population at a target sizexopt, which is lower thanxMsY and depends on the economic discount factor. The population is to be maintained at the target by harvesting heavily if the population is abovexopt and not harvesting at all if the population is below Xopt. This strategy does not suffer from the instability that is likely with a constant harvest policy, but it suffers from a number of other defects in its formulation and implementation.

The formulation assumes that all that is important is a discounted sum of harvests, as is usual in economic optimization. However, the actual time sequence of harvests may also be important. It is difficult to market a product whose availability is variable and unpredictable. Harvesting usually involves a fairly heavy investment in equipment, and the corresponding debt must be serviced. Humans may become dependent on the income stream from the harvests, and hence they may generate intense pressures to con-

7. COLLAPSE, LEARNING, AND RENEWAL 17 5

Box 7-1. Maximum Sustained Yield

D. Ludwig and S. R. Carpenter

The objective of maximum sustained yield (MSY) seems to capture the idea of sustainable development: to achieve as much as possible without compromising future capabilities. To illustrate the concept of MSY, consider the per capita growth rate of an exploited population. If the population is at its largest possible size (the carrying capacity of its environment), then the per capita growth rate is zero-births are exacdy balanced by deaths. If the population is below carrying capacity, less competition for resources leads to a higher birth rate and a lower death rate, and hence the per capita growth rate is positive. The maximum per capita growth rate may be expected at the lowest population size, since the competition is least with a single breeding unit. The net growth rate is obtained by multiplying the per capita growth rate by the population size. The result is a curve that is zero at a population size equal to zero or the carrying capacity, but positive in between (Figure 7-1).

If the population is subject to harvesting, the harvesting rate must he subtracted from the net growth rate when considering the population dynamics. In the simplest case, the difference between the two rates will be positive between two population sizes labeled Xt and x2, and negative outside that interval (Figure 7-1). Hence the population increases between those values and decreases otherwise. If the harvest is constant, the population will move toward x2 if it is above Xt; otherwise, it will decrease toward zero. If the harvest is set above the maximum of the net growth curve (labeled hMsY ), the population will decrease toward zero. Hence any harvesting rate below hMsY is sustainable. The maximum of these sustainable rates is hMsY itself. Note that "sustainability" has acquired a slightly different meaning in this context: the sustainability applies only if x never drops below Xt. If h = h M sY, then x1 = x2 = XM sr, and sustainability applies only if the population never drops below XMSY·

There is a conflict between maximization and sustainability: the higher we set the harvest rate, the more fragile is the sustainability we seek to preserve. There is no margin for error if h = hMsY and x = XM sr. If environmental variation should temporarily decrease the per capita growth rate, a policy based on the previously observed population growth rate may be unsustainable. If environmental variation should temporarily increase the net growth rate, our desire to

continues


maximize returns may lead us to set the harvest rate too high to be sustained over the longer term.

A policy of maximization of the sustained yield can succeed only if information about changing conditions is readily available, and if it is possible to make quick adjustments to changing conditions. However, for many natural populations it is difficult or impossible to monitor the actual population size, and it may also be difficult to monitor and control the harvest rate (and the rate of deaths that are not recorded as part of the harvest).

tinue the harvest even when it might jeopardize future harvests. Hilborn and Walters (1992) discuss such issues for developing fisheries, where the dynamics of the stock are unknown and must be determined as part of the optimization process. Their main conclusions are summarized in two principles: (1) one cannot determine the potential yield from a fish stock without overexploiting it; (2) the hardest thing to do in fisheries management is to reduce fishing pressure.

Clark (1990) describes another complication in stock dynamics: the per capita growth rate may decrease at low population sizes. This phenomenon of depensation is seen in diverse fish stocks (Lierman and Hilborn 1997). Possible causes of depensation include an inverse relationship between predation on the stock and stock size, perhaps related to schooling behavior of the stock or its predators (Walters and Korman 1999). Steele and Henderson

XMSY population size

Figure 7-1. Rate of population growth or harvest versus population size for a fish stock. The harvest rate at maximum sustained yield ish M SY . The horizontal line shows a harvest rateh < hMsY, for which there are two equilibria,x1 andx2 •

7. COLLAPSE, LEARNING, AND RENEWAL 177

fast variable e.g., stock size

abundant stock

Figure 7-2. Equilibria of a fisheries model with two stable states, one with low stock sizes (collapsed stock stable states) and one with abundant stocks, as a function of a variable that changes slowly (e.g., predation intensity) and a variable that changes more rapidly (stock size). The circle shows the position of the ecosystem.

(1984) discuss a different complication caused by switching behavior of predators within the fish community being exploited. Both depensation and switching behavior lead to a net growth curve that has a number of local maxima. In the absence of harvesting, the stock may have a high stable equilibrium and a low stable equilibrium, separated by an unstable equilibrium (Figure 7 -2). Hence if harvesting brings the population below the unstable equilibrium, the stock may never recover because the upper equilibrium is unattainable. The phenomenon of fish stocks that never recover from overexploitation, or that require many years to recover, is unfortunately very common. A high discount rate can lead to such overharvesting, by undervaluing long-term future harvests. Fisheries stock assessments are notoriously variable, and a sophisticated body of statistical tools has been developed to quantify this variability and account for it in making decisions (Lindley 1985; Hilborn and Walters 1992). A fish stock can be overexploited if its variability is underestimated or ignored. Recognition of this danger leads to a precautionary principle that harvests should be reduced to maintain a safety margin for stocks above the nominal optimum size (computed assuming no variability).

The theory of fisheries management has progressed far beyond these simple examples, as explained in Hilborn and Walters (1992). Why have fisheries been managed so badly in spite of good science? Consider the work of Gordon (1954) on open-access resources. The term open access applies to a situation where the resources are available to all. Gordon assumed that the net rate of economic return was a decreasing function of the total exploitation rate, and that new entrepreneurs or new investment

178 CARPENTER,BROC~LUD~G

would be attracted as long as there was positive net return. The result is a "bionomic equilibrium," in which the marginal net return from the resource is zero. Gordon pointed out that widespread overcapitalization of the fishing industry is a consequence of open access to the resource. His insight is equally valid today. It is widely recognized that the fishing industry is overcapitalized and that restrictions on access are required to maintain the resource. However, effective action to impose such restrictions or limit investment has been rare.

The phenomenon of overcapitalization and government subsidization of destruction of resources is not confined to fisheries. Repetto and Gillis (1988) detail numerous examples of government subsidization of overexploitation of forests under a wide variety of political systems. Systems of accounting that include environmental externalities routinely show severe economic losses caused by such practices, yet they continue unabated. Why are such economic insights not applied? Some answers are suggested by Scheffer et al. (Chapter 8) and Pritchard and Sanderson (Chapter 6), as well as Magee et al. (1989), Wilson (1989), and Axelrod (1997).

We infer that models of resource exploitation are not invalid, but are only partial representations of the interaction between natural and social systems. To understand why the lessons are not learned, and how they might be learned, we seek models that include the aftermath of collapse, the processes of organization and reorganization that lead to the initiation and continuation of exploitation. In the following sections we describe our attempts to build and understand such models.

More Complete Models of Social and Natural Systems

A more complete model of interacting social and natural systems should exhibit the cycle of collapse and renewal seen in many environmental management systems described in this book. We have found that such cycles emerge from models with the following features: (1) an ecosystem with three interacting components with distinctly different turnover times; (2) a social system made up of diverse actors, each making decisions about a world they cocreate; and (3) mechanisms for assessing and forecasting the status of the economy and ecosystem. While different disciplines have developed models for each of the three features separately, we will show that in combination these three features lead to outcomes and insights that are not apparent from the individual parts. The range of models capable of exhibiting adaptive cycles remains an open question. We do not yet have a general theory for social-natural systems (although the beginnings of such a theory appear in this book and in the references cited). Most important for this chapter, we do not yet know the minimal mathematical properties necessary for a model to generate adaptive cycles. Rather, we present examples of models that combine the three properties listed above, represent real environmental conflicts, and have proven useful in evoking insight, collective learning, and


action in discussions among diverse stakeholders. So far, our progress with these models has been guided by simplicity, usefulness, and success in applications rather than deduction from first principles or mathematical rigor. This does not detract from the importance of our results, but it does suggest useful avenues for further research.

A Lake Fishery

A fishery is a useful place to begin, because we can build on the familiar foundation of MSY and related ideas. Assume that the fish stock of a lake is subject to exploitation by resident anglers and tourists (Figure 7-3). A manager sets a bag limit each year, with the goal of sustaining both the fish stock and the people in the system. In the diagram shown here, the game player (i.e., the operator of the computer) is the manager. However, in other versions of the program the game player may be a resident, a tourist, or a scientist. At each time interval, the manager receives information from a fisheries stock assessment as well as social information on the human agents. The manager uses this information to set a new bag limit.

The tourists choose where to fish on a day-by-day basis, based on their personal predictions of fishing opportunities on the focal lake and other nearby lakes. They derive these personal predictions from recent experiences. The residents choose whether to purchase or sell property on the lake, based on their personal predictions of future fish catch rates and opportunities on other, nearby lakes. Residents evaluate catch rate and make forecasts over a longer time horizon than tourists. The residential market involves a time lag in purchasing or selling property. The number of residents affects the input of downed trees into the lake, with a long time lag due to slow growth of the trees (Christensen et al. 1996). Residents do not like to

environment

Figure 7-3. Flowchart of the lake fishery model.

180 CARPENTER,BROCK,LUD~G

have too many trees on the shoreline or in the lake near shore, because they interfere with boating and fishing activity. This slowly changing variablefallen trees in the lake-affects fish recruitment. The fish population dynamics in the model depend on habitat (fallen trees), harvest, and stochastic annual variations in recruitment.

We have found it useful to depict model results in three dimensions, corresponding to the ecological capital (here stock size), dependency of humans on ecological systems (here human use as indicated by the total number of fishers), and ecological resilience. Here ecological resilience is measured as the width of the desirable attractor (the one with abundant stock in Figure 7 -2), measured in units of the fast variable (Carpenter, Brock, and Hanson 1999). This definition of resilience was introduced by Holling (1973b). In the ball-and-cup diagrams used in this book, it represents the width of the desirable cup.

Preliminary tests of the model in workshops of the Resilience Network and in courses at the University of Wisconsin-Madison revealed some interesting responses from the users. A typical cycle generated by a player of the lake fishery game resembles the adaptive cycle (Figure 7 -4). During most of the simulation, the stock declined slowly while human use slowly grew (r to K). Fluctuations occurred from year to year, but the overall trend was for the stock to decline while human dependency grew (except for the initial transient period when the stock appeared to grow as human usage grew). Throughout this long period of time, the attractor size declined only slightly. Then the attractor abruptly collapsed over a period of just a few years (K to Q). The stock remained low, and fishers left the system to seek opportunity

Figure 7-4. Adaptive cycle from the lake fishery model. Stock size is the average measured by the stock assessment. Human use is the total number of resident and tourist anglers. Attractor size (note log units) is the width of the basin of attraction for the desirable stable state of the system (the stable state with a relatively large fish population and catch rates).


elsewhere. Economic indicators (not shown) collapsed to minimal values. Over a period of some years, the attractor expanded (Q to a). Because resident fishers were at low levels, the riparian forest regrew and fish habitat improved, thereby increasing the size of the attractor and then the size of the stock. Rather quickly, fishers returned to the system (a to r), and another cycle of exploitation began.

This model is more complete than traditional fisheries exploitation models, because it addresses the full cycle, including collapse and reorganization. Gaming with the more complete model prompted discussions of ways to organize the fishery that may anticipate collapse of the attractor, minimize the social impact of collapse, and sustain both the stock and human use. Typically, participants in the game experimented with bag limits to explore alternative management plans. Because it takes a long time to learn about the slow habitat variables, they eventually incorporated experimentation as an ongoing part of the management process. Among the players, there was a shift from steady exploitation guided by automatic rules to careful experimentation with new ideas that might improve the current situation, illustrating Samuel Johnson's adage that "natural flights of the human mind are not from pleasure to pleasure, but from hope to hope."

Participants eventually began to demand refinements in the software. They asked for a better stock assessment (one with lower observation error) so attractor size could be estimated more precisely. They asked for more direct data on the slow variable, so that consequences of policy options could be forecast more accurately. They asked for management tools other than bag limits, so that a wider range of policy experiments could be performed. For example, they asked for the ability to limit length of the fishing season, or to ban harvest of riparian trees. By this time, a fairly sophisticated conversation was under way about how the fishery might be sustained. It remains to be seen how different types of participants may react to the opportunities inherent in the model. For example, will training, experience, education, or age affect the choices? How do the gaming exercises interact with actual social dynamics in environmental management? These and many other research questions remain to be answered.

Agriculture and Water Quality

While fisheries are a familiar, even iconic, model for sustainability studies, our approach can be applied to diverse environmental issues. In this section, we describe a second model for a very different issue, agriculture and water quality. This issue involves pollution rather than exploitation of a living resource. The socioeconomic system involves decisions by farmers and conflicts between multiple users of water resources. Despite these many differences, model results resemble the adaptive cycle. As in the fisheries exercises, the model can be used to promote discussion of uncertainties, tradeoffs, and options for coping with problems of agriculture and water quality.


Intensive agriculture uses fertilizers such as phosphorus and nitrogen to support crop yields. Animal feeding operations increase the concentration of fertilizers on the landscape, because nutrients are imported in the form of animal feeds and remain in the form of manure. Excess fertilizers and manure wash into rivers and lakes, leading to eutrophication, a condition marked by blooms of toxic algae, anoxia, fish kills, disease, and increased costs of purifying the water for drinking, irrigation, or industry (Carpenter et al. 1998). The United Nations Environment Programme (UNEP) estimates that a third of the world's people live with insufficient water; by 2025 this proportion is expected to grow to two thirds (Watson et al. 1998). Half of Earth's available, renewable freshwater is already used by people; unless water management is improved, all of the renewable freshwater will be needed by 2035 (Postel et al. 1996; Watson et al. 1998). Land use is a major cause of freshwater degradation worldwide, and degradation of freshwater exacerbates water supply problems. The UNEP report further predicts that water degradation will increase over the next few decades, and that providing safe water will continue to be a major concern (Watson et al. 1998). Integration of water use across sectors (agriculture, domestic, industrial, and ecosystem services) will be essential to prevent catastrophic shortfalls. Conflicts among sectors, and among nations sharing water resources, will increase. Thus we can expect that an increasing number of freshwater systems will enter the backloop of the adaptive cycle.

In the western Great Lakes region of North America, agriculture is frequendy in conflict with other sectors dependent on water quality (National Research Council 1992, 1993). Phosphorus (a component of fertilizer) is used excessively in certain fanning practices and runs off into surface water, degrading the water quality. This degradation creates risk to human health and causes economic losses related to increased costs of processing water for

. municipal usage, declining fisheries, and lost recreational opportunity. We have produced several models to evoke discussion of the policy problem (Figure 7-5; Carpenter, Ludwig, and Brock 1999; Carpenter, Brock, and Hanson 1999). The software for running selected models is downloadable from the Internet (Carpenter, Brock, and Hanson 1999). The models consider three phosphorus compartments with varying speeds associated: the soil (slow turnover), lake mud (intermediate turnover), and lake water (fast turnover). Farmers are assumed to choose between two styles of farming, a phosphorus-intensive one (such as high-density dairy farming) and a phosphorus-conservative one (such as conservation tillage with low animal densities). The farmers' decisions are based on their personal forecasts of regulatory behavior and an external market for farm products. In making their decisions, farmers consider the time lag involved in building or selling off a dairy herd. In aggregate, farmers' decisions affect the overall amount of phosphorus in the soil and inputs to the lake. At each time step of the model, a policy maker receives information on the status of the ecosystem and the status of the economy (which depends on both farm productivity and water


environment

assessment and forecast

agent behavior sum of individual independent agents

,-----·-f-;;-;;~~;tt~iur;· ··- -1 , regula~ions and incentives l

r ·- { ... • 1

calculate pollutant emission

Figure 7-5. Flowchart of the agriculture and water quality model. (Reprinted from Carpenter, Brock, and Hanson 1999, with permission of Conservation Ecology)

quality). The policy maker sets a goal for lake water phosphorus level, with the objective of sustaining both farming yields and water quality. A regulator then establishes economic penalties and incentives intended to modify farmer behavior to attain this goal. In Figure 7-5, the game player (i.e., the person running the computer) is the policy maker. Alternatively, the program can be set up so that the game player is an ecologist, economist, regulator, or farmer.

A series of cycles generated by the agriculture-water quality model shows slow growth of soil phosphorus (with some oscillation), cycles in mud phosphorus of about two hundred years, and occasional outbreaks of high phosphorus in the water (Figure 7 -6). The proportion of phosphorusintensive farms is high when water phosphorus is low but decreases to very few phosphorus-intensive farms when water is highly polluted. The outbreaks of high phosphorus in the water are followed by extreme declines in total economic performance. Economic performance recovers gradually as


Figure 7-6. Typical time series for a 1,000-year simulation from the agriculture and water quality model. Pollutant levels in three compartments are shown in the top graph. Utility and farming practices are depicted in the lower graph. (Reprinted from Carpenter, Brock, and Hanson 1999, with permission of Conservation Ecology)

the lake water phosphorus level declines and finally returns to relatively high levels when the lake shifts to the low-phosphorus steady state and farmers switch back to phosphorus-intensive practices. The cycles depicted in Figure 7-6 were generated by replacing the decision maker with a simple automaton that makes the same mistakes over and over again. When people play the game, they tend to stabilize the system.

The agriculture-water quality model-like the fishery model-generates oscillations that resemble the adaptive cycle (Figure 7 -7). During the transition from exploitation (r) to conservation (K), connection between agriculture and water quality intensifies, as measured by the high proportion of phosphorus-intensive farms. At the same time, the ecosystem becomes more fragile because of the accumulation of phosphorus in upland soils and lake mud. Eventually, phosphorus levels in the lake water build up to unacceptable levels and the regulator takes steps to reduce the proportion of phosphorus-intensive farms. However, the system remains near a stability boundary, and eventually a high-stochastic-input event causes the desirable attractor to vanish-a catastrophic loss of resilience. This leads to the omega phase. Drastic reductions in the proportion of phosphorus-intensive farms eventually cause the desirable attractor to reappear, leading to an alpha phase. This initiates a new cycle.

Can Perfect Science Prevent Collapses?

When we discuss these models with users, they frequendy ask about the representations of scientists' knowledge of the ecosystem, the variables that the scientists are able to monitor, and the methods used by the scientists to estimate parameters and calculate forecasts (Brock and Durlauf 1999). Debate


Figure 7-7. Adaptive cycle from the agriculture and water quality model. (Reprinted from Carpenter, Brock, and Hanson 1999, with permission of Conservation Ecology)

also develops about how, and to what extent, scientific advice influences managers' decisions. The representation of science and its links to policy can affect the cycles and bursts of instability exhibited by the models (Carpenter, Brock, and Hanson 1999). Yet we have little empirical experience to guide us in choosing one learning scheme versus another. The models also evoke debate about the representation of political structures and the effectiveness of various regulatory schemes. Numerical experiments suggest that representations of politics and regulation impact the results (Carpenter, Brock, and Hanson 1999). In other words, specific predictions of the models are highly uncertain. This uncertainty reflects the context in which the models are used. The modeling is intended to help expose these uncertainties and facilitate discussion of possible responses, which may include various precautionary actions, steps to increase or maintain social flexibility and ecological resilience, and/or research and monitoring schemes to reduce uncertainty.

Users sometimes argue that research should be able to reduce uncertainty to the point where the system is easily sustainable by a rational social planner. As an antidote to this myth, it is useful to imagine an extreme case in which the scientists are omniscient and the regulatory scheme is perfect. Suppose the scientists understand the structure of the system perfectly, and they know the exact values of all the parameters that describe the system. They know the probability of disturbances to the pollutant input, but they cannot know the exact outcome of a particular disturbance until it occurs. They provide the manager with all of the information required for optimization. The optimal policy is chosen by the standard economic criterion of maximizing the discounted net flow of benefits over infinite time. Further, suppose the manager is able to precisely control the behavior of the farmers, adjusting the mix of pollutant-intensive and -conservative farms to the calculated optimum with no time lag. Thus, the outcome is not affected by farmer


learning or by the associated variability and time lags. Of course, none of these assumptions is satisfied in real life. Instead, the point is to explore the outcome if these conditions were met.

Numerical experiments show that collapses and cycles can occur even if the scientists are omniscient and regulators achieve perfect command and control of the social system (Carpenter, Brock, and Hanson 1999). This behavior is parameter dependent but does occur with a wide range of realistic parameter sets, for example, using parameters estimated from extensive studies of the Lake Mendota watershed in Wisconsin, USA (Carpenter, Ludwig, and Brock 1999; Bennett et al. 1999).

To get a general idea of how collapses of water quality can occur even with omniscience and perfect regulation, think about the basins of attraction in relation to the economically optimal policies (Figure 7-8). For the Lake Mendota system, we find two stability domains if the proportion of

A ~· ~ .!: c: s -" 8. ~·

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900 x103'--------------'-l --' 0.00 0.05 0.10 0.15 0.20

pollutant-intensive farms (proportion of total)

Figure 7-8. Management at equilibrium when ecosystem parameters are known with zero error and policies are followed perfectly by all agents. In all cases the horizontal axis is the proportion of farms that are pollutant intensive. (A) Pollutant levels in the lake water, showing stable attractors (thick lines) and repellors (dotted line). (B) Width of the clean attractor (solid line) and standard deviation of the disturbance to pollutant inputs (dashed line). (C) Width of the clean attractor divided by the standard deviation of the input disturbance. (D) Net discounted value for the clean attractor. The maximum point is translated to all panels by the vertical dashed line.


phosphorus-intensive farms is less than about 0.195, and only a highphosphorus domain for higher levels of phosphorus-intensive farming (Figure 7 -SA). Resilience (measured as the width of the desirable attractor) is inversely related to phosphorus-intensive farming, while the variability of phosphorus inputs to the lake rises with phosphorus-intensive farming (Figure 7-SB). An index of stochastic resilience (attractor widthldisturbance standard deviation) declines as the proportion of intensive farms rises (Figure 7 -8C). The economic optimum for Lake Mendota occurs when the proportion of phosphorus-intensive farms is about 0.18 (Figure 7 -8D). Thus the system becomes vulnerable to collapse because economically optimal policies move it to an intermediate level of phosphorus-intensive farming, where resilience is relatively small in comparison with the disturbances to phosphorus inputs. Eventually, a particularly large disturbance shifts the system into the high-pollutant domain of attraction. This analysis is consistent with the apparent fragility of the lake and current concern about restoring it (Carpenter, Ludwig, and Brock 1999). This behavior is similar to the loss of resilience and subsequent collapse of fisheries managed according to maximum sustainable yield theories.

The preceding example illustrates how collapse can occur even if the ecosystem dynamics are perfectly known and management has perfect control of the human actors. The economic optima (which are computed using simple models) draw the system into a region where resilience is small relative to noise, so a disturbance eventually shifts the system to an undesirable domain. Such a disturbance will occur; the only question is when (Lindley 1985). One way to prevent such a disturbance would be to maintain the system in a region where resilience is large, but such a policy would be inconsistent with the economic optimization criterion. Economic theory suggests that collapses could be avoided if future rewards are discounted lightly and input disturbances are narrowly bounded. Ecological data, however, suggest that input disturbance distributions are broad and show occasional large disturbances (Ludwig 1995; Lierman and Hilborn 1997; Reed-Andersen 1999), while the slow changes in some variables call into question the assumption the system does not change in time. In other words, slow variables and the prospect of large, unique disturbances-key features of ecological systems (Turner and Dale 1998; Carpenter and Turner 2000)help set the stage for collapse of ecological-socioeconomic systems.

There is no easy technical solution for the large uncertainties that affect environmental decisions. For the foreseeable future, important responses to environmental uncertainties will include creation and conservation of social mechanisms that promote flexible, adaptive response to novel and emerging issues, and increasing or maintaining the resilience of ecosystems to cope with novel perturbations. This does not imply that scientific information is unimportant for environmental decisions, or that we should not seize the opportunity to reduce uncertainty where such reductions are possible and useful. Adaptive management experiments, discussed below, can help.


Uncertain Science and Bounded Rationality

While the scenario with omniscience and perfect control is an interesting extreme case, it is of course not realistic. In more realistic scenarios, the scientists' knowledge of the structural model would be changing over time (Brock and Durlauf 1999); parameter estimates will be uncertain and dynamic (Walters 1986); agents' behavior will be individualistic and based on their own imperfect forecasts (Brock and Hommes 1997; Grandmont 1998); and the lake itself may change in response to both human and natural disturbances. These complications raise the possibility of diverse behaviors of the coupled ecological-socioeconomic system (Carpenter, Ludwig, and Brock 1999).

Uncertainty affects the dynamics in complex and surprising ways. For example, if the scientists assume that the ecosystem fits an overly simple model, this can sometimes be stabilizing compared to the assumption of a more sophisticated model (Carpenter, Brock, and Hanson 1999). However, if competing scientists discovered a more sophisticated model that enabled extraction of more utility from the system, the simple, stabilizing model would be replaced (Brock and Durlauf 199 5), perhaps leading to destabilization.

Some of the gaps in our understanding can be reduced by careful experiments (see below). Optimizing behavior, however, works against experimental behavior because at least some of the necessary treatments will appear to be economically suboptimal. For example, to learn how the lake responds to a change in P-intensive farms, the safest experiment may decrease the number of P-intensive farms. This may involve considerable economic sacrifice. The tendency to fix upon an unchanging, putatively optimal policy is promoted by the application of scientific indicators of sustainability. If the indicators say the system is sustainable, then policy may be fixed at the economic optimum, thereby preventing the experimentation and learning that may be needed to forestall collapse in the long run.

Social Factors in Destabilization of Ecological-Socioeconomic Systems

Diverse social phenomena also affect stability. For example, disinformation campaigns (Ehrlich and Ehrlich 1996) and calls for further research can delay implementation of policies that would increase resilience. Such delays are destabilizing (Carpenter, Ludwig, and Brock 1999). A recent study of responses to global climate change has shown that the most important feature of successful policies is timely and appropriate response to changes as soon as they become apparent (Lempert et al. 1996). Policies can break down due to learning by the agents being regulated; for example, numerous examples of loophole searching are known from fisheries management (Hilborn et al. 1995). Scheffer et al. (Chapter 8) discuss some of the causes of mis-incentives in political systems. Modem incentive design theory suggests ways to design a regulatory scheme and to motivate the regulators themselves to help control loophole searching (Brock and Evans 1996; Dewatripont et al. 1999).


Nevertheless, this ancient problem is likely to be an ongoing issue; as noted by Lao Tzu, "People are difficult to govern because they have too much knowledge." Some factors that separate social systems from ecosystems have the potential to be stabilizing or destabilizing (Chapter 3). Both outcomes have been observed in our explorations of minimal models (Carpenter, Brock, and Hanson 1999).

Successful management institutions tend to focus programs relatively narrowly and suppress innovation, ultimately creating conditions that destabilize the natural-social system. Clarke and McCool (1985) showed that U.S. federal resource agencies with single, narrow missions were better able to attract political support than agencies with multiple and sometimes conflicting objectives. Wilson (1989), in a classic empirical study of government agencies, showed that successful agencies reject vague objectives and define a focused mission. Natural resource professionals play an important role in achieving focus on well-defined, verifiable, yet narrow goals. For example, the Tennessee Valley Authority (TVA) emphasized regional planning to accomplish a bounded mission of producing electricity for a region that sorely needed electrification, while deliberately neglecting broad strategic planning and environmental concerns (Wilson 1989). The TVA's clear mission and early success depended on hiring of a preponderance of engineers. Agencies dominated by professionals are perceived as more effective and nonpolitical and thereby gain a degree of autonomy from the political process, which in turn enhances their effectiveness. Such variables as degree of hierarchy, span of control, division of labor, and norms of communication also affect responsiveness of the system as a whole (Westley 1995). Wilson (1989) also argues that incentives for individuals within successful agencies work against the pursuit of multiple or complex goals by the agency. His findings are mirrored by models of agent behavior within institutions (Dewatripont et al. 1999). These models suggest that agencies with vague, multiple, or complex goals have difficulty motivating employees, and ultimately lose autonomy because politicians and the public are unsure of the purpose of the agency.

The processes described by Wilson (1989) and modeled by Dewatripont et al. (1999) correspond closely to the narrowing of focus described by Gunderson et al. (1995a) during the r to K transition of the adaptive cycle (Chapter 2). Suppose a new phase of exploitation is under way and an institutional structure is being established to manage it. The founders, knowing the insights described by Wilson (1989) and Dewatripont et al. (1999), will make hires and create incentives that focus the agency as quickly as possible. They will emphasize quickly attainable goals and early demonstrations of progress and products. Slow variables and nonlinear processes are harder to monitor, understand, model, and forecast than fast variables and linear processes. Thus, there will be a conflict between sharpening the goals of the agency and conducting exploratory probes that investigate slowly changing variables or nonlinear linkages. The top priority will be to focus on easily


managed indicators and sharply defined, attainable goals that make it easier to justify the agency's existence. Consequently, the agency may lose sight of the limits of ecological resilience or the bounds of social flexibility. Because things appear to be going well, there is no compelling reason for research beyond the minimal monitoring needed to demonstrate ongoing accomplishment. Broad exploratory programs are regarded as diversions that increase the likelihood of political meddling in agency affairs and diminish the careers of individuals who participate in them. These trends resemble those in the case studies of environmental management summarized by Gunderson et al. (1995a). Increasingly narrow management programs, when combined with increasingly fragile ecosystems and growing social dependency, set the stage for gridlock, crisis, and collapse.

Adaptation and Experimentation

Successful players of the agriculture-water quality game discover the importance of repeated experimentation (Figure 7 -9). In this example, an initial cycle leading to collapse was followed by hundreds of years of persistence near the desirable state. Experimental probing is the key to persistence. Deliberate shifts in the policy target lead to responses in the proportion of phosphorus-intensive farms and the lake phosphorus level. On the basis of these responses, the game player chooses a level for a decade or two and then tries a new experiment. Experimental policies are chosen carefully to minimize risk while learning about ecosystem responsiveness and social flexibility. The net result is continual movement of the system over a range of states from near the renewal phase, through the exploitation phase, to the conservation phase.

Figure 7-9. Adaptive cycle with experimentation from the agriculture and water quality model. (Modified from Carpenter, Brock, and Hanson 1999, with permission of Conservation Ecology)

7. CoLLAPSE, LEARNING, AND RENEWAL 191

In reality, informative management experiments may be costly. Utility may be lost, for example, because farm practices and/or water quality are shifted away from optimal levels. In the short run of a few years to a few decades, these losses may be thought of as a cost of learning. Over a longer time horizon, however, the accounting may be reversed if costly collapses are avoided. However, the practice of discounting in economic analysis tends to assign a low weight to such long-term rewards of learning.

Discounting has a long and controversial history in environmental issues. In economic theory, future returns are discounted to reflect the fact that money received at present is worth more than the same amount received some years in the future-amounts received now can be invested to yield a larger return later. In situations where poorly understood systems are exploited, however, sufficiently heavy discount rates can prevent exploration and learning, and fossilize policies that are suboptimal because they are based on incomplete or inaccurate information (Easley and Kiefer 1988). In practice, discounting has been an important impediment to active adaptive management (Walters 1997). Some thinkers point out that discounting should apply only to money, not to life-support systems, which have no explicit economic value because no market for them exists:

The things we are unwilling to pay for are not worthless to us. We simply think we ought not to pay for them. Love is not worthless. We would make all kinds of sacrifices for it. Yet a market in love-or in anything we consider "sacred"-is totally inappropriate. These things have a dignity, rather than a price. The things that have a dignity, I believe, are in general the things that help us define our relations with one another. The environment we share has such a dignity. The way we use and the way we preserve our common natural heritage help to define our relations or association with one another and with generations in the future and the past. (Sagoff 1988)

Sagoff (1988) believes that political institutions offer an alternative to economic mechanisms: "Our democratic political processes allow us to argue our beliefs on their merits-as distinct from pricing our interests at the margin. Our system of political representation and majority vote may be the best available device for deciding on these values .... "

Other chapters in this volume (4, 5, 6, 8, 9, 10, and 13) expand on the role of politics and institutional change in management of the environment.

Heal (1997) shows that notions of discounting changed over the twentieth century. At present, they appear to be in flux. Professional economists differ widely in the discount rates they prefer for environmental cost-benefit accounting (Weitzman 1998), yet on average they tend to discount longterm environmental benefits at a lower rate than the commercial discount rate. Adoption of lower discount rates will increase incentives to adopt forward-looking behaviors and learn about the slow variables that are the key to ecological resilience. Successful players of the agriculture-water quality

192 CARPENTER,BROC~LUD~G

game discover that, if future benefits are discounted lightly relative to current costs, experimental management can be used to maintain the system near a desirable state.

From an economist's perspective, the problem of adaptive management is to realign incentives to favor the sorts of experiments depicted in Figure 7-9. Innovation, adoption of new technologies or management practices, or reorganization of institutions typically involve fixed costs that must be paid initially. Benefits, however, arrive in the future and are by no means guaranteed. Therefore, heavy discounting of future benefits tends to work against socially optimal innovation. If a catastrophic crisis occurs (an omega phase), then incentives may be unleashed to adopt and innovate until the crisis is resolved. The cliche "Necessit:f is the mother of invention" is only partially correct. While necessity plays a role, it cannot explain the huge variation in technological progress across different countries and different time periods. In order to explain such large variations, current theories consider institutional designs that force each agent, manager, or production center to adopt available innovations quickly, for fear that some competitor will do so and displace the laggard. Thus the theories explore how institutional design creates incentives to innovate or adopt new approaches (Brock 1999a).

Although recurring crises may stimulate .enough innovation to get by for a period of time, long-term sustainability may require a level of innovation that can be engendered only by an appropriate structure of incentives. Economists often think of incentive design in terms of inducing people to pay fixed costs on private accounts today, in order to invent or adopt ways of creating future environmental benefits that may not be captured by the private accounting system but are captured by a social accounting system. While there is much work in the literature on more inclusive methods of environmental accounting, there is much less literature on the design of incentives to invent and adopt practices that lead to environmental sustainability.


Collapses, and the subsequent need to innovate, create, reorganize, and rebuild, are a likely, maybe even inevitable, consequence of human interactions with nature. This generally pessimistic observation is tempered by the finding that ongoing learning through probing policies may forestall collapse for extensive periods of time, and perhaps reduce the costs of collapse. Ultimately, the risk of collapse under apparently optimal management traces to slow variables that are mistakenly assumed to be static, a broad probability distribution of uncertainties, shortsightedness due to discounting of the future, and losses of social flexibility and ecological resilience. Therefore, institutions that counter these trends may help ameliorate the risk or severity of collapse. Such forward-looking policies can be introduced during the backloop (omega to alpha tor phases) of the adaptive cycle. In the backloop, relatively simple models can serve as the focus for activities designed to


evoke insight, creative debate, and cooperative learning. We discuss the characteristics of such models and present some useful examples. The models are heuristic devices that simulate reality, give insight into possible human choice mechanisms and their interactions with ecosystems, and provide the practitioner with a chance to explore implications of possible interventions. They are abstract simulations that are not intended to give precise predictions. Instead, their value lies in the kinds of speculation, experimentation, and questioning that they permit, all of which are critical when people confront situations of high uncertainty.

In the modeled worlds, experimental policies may forestall collapse for extensive periods of time. They do not make the probability of collapse zero, but they may make collapses less frequent or less severe. While experiments are cheap, fast, and informative in computer worlds, in the real world experimental policies are difficult to implement. Heavy discounting makes experiments appear costly and thereby militates against them. Strict adherence to apparently optimal policies suppresses experimentation by preventing potentially informative probing. Some notions of sustainability discourage experimentation by implying that there are unique, fixed targets for sustainability and only one way for management to optimize them. Dynamics of slowly changing variables, however, endlessly change the system in ways that cannot be tracked by static management targets. It is ironic that well-intentioned fixed policies foster ecological fragility and social dependency, and set the stage for collapse.

Any institution that gathers better information on slow variables, puts more weight on future returns, narrows the distribution of uncertainties, maintains social flexibility for adaptive response, and maintains the resilience of ecosystems to withstand novel perturbations has the potential to ameliorate the risk of collapse. How can such sustainable approaches be created and promoted? We suggest that minimal models of natural and social systems, as we have defined them, play a useful role. They illustrate, in a general way, the consequences of alternative actions and the roles of diverse actors in tipping the balance among various actions. More important, they invite safe electronic experimentation, open discussion, and collective learning. Such activities can lead to the collaboration and networking that characterize the alpha phase of environmental problem solving (Chapter 8). In this sense, minimalist models can crystallize hope and catalyze change.

Acknowledgments

We are grateful to participants in Resilience Network meetings for criticisms and ideas that improved this work. Tanya Havlicek, Garry Peterson, and Frances Westley provided helpful reviews of the manuscript. Financial support was provided by the North Temperate Lakes Long-Term Ecological Research Program and the Pew Foundation (SRC), the National Science Foundation and the Vilas Trust (WAB), and the Natural Science and Engineering Research Council of Canada (D.L.).

CHAPTER 8

DYNAMIC INTERACTION OF

SOCffiTIES AND ECOSYSTEMS

LINKING THEORIES FROM ECOLOGY,

ECONOMY, AND SOCIOLOGY

Marten Scheffer, Frances Westley, William A. Brock, and Milena Holmgren

E cosystems change in response to the stress imposed by human use, and human societies adjust their behavior affecting ecosystems in response to perceived changes in these systems. A thorough

understanding of this feedback would be the ultimate scientific foundation for designing strategies to achieve sustainable society-nature interaction. A widely recognized barrier on the road to such an integrative theory is the segregation in scientific disciplines that analyze the dynamics of ecosystems and those that analyze economics or social interactions. Indeed, in our experience, not only jargon and methods, but also the perception of "what drives this world," are strikingly different among disciplines. As discussed by Holling and coworkers in the introductory chapter to this book, many theories aim to describe the dynamics of integrated systems, and although they are usually not wrong, they tend to be very partial. Indeed, the primary scientific discipline of the authors usually results in a heavy bias, including information from other disciplines in a rather caricatured or at least highly simplified way. Holling (Chapter 2) provides a heuristic model of nested adaptive cycles that describes patterns that can be found in remarkably similar ways in examples from ecosystems and social systems.

This chapter is the product of the cooperation between scientists from three different disciplines: ecology (MS and MH), economy (WB), and sociology (FW). We combine major theoretical advances in each of our branches of science toward understanding the dynamic feedback between ecosystems and societies. The first section describes the different ways in which ecosystems may respond to changing levels of stress imposed by human use. The next section describes how economic utility from nature could theoretically

195

196 SCHEFFER, WESTLEY, BROCK, HOLMGREN

be optimized, and how the ecosystem responses discussed earlier and differential ability to mobilize political forces interfere with such social utility maximization. The third section zooms in on the role of social network interactions and cultural differences in determining the response of societies to problems such as the collapse of vital ecosystems. In the conclusion, we summarize what seem to be major driving forces of society-nature interactions and highlight a set of crucial ingredients for sustainable use of ecosystems. A short version of material presented in this chapter has been published elsewhere (Scheffer et al. 2000).

Ecosystem Responses to Human Use

In order to be able to develop strategies for sustainable use of nature, it is crucially important to understand how the state and functioning of ecosystems respond to change of conditions resulting from human activities.

Catastrophic versus Smooth Responses

It is often assumed that the impact will simply increase more or less smoothly with the intensity of use. However, evidence is accumulating that the response to increasing stress is frequently far from smooth. Sometimes an ecosystem may seem untouched by increasing stress until it suddenly collapses to another state when certain threshold values are passed.

To clarify differences in the way an ecosystem may respond to changing conditions, we can represent the response in simple graphs that plot ecosystem state as a function of the stress imposed by human use (Figure 8-1). Note that for simplicity these hypothetical graphs consider only one state variable and one stress factor. Usually, many aspects of the ecosystem shift in

A B

Et $Q} ~:e r,

c Et $Q} oo->.til rsoo 0 Q)

stress -- stress -Figure 8-1. Schematic representation of four possible responses of ecosystems to stress imposed by human use. The lines represent equilibrium states. The arrows indicate the direction of change when the system is out of equilibrium.

8. DYNAMIC INTERACTION OF SOCIETIES AND ECOSYSTEMS 197

concert with a few important key state variables. Examples of such key state variables that could be represented by the vertical axis are total plant biomass or number of elephants per unit area. Stress is the general term we will use here for effect of human use. Human use of nature can be through harvesting or destroying biomass (e.g., rain forest harvest, fisheries, cattle ranging), but much of the impact may also be the result of stressing the system by affecting its abiotic conditions (eutrophication, ground water level reduction, climate change). The horizontal axis of the figures may represent any of such stress factors.

The state of some ecosystems may respond in a smooth continuous way to increasing stress (Figure 8-lA); but probably more common is the situation in which the system is quite inert over certain ranges of conditions, whereas it responds more strongly when stress approaches a certain critical level (Figure 8-lB). A crucially different situation arises when the response line is folded backwards (Figure 8-lC, 8-lD). This is known as a catastrophe fold and implies that the ecosystem has two alternative stable states over a range of environmental conditions. The explanations and consequences of this scenario are discussed more extensively in the next section, but in short it implies that when the ecosystem is in a state on the upper branch of the sigmoid response curve, it will not pass to the lower branch smoothly. Instead, when increasing human use has altered the conditions sufficiently to pass the threshold (F2 ), what follows is a rapid catastrophic transition to the lower branch (vertical line with double arrow). Note that when one monitors the system prior to this switch, little change in its state is observed. Indeed, such catastrophic shifts typically occur quite unannounced. Another important feature of the response of such catastrophic systems is that in order to induce a switch back to the alternative state on the upper branch it is not sufficient to restore the stress level that occurred before the collapse (F2 ).

Instead, one needs to go back much further, to beyond the other switch point (F1 ), where the system recovers by shifting back to the upper branch.

Note that the threshold level for a forward switch, but not for a backward switch, may be within the range of conditions that may be easily influenced by humans (Figure 8-lD). Desertification in some xeric areas is an example: An increase in grazing intensity can destroy vegetation, but when conditions are sufficiently dry, soil erosion, severe thermal and water stress on seedlings, and lack of capacity to retain soil water may prevent recolonization by plants even if all grazers are removed.

As explained later, an alternative approach to restoring the alternative equilibrium state is to force the system state temporarily past the threshold represented by the dotted middle section of the sigmoidal graphs.

Since catastrophic changes from one stable state to another have large implications for the dynamics of ecosystem use, we will pay extra attention to systems with this property. Catastrophic switches have been described for various ecological systems. In this section, we briefly describe the insights obtained from studies of shallow lakes that will serve as the main example

198 SCHEFFER, WESTI..EY, BROCK, HOLMGREN

throughout the rest of the chapter. A simple mathematical model for the behavior of systems with catastrophic shifts between alternative stable states is presented in Appendix A.

Eutrophication of Shallow Lakes

In most lakes, light is likely to be a main factor limiting the colonization by submerged plants (Hutchinson 1975; Chambers and Kalff 1985; Vant et al. 1986; Skubinna et al. 1995). A positive feedback is caused by the fact that water clarity tends to increase in the presence of plants (Schreiter 1928; Canfield et al. 1984; Pokorny et al. 1984; Jeppesen et al. 1990). This may allow a vegetated state to be one of two alternative stable states. The explanation in a nutshell is that in very turbid water, light conditions are insufficient for vegetation development; but once vegetation is present, the water clears up and the improved light conditions allow the persistence of a lush vegetation (Scheffer 1989, 1990, 1998).

The case of shallow lakes has been studied quite intensively over the past decades as many shallow lakes and ponds in the vicinity of populated areas have changed into murky waters (see Scheffer 1998 for an overview). The most common reason for this shift is an overdose of nutrients (e.g., phosphorus) due to heavy use of fertilizers on surrounding land and an increased inflow of wastewater from human settlements and industries. Nutrients stimulate the growth of phytoplankton, causing the well-known greenish turbid look of many lakes.

Importantly, phytoplankton blooms also trigger a series of dramatic changes in the underwater world. The algal turbidity prevents light from reaching the lake bottom, and, in the resulting permanent darkness, the lush fields of submerged plants that are characteristic of clear shallow water die off. Wtth the submerged vegetation disappear the countless small animals that depend on plant beds for shelter and food. Many fish species forage on such animals or need the plants to attach their eggs or hide themselves from larger predators. Wtth the disappearance of vegetation these species are lost, and a monotonous community dominated by fish that find their food in barren sediments is left. Perhaps most spectacular is the drop in the numbers of birds that visit the lake. Many shallow lakes are known to harbor thousands of migrating ducks, swans, and coots, which come to forage and rest during fall migrations. When such lakes become turbid and lose their vegetation, bird numbers typically drop by one or two orders of magnitude. Overall, the diversity of animal and plant communities of shallow lakes in the turbid state is strikingly lower than that of lakes in the clear state.

The devastating effect of overloading lakes with waste nutrients (eutrophication) is generally recognized as a major problem, and many programs to reduce the nutrient level in lake water have been set up over the past decades. Large amounts of money are invested in wastewater purification plants, and often farmers are forced to reduce fertilizer use and invest in technology for recycling cattle dung. Although various deeper lakes have re-


covered quite well in response to such eutrophication control programs, many shallow lakes have shown hardly any improvement at all, remaining unattractive murky pools despite all investments. Even if the nutrient load is reduced to values well below the ones at which the collapse of the clear and vegetated state occurred, shallow lakes tend to remain in a highly turbid eutrophic state.

As argued, a positive feedback in the development of submerged vegetation is probably the main explanation. One can easily imagine that the positive feedback may give rise to two alternative stable states: a clear one in which plants clear up the water and thereby protect their own growing conditions, and a turbid state in which plants cannot colonize. At first sight, this seems like a convincing argument that lake ecosystems in general will have alternative equilibrium states. However, the demonstration of stabilizing mechanisms per se is not sufficient to conclude that a lake has alternative stable states, as the presence of alternative stability domains may always disappear depending on the conditions.

Although relatively complex mathematical models are needed to capture the dominant mechanisms that are involved, a very simple graphical approach suffices to illustrate the main point in the shallow lake case (Figure 8-2). The graph is based on three assumptions: (1) turbidity increases with the nutrient level; (2) vegetation reduces turbidity, and (3) vegetation disappears when a critical turbidity is exceeded. In the first two assumptions, equilibrium turbidity can be drawn as two different functions of the nutrient level: one for a plant-dominated situation, and one with a systematically higher turbidity for an unvegetated situation. The third assumption translates into a horizontal line representing the critical turbidity for vegetation survival. Above this line vegetation will be absent, in which case the upper equilibrium line is the relevant one; below this turbidity the lower equilib-

t .~ -o :0 ....

:::1 -nutrients --

Figure 8-2. Graphical model for alternative stable states in shallow lakes. The states of with and without vegetation are depicted as a function of nutrients and turbidity.


rium curve applies. The emerging picture shows that over a range of intermediate nutrient levels two alternative equilibria exist: one with dear-water and aquatic plants, and a more turbid one without vegetation. At lower nutrient levels, however, only the macrophyte-dominated equilibrium exists; whereas at the highest nutrient levels, there is only the turbid equilibrium without vegetation. If the lake is in a clear state (on the lower branch of the graph), an increase of the nutrient level will lead to a gradual and moderate rise in turbidity until the critical turbidity for plant survival is reached (horizontal line). At this point, vegetation collapses and the lake jumps to the turbid upper branch. Reduction of nutrients after this catastrophic transition does not result in a return of plants until the critical turbidity is reached again. However, note that this backward switch happens at a much lower nutrient level than the forward switch. Thus, often, reduction of the nutrient level to values at which the lake used to be clear and vegetated will not lead to restoration of that state. This is precisely the frustrating experience of many lake managers. The explanation in a nutshell is that in the absence of the clearing effect of vegetation, the water remains too turbid for vegetation to return.

Note that this simple graphical model is analogous to the catastrophe folds shown in Figure 8-1 C. And this intuitively tractable lake example allows one to get a feel for the way such catastrophic responses may arise. Clearly, the graphical model is a rather extreme simplification of the functioning of lake ecosystems. However, more elaborate mathematical models and analysis of the behavior of many lakes confirm the main result: shallow lakes may have alternative stable states over a certain range of nutrient levels.

Vegetation Degradation in Arid Ecosystems

At first glance, the parallel between dry lands and lakes may seem quite remote, as the factors governing the vegetation development of those two environments are entirely different. Nonetheless, on a higher abstraction level desertification is, in fact, quite similar to the disappearance of submerged plants from eutrophic shallow lakes. In both cases the abiotic conditions at the unvegetated state may be too harsh to allow (re)colonization. Once vegetation is present, however, the plants may ameliorate the conditions sufficiently to ensure vegetation persistence.

Roughly, three different vegetation states can be distinguished in arid and semiarid areas-in order of decreasing biomass: woodlands, perennial herbaceous vegetation, and a desert state in which most of the soil is barren during most of the year. Many studies have shown that the transitions between these contrasting states are discontinuous and often difficult to reverse, which suggests that they represent alternative equilibria separated by critical thresholds (Noy-Meir 1973; Westoby et al. 1989; Walker 1993; Laycock 1991; Rietkerk and Van de Koppel 1997; Rietkerk, Van den Bosch and Van de Koppel 1997).


Woodlands, once lost, do not often recover due to the fact that seedlings of woody plants are easily eliminated by herbivores, unlike adult trees and shrubs. Thus woodland regeneration may occur only after rare crashes in herbivore populations. An example is the African Serengeti-Mara (Tanzania), where gradual regeneration of woodlands was possible from the 1890s until the 1950s because of low herbivore numbers due to a combination of rinderpest epidemic and elephant hunting (Dublin 1995). Fires seem to be the main cause of recent woodland destruction, a loss that is probably irreversible unless herbivore numbers are repressed again. In dry areas, conditions in the absence of cover by adult trees may be too desiccating to allow the seedlings to survive, even in the absence of herbivores, implying a more severe irreversibility of woodland loss. Dramatic examples of this process have been recorded in Mediterranean ecosystems after the loss of evergreen shrublands in Central Chile (Fuentes et al. 1984), and semiarid woodlands in the Mediterranean Basin (Puigdefabregas and Mendizabal 1998). In such cases only rare combinations of rainy years and collapsed herbivory populations could theoretically allow recovery of this severely threatened and highly diverse ecosystems (Holmgren and Scheffer 2001).

The switch to a state in which most of the ground surface is unvegetated most of the year is generally referred to as desertification and is considered to be one of the main ecological threats globally (Kassas 1987). Soil-plant interactions are thought to play a major role in determining the stability of perennial plant cover (Van de Koppel et al. 1997; Rietkerk and Van de Koppel 1997; Rietkerk, Van den Bosch, and Van de Koppel 1997). Perennial vegetation allows precipitation to be absorbed by the topsoil and become available for uptake by plants. When vegetation cover is lost, runoff increases, and water entering the soil quickly disappears to deeper layers, where it cannot be reached by most plants. Wind and runoff also erode fertile remains of the topsoil, making the desert state even more hostile for recolonization by seedlings. As a result the desert state may be too harsh to be recolonized by perennial plants, even though a perennial vegetation may persist once it is present, due to the enhancement of soil conditions. High livestock densities promote desertification as they cause biomass loss through grazing, and through trampling, which causes further soil compaction and erosion.

In conclusion, overexploitation may lead to the loss of woodlands in semiarid areas and to complete desertification in dry regions of the world. Both shifts seem to represent catastrophic transitions to alternative stable states and are difficult to reverse.

Desertification and lake eutrophication happen locally in many places. Thus multiple natural experiments can be studied, and the findings can be tested in full-scale experiments. As a result, scientific theories about these systems are relatively convincing. Larger ecosystems such as the Baltic Sea and the Florida Bay, however, do not have such a number of replicates that can be studied to allow the development of convincing models of their re-


sponse to stress from human activities. On even larger scales human activities may induce catastrophic shifts in the atmospheric system. In general, with scale, both the risk at stake and the uncertainty about predicted effects tend to increase. Also, the group of people involved in causing the change or potentially suffering from it is much larger. Obviously, these aspects may make the response of societies to predictions of catastrophic change quite different from the response observed in their relation with smaller-scale environmental systems.

Theory and Implications of Catastrophic Change

Central to an understanding of how catastrophic change can come about is the concept of resilience that is treated elsewhere in the book from slightly different perspectives.

The Concept of Resilience

In the discussion of management options for ecosystems such as dry lands and shallow lakes that have more than one stable state, resilience is a key concept. Resilience is the ability of the system to return to the original state after a disturbance. Two specific interpretations of this term can be found in the literature. One uses the time needed to return to equilibrium as an indicator (Pimm 1984); the other uses the maximum amplitude of disturbance that still allows the system to return to the same equilibrium (Holling 1973). The latter is especially relevant in systems that have more than one attractor. The mutual relationship of the two ways of looking at resilience can be most easily understood if we consider the systems dynamics in some detail.

In practice, conditions are never constant. For instance, interannual variation in precipitation may affect the nutrient loading to lakes and the potential growth of plants in dry lands. Also, unusual events (fish kills, fires) may wipe out parts of populations. We call the latter disturbances. In our diagrams (Figures 8-1, 8-2, 8-3) disturbances imply a vertical displacement away from the equilibrium line, as they affect the state of the ecosystem (vertical axis).

When an ecosystem has only one stable state for each condition (Figures 8-1A and B), the effect of a disturbance will be temporal, as sooner or later the system will return to the only equilibrium for the given conditions. However, when the ecosystem has more than one equilibrium (Figure 8-1 C and D), a disturbance may displace the state of the system beyond a threshold (dashed middle part of curves) that marks the limit of the basin of attraction of an alternative equilibrium (Figure 8-3, arrow Dz). As a result, the system will move to an alternative stable state from which it will not return unless conditions change or another disturbance occurs. The probability that a disturbance leads to a switch from one state to the other depends on the magnitude of the disturbance, and on the resilience (sensu Holling) of the current state, which can be affected by stress imposed by human use. For

8. DYNAMIC INfERACTION oF SociETIES AND EcosYSTEMS 203

t

* u; E

i 8 Q)

stress--

Figure 8-3. Equilibrium state of ecosystem as a function of imposed stress, as in Figure 8-1 C. Disturbance D 1 does not pass the line that marks the border of the region of attraction of the upper branch of the curve. However, at a higher stress level, resilience of the desired stable state is smaller, and a disturbance of equal magnitude (D2) is sufficient to bring the system into the basin of attraction of the degraded state.

instance, in Figure 8-3 the two disturbances D1 and D2 have the same magnitude. However, only in the case of D2 is the resilience of the system small enough to allow the disturbance to pass the critical threshold for a catastrophic transition to the lower branch of the graph.

Note that return time to equilibrium (resilience sensu Pimm) approaches infinity as a disturbance comes close to hitting the limit of the basin of attraction. Also in ~e vicinity of the bifurcation points F1 and Fz (Figures 8-1, 8-3) movement of the system is extremely slow, and return times following small disturbances may be very long. Thus, both interpretations of resilience are in fact closely linked. However, in the following text we focus on the Holling interpretation, as it is a practical concept to describe stability properties of catastrophically responding systems that we consider of special interest.

Stability Landscapes

Resilience may be understood in an intuitive way from graphs representing the stability landscapes of the system. Such graphs apply to any system with alternative stable states, but we use the case of shallow lakes as a concrete example (Figure 8-4). The bottom plane of this composed figure shows a line that indicates how turbidity increases with the nutrient level. The interpretation is analogous to that of the main sections of the previous graph (Figure 8-3). The middle part of the folded line represents the critical turbidity for plant survival. The two outer sections represent the clear and the turbid state. On top of this plane there are five subsequent hilly figures. They are cross-views showing the equilibria and their stability at five different nutrient levels. The system, like a rolling ball, will be attracted to the


Figure 8-4. "Marble-in-a-cup" representation of the stability properties of lakes at five different levels of nutrient loading (see text).

valleys. These correspond to stable parts of the folded curve on the bottom plane, whereas the hilltops represent the threshold turbidity corresponding to the dashed middle section of the curve. Note that the front landscape represents a situation with heavy nutrient loading in which just one equilibrium exists, a turbid one, whereas the rear picture represents the pristine state of a lake, a low-nutrient situation in which a dear-water equilibrium is the only possible stable state. Between these two extremes is a range of nutrient levels over which two valleys, and hence two alternative stable states, exist.

The response of a lake with such properties to eutrophication and subsequent restoration efforts can also be seen in this representation. Starting from the pristine state, a moderate increase in nutrient level gives rise to an alternative turbid valley, but if no large perturbations occur, the lake will stay in the clear state. Continuing enrichment gradually causes the size of the clear valley to shrink to nil, however, making the lake more and more vulnerable to perturbations such as storms or plant kills that can bring the system across the hill to the attraction valley of the turbid state. However, even in the absence of perturbations, the period in which the lake stayed relatively clear despite the nutrient loading will finally end with a catastrophic transition into a turbid state as the valley around the dear-water state disappears. Attempts to restore such lakes by reduction of the nutrient level often have little effect, since the system will tend to stay in the turbid valley of attraction.

Restoration through Shock Therapy

The good news is that in this situation, a change to the alternative clearwater equilibrium can be achieved by shock therapy. In terms of the stability landscapes: once the nutrient level has been reduced enough to allow the alternative clear valley to exist, one can force a switch to that alternative stable

8. DYNAMIC lNTERACTION OF SociETIEs AND EcosYsTEMS 205

state by pushing the ball over the hilltop. More specifically, such an intentional perturbation could be a temporary reduction in the turbidity of the lake, sufficient to allow recolonization by submerged vegetation. The latter can be achieved in a surprisingly effective way by drastically reducing the fish stock of the lake, in a process called biomanipulation. In numerous small, shallow lakes that are treated this way, vegetation quickly recovers and the lakes remain in an apparently stable clear and vegetated state for many years (Scheffer 1998).

Biomanipulation is relatively cheap. Early, spectacular results can create overoptimism among lake managers, who ponder, "Why bother about expensive nutrient control if a winter of good fishing can cure the lake?" The theory, however, shows that no stable clear state exists if the nutrient level is too high. Trying to restore a lake that receives a heavy nutrient loading by mere fishing is like pushing the ball uphill in the front stability landscape: if you stop pushing, the ball will inevitably roll back to the turbid valley. Indeed, in most cases the shock therapy is likely to work only if the nutrient conditions have been improved first.

Cycles and Complex Dynamics

Simple graphs are useful for transmitting insights into the functioning of the ecosystem to the human users. Obviously, however, the depicted responses (Figure 8-1) are a quite stylized representation of what may happen in reality. First, it should be stressed again that part of the indicators of the state of the ecosystem will always decrease while others increase. For instance, turbidity of a lake will increase (see the above model) as vegetation biomass decreases.

Second, it should be noted that ecosystems can have more than two distinct stable states or ranges in which they are particularly sensitive to changes in conditions. In semiarid areas, woodland, herbaceous vegetation, and desert are alternative stable states. Shallow lakes may jump from a state with dear-water and submerged plants to a turbid, phytoplanktondominated situation in response to increased nutrient loading. But a further increase of nutrients may cause a next jump to an even more turbid situation, in which the plankton is dominated by cyanobacteria. The latter state is also quite resilient because those organisms are good competitors under turbid conditions and are also able to make the water more turbid given the same nutrient supply, thus promoting their own dominant position.

A third point to stress is that, in practice, the species composition of the ecosystem will change along the gradients depicted in the figures. The examples of switches to other stable states coincide with a marked change in physical structure and abiotic conditions, triggering a large shift in the composition of the community of species that depend on these conditions, as explained in some detail for the lake example. However, also along gradual parts of the response range, species composition changes profoundly. Indeed, community composition tends to follow changes in the environment so


closely that species composition can be used as a quite accurate indicator of abiotic conditions such as nutrient concentrations or pH in terrestrial as well as aquatic ecosystems.

A crucial aspect that has been left out of consideration so far is that fluctuations in the state of ecosystems are not only due to variations in the weather or other external forces. Many ecological systems have the tendency to oscillate, even when the environment would be perfectly constant. The analysis of such oscillations is a vast area of research that we will not consider in any detail here. However, since many systems show oscillations of various types, it is important to sketch at least roughly the outlines of the types of dynamics that can be found.

The Adaptive Cycle and Other Oscillations

A common cycle in ecology textbooks is the predator-prey cycle. Classical models predict that the interaction of populations of efficient predators and their prey leads not to .an equilibrium state but to a situation of eternal cycles. In biological terms what happens over such a cycle is the following: The predator population eats almost all the prey, and because of the resulting food shortage, most predators die, allowing the remaining prey individuals to reproduce and grow freely in an environment with few competitors and few predators. The resulting wealth of food for the few surviving predators allows their population to expand too, however. The recovered predator population consumes almost all prey, and the whole cycle starts from the beginning again.

\Vhen one of the variables of cycling systems has much faster dynamics than the other, some phases of the cycles can become relatively fast. Such slow-fast cycles have a special practical relevance, as they give the appearance of periodic crashes of otherwise relatively constant systems. Well-known examples are the spruce-budworm cycles in boreal forests (Rinaldi and Muratori 1992; Ludwig et al. 1978) and the periodic occurrence of forest fires (Casagrandi and Rinaldi 1999). In simplified form the stories are as follows: \Vhen tree foliage becomes dense, the wealth of food allows a rapid increase of spruce budworms, which defoliate the trees in a short time. The following phase of tree recovery is slow and lasts several years, until foliage is dense enough to allow a new budworm outbreak. Forest fires (the fast phase) occur when sufficient litter has accumulated to serve as fuel. It takes many years after a fire to build up a sufficient fuel stock to feed an intense fire again (the slow phase).

Holling (1986; with Gunderson in Chapter 2) has formulated a conceptual and graphical model of slow-fast cycles that he termed adaptive cycles and has demonstrated that various ecological, social, and economic systems go through such cycles regularly (Figure 8-5). Interestingly, he shows that considered on a higher abstraction level, quite similar processes occur in highly different systems during the different phases of the cycle. Holling and his


coworkers have stressed the importance of understanding the mechanisms behind such cycles for finding new ways of managing them (Holling and Meffe 1996). Note, for instance, that frequent small fires may prevent the accumulation of a stock of litter that would cause an intense and more devastating fire. Indeed, a management practice aimed at frequent disturbances may prevent large collapses in slow-fast systems. This counter-intuitive approach has become common practice in forest management and could well be a key to preventing large crashes in various other systems.

The Link between Cycles and Catastrophe Folds

Note that the catastrophe folds presented in our figures (e.g., Figure 8-lC) can lead to cycles if the factor depicted on the horizontal axis (stress) is not fixed, but instead responds to the change in state in a certain way. Suppose, for instance, that human society would respond to the state of the ecosystem such that below a certain level of the state indicator (Ecrit ), stress imposed by humans would slowly decrease, whereas above that level stress would grow (Figure 8-5). For example, imagine the vertical axis (state) to represent clarity of a lake and the horizontal axis (stress) to represent pollution. If political pressure to reduce pollution increases with deteriorating water clarity and relaxes when clarity has recovered, such dynamics could arise. A lake in the clear state (upper branch) would then be increasingly subject to pollution until it collapses (Fz). Since this brings the clarity below the critical value (Ecrit ), the collapse triggers a slow decrease in stress until the lake recovers (F1 ). Once the lake has recovered, political pressure will gradually relax and pollution will increase again, starting a new cycle. If the crash and/or recov-

dE/dt = 0

t ... .

.. .... ····· ________ .. _____ ____ _ ..

•• •• en .· ~

stress (S)-

dS/dt = 0

Figure 8-5. Cycles can occur if stress (S) responds in a dynamic way to ecosystem state such that below a critical level of the state indicator (Ecrit ), stress decreases slowly, whereas above that level, it increases.

208 SCHEFFER, WESTLEY, BROCK, HOlMGREN

ery phases are fast relative to the change in human-induced stress, these cycles will have a slow-fast character.

Note that cycles occur only if the critical ecosystem state for changing society's behavior (Ecrir) falls between the two inflection points of the curve (F1 and F2). Otherwise, the system will remain either a~ the upper branch (Ecrit > F2) or remain stable at the collapsed state (Ecrit < F2). To see this, note that the horizontal line and the sigmoid in Figure 8-5 represent zeroisoclines of ecosystem state and stress level respectively. That is, they represent the collections of points in which change in either ecosystem state is zero (dE I dt = 0) or change in stress is zero (d S I dt = 0). Only at the intersection point of the two isoclines is the system as a whole in equilibrium. However, an intersection with the stress isocline in the middle segment of the ecosystem isocline represents an unstable state. Only intersections with the two stable segments of the sigmoidal ecosystem isocline are stable. In fact, similar isoclines (usually tilted 90 degrees) are common in predatorprey models, and the link between cycles and catastrophic transitions in such systems has been described (Scheffer et al. 1995).

As suggested by this preliminary model, the dynamic response of society to the perceived ecosystem state is key to understanding the coupled dynamics of ecosystems and societies. However, as discussed in detail in the following sections, societal response depends not only on the ecosystem state, but also among other things on the economic mer-its of the activities that impose stress on the system. Hence, the critical stress level cannot be represented simply by a horizontal line as shown in this example. Also, human cognition may be able to oversee the entire system dynamical properties, implying (at least in theory) a potentially smarter human-nature interaction, as argued later.

Nested Cycles and Complex Dynamics

Holling and Gunderson (Chapter 2) have stressed that cycles in numerous ecological and socioeconomic systems can occur in nested sets along spatial and temporal scales. Importandy, complexity of the dynamics can increase sharply if cycles interact, or if cycling systems are subject to periodic forcing by an external cycle, such as the annual or diurnal variation in light and temperature. As an example consider the dynamics of plankton in lakes (Doveri et al. 1993; Scheffer 1997, 1998).

Populations of waterfleas (Daphnia) tend to cycle with a period of roughly forty days (McCauley and Murdoch 1987). These small animals (1 mm) can become very abundant and filter the lake water in a highly efficient way, removing practically all the planktonic algae. As a result, the waterflea populations collapse due to fooq shortage, the phytoplankton density recovers, and the few surviving waterfleas grow and reproduce to cause a new population peak doomed to collapse again in the next cycle. Complicating factors that prevent a simple everlasting cyclic pattern in this example are the seasonal cycle and the fact that fish can completely wipe out waterflea populations.


Experiments show that fish have little effect on waterflea density until a certain critical threshold is past, at which point the waterflea population crashes completely and algal blooms can develop. A closer look reveals that this sudden shift is in fact a catastrophic jump to an alternative stable state, just as in the case of the disappearance of vegetation from flipping lakes. However, the mechanism is different. In the fish-plankton case, the fish (the consumer) overexploits the food (waterfleas). In fact, this is similar to what waterfleas do to the algae. A difference is that fish can survive on other food as well. Therefore, they do not necessarily die off after the collapse of waterfleas, and as a result, the overexploited state may be persistent rather than transient.

Figure 8-6 summarizes the results of a model of the effect of fish on plankton dynamics. The sigmoidal line in the graph is analogous in interpretation to the catastrophe folds shown earlier. In this case it indicates that with increasing fish densities the population of waterfleas can collapse into an overexploited state. The new thing is that at low fish densities, waterfleas and algal populations oscillate around the equilibrium line in a predator-prey cycle. With increasing fish predation the waterflea population collapses. However, due to the oscillations this does not happen at the usual point, namely the bend (F2 ) in the sigmoidal curve, but already at a much lower fish density, namely at point 0 1 . Here the cycles hit the middle section of the sigmoidal curve that marks the critical border of the attraction valley, and the system inevitably collapses into the alternative state with almost no waterfleas and a dense population of algae. This is called a homoclinic bifurcation. In biological terms it implies a collapse into another state due to a combination of two different mechanisms: first the waterflea numbers crash, due largely to depletion of their food, and subsequently the population is trapped

Figure 8-6. At a critical level of fish predation, plankton cycles hit the border of the basis of attraction of the alternative stable state with high algal biomass, in a homoclinic bifurcation point (01 ).


at this low level by fish predation. Note that, in a sense, the oscillations have the same effect as external perturbations: they increase the probability that the system will shift from one state to another.

In practice, fish predation pressure on waterfleas shows a seasonal cycle. This is largely due to the fact that most fish specialize in eating zooplankton only during the first months of their life, causing a peak consumption of waterfleas in early summer. Depending on the amplitude of the seasonal cycle in predation pressure, this may cause a homoclinic collapse of waterfleas in late spring, resulting in high phytoplankton biomass and turbid water in summer. In autumn when most young fish have died or switched to alternative food, fish predation pressure on waterfleas may drop below the critical point (Ft) where waterflea populations may recover and clear the water once again.

Of course, fish predation is not the only thing changing over the year. Changes in light and temperature are important driving forces. Also, young fish respond dynamically to the availability of waterfleas as a food source. Including this causes the plankton model to display a wide range of dynamic behaviors including chaotic dynamics (Doveri et al. 1993; Scheffer 1997). In fact, chaotic dynamics easily arise if several cycles interact, as is definitely the case in plankton dynamics (Scheffer 1991). Indeed, when considered in detail, many fluctuations in the populations of many animals and plants show erratic patterns that are likely to be the joint result of fluctuating external conditions and interacting intrinsic cycles in the ecosystem.

Parallels between the Dynamics of Ecosystems and Human Societies

Before passing on to the interaction of ecosystems with society, it is good to notice that ecosystems and human societies are both complex systems in their own right. In fact, considered on an abstract level, many mechanisms such as competition and various kinds of feedback operate in nature and human society alike. Therefore, it is not surprising that the different types of dynamics described above for ecological systems can also be found in socioeconomic systems.

Crashes of financial markets, for instance, may be related to positive feedback loops created by loss of confidence; financial problems for companies, banks, and households; plunging currency; rising interest rates; and a slumping economy (Krugman 1999). Cycles due to delayed responses are also well known in economics (Chavas and Holt 1993; Chavas 1999). Chavas (1999) discusses the classic "hog cycle," which in its simplest form is as follows: High hog.prices invoke many farmers to start raising hogs. When the hogs are ready for selling, the market is flooded, thereby causing the prices to crash. Farmers then cut back production, which, with some delay, causes a hog shortage, resulting in high prices, triggering a new cycle.

More sophisticated versions of the story (Brock and Hommes 1997) have some producers doing a better job of predicting the future market price of


hogs provided they spend more resources. If enough producers expend resources predicting the future market price and if those predictions become more accurate, then this tends to stabilize the market and smooth out the price fluctuations. But when the price fluctuations become smooth and easily predictable, many producers stop expending resources on accurate prediction, and the system may slowly build up instability. The instabilities make it worthwhile to use more accurate but more expensive prediction methods again. Thus the cycle repeats. The work of Baak (1999) and Chavas (1999) has been empirically tested for the presence of boundedly rational producers in a setting related to that of Brock and Hommes (1997) and has adduced evidence for the presence of boundedly rational producers.

In economics, detailed dynamical systems modeling of economic phenomena dates back at least to the 1930s when people such as Hicks, Kalecki, Samuelson, and others were in their prime. See Lorenz (1989) for a review that is well informed by recent advances in dynamical systems theory. In other social sciences, less mathematical modeling has been applied so far, but it seems likely that the same types of dynamics found in ecology and economy may arise. For instance, models of cycles between despotism and anarchy in ancient China (Feichtinger et al. 1996) have produced diagrams strikingly similar to our Figure 8-6, and models suggest the theoretical possibility of cyclic dynamics as observed in certain types of love relations (Rinaldi 1998a, b).

Obviously, dynamics of ecosystems and socioeconomic systems are strongly intertwined in practice. Therefore, in order to be able to discuss with some realism how human-nature interactions could be made sustainable, we need to understand not only the ecosystem properties, but also the main forces that drive the socioeconomic system. In the next sections, some major contributions of economic and social sciences are reviewed. These ideas are then related to the ecosystem properties presented in the previous sections, to produce a theoretical framework for analyzing the dynamic interaction between societies and ecosystems.

Driving Forces of Economy and Politics

Ecosystems are usually of importance to several different groups of users (stakeholders) who often have conflicting interests. Lakes, for instance, may be used by industries to get rid of wastewater and by swimmers who want clean water and by fishermen who prefer certain kinds of fish. Also, the lake water may pass through rivers and other lakes before ending up in the ocean, engaging many more distant stakeholders along the way.

The first question we address is how ecosystem use by society might be tuned in such a way that the average benefit for all different users involved is maximal. This is the type of problem addressed by normative economics using the central assumption of economics that all kinds of interests can somehow be expressed in a common currency. One can imagine that in practice this is a formidable task with many difficulties.


Even more complex, however, than solving the question of what should be done is the problem of unraveling the mechanisms determining what is actually done in reality. The dynamics of societies depend on economic and political interactions, but ultimately on the behavior of individuals who respond to their environment in a much more complex way than can be captured by the basic rules of economy and politics. We will very briefly discuss the literature that covers the wide range from plain economic motives to beliefs and ethics.

On the economic end of this range are so-called positive economics or political economics. In this approach, economic analysis is used to measure and to predict the political strength of a coalition of common interest stakeholders. Less mathematical, but closer to the bewildering complexity of real society dynamics, is the approach followed in most branches of social science, as discussed the final section.

Normative Economics: How Utility Could Be at the Maximum

The economists' approach to finding the best solution for society as a whole is to express all interests in a common currency (in practice it is often money) reflecting something termed welfare or utility.

Stakeholders and Their Welfare

In the case of lakes, stakeholders whose welfare is related to use of the ecosystem may be:

• Farmers who allow nutrients from cattle dung and fertilizers to pollute the water in the catchment area of the lake. Reducing such diffuse pollution has a cost for the farmers; thus, this use of the lake has an economical benefit for them.

• Households (or municipalities) and industries that have their wastewater run into the lake. Reduction of pollution from such point sources also has a cost that increases with the required level of cleaning.

• Recreational fishermen, swimmers, boaters, bird watchers, lakeborder estate owners. These users require a certain basic quality of the water and its associated ecosystem.

• Hotels, camps, restaurants, etc., that serve recreational users. Their income increases with the number of recreational users attracted by the lake.

• Drinking water companies that use lake water as a source. Cleaner water is cheaper to process than polluted water with toxic cyanobacteria.

• Users of the chain of rivers, lakes, and oceans that receive water from the outflow of lake.

8. DYNAMIC INTERACTION OF SOCIETIES AND ECOSYSTFMS 213

Obviously, estimating the welfare functions that describe how welfare of each stakeholder changes with its use of the lake is not simple in practice. Although there are various techniques for valuating different ecosystem services that yield reproducible results, the topic is still controversial (Symposium on Contingent Valuation 1994). Probably, it will always remain difficult to express the value of such highly different aspects in a common currency. Also, one may argue whether maximization of the value for human use, rather than other ethical standards, should be the criterion for analysis. Nonetheless, the valuation approach is in our opinion a large step forward as compared to the dominant current practice, which simply leaves many obviously important values of ecosystems out of consideration in the policy-making process.

One way of helping to clarify one's thinking, and to avoid getting bogged down in nonproductive debate on this controversy, is to imagine that the lake and its watershed are owned by one entity (for example, a monopolist). In this scheme, the lake is operated like an amusement park, with the objective of designing pricing schemes to maximize every possible dollar of value that can be squeezed out of the lake and its watershed. For example, potable water could be sold to cities from the watershed itself, provided humans kept the watershed clean enough. Recreational, viewing, scenic, boating, fishing (and other) services could be packaged, much like the packaging of amusement park rides. Admission fees could be levied on visitors to the area, and rentals could be levied on living units within the area.

The monopolistic owner would have an incentive to maintain the lake and its watershed in such a way as to maximize the total sum of these values. One could imagine that such an owner might not sell any loading services at all to agriculture, developers, or leaking septic systems from cottages. Notice that this monopolist owner would charge leakage fees to any cottage owner whose septic leaked into the lake as well as loading fees to any farmer.

The amusement park analogy helps to clarify thinking about the myriad of services that a lake and its watershed generate and the skills that a monopolistic operator needs to extract maximal value from the spectrum of services. This way of putting things might help avoid nonproductive debates about the merits of utilitarianism and the problems with cost-benefit analysis, and get the discussion focused on how society might theoretically extract all of the potential values from the bundle of resources comprised by a lake and its watershed. Chichilnisky and Heal (1998) discuss the practical problem of delivering clean water to New York City. We urge the reader to look at this case as a prototype for design of a watershed cleanup program and an institutional framework to get the job done.

A Graphical Theory of Ecological Limitations to Welfare Optimization

In a society with different interest groups, the monopolistic amusement park owner can be replaced by a hypothetical rational social planner (RASP). This


concept will show more specifically how the trade-off among different lake uses might work. The hypothetical RASP knows how the welfare of each stakeholder is related to its use of the lake, and therefore should be able to decide what combination of uses would yield the highest per capita welfare. However, to do this, the RASP needs to take into account how different uses of the system affect the value for others (for example, swimming is incompatible with algal blooms). Therefore, it is crucial that the RASP also knows how the system changes in response to its exploitation. Thus, the combination of the ecosystem response with the welfare functions serves as a basis for the RASP to find the integrated use that yields the highest welfare for society.

To illustrate the principle of maximizing welfare using knowledge of the constraints imposed by the functioning of the ecosystem, we use the response graphs (Figure 8.1) presented in the previous section. In these graphs the horizontal axes represent conditions such as nutrient loading that are affected by human use. There is usually a clear economic benefit related to such use. Assuming that the intensity of human use increases along the horizontal axis, the economic benefit and hence the welfare of the users will increase along the gradient. The precise relationship will depend on the specific situation, but the increase of welfare will usually diminish at very intense use. For shorthand, users that significantly affect the state of the ecosystem are called Affectors.

The vertical axes represent an aspect of the state of the ecosystem such as plant biomass. As argued, most components of the ecosystem tend to change in concert, and the variable depicted on the vertical axis serves merely as an indicator of the overall state. Many uses of an ecosystem can depend on its state but have little effect on it. For instance, swimming and bird watching are better in clear lakes but have little effect on lake ecology. Also, ecosystems may provide services to a wide group of more distant stakeholders that depend on the state. For instance, in shallow lakes vegetation helps purify the water through natural processes such as denitrification. Many downstream inhabitants will enjoy the benefits of this clean water flowing from the lake into the river system and eventually into the ocean. We will call users that benefit from the system but do not significantly affect the state of the ecosystem Enjoyers for short. In most cases, the ecosystem value for Enjoyers will diminish with increasing exploitation by Affectors. Thus, in the graphs (Figure 8-1 ), the low level of the systems state indicator at high exploitation will correspond to the lowest value for Enjoyers, and the welfare that Enjoyers can obtain from their use of the ecosystem will increase systematically with the level of the state indicator represented by the vertical axis.

Obviously, many more groups of stakeholders exist in practice, and their interests may often be overlapping rather than strictly complementary as in this Affectors-Enjoyers model. However, this distinction is useful for a first exposition of the ideas. We thus assume that overall community welfare obtained from the ecosystem is simply that of the Mfectors plus that of the


Enjoyers. Total welfare will therefore increase along both axes in the ecological response graphs (Figure 8-7). If nature would impose no restrictions, the highest welfare could be obtained by combining maximum exploitation with a maximum value of the ecosystems state indicator. However, the state is a function of the exploitation, and hence the ecosystem's response limits the possible combinations of use by Mfectors and Enjoyers to points on the stable equilibrium lines in the response graphs (Figure 8-1). Projection of these lines on the welfare plane (Figure 8-7) shows in one picture what stable combinations of use by Affectors and Enjoyers are possible, and what their associated welfare is (see Appendix B).

This information allows the hypothetical RASP to guide society in its use of the ecosystem. The highest point on such graphs represents the maximum overall welfare that a society of stakeholders can achieve. Mosdy, it will be good for society to move as close as possible to such a maximum. Note that, depending on the precise shape of the ecosystem response curve, there may be a single optimum (Figure 8-7A, curve b) at an intermediate stress level, indicating that a compromise between Affectors and Enjoyers

A

B

Figure 8-7. Graphical models showing how a theoretical society of ecosystem Enjoyers and Affectors (see text) may obtain optimal welfare from use of an ecosystem. Welfare of Enjoyers increases with the ecosystem state indicator, but welfare of Affectors increases with the level of stress imposed on the system by their activity. Thus total welfare will increase as indicated by the planes. The curves (a, b, and c) on the planes indicate how the ecosystem state responds (A and B) to an imposed stress (as in Figure 8-1). Optimum social welfare compatible with ecosystem dynamics is therefore obtained at the highest point of each curve.


yields the highest overall welfare, or two local optimum points (Figure 8-7 A, curve a) representing biased situations with maximum welfare of either Affectors or Enjoyers. The latter observation is important because it shows that a compromise (which is often the outcome of sociopolitical processes, as discussed later) may well be a bad solution, as it represents a situation with low overall utility. Curve a in our example that results in this situation represents the response of a sensitive ecosystem. Even low levels of stress result in a large deterioration of the state. The reason a simple compromise yields low overall welfare in such situations is intuitively straightforward. Even a small stress level (yielding low gains for Affectors) produces a large loss for Enjoyers. If the ecosystem can be treated in separate spatial units (e.g., if many lakes exist in an area), the obvious solution may be to assign some units entirely to Enjoyers and others entirely to Affectors.

Figure 8-7B illustrates what happens if the response of the ecosystem is catastrophic (Figure 8-1 C, D). In this case, the maximum utility tends to be close to the threshold at which the system collapses. The reason is that in such ecosystems, stress typically has little effect due to the stabilizing feedback that tends to keep the system in the same state until stress has increased far enough to bring it close to the border of collapse. Therefore, Enjoyers will be well off until quite high levels of stress are imposed on the system. This implies that aiming for the maximum welfare may be a hazardous strategy, as a slight miscalculation of the RASP, or some environmental variability (for instance, an exceptionally hot year), may easily invoke a switch to the lower branch of the curve representing an alternative stable state with a low overall utility. In order to restore the system, the stress level has to be reduced to quite low values (at the cost of a further considerable loss of total welfare) before a switch back to the other branch occurs. This implies that for societies that use ecosystems with multiple stable states, it may pay off in the long run to be conservative in their ecosystem management strategy. This aspect is analyzed in some depth by Carpenter, Brock, and Hanson (1999).

Note that the total welfare of a group depends on the welfare of individuals in that group multiplied by the number of individuals in that group. Thus if, for instance, the proportion of Affectors decreases relative to that of Enjoyers, the stress-dependent welfare should be downweighted. In terms of Figure 8-7, this would imply that the welfare plane is tilted and the optimum welfare will be farther away from the critical threshold. Indeed, in societies where the enjoyment type of nature use becomes more important, overall utility of the human community will benefit from an even more careful use of its surrounding ecosystems.

However, as discussed in some detail later, a regulating authority will usually be responding to political pressure from Enjoyers and Mfectors rather than seeking the real social welfare optimum. Such political pressure depends not only on potential individual welfare gains and the size of different interest groups, but also on other socioeconomic aspects that determine


the political power of groups. As argued later, industries and other types of Affectors will often be more powerful in exerting political pressure than Enjoyers, because the latter tend to be socially more scattered, among other reasons. As a result, politics tend to distort the picture, and an authority seeking to balance political pressure from Enjoyers and Affectors will be biased away from the social optimum in the direction of further deterioration of an ecosystem.

In the following sections we use this lake example to highlight several socioeconomic theories about the factors that facilitate or prohibit societies to get the theoretical optimal utility from ecosystems. A formal mathematical framework of these theories is presented in Appendices B, C, and D.

Naive and Smart Ways for Approaching Optimum Utility

An ideal RASP that oversees the entire system does not exist in the real world. In the worst case, a management authority that tries to maximize community utility from the ecosystem may actually know nothing about the overall system dynamics. In that case, one could imagine that the authority might follow a simple iterative hill-climbing strategy to optimize overall utility. The minimum requirement is that the authority can somehow measure the utility that different groups (Affectors and Enjoyers) obtain from the lake. This can be done by measuring the willingness to pay for different utilities. If the authority continuously monitors the rise and fall of utility for different groups, it can iteratively adjust regulations on pollution in such a way that total utility increases (see Appendix B). For instance, if a small increase in pollution load results in an increase of total utility, the regulating authority will allow a small further increase; whereas in the case of an observed decrease in utility, it will reduce the allowance. This hill-climbing strategy results in a gradual iterative movement to increasingly higher utility and can thus guide society to an optimum utility, as indicated in Figure 8-7.

Apart from the question of whether this approach is feasible in any practical situation, there are several fundamental caveats to it to find optimal utility. First, notice that, as mentioned earlier, in a system with alternative stable states, the optimum tends to be close to the threshold at which the system collapses. Since in reality no authority will ever be absolutely accurate, it may well accidentally allow the system to go beyond the flip, which is a little beyond optimum on the diagram, causing the lake to switch to the bad state. Second, after this crash, the hill-climbing method guides the authority farther up along the lower branch, allowing a progressively higher pollution, to the advantage of the Affectors, but not society as a whole. In order to move to the more desirable utility optimum on the good branch of the curve, society would need to move downhill (i.e., to a further decrease in overall welfare) temporarily after the crash, until it reaches the point where the lake recovers to the upper branch to come back to the optimum.

Obviously, it would be much better if the authority has a good insight into the rules that govern the ecosystem dynamics, and adjusts its policy in a


cautious way so as to minimize the chance of letting the ecosystem and its utility for society collapse.

There are many ways in which authorities can regulate, but in practice, tax is the instrument that economists think of first. The idea behind tax as an incentive is that, given the tax rate, Affectors will choose their pollution load in such a way that they maximize their individual net benefit, taking both tax and gains into account. Since the gains will usually not keep increasing at a constant rate with the intensity of the Affectors' activities and the resulting pollution, a fixed tax rate per unit of pollution will lead rational Affectors to fix their pollution to a certain predictable level (Figure 8-8). Theoretically, an authority with sufficient insight into the system can thus set the tax rate in such a way that Mfectors realize precisely the pollution that leads to the social welfare optimum (see Appendix C).

One can easily derive a tax-setting scheme to let society follow the hillclimbing procedure described in the previous section (see Appendix C). However, as argued, such a hill-climbing approach is rather limited. If the system has multiple equilibria or several local welfare maxima, one needs a deeper insight into the ecosystems dynamics. Using that insight, the authority may want to impose a temporary surtax to lower pollution for a period of time long enough for the lake to flip to the good branch. Then the surtax could be lifted. This is something rather like a quantity control placed upon the Affectors to guide them toward the right basin of attraction, and then impose a tax to guide them toward the right level given that basin. It is beyond the scope of this chapter to discuss the design of such more elaborate decentralized regulation schemes. The general theory of mechanism design (Wilson

t 5' !!? 0

Ua t5 (J)

:!;( .9

~ 'S

optimum net benefit

------------~ :T

... .. .. .. .. ..

•• ·~ tax rate ••• • : (T)

: .. ··~ .. ' . ' .. ' .. ' .. ' .. ' . '

Sa

.. .. '~···· .. ..

stress on ecosystem (S) -

.. .. .. ..

Figure 8-8. Tax as a way to reduce stress (a) imposed on the ecosystem by the activity of Mfectors to a desired level a*. For an Affector to optimize net benefit (fJ .a I o T a), she must tune activities to the point where the first derivative of the utility curve equals the tax rate U 0 .aID T.


1992; McMee and Reny 1992) should be useful for design of more elaborate regulatory mechanisms that have good incentive properties and minimize costs of implementation and administration (Brock and Evans 1986).

Positive Economics: Why Societies Do Not Achieve Optimum Utility

In practice the forces that drive societies do not naturally approach an optimum welfare situation. Positive economics, as opposed to normative economics, deals with the problem of analyzing these forces. The basic assumption is that each individual will try to maximize its welfare or play its card in the smartest way. Game theory is the typical tool used for computing strategies that individuals (or groups) would choose on the basis of their prior assumptions of how other individuals (or groups) will respond to problems (Maler et al. 1999). Quite often the tendency. to tune behavior to such prior assumptions results in suboptimal situations from the viewpoint of social welfare. As an environmental example, consider the case in which two individuals (or cities or countries) use the same lake (or ocean or atmosphere). Each one expects that the other will adjust its behavior to prevent the ecosystem from deteriorating. However, precisely for that reason, each one will have less incentive to adjust its own behavior, and the system is more likely to deteriorate (Maler et al. 1999).

In the following section, the Affector versus Enjoyer example is used to show how this type of theory may be applied to analyze forces that determine which interest groups are more powerful in forcing policy in a desired direction.

Polluting Is Profitable: The CCPP Phenomenon

A well-known problem in environmental protection is the so-called CCPP (Communize the Cost, Privatize the Profit) phenomenon (Hardin 1993).1n an unregulated situation, Affectors benefit from their activities, while the costs resulting from a deteriorated ecosystem state are carried by the Enjoyers. In the common situation that Affectors are also partly Enjoyers of the same ecosystem, the costs of their activities usually concern the community as a whole, while the profit from the affecting activity goes exclusively to the Affectors. This imbalance is the core of many environmental problems.

In the absence of any feedback, Affectors may keep increasing the stress on ecosystems, even if the profit associated with further increase is very small. In such saturated utility situations, already a slight tax on stressinducing activities could have a large effect. A fair tax system, as sketched earlier, would ideally force Affectors to take real environmental costs into account, typically inducing a large reduction of the stress imposed on the ecosystem. However, if there is no RASP and there are no regulations yet for this particular Affector's activity, the first step in the direction of a more equitable situation from a social point of view is to mobilize forces of Enjoyers


in order to change the policy. Game theoretical models suggest that the political pressure mounted by groups such as Enjoyers and Affectors depends strongly on their ability to overcome so-called collective action problems.

The Collective Action Problem and Its Effect on Politics

The essence of models that address collective action problems is easy to understand. Suppose a tax T on pollution is proposed by the regulatory authority as a trial. Affectors will want to invest resources to exert political pressure against this policy. The amount of effort will depend on their beliefs about the impact of their total contributions (of resources) on the chances that this policy will actually be implemented. However, all individual Affectors also have an incentive to free-ride on the contributions of their comrades in the common effort to stop passage of T by the authority. In practice, individual Affectors will tend to contribute less than they should if they believe that their comrades will invest properly.

This specific case can be modeled as a simple noncooperative game in which indivudal Affectors form beliefs based on the actions of the other Affectors and choose their contribution level in such a way that it maximizes their expected gain given their prior beliefs. It is easy to show that in such models contributions at equilibrium increase as the stakes are less evenly distributed over the players (Magee et al. 1989). This makes sense because if the losses were all concentrated on one large Affector, that Affector would not face a free-rider problem and would optimize his effort against the policy, whereas if there were two even-sized Affectors, each would tend to free ride on the other's efforts. The same free-rider analysis can be applied to the Enjoyer side of the political struggle.

In some situations, where the regulator is a management agency, a pressure analysis using game theory as above may approximate what actually goes on in practice. However, it should be stressed that such noncooperative an approach is not always appropriate. In a repeated situation where the Affectors are interacting face to face, other more adequate models have been proposed (Ostrom, Gardner, and Walker 1994). Still, in practice, interactions may be much more complicated than those incorporated in such models as discussed in the section the key role of sound structures below.

As argued, political pressure from an interest group depend!>, among other things, upon the tendency of its members to free-ride on the efforts of other group members, and to believe in the effectiveness of the overall pressure. Pressure may be derived from a noncooperative game model, as outlined above following Magee et al. (1989). These analyses suggest that the resources invested by an individual to exert political pressure depend on the interest at stake, but also on what has been termed perceived effectiveness and noticeability (Magee et al. 1989).

The perceived effectiveness depends on the strength of beliefs in the power of the sum of contributions to move policy in the direction desired by


the Enjoyers. This will increase the merit of the Enjoyers' case. However, noticeability, and hence the eventual individual effort, will drop with group size due to the free-rider problem (Figure 8-9). This is because, all else being equal, the larger a group is, the more anonymous each member will tend to feel. Self-interest may therefore lead each individual in a large group to shirk the duty of contributing a fair share of the group effort.

The drop of individual effort with group size depends on how effective the group is in making each member feel noticeable so that he pulls his own weight in the joint effort of supplying pressure. This depends on the forces discussed by Ostrom et al. (1994) that determine how well a social group can muster a collective effort in a commons-like situation like mustering political pressure that serves the common good of that group.

For example, if the Enjoyers are dominated by recreational businesses and those businesses have a formal organization of long-standing tradition like a recreational businessmen's association, then the noticeability would be quite large. Each businessman will be monitored by the association and may be punished for contributing less than the standard expected level of effort. The businessmen's association may have built a relationship with the Authority over the years, which might show up in an increase in the perceived effectiveness that each unit of contribution has upon policy.

Other forces that might act to increase noticeability could be the necessity of each member of the group to have access to a commonly shared factor

·of production (e.g., access to the common milk distribution network for a dairy farmer, access to the dock for a stevedore, access to the multiple listing

t t::

~ a; ::;,

"0 ·s: '6 .~ perceived effectiveness

group size-+

Figure 8-9. Game theory predicts that the effort individuals invest in political pressure to reach the goal of the group depends on "noticeability" felt by the group member and on the perceived effectiveness of the pressure on changing policy in the desired direction. The individual contributions decrease with group size due to an increasing incentive to free-ride on the efforts of others in larger groups, where each member feels more anonymous. Notice that small groups that have a clear case and a social system that reduces free-riding will be politically more powerful than expected from their mere numbers and the welfare at stake.


service for a real estate agent). The necessity of access to such a factor of production may give a group leverage over the tendency of its members to shirk.

Notice the stress placed on the degree of repeatability and the density of the communications network in the examples above as inputs into the strength of the group in monitoring shirkers and free-riders in collective efforts like lobbying politicians and authorities to get the group's desires implemented by the political system (usually at the expense of other groups that are not so successful, relatively speaking, at solving their collective action problems). Further discussion of these forces of relative efficiency of resolving collective action problems is beyond the scope of this chapter. The reader is referred to Magee et al. (1989) and Ostrom et al. (1994) for more details on how collective action is mobilized even though each individual has an incentive to shirk on the joint effort.

The graphical models that show how welfare of society could be maximized (Figure 8-7) can be modified to produce graphs that show the expected outcome of political pressure (Figure 8-10). A formal treatment of the relationship between the two sets of graphs can be found in Appendix D, but the interpretation is intuitively straightforward. The change of focus is that, rather than seeking the social welfare optimum, the authority that reg-

A Enjoyers dominant

political power

B Affeetors }1 dominant "'

political 1~ power >i

~~

i

Figure 8-10. Differences in efficiency at mobilizing political pressure (see Figure 8-8) distort the process of social welfare optimization (Figure 8-7B). The system will tend to an equilibrium in which political pressure from different interest groups is in balance. If Enjoyers are more efficient (A), that equilibrium will be on a more resilient part of the branch representing the desired ecosystem state. However, typically Affectors are the.more efficient group at mustering political pressure (see text), resulting in a situation (B) where the system tends to increase stress on the ecosystem, even after it has collapsed to the lower branch of the curve.


ulates the system is responding to political pressure. As argued, political pressure depends on the interest at stake (i.e., the welfare in Figure 8-7), and on the effectiveness of the interest group to mobilize forces, which depends on aspects such as noticeability and effectiveness perceived by the members (Figure 8-9). Therefore, we can obtain a graph that represents the political force that can be applied by Affectors and Enjoyers to obtain a certain utility from the ecosystem by multiplying that utility with a factor that represents the ability of the group to mobilize forces (Figure 8-10). In a situation in which the Enjoyers are a more coherent and concentrated group than the Affectors, their political power will be relatively strong. In the case of our example, ecosystems with alternative stable states will tend to lead to an equilibrium that is on a relatively safe part of the good branch of the equilibrium curve (Figure 8-10A). The resilience of this situation is relatively high. However, quite often, Mfectors are relatively better organized than Enjoyers, who may often be a large but diffuse group. As a result, the political power of Mfectors is relatively high, resulting in a situation in which there is no local optimum representing a power equilibrium on the good branch of the curve (Figure 8-1 OB). Instead, the political pressure will drive society farther and farther up along the branch with low Enjoyer value, due to the high pressure produced for even slight gains of Affector utility.

More Elaborate Theories of Political Contest

If the regulation (or the regulator itself) is an outcome of a political contest, then the model must take the form of the contest. Median voter theory is the simplest version of this analysis. As an example of how this works, let each stakeholder have a desired optimal pollution level of a commonly used lake at a fixed point in time. If two candidates in an election compete against each other to capture votes from stakeholders, majority rule will force the candidates to locate at the median in equilibrium. The logic is simple. Suppose that one candidate is to the right of the median. The other candidate can locate slightly to the left and capture all votes to the right plus more. This is not equilibrium. Hence both are forced to locate at the median voter position.

There is still pressure away from extremes in many institutional settings. Consider a multiparty race. Even though there may not be a stable equilibrium as in a two-party race, candidates who take extreme positions relative to the median will tend to be beaten by candidates who hover nearer the center. Many regulatory agencies are run by a politician once removed in the sense that the head faces competition from a competing person for the headship, or the head is maneuvering to build recognition and position for a future campaign herself. This creates pressure to stay away from the extremes and to hover nearer the center.

Of course, there are also forces that cause political candidates or parties to move away from the center. This is easily illustrated by the U.S. situation, where political campaigns are very expensive and candidates must garner resources by catering to pressure groups. Each candidate thus faces a trade-off


between deviating from the median position and mustering resources to influence the beliefs of the voters, which amounts to moving the desired position of those voters (Magee et al. 1989).

Usually, politics proceed in a rather more complex way than the mechanisms captured by the median voter approach. Especially the formation of coalitions is important. Riker (1962) is a classic on the coalition approach to political analysis. Here the idea is to imagine a coalition manager who sets the tax in such a way that it minimizes total cost of mustering a winning coalition. If all actors were equally weighted (a pure voting situation), then the manager could assemble a policy to optimize social welfare. As one can imagine, there is enormous variation on Riker-type models. If financial resources are needed to muster a winning coalition, then votes may end up being weighted according to how much money is given by each. There is also variation on whether a simple majority (more than 50 percent) or a super majority is needed to win.

Although we shall not say much about administrative science models here, they are too important to leave out of any discussion of how administration of policy takes place in reality. The term administrative science refers to the basic work of Simon (1960) and related works such as that of Williamson (197 5). Sent (1997) has written a very interesting recent historical study of Herbert Simon's work. His work on administrative science was his earliest work. It stressed how organizations are structured to deal with the cognitive limitations inherent in actual human decision making. Hence, it is direcdy relevant to the problem of understanding how policy-making authorities make policy independent of the political pressures discussed above.

In conclusion, in any political system there will be a tendency to move to the center position, balancing pressure from different groups, but different systems will have different sensitivities to be biased away from a pure voter situation by pressure from groups that are more powerful than others in their attempts to steer politics. Therefore, the various aspects that affect the ability of groups to muster poJitical pressure are likely to be key to understanding how societies deal with ecosystems.

These analyses are extremely stylized, even trivial, but they suggest a diagnosis. A key problem is the differential organizational efficacy of Affectors relative to Enjoyers at mustering political power. The ultimate roots of this differential ability lie not in corruption but in the superiority of Affectors in overcoming the type of collective action problems described by Magee et al. (1989) and Ostrom et al. (1994). Enough is known now about what kinds of forces determine relative efficacy of collective action that one could imagine designing policies to even the collective action organizational playing field across the two groups. An ideal would be a surrogate for a tax on the negative externalities that the Affectors load onto the Enjoyers through their relative efficiency at using the political system. Notice that the relative efficiency of the Affectors may have nothing at all to do with things like bribes, private jet rides to Tahiti for politicians, etc., that capture the imagination of


the public and the news media, and generate public outrage. The real cost is the slow, subtle education of the politicians and the regulatory authorities, caused by the steady daily contact with Affector agents financed by their superiority in mustering more resources per unit of stakeholder interest than the poorly organized Enjoyers.

For example, an association of real estate agents in the United States can be much more effective with legislators than a collection of individual homeowners because real estate agents must interact intensely with each other in order to match up buyers and sellers. This intense social networking of real estate agents produces collective action for other objectives such as convincing legislators of certain viewpoints, as a byproduct of the microeconomics of their professional practice. Ideally, some kind of tax would be levied on such associations in order to even the balance of pressure on politicians.

Another logical approach to attack the power bias and push the political balance back in the direction of the social welfare optimum would be to aim at an institutionalized valuation of a broad form of cost-benefit analysis in public policy making, provided that it was based on a socially relevant accounting system (broad in the sense that a wider spectrum of values is considered rather than the narrower monetary values considered in traditional cost-benefit analysis). Given that the current policy-making process tends to select a far worse alternative, one may decide to support this form of cost-benefit analysis even though it suffers from critiques such as that of Bromley (1990).

The Key Role of Social Structure

While it is possible to imagine an ecosystem managed by a rational decision maker or by a political actor who handles the relationship between Affectors and Enjoyers through taxation, etc., this is not an approach that can be simply followed by an average scientist or ecosystem manager such as found in industrialized societies. Managers are embedded in two sets of interactive processes. The first is organizational. Managerial responsibility is generally linked to specific agencies, generally government departments. Action on the part of the individual manager therefore takes place in a hierarchy that controls the resources and authority to manage the ecosystem concerned. In general, these systems are as much political as rational, and the individual manager may have very little influence over this process. The second is interorganizational. While the particular agency or government office may have jurisdiction over an ecosystem (say, a particular forest or lake), it rarely has complete control over what happens in that system. However, a manager aware of the dynamics of flipping systems and the stress that the combined demands of Affectors and Enjoyers place on that system is not without some tools for managing both the ecosystem and the social system. We have noted above some of the ecological, economical, and political interventions that can be made. In this section we tum to the question of what kind of social interventions may be important.


Social Dynamics around a Common Problem

One of the challenges of managing ecosystems is that the social system that interacts with the ecosystem has a dynamic at least as complex as that of the ecological system. Emery and Trist, in a seminal article on organizational theory written in 1965, coined the concept of turbulent fields to frame in sociological terms some of the ideas that today have become central to complexity theory in general (Emery and Trist 1965). In that article they contrasted four types of what they call causal texture (i.e., processes through which parts of the environment become related to each other):

• placid, randomized (in which the elements in the social environment stay fairly constant);

• placid, clustered (in which elements of the social environment form interacting subsystems but still are fairly consistent over time);

• distributed-reactive (increasingly competitive social environments where in order to move, the actor must take into account interactions between clusters of organizations as well as between those clusters and the actor);

• turbulent fields (dynamic, with dynamic properties arising not simply from the interaction of the component organizations but also from the field itself so that the ground and the figure move simultaneously).

While Emery and Trist were referring to social systems and their social environments, the interaction of social and environmental sets presents an excellent example of a turbulent field. Holling (1986) has noted that ecological surprises are more likely to occur as the systems become more interconnected over larger spatial scales. Similarly, social system turbulence would appear to increase as these systems become both larger and more richly connected. Surely, when two highly interconnected turbulent systems interact, as in the case of ecosystem management, one may expect the uncertainty to become very high indeed (Gunderson et al. 1995).

Stability (from the point of view of any agent) in such circumstances is difficult to come by but can be aided, according to Emery and Trist, by "the emergence of values that have an overriding significance for all members of the field" (1965). Values provide a guide and act as power fields to help all members of such complex systems coordinate their actions (Lewin 1935). They can help to stabilize and simplify turbulent fields if they represent the new environmental requirements. They are the equivalent of the image as a guiding force for complex action systems, as determined by Boulding (1956). Trist (1983) advocated bringing together stakeholders into referent organizations, representative of all stakeholders, who would discuss priorities and agree to cooperate even when they were generally in competitive relationships. Industry associations, where competing companies meet on a regular


basis to agree to collaborate on such things as research and development or supply sourcing, are examples of such referent organizations. Emery and Trist (1965) felt that some degree of collaboration or cooperation, in the interests of forging such common value sets, would be a prerequisite for reducing uncertainty and surprise and increasing resilience.

Problem Domains

In later work, Trist (1983) referred to such turbulent environmental fields as interorganizational problem domains. A problem domain is defined as the entire group of people or organizations involved in a common set of problems. The destruction of the ozone layer and the restoration of the Mediterranean Sea (Haas 1990) are good examples of problem domains, demanding that a wide variety of stakeholders (governments, industry, residents, NGOs) collaborate to seek solutions.

Problem domains can also fall along a continuum of relatively placid to volatile, defined as "domains characterized by large power differences between concerned stakeholders, histories of problematic conflict, and potential for surprise and turbulence" (Gray, Westley, and Brown 1998). The challenges of managing shallow lakes in Holland or the matorral forest systems in Chile clearly represent volatile problem domains. They are turbulent fields in which the dynamics of the ecological system and the dynamics of the social system have become interconnected with a concurrent increase in uncertainty and surprise for the human actors and flips in the ecological system. In addition, they are contested resource bases where actors of different power levels seek to secure ecological services. More powerful parties can shape the rules and rituals for how the domain will be organized (Hardy et al. 1998), at times even suppressing differences and quelling conflict among stakeholders by silencing certain voices entirely (Bacharach and Baratz 1963; Gaventa 1980; Foucalt 1980). For example, research in political ecology has argued that political and economic elites have sought to justify specific, usually highly unequal, patterns of human use of the environment in terms of the greater social good, at times explicitly employing the discourse of scientific management to justify unequal appropriation of ecological services (for example, in commercial timber extraction). In the past, such volatility has resulted in outbursts of environmental violence, which some researchers expect to continue and indeed increase (Homer-Dixon 1991).

So what can a manager confronted with this level of complexity do in order to deal with and manage surprise and to increase the resilience of the social system? The keys to management action would seem to lie in (1) understanding how to analyze problem domains, both in terms of structure and in terms of dynamics; (2) understanding the role of collaboration in transforming the domain; and (3) understanding how values and social capital formation can help in the management of these complex processes. We will first review the concepts of organization and collaboration and their role in

228 SCHEFFER, WESTLEY, BROCK, HoLMGREN

the transformation of volatile domains. We will then look at the construction of social capital and values as critical tools in facilitating collaboration. Last, we will look at these concepts as they appear specifically in our two cases. Our concerns here will be to determine (1) who the relevant stakeholders are who form the organizational field and (2) to what extent the values and culture of the relevant stakeholders, collaboration and competition, and the formation of social capital have had a role in reducing uncertainty and surprise on the part of those in charge of the system.

As social-political-cultural-economic systems become more interconnected, new forms of organization seem to be demanded in order to effect change in a particular problem area (Trist 1983; Cooperrider and Billmoria 1993; Westley and Vredenburg 1997). Command and control approaches to ecosystem management seem designed to produce the kind of surprises and uncertainty managers hate and increase the overall turbulence of the socioecological system (Gunderson et al. 1995). On a practical level, therefore, it is important to understand the dimensions of domain organization, what it involves and how it affects the domain problem transformation, and how specific actions on the part of managers can facilitate these processes.

Factors That Govern Domain Stucture

The dynamics of volatile interorganizational domains are in part a product of the structure of that domain, while at the same time these dynamics restructure and reshape the domain. Structure has been the subject of considerable study at the level of organizations and industries (Spender 1989; Porter 1980). The structure of interorganizational domains, however, has been more difficult to grasp. Most discussion of structure has focused on social networks and exchanges among parties (Granovettor 1985; Burt 1997) rather than at the level of the domain as a whole. In part this may be because interorganizational problem domains are relatively fluid, emergent, and loosely coupled. Nonetheless, an understanding of how such organization comes about is clearly essential to guide the search for successful strategies to the resolution of problems of the interaction of societies and ecosystems, which requires an approach at this high hierarchical level.

Since the 1950s a variety of sociologists have developed theories that attempt to link the micro level of social action with the macro level of social structures and institutions (Parsons and Shils 1951; Berger and Luckman 1967; Collins 1981). Among these, Giddens's (1976, 1979, 1982, 1984) theory of structuration provides a guide to the process of social organization that combines the notion of emergent process with the notion of enduring institutions. Because it links emerging order to enduring institutional patterns, this framework is ideal for exploring how domains become organized, the degree to which domain order is negotiated, and the extent to which it is institutionalized. It also allows us to consider the interaction between social capital and domain organization on the one hand and domain transformation on the other.

8. DYNAMIC lNrERACTION OF SOCIETIES AND ECOSYSTEMS 229

Giddens suggests that social systems are structured in three ways. First, social systems are made up of structures of legitimation-the sets of norms and rules that regulate how we interact with other people. Rules are linked to social action through agreed upon rights and obligations and an encompassing set of social sanctions that enforce conformity. Second, social systems are made up of structures of domination-allocative and authoritative resources that are distributed among social actors to facilitate goal-oriented action. Taken as a whole, this pattern of resource distribution undergirds the power structure of social systems. Last, social systems are made up of structures of signification-the interpretations or meanings that individuals use to make sense of their experience. Structures of signification are linked to social actions when these meanings become generalized in interpretative schemas or maps. These processes are embedded in the communication systems of any society.

Giddens argues that these three aspects of social structure can be observed at a macro, or institutional, level (in laws, political systems, and cultures), where change is slow and rare, but also at the micro level, where they are reproduced or subtly transformed on a daily basis by individuals in interaction rituals (Goffman 1967), the simplest of which is the conversation (Collins 1981; Westley 1990). When participants share and agree upon the basic rules and rituals of engagement, interactions are relatively straightforward. However, when parties from radically different S<?cial systems meet and attempt to engage, new rules of legitimation, domination, and signification must be negotiated. This is particularly important for the interorganizational problem domain, as it, by definition, involves stakeholders who come from different systems and retain loyalties to those systems. For example, discussions around lake management in Minnesota may involve sports fishermen, commercial fishermen, cottage owners, farmers, several different levels of government, scientists, NGOs, hobbyists, native groups, and others.

The Dynamic Nature of Problem Domains

The structure of social interactions with respect to a certain common problem may change profoundly from the moment the problem becomes recognized to the moment when a solution is found. At early stages of a domain formation, many involved stakeholders may not even recognize that they have a stake (Westley and Vredenburg 1991). For instance, a chemical firm may be unaware that its operations will be impacted by an environmental group that was formed to protect water quality in a nearby town, and many citizens may be unaware that their health has already been affected. At the other extreme, at very high levels of domain organization, all stakeholders may find themselves entrenched in conflictual positions, making negotiations and coordination almost impossible (Lee 1993).·For instance, municipalities, environmental groups, government agencies, native groups, and industry groups increasingly resort to the use of lawyers to. contest rights to water use and exploitation in the Columbia River Basin. Each fully knows


the position of the other, is unwilling to compromise, and looks for a higher authority to settle the dispute. In such cases, contests over the perceived legitimacy of certain stakeholders and their right to define the domain will likely erupt (Gray 1989; Westley and Vredenberg 1991; Hardy et al. 1998). This may be part of a successful process of domain transformation, provided that such conflict coexists with participation in domain level collaborations (Brown and Ashman 1996). However, a common characteristic of highly volatile domains is that repeated failures of interaction rituals reduce trust and increase stereotypes, preventing cooperation. With dysfunctional or nonexistent rituals of interaction, the potential for disruptive conflict is high (Brown 1980, 1982).

Transformation of the domain becomes particularly restricted when either patterns of collaboration or patterns of conflict become so established and routine that they become rigid and ritualistic. We say then that the domain is suffering from overorganization, tight coupling, a high degree of structural rigidity and ritualization and homogeneity. In such cases, initiation of domain-level collaboration requires an opening up, destruction, or deinstitutionalization of the system to include a greater variety of stakeholders and/or to negotiate new rules, meanings, and resource allocations (Gray, Donnellan, and Bougon 1985). For example, an environmental round table of industry, government, and environmental experts may have evolved over time from a think tank to a club of friends, used to each other and to solving problems by formula. It may need to be expanded to include new organizations and individuals in order to deal with new or unanticipated challenges.

Frequently, such efforts occur only after grassroots mobilization efforts have called attention to the problem, often using confrontational tactics (Gamson 1975; Gricar and Brown 1981; Fox and Brown 1998). For example, in many parts of Canada, native rights groups have become increasingly organized, putting pressure on the government to secure their traditional rights. As a result, the government has intervened to take away the rights to some prime logging areas. This in turn has made some of the dominant pulp and paper companies, which have traditionally been able to resist the protests of environmentalists, much more vulnerable. The result is that for the first time those companies have been prepared to seriously consider alternative technologies to clear-cutting, and to seriously negotiate with less powerful stakeholders in the domain. Indeed, such collaborative efforts that foster interaction among stakeholders with differing power and interests can generate substantial improvements in the domain (Gricar and Brown 1981; Gray 1989; Uphoff 1992; Brown and Ashman 1998).

Of course, a domain problem such as the collapse of an ecosystem is generally nested in a larger institutional polity and culture, and both can play important roles in facilitating or impeding domain-level collaboration. For example, a culture that values collaboration may make it much easier for stakeholders to negotiate differences. If a high value is placed on a pristine environment, it may help various stakeholders to get by their differences to

8. DYNAMIC INTERACTION OF SOCIETIES AND ECOSYSTEMS 2 31

make joint decisions. Values such as scientific integrity are more problematic but can also play an important role in creating common ground and coordinated proactive action on the part of stakeholders (Yaffee 1994). There is no question that national cultures vary considerably in the value they place on nature, as they also vary in their comfort with collaboration as a problemsolving process (Trompenaars 1994; Berkes, Folke, and Gadgil 1995). However, these are elements outside of the manager's or even the management agency's control, and can only be appreciated and anticipated.

On the other hand, mandated reorganization of the domain, through external judicial decisions, can also produce the necessary destabilization that opens the door for new rituals of engagement (Gray, Westley, and Brown 1998). If management agencies are able to influence social policy, they can influence some of these contextual factors. Perhaps even more important, however, individual managers can work to increase and mobilize social capital in the domain.

The Role of Social Capital

A key concept in our analysis of domains is social capital. Social capital refers to the aggregate of actual or potential resources that can be mobilized though social relationships and membership in social networks (Nahapiet and Ghoshal1998). In essence, the concept focuses on the value of relationships for the individuals, groups, and organizations that participate in them (Loury 1977; Bourdieu 1990; Coleman 1990; Burt 1997; Evans 1996; Putnam 1993a, 1993b; Fukuyama 1995). At the level of the interorganizational domain, social capital represents a key resource for managers seeking to link key actors in collective action. Managers who can build or tap social capital can play a role in forging, catalyzing, and enabling the action energy stored in these relationships to be used for managing ecosystems adaptively.

Social capital is built up through investing in social relationships. For example, an individual can give another individual support, information, free labor, free contacts, and other forms of favors. Depending on the history of the relationship (established and tested v. new and untested), investments can be high or low yield, secure or risky, fixed or fluid (Westley 1998). However, source capital is always based on personal exchanges and builds with reciprocity, which endures over time (U zzi 1997). If a favor is done and no reciprocation follows, even on request, the investment is essentially lost. Some investments are therefore more risky than others, primarily those that are made in the context of new, untested relationships or weak ties (Granovettor 1973). On the other hand, much as with financial investments, these risky social capital investments may have the largest return.

·Structural Holes in Social Networks

As Burt (1997, 1992) has pointed out, network ties across structural holes (linking two individuals whose primary networks are linked in no other way)


represent the greatest increase in resources for the individual. From the point of view of domain transformation described above, such links also bring new groups of stakeholders into exchange relationships and so may be of key importance.

In volatile interorganizational domains, the history of conflict and power differences often means that relatively little social capital bridges the differences between low- and high-power actors. There is often relatively little social capital within low-power groups as well. Transforming interorganizational domains may require the development of social capital that supports cooperation among key actors that have not been linked before. Sometimes this may involve increasing the social capital that links actors with similar concerns and interests, such as the mobilization of many low-power grassroots actors for collective action (Gamson 1975), or the development of coalitions of like-minded organizations to exert influence at levels of aggregation that cannot be influenced by any one of them working alone (Fox and Brown 1998). This may be called horizontal or formative social capital. On other occasions it may involve creating social capital that links diverse domain participants, such as grassroots groups, nongovernmental organizations, international agencies, and government actors (Fox and Brown 1998; Gray 1989). This kind of social capital can be referred to as vertical or bridging social capital. Both kinds of social capital seem to be necessary for domain transformation (Westley 1998) and may occur at different stages in the transformation. From the point of view of the lower-status or poorer actors, such links with higher-power/resource stakeholders may, again, be particularly high yield, as the value of cooperation/reciprocity of such high-status actors is worth more than that of low-status actors, in terms of affecting real change. However, from the point of view of the high-status actors, entering into these reciprocities is correspondingly risky and therefore often to be avoided. It is for this reason that a destabilization of the institutional context (such as outside intervention) is sometimes required, before high-status actors will enter into such collaborations.

Common Culture as a Catalyzer

It is important not to neglect, however, the role of common culture and meaning in the creation of social capital, both horizontal and vertical. Particularly in the absence of a long history of reciprocity and the trust that engenders, stakeholders will often make the decision to enter into the initial reciprocities on the basis of their belief that they share representations, interpretations, and systems of meaning with the other party or parties (Nahapiet and Ghoshal 1998). This in part explains the key role of domain entrepreneurs or visionary leaders in domain organization. They have the ability, among others, to tell a story (create a structure of signification) that can appeal to many different stakeholders (Gardner 1995) or to tailor a story so as to secure the cooperation of key stakeholders (Westley 1992). Sense making is an ongoing and perpetual process in domain transformation as in

8. DYNAMIC INTERACTION OF SOCIETIES AND EcOSYSTEMS 233

organizational transformation (Weick 1995; Powell 1995) and requires an understanding of how to create and re-create negotiated orders among divergent and sometimes competing viewpoints (Strauss et al. 1963).

Repeated Patterns of Domain and Social Capital Dynamics

As noted earlier, when managing ecosystems, managers must deal with a social system (domain) as complex and dynamic as the ecological system itself. It appears, however, that these domains often move through more or less predictable patterns of transformation. For example (Figures 8-11 and 8-12), in the early stages of a problem-say, a polluted lake system-the social actors may represent a highly scattered and diffuse (undifferentiated) collectivity, unaware of the problem and their stake in it and with poorly articulated or differentiated responses. In this stage there is much free-floating or kinetic social capital available for use in organizing, but as yet the domain is uniformly undeveloped or disorganized (hence, high in homogeneity or connectedness). This hypothetical domain may begin to be transformed by an individual or group identifying, articulating, and naming the probleme.g., destruction of the lake-and trying to get others interested in collective action. Generally, the first stage involves forming links with like-minded stakeholders, who begin to differentiate themselves into groups or coalitions. We may term this lateral or horizontal social capital utilization. In the process, the domain becomes less homogeneous, and free-floating social capital becomes locked up in specific reciproCities. The domain moves from a scattered to a mobilized configuration. The next stage is to secure a redistribution of resources, changes in norms or regulations, and increasing articulation of a we-them perspective. Conflict in the domain may increase

III

~-\ / ', : '

I / ',~,L \II

~ <----'~ ---------

Figure 8-11. An example of social network structures. Group I has strong withingroup links but few links to other groups. Group II has weaker within-group links but is well linked to other groups both horizontally and vertically to the higher hierarchical level (Group III). The rareness of links between groups is referred to as structural holes. Links that bridge structural holes are essential for solving problems that concern more than one group (see text).


invested r-----------, (potential) mobilized

diffuse polarized (kinetic) '--------------'

heterogeneous connectedness ~

homogeneous

Figure 8-12. Domain transformations that usually occur after the emergence of a problem. Initially, few of the stakeholders that are potentially involved (the domain) recognize the problem, and few social links have evolved around it (scattered phase). When recognition grows, the affected group becomes mobilized and the social capital invested in links increases. When horizontal and vertical links between groups exist, an agreement may be reached and institutionalized. However, in a segregated situation with few horizontal links, a phase of polarization may delay institutionalization of a solution for a long time.

as the mobilized coalitions pressure for change. Differentiation increases, and social capital again shifts to a more kinetic form as alliances and new realignments are sought. If this conflict persists, the problem domain may be frozen in a polarized configuration. If, however, these actors are able to invest successfully in a new pattern of reciprocities, linking low- and highpower groups, the collective values may be linked to resources and hence institutionalized, at least for a time. New legislation may be enacted and resources redirected toward protection of the lake.

Importantly, the quality of the solution that is reached during the social process may actually benefit from the occurrence of some conflict (in combination with cooperation). To see this, consider the distinction that Mary Parker Follett (1924) drew between integrative and compromise solutions. Integrative solutions are much more difficult to achieve than compromises. For example, if two people working in the same room start to argue about whether the window should be opened or closed, a simple compromise is to leave the window half open. However, if what one person wants is to have air and the other to avoid draft, the compromise meets neither objective. Analternative is opening a window in an adjacent room. That way both people get what they want. Such an integrated solution is better than a compromise but takes more work, greater understanding of the needs of all parties, and more creativity.

Of course, most large-scale or meta-problems, and certainly those presented by such complex systems as the natural environment, are never permanently resolved. Change will occur, demanding adaptation on the part of the collectivity. With the domain in the fully institutionalized form, ho-


mogeneity is high and social capital tied up in existing reciprocities and arrangements. For the system to be considered resilient, it must not become too monolithic. As the environment changes, the social system may need to deinstitutionalize and return to a more scattered state in order to incorporate new stakeholders or allow for a new definition of the situation. If the social system is to continue to adapt to the changing ecosystem, the process of scattering, mobilizing, polarizing, and institutionalizing needs to be continuous over time (see Figure 8-12). Ultimately, this infinite loop resembles something like the Holling four-box adaptive cycle.

Two Contrasting Examples of Society-Nature Systems

The challenge confronting the manager seeking to manage ecosystems adaptively is to anticipate and facilitate the process of domain transformation so that the system remains flexible enough to respond to ongoing changes in the ecosystem with appropriate collective action on the part of stakeholders. Domains at different stages require different managerial interventions. We now turn to a comparison of two cases of flipping ecosystems whose social systems present radically different configurations.

Holland and Its Shallow Lakes

The Dutch culture generally values collaboration or cooperation. Usually, significant policy issues are widely discussed among groups in Holland. This attitude has been institutionalized in a set of formal collaborative consultations about the use of lake systems. The shallow lakes in Holland offer ecological services to a number of different stakeholders: farmers, who use the lakes as phosphorus sinks; fishermen and recreational users who want clear water and healthy and varied fish; municipalities, which need clean water. However, the value of collaboration means that groups feel some pressure to compromise with others, as opposed to outright competition for the resource.

This may mean that a negotiated order is possible. However, a drawback is that the emphasis on collaboration may ultimately result in compromises, which in many cases can turn out to yield an unsatisfactory final outcome, as discussed in earlier sections. Indeed, in the case of the Dutch lakes, compromise was sometimes accepted as a solution with negative consequences, due to the particular dynamic of the lakes. For example, in order to restore a lake that has flipped, the fish may need to be reduced by more than 80 percent. A compromise that reduces the price of the operation may lead to a 50 percent removal, however. But this has negligible restorative effects on the lake.

The Dutch water system has been managed for centuries at the local level. Water boards have traditionally regulated water flows and prevented local inundation, but they have evolved over the past decades into institutions that are responsible as well for water quality and ecosystem values. In the case of the shallow lakes, these local organizations are now linked to a


central scientific organization, the Research Institute for Inland Water Management (RIZA), largely through a key individual at that institute who acts as a domain entrepreneur. That individual had personally built up considerable social capital with members of the local water boards concerned. He also had social capital with Dutch scientists, and through RIZA he influenced policy at the ministerial level. The web of social capital that he had helped to engineer, therefore, spanned both the horizontal and vertical domains and appeared to be key. These links allowed him to prepare the ground for implementing scientific experiments with the local water boards and at the same time to alert policy makers of the policy implications of these experiments. The main challenge (getting buy-in from the farmers) was somewhat reduced by the relative power of the two ministries (agriculture and water/transportation). His own ministry (water/transportation) was better coordinated internally and had greater clout at the policy level. Nonetheless, the Dutch emphasis on collaboration meant that there were ongoing efforts to include the farmers in the discussions as well as ongoing effort to forge consensus.

The Dutch case represents a good example of resilient collaboration. The problem domain, taken broadly as managing water, has been recognized for several centuries. Effective interorganizational action has been mobilizedinvolving scientists, local users, national government, and fishermen-and institutionalized into structures such as the water boards. The social capital in the domain has been continuously restocked by such individuals as the domain entrepreneur. And the Dutch value of collaboration creates a strong norm of reciprocity and consensus building such that when new challenges are presented (e.g., the destructive possibilities of phosphates), new stakeholders (e.g., the farmers) are coming to the table and entering discussions. The main danger seems to be that the same value on collaboration may result in compromise, which in fact will hinder the lakes recovery.

Chile and Its Evergreen Shrublands

In sharp contrast is the Chilean case. As in the shallow-lake case we have a fragile ecosystem (semirarid shrublands and forests called "matorral"), which also flips into an alternate stability domain (degraded, poorly vegetated land) with little hope of recovery, but the human domain is very different. The political instability of the country over the past thirty years has reduced the social capital and trust between groups and hence the ability to organize around a problem. In addition, collaboration and consensus building do not have the same pride of place in Chile as they do in Holland. Rather, the country has been dominated by powerful families, some of them owning large tracks of the matorral vegetation. In some cases family pride demands its protection. However, as the protection rests on the whim of the family, it can as quickly be turned over for development or other uses. There seems to be very little coordination at the ministry level and very little power vested in

8. DYNAMIC INTERACTION OF SOCIETIES AND ECOSYSTEMS 2 3 7

the agencies whose job it is to protect these resources. Probably one of the crucial issues is that many Chileans, including some policy makers, do not appreciate the matorral as an inherently valuable ecosystem, special and diverse, that needs to be preserved. This contrasts with the international biodiversity agenda that has included it as one of the conservation priorities. As a result, the matorral is not strongly legally protected, it has a very small proportion of its surface within national parks, and most of it falls into private hands. It would be fair to say that for the most part the problem domain in Chile is in a scattered state, with little or no common awareness of the problem and the resulting stress on the ecosystem.

Some efforts have been made to mobilize action to protect the land. There are associations of small farmers that undertake restoration projects. Individual scientists, working with national park managers in some cases and indigenous groups in others, work to create awareness and to mobilize actions. However, in some cases this has resulted in increased polarization and conflict, particularly in the south. What is still in relatively short supply is the kind of vertical social capital formation required for domain transformation, although, again, some scientists, working at the interface of science, policy, and action, have gone to some lengths to get environmental impact assessments on the agenda for new development projects. Last, the NGOs working in Chile have forged links on an international scale with similar groups around the world. Much of the pressure for change is coming from outside the system, but whether it will raise awareness of the problem quickly enough to prevent an irreversible loss of resilience in the matorral is up for question.

What is interesting about these two cases is that they represent very different configurations of domain organization and social capital formation. In the Dutch case, the domain has been institutionalized but is kept resilient by the constant release of potential social capital into kinetic social capital at the micro, managerial level and the ethic of collaboration at the macro, cultural level. The Chilean case is just moving from a scattered to a mobilized state of organization and social capital formation. To the extent that it is moving toward domain transformation it is also heavily affected by scientists who are prepared to act as domain entrepreneurs, working with the various stakeholder groups to create awareness and with the government agencies to formulate policy. However, at the macro, cultural level, Chilean managers wishing to manage the matorral in an adaptive fashion are confronted with an ethic that values the unfettered decision making of powerful families and unrestricted development, and a history of fear that makes collaboration difficult.

These two cases illustrate that the large-scale cultural variables may indeed be important shapers of domain transformation, working as slow variables. Also, they point to the fact that the scientist's willingness to get out of the laboratory and work to build social capital in the domain can be as important as the scientific experiments themselves.

238 SCHEFFER, WESTLEY, BROCK, HOLMGREN.


The theories that we reviewed from systems ecology, and a diverse set of socioeconomic branches of science, suggest some emerging patterns and driving forces that seem essential to take into account if one wants to construct a theoretical framework for understanding the dynamic interaction of societies and nature. The main points are:

•

•

•

•

•

Human activity imposes a continuously increasing stress on most ecosystems. That stress causes ecosystems to change. The change can be smooth, but some ecosystems collapse unexpectedly at a certain critical stress level and are difficult to restore.

Usually, only a subgroup of society benefits from the activities that cause the strongest stress to the ecosystem, whereas the costs of environmental degradation are largely on the account of other groups, or society as a whole (the CCPP strategy: Commonize the Cost, Privatize the Profit). Normative economy aims at finding the ecosystem use that yields the long-term maximum benefit for society as a whole.

The key to finding ways of realizing such sustainable use in practice is in the understanding of what really drives the dynamics of societies in response to the ecosystems they depend upon. Economic, political, and other social sciences have generated many useful insights in this respect.

Political economy models predict how much effort different groups of stakeholders will invest in influencing the political process and stress that groups that are better able to motivate all members to invest (avoid free-riders) will be most successful in getting what they want. Various ways of setting taxes and other regulations are the main tool proposed to arrive at a fair and sustainable use of nature.

Management scientists and sociologists have stressed the dynamic nature of the group of stakeholders involved in problems and the crucial role of social network structure for the final outcome of a conflict: A problem (degradation of an ecosystem) will first be noticed by a few perceptive individuals (often scientists or directly affected persons). These individuals may try to stop or reverse the degradation, investing their kinetic social and other capital into advocacy or change groups. If they have a clear and convincing story (signification) and good social links with fellow stakeholders they will be able to mobilize a large interest group. H woever, other interest groups may mobilize in reaction. If few social links between groups exist, a prolonged polarized phase may occur.


Between-group links and links to higher hierarchical levels may help to find a solution, which becomes institutionalized. In many cases, social links outside the direct interest group seem crucial in "getting what you want."

• Various social and political processes tend to lead to a compromise. A combination of ecological and normative economic analysis shows that such compromise may in many situations be bad for overall community utility. Avoiding these lose-lose situations requires a good insight into the response of the ecosystem to human use, an insight into different utilities, and suitable social structures.

• In conclusion, we identify four key ingredients for a resilient sustainable human-nature interaction:

1. a clear and widely shared insight into ecosystem-dynamic responses to human use;

2. a br9ad and widely shared inventory of ecosystem utilities-short and long term, local and global;

3. avoidance of bias due to differences in the organizational power of different groups of stakeholders; and

4. social network structures that bridge gaps between interest groups and hierarchic levels.

Each of these key aspects would be worth addressing explicitly in projects aimed at developing strategies for sustainable use of ecosystems by societies.

CHAPTER 9

A FUTURE OF SURPRISES

Marco A. Janssen

Surprise is always relative, which explains why,

whenever something unexpected befalls us, there is always someone who "saw it coming. "

-Michael Thompson

T:e aim of this chapter is to discuss some recent developments in inegrating human perceptions into ecological-economic models and o use these models to explore the possibilities and consequences of

changing perspectives on sustainable futures. Integrated models describe the interactions among people, economies, and nature to explore possible outcomes. These models incorporate simplifications of human behavior to begin to study the interactions between dynamic biophysical systems and subjective perceptions of reality.

The variety of expectations about the future can be nicely illustrated by the behavior of a financial market, the place where expectations of companies' futures are valued. According to the "efficient market hypothesis" in economics, price fluctuations are an immediate and unbiased reflection of incoming news about future earning prospects. However, financial markets have experienced large price fluctuations that are not directly related to external disturbances but caused by internal dynamics. Behavioral economists argue that psychological factors often lead to more quasi-rational decisions (Thaler 1992). Multi-actor models are used to study the financial market behavior, where actors have different strategies in determining expected prices (Lux and Marchesi 1999). These multi-actor models have been found useful to explain observed fluctuations on financial markets ..

This chapter focuses on perceptions of sustainable development by multiple actors in human-ecological systems. Sustainable development is a rather vague concept related to maintaining opportunities to meet the needs of future generations. Because it is not clear what those needs will be, or how they might be satisfied, the implications for environmental policy of the

241

242 }ANSSEN

desire for sustainable development have been variously interpreted. Should this policy be preventive, adaptive, or reactive? The different perceptions of reality will lead to surprises when expectations significandy differ from observations. As suggested by the adaptive cycle (Chapter 2), these surprises can trigger changes in perception of reality and related resource management.

Controversies and different interpretations have a long history in determining how to manage the environment. For instance, Malthus (1798) regarded food production as a land-limited resource that could not possibly be increased quickly enough to keep pace with a growing population. His expectation did not come true for various reasons, among them the sharp increase in agricultural productivity and the decrease in birth rates. Another example is provided by the Limits to Growth report to the Club of Rome (Meadows et al. 1972), which concluded from a model-based analysis that the continued depletion of resources would result in a collapse of the world economy. However, the oil crisis of the 1970s led to intensification of exploration efforts that located additional oil reserves, and induced investments in energy efficiency and renewable energy sources (Meadows et al. 1991). The simulations made in 1971 did not include either the oil crisis or possible responses to it. The complexity of the human-ecological system is seriously underestimated in such analyses. This is particularly true with respect to the response and adaptation options and the capability of humans to apply and expand such options.

In this chapter the inclusion of perceptions of reality and surprises within integrated models is explored. First, the field of integrated modeling is discussed in the context of the theories on scale and resilience as applied in this book. Then theories of different perceptions of reality are explored, especially the cultural theory. This theory is then used to construct an approach to explore institutional change, which is finally applied on an integrated model of global climate change.

In line with other chapters in this volume (Chapters 7, 8, and 10), simple models are used in this chapter to study the interactions between human activities and ecosystems, in patterns suggested by the adaptive cycle. Such models link simplified versions of expert models into an integrated framework Qanssen 1998). Integrated models can be used for a variety of reasons. Understanding of complex human-ecological systems is one reason, management is another. Ideally, models should integrate insights from various disciplines such as economics, psychology, ecology, and physics. The integration should be clear and acceptable to leading scholars in various disciplines. The purpose of such models is to study key interactions between the various elements in a qualitative way and find ways to improve the future quality of the system, however it is defined.

Modeling Human Behavior

Of all elements in integrated models, the behavior of human beings is probably the most complex. Since theory in social science is rather fragmented,

9. A FUTURE OF SURPRISES 243

models of human behavior that are useful for simulation models are not generally agreed upon. Integration of human behavior into integrated models is therefore biased at the start, through the elements of social science that are assumed to be important for our purposes and that can be included into a formal model. Although formal models cannot include every nuance of our understanding, they pose clear assumptions and consequences.

Traditionally, economics has been the social science that developed formal models of human behavior. Conventional economics assumes rational actors in order to study human behavior. The rational actors are self-regarding individuals maximizing their own well-being. However, the powerful concept of the rational actor has not been validated by experimental research in economics and psychology and is therefore an oversimplified model of human behavior (Gintis 1997; Loomes 1998; Ormerod 1994; Thaler 1992).

Since the early 1950s, social scientists have used computers to simulate behavioral and social processes. Economist Herbert Simon pioneered in developing models of bounded rationality (Simon 19 57, 1996). Due to the development of new simulation techniques, such as cellular automata, genetic algorithms, and neural networks, and the widespread availability of personal computers, social scientists now explore new ways of modeling human behavior (Vallacher and Nowak 1994; Gilbert and Doran 1994; Gilbert and Conte 1995; Conte et al. 1997; Liebrand et al. 1998; Jager 2000). These simulation models use interacting agents to study social processes in simple and complex environments.

A General Framework

As with ecological processes, we can describe the various components of integrated models in line with the adaptive cycle. In this chapter a general framework of systems will be used that is based on the many case studies described in this book and other literature (e.g., Berkes and Falke 1998; Diamond 1997; Giovanni and Baranzini 1997; Gunderson et al. 1995b). The so-called complex ecological-economic systems refer to the transdisciplinary approach of ecological economics and to the study of complex systems (Anderson et al. 1988; Costanza 1991; Holland 1995; Waldrop 1992). Four basic elements dominate the descriptions of the case studies: economic agents, institutions, physical economic systems, and ecosystems. Studying complex ecological-economic systems requires a transdisciplinary approach to study these four subsystems and their interactions. The four components can be described as follows (Table 9-1):

• Economic agents are the total of consumers and producers in an economic system. Decisions made by these agents-the households, the companies, etc.-are based on the satisfaction of needs, which vary from subsistence (physical and mental health of

244 JANSSEN

people, profits of a company) to identity (big car, market leader). How to satisfy these needs is based on the abilities and the opportunities of the agents.

• Institutions can be defined as a set of rules used by a group of individuals to organize repetitive actions that affect this group and can affect others. Institutions are made up of formal and informal constraints. Formal constraints are rules, laws, and constitutions. Informal constraints are norms of behavior. Institutions often react to surprises by adding additional rules to repair external effects. New rules can be added in a relatively brief time, but changing or removing rules is usually a slow process.

• The physical economy can be described in stocks and flows of energy and materials. In fact, the physical economy can be considered to be the metabolism of the economic system. The stocks and flows are designed with a functional purpose: houses to live in, infrastructure for transport, electric equipment to make housekeeping more comfortable, etc. Materials and energy often disperse in the economic and environmental systems in low concentrations. The flows of materials and energy use can change relatively rapidly compared with the slow changes in the accumulation of materials in various stocks in the economy and the environment.

• Ecosystems are the collections of living and non-living components of the environment functioning together. The human population and human-made environment were described in more detail above in the three other components of complex ecologicaleconomic systems. Ecosystems also involve physical, chemical, and biotic constituents of remarkable complexity. Some constituents of ecosystems change rapidly (e.g., certain chemical reactions or interactions among organisms), while others change slowly (e.g., geomorphological changes or soil weathering). Evolutionary changes in the biota can adapt to changes in the environment and account for much of the resilience of ecosystems. But the rate and capabilities of evolutionary change have limits. Human disturbance can produce irreversible changes in ecosystems, such as biodiversity losses, as well as changes from which recovery is slow, such as deforestation. These are the changes that forward-looking institutions must anticipate to avoid severe social costs.

Each subsystem can be described in line with the adaptive cycle. They can all be described as a dynamic process in which change is triggered by surprises. Surprises can arise from internal or external sources. Internal surprises evolve from the subsystem itself and can lead to changes in other subsystems. For example, the pig plague in the Nether lands during the 1990s was started at farms in Germany where boars and pigs lived together. Illness


Table 9-1. The Characteristic Elements of Complex Ecological-Economic Systems

Economic Physical Agents Institutions Economy Ecosystems

Components consumers formal and material and populations and and producers: informal energy stocks non-living households constraints and flows environments and companies

Diversity needs, rules, laws, functional genetic, opportunities, norms, and functional abilities traditions (biodiversity)

Surprises bankruptcy, external effects technical or fire, floods disease physical

collapse

Fast variables individual adding new material and behavioral decisions rules energy flows change

Slow variables habits changing or material stocks evolutionary deleting rules change

among the boars led to a pig plague in Europe. Because of the high density of the Dutch pig industry, stimulated by government subsidies, the consequences were severe for the Netherlands. The financial costs reached a billion dollars. The pig plague provided the government with the opportunity to change the pig industry in the Netherlands and reduce acid-rain-causing emissions.

In the case of global climate change, various stakeholders have different interests in using or producing energy. The physical economic system consists of capital and energy production. Institutions can change the rate of change of the capital stock and the degree of reliance upon alternative energy resources. Stakeholders can become surprised when the changes of ecosystems are different than expected. This can trigger the collapse of current institutions and the initiation. of new types of institutions.

Although many different possible surprises can trigger structural changes in the system, this chapter will focus on how perceptions of reality can lead to unexpected behavior in some component of the integrated system. The next section describes some classifications and dynamics of perceptions of reality.

246 JANSSEN

Perspectives on Reality

Thompson et al. (1990) developed cultural theory to provide perspectives on social relations in natural and human systems. That theory will be used in this chapter to illustrate the possibilities of modeling changing perspectives. The motivation to use that cultural theory, and not another classification, is that it includes different perspectives that describe the relationship between human and natural systems, a claimed generality, and a deterministic rationality for each of the different perspectives. This makes it suitable for modeling purposes. This does not mean that the modeling approach described in this chapter cannot be applied using other classifications of human behavior (Janssen 1998). The large number of theories in social science force us to make a choice for one theory without abandoning the others.

Thompson et al. (1990) borrowed anthropological insights from Douglas (1982) and combined them with ecological insights elaborated by Holling (1973a, 1986). Thompson et al. claim that notions of human and physical nature are socially constructed, and that the four myths of nature derived from ecologists closely coincide with certain ideas of nature. These myths of nature are in line with car!catures of nature flat, balanced, and anarchic as described by Holling et al. (Chapter 1). The crux of their theory is that societies can be characterized along two axes, labeled "group" and "grid" (Figure 9-1). Douglas and Wtldavsky (1982) proposed the grid-group typology to characterize societies along two axes. The group axis reflects the extent to which an individual is incorporated into bounded units. The greater the degree of incorporation, the greater the subordination of the individual choice to the group determination. The "grid" axis denotes the degree to which an individual's life is circumscribed by externally imposed

+ .--------, r--------, fatalist hierarchist

group (incorporation in unit)

+

Figure 9-1. Categories representing four different cultural perspectives (Schwartz and Thompson 1990). Each type is contrasted along grid and group axes. The surfaces within each box correlate each type with a myth of nature (Chapter 1, Figure 1-1).

9. A FuTURE OF SURPRISES 247

prescriptions. The more binding and extensive the range of prescriptions, the less scope there is for individual negotiations. The social control sets the perspectives apart from each other. The group-grid characterization yields four different perspectives (or worldviews). They inform the individual's perception of the world and his or her behavior in it, and are labeled in turn as: the hierarchist, the individualist, the egalitarian, and the fatalist.

Three of these perspectives or paradigms are active. Holders of these perspectives think they can structure the world. The hierarchist lays down the rules. The individualist is the pioneering innovator. The egalitarian criticizes both the rules established by the hierarchist and the exploitative attitude of the individualist. The fourth category, the fatalist, is passive. The fatalist is a necessary loser in the world of the individualist, and fatalists occupy the lower echelons in the hierarchy of the world as envisaged by the hierarchist. For the egalitarian, the existence of fatalists is evidence of the injustice and irresponsibility of the other two ·active perspectives.

Each individual represents a mixture of perspectives, and the mix changes over time. Thus the adoption of perspectives by actors is a dynamic process. Change occurs because of "surprise"-that is, the discrepancy between expected and the actual-which is of central importance in dislodging individuals from a previously adopted perspective. Adherents to each of the four perspectives are, as it were, in competition for new adherents to their particular perspective but are dependent on one another at the same time. In other words, all of the perspectives are needed to ensure each one's viability (Thompson et al. 1990).

Although some use the cultural theory to describe individuals' behavior, it has also been used to describe different types of institutions (Rayner 1991; Thompson and Rayner 1998; O'Riordan and Jordan 1999). They consider only three "active perspectives"-that is, the individualist, the hierarchist, and the egalitarian-that correspond to three different types of institutions: market, hierarchy, and community, respectively (Rayner 1991; Table 9-2).

Individualists and market institutions: Market institutions are based on short-term expectation and immediate returns on activities and investments. They pay little attention to inter-temporal responsibility. Future generations are assumed to be adaptive and innovative in response to problems then, just as the present generation copes with current market conditions. Human impacts on ecosystems will be reduced by markets only when environmental damage causes markets to adapt.

Hierarchists and hierarchy-based institutions: According to the hierarchybased institutions, economic organization and social behavior are legitimated by top-down, rule-bound structures that intervene in the dominant social order. The hierarchical regimes contribute to an ordered expectation of the future. Concern for future generations is strong but is balanced by the needs of the present generation. Scientific research will help to identify the boundaries within natural systems that are stable. Often hierarchical institutions use cost-benefit analysis to help balance the risks.

248 JANSSEN

Table 9-2. Characteristics of Cultural Perspectives

Individualist Hierarchist Egalitarian

Worldviews

Idea of nature skill-controlled isomorphic accountable cornucopia

Myth of nature benign perverse, tolerant ephemeral

Concept of self-seeking sinful born good, human nature malleable

Management style

Institution market hierarchy community

Driving force growth stability equity and equality

Type of management adaptive controlling preventive

Attitude to nature laissez-faire regulatory attentive

Attitude toward channel rather restrict behavior change social humans than change environment

Attitude to needs expand resource base rational allocation of need-reducing and resources resources strategy

Economic growth preferred aim: to create preferred aim: to not preferred personal wealth avoid social collapse

Attitude to risk risk-seeking risk-accepting risk-aversive

Egalitarians and communities: Egalitarian groups feel a strong responsibility for the future, but their trust in formal institutions is weak. Communities are based on equity with other actors, nature, and future generations. To reduce the risks to future generations and to sustain nature, egalitarians prescribe precautionary measures to limit disturbances of our fragile natural system. Limiting the pressure on the environment is implemented by voluntary reduction of the needs for harmful consumption patterns.

Of course, in the real world, agents and institutions rarely express their views in such a simple way. They are in constant interaction and have strategic and public relations in mind as well. Moreover, positions may be nonidentical or even inconsistent when stakeholders and institutions share only part of the underlying values and judgments. Nevertheless, this framework captures the

9. A FuTuRE OF SURPRISES 249

crucial idea that a set of heterogeneous agents can have different worldviews and related management styles (Janssen and de Vries 1998).

Trisoglio et al. (1994) have characterized perspectives according to two dimensions: (1) how the world actually works-the functioning of natureand (2) how should it be acted upon-the management style or institution (Table 9-3). A management style is correct insofar as it is based on a corresponding view of how the· world functions. Trisoglio et al. refer to this situation as utopian: the management style and worldview of agents correspond with the functioning of nature. If, on the other hand, a management style is inconsistent with the way nature works, the situation is dystopian. For example, a fisheries that assumes that the fish population will recover very fast after each catch will be confronted with a dystopia when overharvesting leads to resource depletion.

The literature on utopias has a long history (More 1516; Kumar 1987; Proops 1989; Achterhuis 1998) and generally depicts utopias as blissful and positive scenarios. Achterhuis clearly explained that utopias may be dreams of an individual yet will tum out to be nightmares in practice because of the rules that force individuals to behave in a utopian way, in the spirit of "all people are equal, but some are more equal" (Orwell 1946). The Orwell quote refers to the communism regime, implemented in line with an ideology on the how the world should function. We now know that many countries did not function in line with the assumptions of communism, and the supposed utopian paradise became a dystopian nightmare. Finally, the communistic institutions collapsed, although in some countries, like Russia, the system seems to remain in the « phase.

The previously mentioned doomsday scenarios of Malthus (1798) and Meadows et al. (1972, 1991) can be seen as dystopias because human behavior and (lack of) policies are discordant with nature's resource potential and resilience. Meadows et al. (1972, 1991) also present scenarios to avoid dystopias by the generation of new policies that can be interpreted as utopian. Bossel and Strobel (1978) simulate utopias by inclusion of explicit adaptive behavior.

Table 9-3. Different Combination of Functioning of Nature and Management Styles

Institution

community hierarchy market

unstable utopia dystopia dystopia

Functioning stable within limits dystopia utopia dystopia of nature

stable dystopia dystopia utopia

250 JANSSEN

Surprises and Institutional Change

The utopia-dystopia approach can be used to explore a variety of images of the world's future (Rotmans and de Vries 1997). However, this approach is static in the sense that an emerging dystopia does not induce adaptive behavior. If the system collapses, the agents do not respond. Hence, the scenario outcomes are rather implausible, both for utopias and for dystopias, although they give interesting insights into the role of uncertainties.

The approach discussed here assumes that the agents change their preferred management style if observations about the world are surprising enough, that is, if those observations differ enough from what the agents expect on the basis of their worldview (Thompson et al. 1990). In line with Gunderson et al. (1995b), the adaptive cycle can be used to describe changes and adaptations of institutions (Figure 9-2).

Agents' management style can be influenced by variations in their myth of nature. Gunderson et al. (1995b) define the adaptive models to describe the dynamics of resource management institutions, based on a large number of case studies.

The r phase is defined as formulation of a policy. If that policy is successful it leads to increasing bureaucratic processes to formalize and institutionalize policies. The expectations of the institutions are mainly based on insights and information during the time policies were formulated. Since policy was considered to be successful, no new investigation is done on the quality of the expectations. Those groups with other perspectives on reality, leading to other expectations and preferred policies, will challenge ruling institutions. In the event of a surprise, the ruling institution is con-

a alternatives implementation K

Figure 9-2. Four-phase heuristic for institutional dynamics. This rendition of the adaptive cycle indicates how the domination of different groups at different stages of policy development generates new institutional arrangements. (Modified from Gunderson eta!. 1995b)

9. A FUTURE OF SURPRISES 2 51

fronted with evidence that its expectations do not hold anymore, which can result in a crisis. Such surprises can be natural disasters, economic collapses of companies or nations, epidemic diseases, scientific or technological revolutions, and so on. After the start of such a crisis, a period will begin in which various alternative policies react to the surprise. This can lead to continuation of the ruling type of institution with new policy initiatives, or a flip to a new type of institution.

The three types of institutions defined in the previous sections lead to a scheme of possible flips between institutions. Each type can be viewed as a stable state in a dynamic process but can flip to another state when a surprise shakes the existing institutional system.

In the following section an application on global climate change is described. Uncertainty, unclear signals, and long time scales are important elements of the climate change problem. Various myths of nature are claimed to hold for the climate system by the important stakeholders. Because of these elements, the global climate change problem is a perfect example to illustrate the modeling of institutional change in line with competing worldviews.

The model used for this application can be downloaded by the reader at www.sustainablefutures.net. The reader can then explore the consequences for alternative assumptions in an interactive way.

Changing Perspectives on Global Climate Change

During the past two centuries, the atmospheric concentrations of greenhouse gases have increased. The most important greenhouse gas, carbon dioxide, increased steadily from 280 ppmv (parts per million by volume) in the early 1800s to 360 ppmv in the 1990s. Increases in the atmospheric concentrations of greenhouse gases will increase the global mean surface temperature of the earth. However, the magnitude, the rate, and the patterns of global climate change that these increases will produce is uncertain, and their impact on the biosphere and humanity is even more uncertain.

Understanding the consequences of climate change is a complex issue, because of our limited understanding of the global system and because observations of the climate system are influenced by various natural factors such as volcanic activities and fluctuations in solar activity and anthropogenic factors such as variations in albedo due to land-use changes, changes in tropospheric and stratospheric ozone concentration, and sulphur emission from industry.

Perspectives on Climate Change

Given the enormous uncertainties and the important economic consequences of a severe climate change or strong emission reductions for various economic sectors and regions, it is of no surprise that many controversies exist around human-induced climate change. Important problems relate to

252 JANSSEN

the unequal vulnerability of ecosystems, the unequal responsibility for historical emissions, and the unequal economic and technological perspectives to reduce emissions. For example, the agricultural sectors in Canada and Russia will benefit from a climate change, while countries with large river deltas like Bangladesh, and the small island states in the Pacific Ocean, will be heavily affected by a sea level rise. Furthermore, measures to reduce C02 emissions will have negative effects for stakeholders like coal producers in the United States and oil producers of the OPEC, while stakeholders who have invested in alternative energy supplies will benefit. Institutions as defined in the last section can be characterized as follows for the energyclimate debate (de Vries and Janssen 1996; Janssen and de Vries 1998). The classification of cultural theory will be used to describe how different views of stakeholders generate alternative scenarios. The perspective of each is described in the following paragraphs.

Individualists: For the market institution, entrepreneurial freedom and the unhampered working of market forces give the best guarantee of increasing material wealth and at the same time solving resources and environment problems. If energy supply companies can operate in a regime of free trade and with a minimum of government regulation and interference, price signals will steer the transition away from fossil fuels before they are depleted. The key resource is human ingenuity: human skills generate science and technology, which will bring options we cannot even imagine at the present. Concerning the climate change debate, the market institutions' view of a benign natural system leads them to believe that climate change will be mitigated by known and hitherto unknown dampening feedbacks. The market institution emphasizes the opportunities that arise from the search for new resources and new technologies to supply and conserve energy. Policy measures like a carbon tax are viewed as unnecessary and may actually be quite harmful to the legitimate aspiration of the less developed countries to spur economic growth.

Hierarchists: The hierarchist wishes to avoid disruptions to the smooth functioning of the energy system in view of its consequences for economic growth and voter behavior. To this end the hierarchist institutions of society will anticipate and respond on the basis of scientific expert knowledge. There is a preference for a risk-reducing control approach and for reliance on and legitimation by the outcomes of cost minimization and cost-benefit analyses. The hierarchist will make a prudent assessment of the potential for energy conservation and have an institutional bias toward large-scale supplyside options. There will be a cautious approach to the issue of climate change, judging it in terms of "acceptable risks." Hierarchists will support cost-effective "no regrets" measures that reduce the risk of climate change, but they are keenly aware of the fact that fast and stringent cutbacks in C02

emissions may be socially disruptive and may create competitive disadvantages. Hierarchists prefer unambiguous, scientifically robust indicators on which to found their analyses and policies.


Egalitarians: The egalitarian or community-based institution wishes to reduce inequity and stresses the rights of those without a voice: our children, the poor, and nature. High and rising C02 emissions are seen as one more sign that humans are maltreating the earth and that this may lead to catastrophes. Being risk aversive, community institutions consider all uncertain processes and feedbacks to amplify climate change. They also wish to take account of feedbacks or catastrophic impacts, which are strongly disputed within the scientific community. On the other hand, egalitarians tend to ignore potential negative feedbacks. This leads to a preference for the "precautionary principle." Energy futures will be judged not only in terms of costs, but also with regard to distributive aspects and ecological impacts. Hence, policies should be based on assessment studies of the possible impacts from anticipated increases in temperature and sea level. No, or only modest, economic growth is to be preferred. There will be a preference for decentralized and clean technologies, and therefore a natural tendency to focus on energy end-use needs and efficiency. Egalitarians' estimates of fossil fuel resources are on the low side, whereas the prospects of renewable energy sources are usually on the high side, if compared with the hierarchist perspective. Egalitarians believe that development of renewable sources should be strongly supported by government-sponsored research and technology programs.

Utopias and Dystopias of Climate Change-Oriented Futures

A simple integrated model of economics and the climate system is developed to explore different perspectives on climate change. This model is based on existing economy-climate models, such as those found in Nordhaus (1994), Manne et al. (1994), Hammitt et al. (1992), and Lempert et al. (1996). Previous versions of the model are described in detail in Janssen (1998) and Janssen and de Vries (1998). The version used here can be found on the Web site (www.sustainablefutures.net).

In the economic part of the model the economic output is simulated as a function of capital and labor inputs, technological progress, and climate change-induced damage costs. C02 emissions are related to the fuel mix of supplies, energy demand, and energy intensity. The climate system describes the concentration of C02 resulting from anthropogenic emissions using a reduced-form carbon-cycle model (Maier-Reimer and Hasselmann 1987). Then the radiative forcing and the resulting temperature change are calculated.

The economic agents have to decide how much they will invest from the economic output in new capital, and how much they will consume. Furthermore, they have to decide how fast the share of fossil fuels should be reduced. However, in making these important decisions, the agent is confronted with large uncertainties about the pace of technological improvements in the economy and the pace of the energy conservation transition. Moreover, large uncertainties exist about the sensitivity of temperature

254 }ANSSEN

change due to increasing C02 concentration, the economic cost of reducing COz, and the damage to the economy by a possible climate change.

Given the uncertainties in the integrated economy-climate system, different possible functions of nature are defined in model terms, using the myths of nature. Moreover, by assuming a variety of responses from agents with different perspectives, we can define the institutions' management styles.

The utopia of each perspective is presented assuming that the worldviews of the agents fit with their management style. By implementing the assumptions of Table 9-4 into the integrated model, projections are derived for economic output, fossil C02 emissions, and temperature change for each utopia (Figure 9-3). Note that there are already differences in the present temperature change that show the different estimates of human-induced temperature change over the past one hundred years. In the utopia of the individualist, economic growth is greater than 2 percent per year throughout next century. Because the market institution expects only a modest decline in energy intensity, C02 emissions soar to over 30 GtC (gigatons of carbon) in 2100. In the worldview of the individualist, the climate system is also believed to be quite insensitive to human disturbances of the carbon cycle; hence, these high emissions cause only a small increase in the global temperature of 0.5° C in one hundred years. This temperature change has no significant impact on economic activities, so there is no policy response and the use of fossil fuels is not restricted .

. Table 9-4. Assumptions for Implementing Perspectives in an Economy-Climate Model

Market Hierarchy Community

Worldviews

Technological high moderate low development

Climate sensitivity low (0.5°C) best guess (2.5°C} high (5.5°C}

Damage costs none moderate high

Mitigation costs high moderate low

Management style

Investment maximizing economic stable long-term no expansion of growth growth capital stock

Climate policy only policy when increase efforts when fast reduction of damage costs temperature remains use of fossil fuels become severe increasing


@200~------------------------~

A § ,

B

~ 150 ~~\.,' 'S 0~'0'1,. .. % 100 \~ .....

i so L-...::.=· :::· ·:::::·:::·::::.· ~-_ .. :: .. _~ .... _ ... :_ .. :._.:._.~-~_ ...... .:::-:~1!;:-~~:::.::~:;;;-~;;"'":_·····_l ~ 04-----~--------~--------~

~ "' c

.Q

lll ·e: " "' 0 ()

40~-------------------------.

2000

·································

c'rt\s\. "\~!~~---·······

·········· \nd\vidu~l\~t_ . ............... "' .. "' ...............................

2020 2040 2060 year

2080 2100

Figure 9-3. Time course dynamics of (A) economic output, (B) C02 emissions, and (C) temperature change under different climate change scenarios. Each line represents the results based on a utopian future of each of three institutional groups.

In the hierarchist utopia, the economy grows at a stable rate of 1.5 percent per year. The C02 emissions keep increasing and so does temperature change. However, the hierarchist management style responds to the rising temperature by accelerating the phasing out of fossil fuels and the temperature increase can be stabilized at about 1.5· C above present values. This is assumed to be the upper range of what is considered acceptable in many official (government) studies.

l1;1 the utopia of the egalitarian, economic growth is approximately 1 percent per year, and the C02 emissions from fossil fuel combustion start falling after 2015 because of the policy to accelerate the fossil fuel transition. Due to the sensitivity of the climate system, the temperature increases up to 2.5" C in the utopia of the egalitarian.

Interesting situations emerge in dystopias--scenarios in which the functioning of nature and the management style are not in agreement. Figure 9-4 presents the most profound dystopia for the same three model variables: the nightmare of the egalitarian. The worldview of the egalitarian is assumed to be correct, that is, the climate system is quite sensitive to increased C02 concentrations, but economic aspirations and human-related feedbacks to temperature rise are based on a management style of the market institution. In this situation, with the integrated system functioning according to the worldview of the egalitarian, a management style of the market institution

256 JANSSEN

A ih 60

U) c: g a 40 s % 0

.!1 20 E 0 c: 8 " 0

B §' !2. 12

:g .Q

.~ E

" 0' ()

c v " 4

~ -5 3

~

" 2

l 1 E .!!l

0

2000 2020 2040 2060 2080 2100 year

Figure 9-4. Time course dynamics of (A) economic output, (B) C02 emissions, and (C) temperature change under different climate change scenarios. In this case, an egalitarian utopia is compared with an egalitarian nighnnare (see text).

leads to a collapse in economic development due to high economic growth aspirations together with severe impacts of climate change. The emission reduction measures are implemented too late to avoid a temperature increase in excess of 4°C. This type of dystopia is the one that has been sketched regularly by environmental groups who fear that the prevailing economic growth aspirations will spell environmental catastrophe.

Changing Perspectives in the Face of Climate Surprises

The model experiments in the previous section have an important assumption: that human society does not learn from observations about how the real world actually behaves. In the case of a utopia, since the world fits one's expectations, neither learning nor adaptation is needed. However, in the case of a dystopia, there is a mismatch between expectations and observations. In this section, agents are assumed to be able to learn and adapt so as to avoid a dystopia instead of rigidly sticking to a fixed policy as disaster unfolds.

According to Thompson et al. (1990), people are assumed to abandon their perspectives in the event of surprise-that is, if observations differ from expectations. People who adhere to a certain worldview will switch to another one if it can better explain the observed behavior of the system. Here, institutions are assumed to follow the adaptive cycle as described in


Figure 9-2. This is implemented by a set of simple rules. For each type of institution a fitness function is defined that values the difference in expectations and observations of the indicator's temperature change and economic growth. A threshold of the minimum fitness value for the ruling institution is defined. \Vhen the fitness value of the ruling institution drops below the threshold, a period of crisis starts. The type of institution with the highest fitness value is assumed to be chosen as the new institution. The longer a certain type of institution rules, the more bureaucratic forces that maintain the institution typically cause it to increasingly ignore differences between expectations and observations. The increasing ·ignorance can be modeled by reducing the degree that a mismatch between expectations and observations lowers an institution's fitness. The resulting framework is depicted in Figure 9-5. The circle represents the ecological economic model of the real system. The triangle represents the fitness of the different myths of nature. The little circle in the lower triangle represents the average myth of nature of the population of agents. The more a myth of nature fits exclusively to the observations, the more the circle will move to one of the corners. A crisis occurs when the observed myth of nature differs significantly from the related institution. Based on the worldviews of the agents, the institutions remain the same or flip to another type of institution.

The dystopias depicted in Figure 9-4 may change over time, so that the market institution becomes unfit and gives way to dominance by the community institution. The results shown in Figure 9-6 represents modeling

Institutions

~ 8-8

effects ~'/:actions based '/• Otlpo/icies

real world

obs8rVed congruity --+

myths of nature

Figure 9-5. Schematic overview of the framework linking the real world, institutions, and myths of nature. T he circle represents an ecological economic model of the real system. The agreement between the model and pattern from the real world is represented by fimess of different myths of nature (triangle, lower right). The more a myth of nature fits exclusively to the observations, the more the circle will move to one of the corners. A crisis occurs when the observed myth of nature differs significantly from the related institution. Based on the worldviews of the agents, the institutions remain the same or flip to another type of institution (ovals at top).

258 JANSSEN

A ifi 60

l!! ~ e 40 => "-5 0

" 20 E 0 c 8 .,

B G §_ 12

"' c 0

·;;;

"' E ., 0 0

c ~ ., g>

"' .r:; 3 " i" => e "' "- 1 E J!l

2000 2020 2040 2060 2080 2100 year

Figure 9-6. The effect of learning on (A) economic output, (B) C02 emissions, and (C) temperature change compared between the utopian and dystopian cases. The lines in the shaded area represent the cases (a) learning, (b) ignorance, and (c) variability.

exercises with changing institutions. Due to changing management styles, the phasing out of fossil fuels is started earlier, which prevents extreme temperatures and high damage costs.

Three types of possible adaptations are implemented. The first adaptation is designated as Learning. The market institution remains in power until 2030, when the observed temperature change differs significantly from expected values, and the damage costs due to climate change begin to increase significantly. The shift to a community institution leads to a phaseout of fossil fuels around 2050. Compared with the dystopia, the change of institution leads to a reduction of temperature change of 1 oc. If we introduce ignorance of a modest degree, the adaptation is delayed with twenty years, and the temperature change in 2100 is only OSC lower than that of the dystopia. When the Learning case is confronted with variability in temperature change, surprises occur earlier, because of a higher chance of extreme events. This results in a somewhat earlier change of institution. In sum, adaptation of management style prevents extreme consequences of climate change. Ignorance will slow adaptation, and climate variability can accelerate adaptation.

Learning in a world ruled by uncertainties will not lead to utopian values of the main indicators. Due to the slow dynamics of the carbon cycle and the inertia in the energy system, a buildup of atmospheric C02 cannot be


reduced at once. In fact, the range between the utopia and the dystopia indicator values represents the space of possible paths in case of learning agents.

For each type of functioning of the system, a large number of simulation runs have been performed using different initial management styles. It is not surprising that highest economic outputs are derived when climatic change is small (Figure 9-7). Economic systems adapt to observed climatic changes. The success of adaptation varies with the initial institution.

What might be surprising is the fact that in a world where humaninduced climate change does not occur, economic output over a century is higher when the dominant institution initially is community based rather than hierarchic. The explanation is that a community institution is surprised much earlier than the hierarchic one, so that the institution changed earlier to a market institutions. This also results in an early relaxation of C02 mitigation policy.

Based on these sensitivity runs, we can conclude that the market institution can lead to collapses of the system, while the community institution can result in lost opportunities. The hierarchical institution is too slow to adapt to surprises, leading to moderate collapses or moderate lost opportunities.

What does this modeling exercise tell us? We can consider these scenarios as possible futures for different types of assumptions. Policies aimed at increasing the capacity for learning, adaptation, and innovation are recommended. Bureaucratic control regimes are likely to reduce the ability to adapt. Current climate change policies are mainly focused on technical measures and institutional regimes to reduce or store C02. A resilient climate policy would invest more in new energy supply and demand technol-

;> 160 VJ

c ~ e 120

s .9- 80 " 0 .II E 0

40 c 8

0 (I)

~ 4 ., VJ as 3 .,

.~ 2 .,

5 "§ ., 0. E ~ 0

unstable stable stable within limits

Figure 9-7. The average values of economic output (top chart) and temperature change (bottom chart) in 2100 for three possible worlds where agents do learn and adapt. Results are arrayed based on starting type of institution (egalitarian, hierarchist, or individualist) and worldview of nature.

260 JANSSEN

ogy and social and physical infrastructure. In case our global ecosystem is sensitive to greenhouse gas emissions, effective learning and adaptation make the difference between a moderate climate change and catastrophic climate changes. To improve the resilient capacity of our global community, we should invest in three types of mitigation:

• precautionary: new technology and behavioral patterns can reduce the addiction to fossil fuels and improve our ability to reduce emiSSIOnS.

• adaptation: a certain degree of climate change seems to be unavoidable, which leads to improved adaptive capability of ecosystems and economic sectors.

• reactive: due to the unavoidability of climate change, extreme events can occur, which can lead to important damage of ecological and economic systems. Policies need to be developed to react to such extreme events.


Surprises are an essential and certain element of the future. In exploring possible pathways of the future, surprises should explicitly be taken into account. With regard to resource management, the consequences of surprises for resource managers and institutions are of interest. In this chapter some examples of models of possible reactions of resource managers and changing of institutions are discussed. The use of multi-agent models is central in the discussion, where agents differ in their worldview of the system and the related preferred management style.

It should be clear that modeling of changing perceptions of populations and changing institutions is in an embryonic state. The example of climate change shows the importance of the mixture of adaptive, precautionary, and reactive policies. Precautionary policies are necessary to limit harmful surprises, but due to the current trends of change, it is inevitable to prepare for system changes. Therefore, adaptive policies are necessary to increase the adaptive capacity of nature and society. Finally, surprises can still lead to extreme events not prepared for, such that reactive policies need to be available.

Various types of models should be explored in the coming years to understand the interactions between worldviews, management, and ecosystems. Improved understanding of these relations can improve our insights into which types of policies and institutions are resilient in the longer term for both society and ecosystems.

Acknowledgments

The author would like to thank Steve Carpenter, Lance Gunderson, and Garry Peterson for valuable comments on an earlier version of this chapter.

CHAPTER 10

RESILIENCE AND SUSTAINABILITY:

THE ECONOMIC ANALYSIS OF

NONLINEAR DYNAMIC SYSTEMS

William A. Brock, Karl-Goran Maler, and Charles Perrings

T he discussion of resilience in this book relates mainly to the properties of dynamical natural systems, especially hydrological and ecological systems. In this chapter we consider the implications of

the resilience of ecological systems on the economics of natural resources. One implication of ecological resilience pertains to some fundamental assumptions of economic theory. We argue that in ecological systems where resilience is exceeded, or where it can flip from one state to another if sufficiently perturbed, one must relax the assumption regarding convexity of production sets. The relaxation of this assumption has far-reaching consequences for the economics and management of natural resources, and for the sustainability of resource-based development.

Economists have been aware of the general problems posed by the nonconvexity of preference and production sets for a number of years (Brown 1991). However, they have paid little attention to the implications of nonconvexities for the dynamics of economy-environment systems. Nor have they explored the implications for management and policy. Both of these are discussed in this chapter.

We first identify the way basic resource allocation may be affected by non-convexities and consider the implications of this for the efficiency of the economic process. In the next section, we explore the effect of non-convexities on equity, sustainability, and resilience in economy-environment systems. We also relate this to the variability of natural resources and to the related problems of uncertainty and irreversibility. We then illustrate our comments through a specific example of lakes that are subject to nutrient loading. We conclude with a discussion of the implications of non-convexi-

261

262 BROCK,~ER,PERR]NGS

ties for resource management and policy, and for the economics of the environment in general.

Convexity and Resource Allocation Mechanisms

The objective of most societies is to maximize the well-being of their citizens. To meet that objective we need to be able to represent social well-being in a practical way and to be able to compare the well-being of different individuals. We will start with the impact on human well-being due to changes in the quantity or quality of environmental resources and will focus on individual well-being.

A utility function is a practical device by which we can represent the preferences of an individual. It is an assignment of real numbers to a bundle of commodities and services (including natural resources or ecological services) available to the individual (Box 10-1).

The principle of consumer sovereignty is an ethical hypothesis. The principle states that individuals are the best judge of their own welfare, given that they have no less information than anyone else about the technical and

Box 10-1. Representation of Preference Functions

W. Brock, K. G. Maler, and C. Perrings

Let x and y be two bundles of commodities. If an individual prefers x toy, one writes

X :::::y.

If she strictly prefers x toy, one writes

X>- y,

and if she is indifferent between the two bundles, one writes

y ~X.

We want (for practical reasons) a simpler representation of the preferences. It can be shown (under some reasonable assumptions) that there exists a function u mapping commodity bundles to real numbers such that

x >- y_u(x) > u(y)

and

y ~x _ u(x) = u(y).

The function u is the utility function of the individual.

10. ECONOMIC ANALYSIS OF NONLINEAR DYNAMIC SYSTEMS 263

If u(x) > u(y), we will also interpret that as meaning that the individual is better off with x than with y. Otherwise, she would not prefer x toy! This reflects the assumption of consumer sovereignty. Usually, we impose on the utility function various structures, and for doing environmental valuation, special structures are needed.

Note that if we use a utility function to represent the preferences of an individual, that function is not unique. If y ~ x implies that u(y) ::::_ u(x), then any monotonic increasing function of the utility function is also a utility function. For example, if u = xy, then In u =In x +In y or u2 = x2y2 is also a utility function representing the same preferences. They are equally valid representations.

scientific characteristics of the bundles they are considering. On the basis of this principle it is clear that the utility function can be interpreted as an index of the individual's well-being. This is stated in the following theorems of welfare economics.

The Two Fundamental Theorems of Welfare Economics

Welfare economics is that part of economics that is normative. It is used to make recommendations regarding policies and actions. Two basic theorems in welfare economics are based on relationships among utility and allocation of resources among individuals. These are referred to as Pareto criterion and Pareto optimal.

A Pareto criterion refers to an allocation of resources (A) that is socially preferred to another allocation (B), given that at least one individual is better off with allocation A and no one is worse off with allocation A. Note that this is a partial ordering; not all allocations can be compared in this way. Two allocations cannot be compared by applying the Pareto criterion if some people are better off in the first and others are better off in the second.

An allocation that is Pareto optimal is one in which it is not possible to improve the situation of one or more individuals without harming other individuals. A Pareto optimal allocation is thus a "maximal" allocation with regard to the Pareto criterion. Note that a given Pareto-optimal allocation may not be socially desirable at all because the distribution of well-beingthat is the utilities-may be very uneven.

The First Theorem of Welfare Economics:

If there is a competitive equilibrium such that all goods can be assigned property rights (and therefore be traded), if no individual and no firm can affect the prices, if producers are maximizing their profits, if consumers are maximizing their utilities, and if all markets clear, then the resulting allocation of resources is Pareto optimal.

264 BROCK,~ER,PE~GS

The assumption regarding assignment of property rights does not correspond to the real world. It has been studied in depth in many contributions to economic theory, and the importance of the lack of defined property rights is well understood (Ostrom 1990; Ostrom et al. 1994). The assumption of perfectly competitive markets is also violated in the real world but is probably of less importance than the first assumption.

There is, however, one problem that this theorem does not address, namely whether a Pareto-optimal resource allocation generated by markets is socially desirable. The outcome of the market process may be a distribution of well-being among citizens, which is not regarded as good. In fact, the resulting allocation may be highly inequitable. Society may accordingly prefer an allocation that is not Pareto optimal but is more equitable. The second theorem addresses this problem.

The Second Theorem of Welfare Economics:

If production sets are convex and closed, if preferences are convex and continuous, and if all goods and services can be assigned property rights, then for each desired Pareto-optimal allocation one can find an initial distribution of wealth such that the resulting competitive equilibrium is the desired allocation.

This means that if the assumptions hold, any desired allocation can be achieved using market mechanisms and by redistributing initial wealth among individuals. Thus, we can let the markets do almost everything if we first redistribute the initial wealth in a suitable way. We will later discuss briefly how we should think of this redistribution and how one should make the trade-offs between equity and efficiency.

The three conditions for the second theorem are based on assumptions of convexity, continuity, and assignment of private property rights, as discussed in the following paragraphs.

t -~ 0. -~ 0 feasible

sets

input (labor) -

Figure 10-1. Convexity of a production set with a single input Qabor).


"'5 % 0

~ :0« .........

tO ?b . ~ ~

~

Figure 10-2. Convexity of a production set with two inputs (capital and labor).

Convexity

Convexity in production sets means loosely that we have diminishing returns. More strictly, a set of points is convex if, given two arbitrary points of the set, the line connecting those points belongs to the set. Production is convex if the production set-that is, the set of all technically feasible bundles-is convex (Figures 10-1 and 10-2). There are good theoretical and empirical reasons to assume that this is the case in most instances, but as we will see it may not be the case in every instance.

Convex preferences are defined in a slightly different way. Indifference curves are curves connecting bundles that provide the individual with the same level of utility-that is, the individual is indifferent among bundles. Since convexity of preferences is of little concern in this chapter, we will not discuss it any further.

We can now understand the economic importance of convexity and the two theorems of welfare economics. Consider individuals who are both producer and consumer. Assume they can produce two goods, x and y. As producer, they have to choose a bundle of the two goods that is feasible in the sense that it belongs to the production set defined in Figure 10-3 by the transformation curve T-T and the area below it. As consumer, they have to choose a bundle that yields the highest attainable level of utility. It is apparent that it is best to produce and consume at the point where the indifference curve 1-1 is tangential to the transformation curve. At this point, the two curves have a common tangent, P-P. Convexity of the production and preference sets is what guarantees that the indifference and transformation curves can be separated by a straight line. Without convexity, this cannot be guaranteed.

More importantly, the convexity of preference and production sets underpins the efficiency of prices in the allocation of resources. The slope of the tangent may be interpreted as the relative price of y in terms of x. The importance of this is as follows. If individuals as producer maximize profit,

266 BROCK,~ER,PE~GS

t >.

"0 0 0 0')

I

good x-

Figure 10-3. The optimal allocation of resources with convex preference and production sets. T-T is the production possibility frontier. P-P is the price line. I-I is the indifference curve at optimum. Both producer and consumer facing P-P want to be at point A.

they will choose the production bundle that corresponds to the point of tangency between the price line P-P and the transformation curve T-T. If individuals as consumer maximize utility, they will choose the consumption bundle that corresponds to the tangency of the price line P-P and the indifference curve I-I. These points are the same. Therefore, we can decentralize production and consumption decisions while still achieving the social optimum. The producer can choose the production bundle knowing only the relative prices of x and y. The consumer can choose the consumption bundle knowing only the same relative prices. This is the intuitive background for the second welfare economics theorem.

Now consider what happens if the production set is not convex. In Figure 10-4, the production set defined by the curve T-T is not convex. The optimum point for the individual as both producer and consumer is given by point A, where the indifference curve I-I and the transformation curve T-T are tangential to each other. However, at this point, the production and the consumption decisions cannot be "separated" by prices, and the second theorem of welfare economics does not hold. The outcome is not a social optimum. Given the prices defined by the slope of the tangent at A, the producer could make higher profits at some other output combination. At point b, for example, the producer would make a profit corresponding to the dotted line. Thus, with this non-convexity it is no longer possible to decentralize decisions between consumers and producers and still achieve a social optimum.

Continuity

Continuity is a mathematical device that is usually (but not always) without significant economic meaning, and we will not dwell on this concept. We later consider models of ecological systems that superficially seem to gener-


T

good x-

Figure 10-4. The optimal allocation of resources with a non-convex production set. TT is the production possibility frontier. I-I is the indifference curve at optimum. Lower dotted line is consumer's budget line at point a. Producer wants to be at point b.

ate discontinuities, but don't. In particular, we will look at a lake that flips between an oligotrophic state and a eutrophic state and a rangeland that flips between a wooded and a grassy state. It may seem that such flips imply a discontinuity, but as the flip takes time, it is not mathematically a discontinuity.

Property Rights

Without well-defined property rights, markets will not be established for all goods and services, and as a result, incentives will be distorted. The reason is that well-defined property rights provide the owner of the rights with an incentive to manage the resource in a socially efficient way. Someone who owns an asset, such as a piece of land, has an incentive to manage that asset efficiently because he himself will bear the cost of mismanagement. \Vh.en property rights are not well defined, someone else will bear that cost. A lack of property rights in such cases leads to externalities, or effects that are external to the transactions among individuals.

Take the example of grazing land. If there were no well-defined property rights to grazing lands, then typically the lands would be overgrazed because no single herder would have to consider the full social cost of adding more cattle. If the grazing land were a common property with unlimited access by the members of a particular community, there would still be overgrazing because each member of that community would have an incentive to add cattle beyond the socially optimal level. However, if the common land were managed by some social device that limited the access enjoyed by rightholders, then the grazing land could be managed efficiently. Similarly, if the land were divided into privately owned pieces, each land owner would have an incentive to use his land in such a way as to be able to use it again in the future. Once again, the grazing land could be managed efficiently. Many environmental problems are due to property rights failures.

268 BROCK, MALER, PERRINGS

Policy decisions and the costs of establishing property rights are two basic reasons for property rights failures. In many countries, environmental resources are regarded as either publicly owned or open to all. For example, land that has not been claimed by anyone is often regarded as open to all. Thus there are incentives to overuse that land. One reason for this may be that the costs of introducing property rights in environmental resources are very high due to the nature of the resource. For example, it is difficult to think of how property rights might be assigned to the global climate. The reason in this case is the public-good nature of the resource. A change in climate that affects one will also affect others. Of course, individual rights to emit greenhouse gases can be assigned, but the climate itself will continue to be a public good.

The Coase theorem (Coase 1960) addresses this problem. The theorem states that if there are well-defined property rights in some environmental effect, if there are no transaction costs, and if wealth effects are negligible, then the outcome of bargaining between the interested parties will be Pareto optimal regardless of the distribution of those rights. Furthermore, lack of agreement about such effects is an indication that the transaction costs are too high to motivate the establishment of a market in the effect. The absence of an agreement is therefore socially optimal. Applied to a simple pollution problem, the Coase theorem says that if those who are damaged by pollution are not able to bribe the polluters to reduce pollution, then it is socially desirable to have that pollution. The gains from pollution control will not outweigh the cost of abatement and the transaction costs.

The Coase theorem has been criticized on many grounds. First, if there are more than two parties involved (and generally there are), the incentives may go completely astray, and the Coase theorem is no longer valid (Dasgupta and Maler 1995). Second, if there is asymmetric information between the affected parties (some parties know more than others), the outcome of negotiations will not generally be Pareto optimal. However, in that situation the important limitation of the Coase theorem is that the transaction costs (the costs of assigning rights and carrying out negotiations between right-holders) will increase substantially when the feasibility sets for the parties are non-convex. The reason is that if these sets are convex, the parties can arrive at the optimal solution by following a step-wise process known as the gradient process. If, for example, the parties make incremental bids in such a way that the bidder is better off if the bid is accepted, then the process will converge at the efficient outcome. However, without convexity, such a process will generally not converge to the optimum. We consider an example of this later.

The main point of this brief discussion of basic resource allocation mechanisms is that they all require the convexity of production sets in order to operate smoothly. These convexity assumptions are often (but far from always) valid when we limit our analysis to conventional production functions. As we shall see later, however, extending production functions to


include nonmarket environmental inputs frequently compromises the convexity of the production set.

Equity, Sustainability, and Resilience

If we are to judge whether one allocation is better than another, we need to compare outcomes with different implications for the distribution of wealth and income. The only way to judge whether some change increases welfare is by explicitly introducing ethical values on the distribution of welfare. This is typically done through a social welfare function (Box 10-2).

The welfare function provides a very useful way of thinking about the sustainability of economic and environmental change. There is no generally accepted definition of sustainable development (and it is doubtful whether the concept has scientific validity). The Brundtland Commission defines sustainable development as development that meets the needs of present generations without compromising the ability of future generations to meet their needs (World Commission on Environment and Development 1987).

Box 10-2. Social Welfare Function

W Brock, K. G. Maler, and C. Perrings

Let there be H individuals that are affected by a particular change or policy and let each individual have utility uh. Let there be a function W(u 1, ••• , uH). which we will suppose is increasing in all its arguments. We will interpret this function as a social welfare function if it represents the preferences of a social decision maker. We know from Arrow's impossibility theorem that it is impossible to derive the social welfare function from individual preferences in a way that is consistent with some reasonable conditions. However, there often does exist information that makes aggregation of individual preferences possible. One can therefore interpret the welfare function either as an aggregate of individual preferences (given certain assumptions on information) or as representing the preferences of a social decision maker.

Consider a change in society from A to B. This change implies that individual utility levels change from uh A to uh 8 , and that social welfare changes accordingly

~W = W(u, 8 , ... , uH 8)- W(u,A, ... , uHA)

If ~ W > 0, then social welfare increases according to the welfare function.


The Brundtland definition suggests something about the change in welfare over time. Pezzey (1997) identifies three possibilities for such change:

• welfare is non-declining over time (dW/dt > 0 always),

•

•

welfare does not exceed the maximum constant level of welfare (dW/dt :::=: W nnu always),

or welfare does not fall below some prescribed minimum (dW/dt ;::: W, always).

He defines these as, respectively, sustained, sustainable, and survivable development. But for whom is W a social welfare function in these definitions-the present generation or the present generation and generations yet to come? In the discussion on the meaning of sustainable development, many have argued that W(t) should represent the welfare of the generation living at timet. However, this would severely restrict policies. For example, poor countries may need to reduce their current consumption in order to increase the capital stock for future generations. Taking such a criterion as non-declining or constant welfare seriously would therefore imply that such countries could never increase the savings rate. A more flexible interpretation is that W(t) represents the welfare of current and future generations together.

Dasgupta and Maler (2000) show that with some technical assumptions, shadow prices p, always exist on all resources (including ecological resources) X, such that an operational criterion for sustainable development is that

L;p; dX;/dt :::: 0

in the present and in all future time periods. Thus, economies are on a sustainable development path if and only if the sum of the values of changes in capital stocks does not decrease over time. This corresponds to what has been called genuine savings in reports from the World Bank (Pearce and Atkinson 1993). However, among these technical conditions guaranteeing the existence of the appropriate shadow prices is the differentiability of the so-called value function with respect to the initial endowments of assets, that is, of the social welfare function for present and future generations. It can be shown that for non-convex feasibility sets, these shadow prices may not exist for all possible initial stocks. Prices may not exist to inform us whether we are on a sustainable path.

Resilience and Sustainability

An alternative approach is to consider the sustainability of an economy and its supporting environment in terms of its capacity to absorb stress and shock without fundamental change. For any economy there are many possible states, each delivering different levels of welfare to society. In this approach the sustainability of any particular state depends on the properties of the sta-


bility domain corresponding to that state. In the ecological studies reported in this volume, this is typically analyzed in terms of the resilience of the system in each state.

One measure of resilience is the magnitude of disturbance that can be absorbed before a system flips from one state to another (Holling 1973b).l Holling (1986; with Gunderson in Chapter 2) describes ecosystem behavior in terms of sequential interaction of four system phases: exploitation or colonization; conservation; creative destruction, where an abrupt change caused by external disturbance releases energy and material that have accumulated during the conservation phase; and reorganization, where released materials are mobilized to become available for the next exploitative phase. Resilience is measured by the effectiveness of the last two system functions. It is crucial to the ability of the system to satisfy "predatory" demands for ecological services over time and to cope with both sustained stress and shock. This measure is the one that is reflected in the chapters in this volume.

It has been argued that this measure, and the concept behind it, offers a useful way to address the sustainability not just of ecological systems, but also of jointly determined ecological-economic systems (Common and Perrings 1992; Levin, Barrett et al. 1998). Indeed, the approach has implications for the way we think about the dynamics of any stochastic, evolutionary system. Levin et al. argue that sustainability as a concept is more pertinent in stochastic systems away from equilibrium than in deterministic systems at equilibrium.

The link between the resilience of systems and the probability of their collapse or change of state is reflected in the literature on the analysis and management of environmental risk. Although deterministic bio-economic models for the optimal utilization of natural resources generate sustainable (steady-state) solutions, they often ignore inherent or environmental stochasticity in modeled relationships. Stochasticity has been incorporated into such models by randomly changing model parameters, or by random catastrophe, or through density-dependent risk of collapse (Reed 1988; Tsur and Zemel 1994). Density-dependent risk of collapse includes both the existence of a density-dependent hazard function (Reed 1979, 1988) and thresholds that, if reached, trigger the immediate collapse of the stock (Tsur and Zemmel 1994, 1997). A density-dependent threshold implies that increasing stress on the system raises the probability that it will flip from one state to another, and so corresponds well with a measure of resilience (sensu Holling 1973b). The implications of this for the management of susceptible systems are only now being considered (Chapters 7, 8, and 9).

The relationship among resilience, diversity, and risk has been studied in ecology. A long-standing dispute contrasts the relation between the complexity of ecological systems, their diversity, and their stability (May 1973; Elton 1975), and an alternative proposition that diversity supports not stability but resilience (Holling 1973b, 1986) and ecosystem functioning (Schulze and Mooney 1993). Experimental research of grasslands has now shown that

272 BROCK,~ER,PE~GS

ecosystem productivity increases significantly with plant biodiversity (Tilman et al. 1996). This is because the greater the diversity of species, the more effectively the main limiting nutrient, soil mineral nitrogen, is utilized. These results have led to the proposition that the sustainability of soil nutrient cycles and soil fertility increases with biodiversity.

More generally, the resilience of any ecosystem, with respect to variation in environmental conditions, depends on the existence of species capable of supporting the key ecological functions as conditions vary (Perrings 1995). Deletion of a species that is important under some conditions will have little effect on ecosystem functioning if other species are capable of stepping in as substitutes. If there are no substitutes, however, the deletion of some species can trigger a fundamental change from one ecosystem type to anotherfrom forest to grassland, or grassland to a shrubby semi-desert, for example (Westoby et al. 1989). The importance of the mix or diversity of species for the resilience of ecosystems lies in the fact that species that are "redundant" in one set of environmental conditions may be critically important in other environmental conditions. Resilience has been shown to depend on the functional diversity of species that support critical structuring processes (Holling et al. 1995).

The significance of this for resource-based development is that agroecosystems (ecological systems whose species mix is transformed for the purpose of agriculture) may be especially sensitive to species deletion precisely because they are already simplified by the exclusion of competitor or predator species (Conway 1993). The specialization gains from simplification of agroecosystems typically involve a reduction in the resilience of the system. The costs of a reduction in resilience include, for example, the costs of the herbicides, pesticides, fertilizers, irrigation, and other inputs needed to maintain output in the simplified system. They include the cost of relief where output fails, relocation where soils or water resources have been irreversibly damaged, rehabilitation where damage is reversible, and insurance against crop damage by pest or disease. If the system loses resilience and flips from one state to another, they include forgone output under the new state.

There have been few attempts to estimate the impact of changes in relative prices on nonmarketed biological resources and fewer still that relate these changes to the resilience of agroecosystems. Loss of resilience implies both an increase in the time taken to return to equilibrium following some shock, and a narrowing of the range of environmental conditions over which the system can maintain the flow of ecosystem services. It is economically interesting if: (1) it alters the risks associated with a given set of environmental conditions, and (2) the value or potential productivity of the new and old states is different. If changes of state are either irreversible or only slowly reversible, a change in the potential productivity of the system imposes costs or confers benefits on both present and future users of that system. That is, it has implications for our measure of welfare. If the loss of resilience of a managed or


impacted ecological system is associated with a change in its long-run productive potential, it is in principle observable through its effects on the value of economic output. Perrings and Stem (2000) estimate change in the potential productivity of rangelands in Botswana using the Kalman filter to model productivity change as a stochastic trend. In particular, they use the Kalman filter to estimate the state of the range as a latent variable in much the same way that the state of technology has been treated as a latent variable by Slade (1989) and Harvey and Marshall (1991). They show that change in the current and long-run equilibrium-carrying capacity of an ecological system may be treated as a nonstationary trend. This enables them to measure both the speed of a return to equilibrium and the threshold effects that occur when the system loses the capacity to absorb shocks of a given magnitude.

Resilience, Institutions, and the Evolution of Economy-Environment Systems

Consider the following simplified way of thinking about the evolution of an economy-environment system. Suppose that a finite number of resources are denoted Xr = (x1r, ... , Xnr). Decision makers are assumed to allocate resources through actions, a. These actions describe the consumption and production activities of economic agents. They affect the probability that the system in one state will converge on any other state. That is, P(a), defines the transition probability between states as a function of the consumption and production activities of decision makers. The activities of economic agents are, in tum, determined by a set of behavioral rules that depend on the institutional and cultural conditions in society. A familiar example of such a behavioral rule is profit maximization, which is associated with the decentralized decisions corresponding to the institutional conditions in competitive market economies. As we have already remarked, given relative prices, application of this rule determines the optimal combination of inputs in production and the optimal combination of outputs.

We can call such behavioral rules "policies," and denote them by Ur.

Hence the set of resources evolves according to

Xr = F(Xr-t. Ur)

The policies that guide people's activities are to a large extent determined by the institutional conditions-the rules of the game-within which they are made. That is, institutional conditions determine the logic of optimizing behavior in a way that makes the decision maker's behavior fully predictable once the institutional conditions are given. The logic of open access, as we have seen, ensures that decision makers will choose to use resources up to the point where total revenue and costs are equal, and so on. The implication of this is that for given institutions we can identify the longterm probabilistic evolution of the system. This does not stop us from thinking about the effects of changes in institutions or rules. Indeed, it offers


a very natural and structured way of doing that. A change in institutions induces a change in policies and hence the probability that the system will develop in particular ways.

We take the policy to be to maximize a measure of social welfare. Specifically, the expected welfare from the policy is:

00

wu(i) = Eu LW(Xr, Ur(Xo, ... 'Xr)) t=O

The advantage of thinking about the development or evolution of an economy-environment system in this way is that it makes it easy to identify the resilience of the system in a given state. The ways decision makers can influence the process depend on the structure of the system. If the time path for the state variables, x1, given u1 , is

Xr = fr(Xo, UJ, ... ur)

then the set of all states that are reachable from xo at time t depends on the current state of the system and the transition probabilities, P. It is useful to distinguish between transient and recurrent states. States that are revisited are said to be recurrent. Recurrent states are either occupied permanently or revisited periodically; transient states are left after some finite time and never revisited thereafter. It is quite natural to associate recurrent states with the long-term equilibria of a system, and transient states with far-from-equilibrium positions.

To relate this to the concept of resilience, recall that resilience is frequently measured by the size of the disturbance the system can absorb before flipping from one stability domain to another. The transition probabilities just described define the probability that a system in one state, and subject to some disturbance regime, will change to another state. This is exactly what the Holling measure requires. However, it is much more general than the Holling measure. It defines the transition probability from one state to another state whether or not that other state lies in a different stability domain.2

The main point here is that the evolutionary potential of an economyenvironment system is limited by institutional conditions-or the rules of the game. The evolutionary possibilities of a system are summarized by the probability law pu. This depends on institutional conditions, the decision maker's objectives, the admissible policies and actions, and the strategic behavior of agents. So for a given set of property rights, a given disturbance regime, and a given state of nature, it may be possible to estimate the probability that the system will converge by some finite time on some other state of nature. The connection with the notion of sustainability is direct. If the transition probabilities are known, it is possible to estimate either the time the system occupies a particular state (the sustainability of that state) or the time of convergence on any other state (the loss of resilience).

The resilience and hence sustainability of the system in any one state depends on the way it is used-the control policy applied. For most economies the process of development involves a sequence of states. Indeed, early devel-

10. ECONOMIC ANALYSIS OF NONLINEAR DYNAMIC SYSTEMS 2 7 5

opment theory was all about the transition between equilibria-about escaping from states associated with low levels of well-being and moving toward states associated with higher levels of well-being. Strategies for sustainability are about enhancing or protecting the resilience of the system in desirable states and reducing the resilience of the system in undesirable states (poverty traps, subsistence or semi-subsistence equilibria; Perrings 1998).

The Dynamics of Lakes

Many ecological systems display discontinuities in the equilibria of the state of those systems over time. A famous example is the interactive dynamics of the spruce budworm, its predators, and the boreal forest (Ludwig et al. 197tl). As the forest grows, equilibrium budworm numbers are relatively low in the beginning, but at a certain point they suddenly jump to relatively high numbers. As a consequence, the dynamics of the forest are reversed and living conditions deteriorate, but for a while the budworm density remains relatively high before it returns to low numbers again. This hysteresis effect is due to a nonlinearity in the dynamics of the spruce budworm, reflecting the role of its predators. It implies that for a range of values for the living conditions, high and low equilibria for the budworm numbers exist with separated domains of attraction. Other examples of ecological systems that display this phenomenon can be found in Ludwig, Walker, and Holling (1997).

These hysteresis effects are also important for the interaction between human behavior and ecological systems. Here we take the example of the eutrophication of lakes (Carpenter and Cottingham 1997; Scheffer 1999), but the situation can be viewed as a metaphor for many of the ecological problems facing us today. Due to increasing agricultural activity, more and more phosphorus is released into the lake. Initially, the effect is small, but at a certain point the lake flips from an oligotrophic state with a relatively high value of ecosystem services to a eutrophic state with a relatively low value. Because of the hysteresis effect, this drop in ecosystem services is reversible only at a high cost because the agricultural activity has to be reduced far below the level where the flip occurred. In other cases, the loss of ecosystem services may be irreversible.

Management of the lake has to consider the trade-off between the benefits of agriculture and the benefits of the services that the lake can provide such as fishing, recreation, and the use of water for industry and consumption. It will be shown that for a general class of welfare functions, optimal management chooses a level of agricultural activity that keeps the lake in an oligotrophic state but close to the point where the lake flips to a eutrophic state. This implies that small mistakes can have large costs, because the lake can flip and, due to the hysteresis, agricultural activity must be reduced first below the original level and then increased again to reach the optimal point.

In case such a lake is shared by different communities, each with a welfare function as described above, a game is played between those com-

276 BROCK,~ER,PER~GS

munities. The services are public, and all communities influence the state of the lake by the release of phosphorus from their agricultural activity. It will be shown that typically two Nash equilibria exist. The first one leaves the lake in an oligotrophic state, but it is located somewhat closer to the flip point than the cooperative outcome. The second equilibrium, however, leaves the lake in a eutrophic state with low welfare. If the communities end up in the second Nash equilibrium and decide to coordinate their policies in order to reach the cooperative outcome, the coordination is much more difficult and costly than from the first Nash equilibrium because of the hysteresis. In case the first flip of the lake is not reversible, it is even impossible to reach that point.

Under the assumption that the dynamic processes in the lake are very fast so that the state of the lake adjusts instantaneously to new loading levels of phosphorus, the analysis is essentially static and relatively easy. A full dynamic analysis, however, requires optimal control techniques for nonlinear systems. We begin with the steady-state economics of lakes. We then consider the full dynamics. The dynamics prove to be very complex.

The Lake Model

Lakes have been studied intensively over the past two decades, and it has been shown that the essential dynamics of the eutrophication process can be modeled by the differential equation:

. _ P(t)2 _ P(t)-L(t)-sP(t)+r P(t)Z+m2 , P(O)-Po, (1)

where P is the amount of phosphorus in algae; L is the input of phosphorus (the "loading"); s is the rate of loss consisting of sedimentation, outflow, and sequestration in other biomass; r is the maximum rate of internal loading; and m is the anoxic level (Carpenter and Cottingham 1997; Scheffer 1998). Estimates of the parameters of the differential equation for different lakes vary considerably, however, so that a wide range of possible values has to be considered.

By substituting x = P jm, a= Ljr, b = smjr, and by changing the time scale to rtjm, the previous equation can be rewritten as:

i(t) ~ a(t)- bx(t) + x(;)~): 1 , x(O) = Xo. (2)

The last term in the right-hand side-the internal loading-has a feature that is the cause of the interesting dynamics. This term is convex over a certain interval and concave over another interval. This means that we lose the convexity of the analysis, the convexity that is the basis for decentralized decision making. We will see this in more detail in what follows.

In order to understand the essential features of the model, suppose that the loading a is constant. For high values of a, equation 2 always has one stable equilibrium. For lower values of a, three things can happen depending on the value of the parameter b. If b :::: 3;-f,, all values of a lead to one stable equilibrium. If b ::::; 0.5, a range of values of a exists, starting at 0, where equation 2 has one high stable equilibrium and one low stable equilibrium, and


t Q)

a; UJ ::::J ..... 0 .c c.. UJ 0 .r:: c..

eutrophic poospho~us at

equi\ibnum --------------~--~--~------~-~~d~~,::dt~~~o

loading (a) ~ Figure 10-5. Hysteresis effects in the management of shallow lakes.

f g tJ) c::

~ _Q

]! g

phosphorus in lake (X) -+

Figure 10-6. Phase diagram for the open-loop Nash equilibrium.

=0

where the third root of the right-hand side of equation 2 determines the borderline between the two domains of attraction. If 0.5 < b < 3/f, the range with two stable equilibria is preceded by a range of low values of a with again only one stable equilibrium. Figure 10-5 shows the three different situations.

It is easy to see the hysteresis effect now forb < 3fj. If the loading a is gradually increased, at first the equilibrium levels of phosphorus remain low so that the lake is in an oligotrophic state with a high value of ecological services. At a certain point, however, the lake flips to a eutrophic state with

278 BROCK,~ER,PE~GS

high-equilibrium levels of phosphorus and a low value of ecological services. If it is now decided to lower the loading a in order to try to restore the lake, the lake has to be decreased below this flipping point until a point is reached where the lake flips back to an oligotrophic state. If b ::::; 0.5, the lake is trapped in high-equilibrium levels of phosphorus, as can easily be seen from Figure 10-5, which means that the first flip is irreversible. In that case only a disturbance of the parameter b of the model-e.g., by changing the fauna of the lake-can possibly restore the lake.

Steady-State Economics

Several interest groups operate in relation with the lake modeled in the preceding section. Since the release of phosphorus into the lake is due to agricultural activity, at least the farmers have an interest in being able to increase the loading. In this way the sector can grow without the necessity of investing in new technology to decrease the runoff-output ratio. On the other hand, a dean lake is preferred by fishermen, drinking water companies, other industries that make use of the water, and people who spend their leisure time on or along the lake.

Many potential conflicts can arise on the use of the lake. The conflict that one would think of perhaps first of all is the conflict between farmers (and by extension consumers of agricultural products) and those who demand an oligotrophic lake in order to sustain their well-being. We will come back to this conflict later. For now, we will instead focus on conflicts between different communities.

Suppose that a community or country, balancing the different interests, can agree on a welfare function of the form In a - cx2 . The lake has value as a waste sink for agriculture, and it provides ecological services that decrease with the amount of phosphorus in algae. Furthermore, suppose that the lake is shared by n communities or countries with the same welfare function. If the amount of phosphorus adjusts instantaneously to the loading levels chosen by these communities without costs, just a static steady-state problem results. Suppose also that b = 0.6 in equation 2, so that according to the above analysis, the lake displays hysteresis, but a flip to a eutrophic state is reversible. This is the most interesting case, but the analysis for other values of the parameter b can of course be performed in a similar way.

First we will concentrate on the optimal management problem, which implies the maximization of 'Ei In ai- ncx2 , c > 0, subject to

n 2

L ai - 0.6x + 2\ 1 = 0. (3) i=l X

The logarithmic functional form will lead to an outcome that is independent of n, so that it can be a benchmark for Nash equilibria regardless of the number of communities. It is also assumed that the area around the lake is large enough, that adding new communities does not lead to crowding out and the objectives can be additive in the number n.


and the objectives can be additive in the number n. Simple calculus shows that the optimal steady-state amount of phospho

rus x* is determined by

0.6- (x2 ~ 1)2 - 2cx(0.6x- x/: 1 ) = 0. (4)

If c = 1, this yields x* = 0.33, with the optimal steady-state loading ~a;*= 0.1. It means that the lake is in an oligotrophic state, but note that it is also possible to end up in the eutrophic state x = 1 for the same level of loading, which will happen if the initial amount of phosphorus is in the upper domain of attraction.

Optimal management, of course, does not necessarily have to lead to an oligotrophic state of the lake. If the welfare function attaches a relatively low weight to ecological services, it can become optimal to choose a eutrophic state with a high level of agricultural activities. In the steady-state objective, c denotes the relative weight of the loss of ecological services with respect to the value of the lake as a waste sink for phosphorus. For large values of c, the optimal steady-state problem has one maximum for an x below the flipping point. As c decreases, first a local maximum appears for a high x while the global maximum is still reached for a low x, but for c low enough (c ::::; 0.36) that the global maximum occurs for a high x beyond the flipping point. If enough weight is attached to the services of the lake, it will be optimal to aim for an oligotrophic state. This is guaranteed by taking c = 1.

Flipping occurs when total loading is increased to a= 0.1021, which means that the lake will be managed not far from what can be called the "edge of hysteresis" (Carpenter et al. 1999). Small perturbations that cause a flip will have high costs, not only directly by a jump to a high x but also indirectly because of the long path of return to the optimal situation. Therefore, policy considerations might lead to a precautionary principle.

Suppose now that the communities or countries do not cooperate in optimally managing the lake so that it is appropriate to search for Nash equilibria for the objectives In a; - cx2 , i = 1,2, ... , n, subject to equation 3. Simple calculus shows that the steady-state amount of phosphorus in a Nash equilibrium has to satisfy equation 5:

0.6- (x2 ~ l)2 - ~2cx(0.6x- x/: 1 ) = 0. (5)

For the sake of exposition we taken= 2 and c = 1, so that optimal management of the lake leads to the oligotrophic state x* = 0.33 with optimal loading levels a;*= 0.05, i = 1,2 (see above).

Equation 5 has three solutions, but only two of them relate to Nash equilibria. The first one is x = 0.36 with equilibrium loading levels a; = 0.0506, i = 1 ,2. This equilibrium point lies between the full cooperative outcome and the flipping point, which shows that noncooperative behavior not only leads to a standard loss in welfare but also brings the lake closer to the edge of hysteresis.

More interesting is that another Nash equilibrium exists that yields the


steady-state amount of phosphorus x = 1.51 with equilibrium loading levels a; = 0.1054, i = 1,2. If the communities focus on this equilibrium point, the lake is in a eutrophic state. Welfare is much lower here. In the full cooperative outcome, the communities each have a welfare level of -3 .1068; and in the Nash equilibrium with an oligotrophic state, the welfare level of each community is -3.1134, but in the Nash equilibrium with a eutrophic state, the welfare level is -4.5301.

Each community will have some policy, like a tax on emissions, in order to regulate the release of phosphorus from agricultural activities to a desired level. If the communities are in the first Nash equilibrium and decide to cooperate, it is relatively easy to redesign the policy in order to regulate the loading to the optimal level. However, after a flip of the lake has occurred and the communities are locked into the bad Nash equilibrium, it is much more difficult for the people to reach the cooperative outcome in reaction to a higher tax. It is not enough to reduce the loading levels from a; = 0.1054 to a;*= 0.05, i = 1,2. The reason is that the lake will then still be in a eutrophic state with the steady-state amount of phosphorus x = 1, because the adjustment process started in the upper domain of attraction. What is needed is first a further reduction to the loading levels a; = 0.0449, i = 1,2, where the lake flips back to an oligotrophic state. After this flip back has occurred, the loading levels can be increased again to a;*= 0.05, i = 1,2. The two stages in the reaction process to higher taxes are a consequence of the hysteresis in ecological systems. Lack of care in the neighborhood of the flipping point may lead to the bad Nash equilibrium and may, therefore, considerably increase the costs of restoring cooperation. Another way of saying the same thing is that transaction costs increase because of the existence of non-convexities.

Dynamic Analysis

The problem we wish to consider here is the existence of shadow prices for lake problems. Flips from one basin of stability to another take time (years to decades for some lakes). There are accordingly no discontinuities, but the problem is a dynamic one. The static analysis of the previous section is not sufficient. In order to find the optimal management strategy, we need to take the transients into account. The way to do this is to find the time path of the runoff that will maximize the present value of future utility. This can be analyzed by using Pontryagin's maximum principle (Brock and Starrett 1998; Maler et al. 2000).3

Remember that the maximum principle introduces an auxiliary or costate variable that can be interpreted as a shadow or accounting price on the state variable. In the lake case, there will be a price on the stock of phosphorus in the algae in the lake. This price will change as follows:

'Z = [o + b- (I ;:2)2)p, (6)


where & is the utility discount rate and 1/a + p = 0. Thus, the price must be negative, reflecting the fact that x, the amount

of phosphorus in the lake, is bad. By using this relation we can rewrite the equation for the dynamics of P as an equation of the dynamics of A:

~~ = -[(b + 8)- (l;;2) 2 ]a + 2cxa2 (7)

By combining the equation daldt = 0 and the equation dx/dt = 0, we are able to characterize the optimal management of the phosphorus flow into the lake.

That there are three points of intersection between the curves ~~ = 0 and ~~ = 0 is arbitrary and depends on the parameters. There can be one, three, or even a countable number of equilibria, but the generic case will have a finite odd number of equilibria under plausible economic restrictions. These intersections between the two curves, ~~ = 0 and ~~ = 0, indicate potential equilibria (Figure 10-6). However, the middle one is unstable and can be ruled out. The remaining two are saddle points and may be steady states. To which will the optimum path converge? That depends on the initial stock of phosphorus in the lake. If the lake is much polluted initially, the eutrophic steady state will be the ultimate outcome, while if the initial pollution is low, the optimal path will bring the lake to an oligotrophic equilibrium. Which equilibrium the optimal path will converge on is determined by the initial stock of phosphorus in the lake. If it exceeds a certain level Xs (the so-called Skiba point after the mathematician who first studied these convex-concave dynamic problems), the system will converge to the eutrophic equilibrium and vice versa.

Assume now that phosphorus flows into the lake. We may now prepare a phase diagram of the system by plotting a on the vertical axis and x on the horizontal. The curves ~~ = 0 and ~~ = 0 divide the phase diagram into four regions: (1) ~~ < 0, ~~ < 0; (2) ~~ < 0, ~~ > 0; (3) ~~ > 0, ~~ < 0; (4) ~~ > 0, ~~ > 0. By visual analysis of these four regions and the directions of movement of the variables a and x in each, it is possible to locate all initial pairs (x, a) such that the dynamics converge to a steady state in both forwards and backwards time. Candidate optimal paths located on the phase diagram are the same as those that converge to a steady state in forward time. Techniques are available (Skiba 1978; Dechert and Nishimura 1983; Brock and Malliaris 1989) to compare the value of the objective on each of these candidates without having to do the actual integration required to explicitly evaluate the objective.

Using this type of analysis, Brock and Starrett (1998) provide a complete analysis of the location of the optimum for two cases: (1) where there is only one steady-state equilibrium, and (2) where there are only three steady-state equilibria. They indicate how to generalize this analysis to the generic case of any odd number of equilibria. In case 1 the analysis is standard. One locates the optimum trajectory on the phase diagram by locating for each initial x the value a(x) such that the pair of differential equations converges to

282 BROCK,~ER,PERRTINGS

the steady state as time advances. Consider case 2, where there are three equilibria. The low x and high x

equilibria are saddle points, whereas the middle x equilibrium is an unstable spiral. The middle steady state can always be shown not to be an optimum. Notice that in case 2 there are always initial values of x where there are two candidate optima. For each initial x there are two values of a, one low, one high, such that the trajectory starting from each of these converges to one of the steady states. More particularly, a high a leads to high steady state x, and low a leads to low steady state x. Case 2 divides into three subcases: (A) it is always optimal to go to the low x steady state, no matter how big the initial x is; (B) it is always optimal to go to the high x steady state, no matter how big the initial x is; (C) there is a cutoff point, the Skiba point, such that if the initial xis below (the high steady-state) the Skiba point, the optimal path converges to the low x steady state (above). The paths yield the same value at the Skiba point.

Let us use the above discussion of our very stylized model to inquire into changes that have to be made in conventional "convex" economics to deal with this type of situation where hysteresis and irreversibility may be endemic due to non-convexity of the state dynamics. We do this in several parts. First, we consider the case where the utility of loading u(a) (which we are setting equal to log( a) for illustrative purposes) is generated by the sum of profits of firms located in the watershed of the lake and where the farmers are numerous enough that strategic interactions may be ignored. We consider the workability of taxation of loadings at marginal social cost.

Parenthetically, we remark that in practice taxes may not be the most efficient instrument, especially if restoration of the lake is more easily achieved by ecological sequestration of harmful materials than by stopping pollutants from getting into the lake in the first place. Sequestration might be done more efficiently by, for example, co-payment schemes for buffer banks along stream course ways and riparian reserves rather than direct taxation of loadings. This is likely to be the case when (1) sequestration is quite easily done by inducing direct effort to sequester runoff nutrients by devices such as buffer banking streams, (2) the elasticity of substitution between taxed inputs and other inputs is low in production functions, and (3) risk aversion of individual firms is high (because of incompleteness of hedging markets and the tax translating into a fixed cost equivalent via low substitution elasticity and high administrative costs, perhaps). It is beyond the scope of this chapter to analyze other instruments of regulation beyond stressing to the reader that other instruments should be considered in any practical application.

We now return to a discussion of implementation of taxation on loadings. Our initial remarks at the beginning of this chapter stressed the role of convexity of the abstract production technology set in the design of conventional decentralized regulatory schemes such as taxation at marginal social cost at each point. We discuss here the workability of this type of decentralization in the above setting, where not only are there two cases but case 2 has three sub-


cases. Note that except for case 2C with initial x at the Skiba point, there is a unique socially optimum path; call it {a*(t), x*(t), x*(O) = xo given}. Let each firm fin the lake's watershed have profit function u(a(f), f), where firm f loads a(f) into the lake. Let u(a) denote the maximum of the sum of u(a(f), f) over all firms J, subject to the constraint that the a(f) sum to less than or equal to a. This is a concave problem provided each u is concave increasing in a(f). Hence, under regularity conditions on each function u(.,J) we can implement decentralization schemes using conventional economics for each target level of total loading. The regularity condition needed is that the derivative of each u be decreasing in a, be very large for very small a, and be very small for very large a. That is, the marginal product of loading must fall with loading and be very large (very small) for very small (very large) loadings.

Notice that a linear utility has constant marginal product and, hence, will not satisfy the regularity condition. But u(a) = log(a) satisfies it. Under the regularity assumption-call it the controllability condition-we can induce each firm to load an amount a*(f, t) that sums across firms to the socially desired target total loading a*(t). This may be done by imposing a tax T*(t) on each unit of loading at marginal social cost - p*(t), where p*(t) is the shadow price of x evaluated along the socially optimal path (a*(t), x*(t), x*(O) = xo given). p*(t) is negative because it is the derivative of the optimal value function w.r.t.x and xis a bad. Indeed, if each u(.,f} is controllable, we can induce any choice of a(f) we wish by charging an appropriate "tax price" T. The tax T*(t) can be written as a continuous function of the state variable x*(t) for case 1 and cases 2A and 2C but not for case 2B. But this discontinuity in the tax function presents no problem for the optimum planner in decentralizing this community of firms. All she need do is design incentives to induce the firms to produce the total target socially optimal loading a* (t) at each point of time t. The resulting system solves the differential equation (equation 8), given x(O} = xo

~; = a*(t) + g(x). (8)

But x* itself solves the differential equation (equation 9), given x*(O) = xo

d;; = a*(t) + g(x*). (9)

A trivial adaptation of the usual argument for uniqueness of solutions to differential equations proves that solution x equals solution x* since the control a* is the same. Even the case 2B can be decentralized. For initial x not equal to the Skiba point, simply follow the above. For initial x equal to the Skiba point, simply make up your mind which path (they both have the same welfare) you want to follow and design T*(t) as above to induce a*(t) for the total loading along that chosen path. This treatment looks more special than it really is. For example, imagine that each firm has a vector of state variables (i.e., slow variables) that are cosdy to rapidly adjust as well as flow variables like the above. As long as each firm's problem is strictly

284 BROCK,~ER,PE~GS

concave, we may form a global overall optimization problem, optimize it to get the optimal loading of the lake, tax the loading of each firm at marginal social cost T*(t), present each firm with this tax schedule, and tell each to go ahead and maximize the capitalized value of their profit stream net of taxes. Since their optimum problem is still concave, they will reproduce their part of the socially optimum path of loading, as above.

It follows that introduction of slow and fast variables on the firm side presents no problem for standard decentralization theory so long as the firms' problems remain concave in the relevant arguments and an analogue of the controllability condition holds. Spatial scale considerations present no problem in this idealized full-information, deterministic world either. If there are multiple watersheds, the overall social optimization problem may be solved, and taxation may be imposed on the firms at marginal social cost as before. As before, so long as the firms' problems are concave in their state and control variables, and a dynamic analogue of the controllability condition is satisfied, the tax schedule may be designed so that firms are induced to follow the overall social optimum.

The introduction of uncertainty presents no problem provided there is common agreement among the firms and the regulator on the true distribution of stochastic shocks to the system and provided that the firms face concave optimization problems and that a stochastic analogue of the controllability condition is satisfied. Hence, the essence of the tax problem from an abstract theoretical point of view is that the targets of the taxation-i.e., the firms-all face concave optimization problems where an analogue of the controllability condition holds. The overall social optimization problem of the coupled economic ecological system can be nonconcave (non-convex overall production set in the language of welfare economics exposited at the beginning of this chapter). What is key to successful decentralization of incentives is whether the system can be decomposed so that the targets of the decentralized regulatory scheme themselves face concave problems that are "controllable" (the firms in our case).

In this case the targets can be "controlled" via dynamic state-dependent tax schedules to reproduce the socially optimal values of their inputs into the overall economic-ecological system. The ecological system, even though it:S dynamics are non-convex, will reproduce the socially optimal path for the state variables, given that the inputs from the economic side are controlled at their socially optimal levels. Although what we have said above is sound from the point of view of pure theory, it needs revision and supplementation for the complexities of actual practice. In actual practice, the dynamics of both the economic system and the ecological system are not known and must be estimated. The distribution of stochastic shocks must also be estimated. Ludwig (1995) studies a harvesting problem and treats several levels of uncertainty including difficulties in measuring the state, uncovering the true dynamics, administering control, and uncovering the true distribution of outside shocks to the system. He also treats not only the case of continu-


ally occurring small shocks but also the case of rare but large shocks. He argues that precautionary principles tend to get strengthened as one adds more layers of uncertainty.

The optimal design of regulatory instruments becomes much more complicated in this more realistic world. However, if there is a presumption that the economic side of the coupled economic-ecological system displays enough "regularity" in the sense of concavity of production, (i.e., convexity of production sets) and quasi-concavity of utility functions, then we may borrow from a large literature in economics to induce agents to choose in a decentralized manner whatever loading choice is deemed optimal from the ecological side. The extra complications created by scientific ignorance about the ecological dynamics, stochasticity, ecological non-convexities, cross-scale interactions, slow and fast variables, unobserved slow variables, etc., may manifest themselves much more in the decision about the amount of loading to be tolerated than in the exact manner that that chosen amount of loading is to be implemented by the agents of the economy. Recent work on robust control may be a useful way to extend the work discussed here. Robust control theory gives a precise way of modeling the type of risk present when mis-specification error looms large and the social planner wishes to ensure against mis-specification risk when designing regulatory policy.

Extension and application of this literature to mis-specification of nonconvex ecological dynamics and the impact of this mis-specification of non-convex dynamics with potential alternative stable states upon regulatory design seems to us to be a very worthwhile research project. But let's return now to discussion of optimal taxes when model specification is correct. Notice that the optimal tax is the marginal social cost of loading, which depends upon the state (i.e., the fully specified history in the stochastic case) of the system. In our simple time-stationary recursive system, this tax can be specified as a time-stationary function of the state "x" of the system, but in general nonstationary stochastic settings the state description will be more complicated. Complexities of practical implementation of such detailed contingent specification of policies argue for implementation of simple approximate policies. This consideration argues that computational work like that of Dechert (1999), which computes social welfare from simple policies and evaluates the cost of simplicity, will be valuable.

Economists commonly argue in favor of tax instruments over many other modes of regulation such as quotas because of flexibility. However, in practice there are many complications that may modify this prescription: (1) Firms may face overhead costs that must be covered or they will go out of business. This generates a nonconvexity. (2) Firms may have dimensions of adjustment to incentives that are not captured by estimates of their technology used in setting the level of taxes, quotas, loading reduction copayments, buffer bank co-payments, or any other regulatory instrument. (3) Any mode of regulation induces costs on the regulated as well as the regulator. These administrative costs as well as heterogeneity of firm types argue for regula-

286 BROCK,~ER,PERRINGS

tory tiering in practice (Brock and Evans 1986). Furthermore, it is common to find that a small number of firms cause the

bulk of the problem. This is suggested by Gibrat's law: the size distribution of firms in actual practice is roughly log normal. Brock and Evans (1986) present studies of Gibrat's law, regulation of pollution, and other negative externalities in actual practice. Hence, one might avoid both political and administrative costs by regulating only the small number of operations that are responsible for the bulk of the problem, and simply leave the rest of the firms alone.

The Small Numbers Case

We tum now to the case where there is a small number of strategically interacting firms in the lake's watershed. We sketched a theory above that suggests that regulation of a large number of firms (a large enough number so that incentives to act strategically are minimal) might be easily decentralized because each firm is solving a concave, tax-controllable, problem at each point in time. Hence all the decentralizer has to do is design a system where the firm sector is induced to load the target a*(t) at each point in time t. This can be done with taxes, but could also be done with emission permit markets where a firm must have a permit to emit at any date t, a permit to emit lasts only one "period," the permit market is open each "period," and the agency sets a*(t) permits to be sold each "period." Of course this is very idealized, but it suggests that decentralized regulation may be achievable for large numbers cases where strategic interactions may be ignored. Decentralization is more difficult in the small numbers case discussed above in this section because there each operator took into account the impact of the others' actions on the dynamical state equation.

We sketched the case of an open-loop Nash equilibrium above. We tum to a very brief sketch when it is appropriate to focus on other concepts of equilibrium. First, there is the issue (Ostrom 1990; Ostrom et al. 1994) oflocating conditions where the small group might self-evolve institutions that do better than Nash noncooperative equilibrium. For example, the temporal and spatial scale of the problem may interact with the determinants of the ease or difficulty of organizing collective action as detailed by Olson (1965) or Ostrom (1990). Ostrom (1990) lists factors (and cites case studies to back them up) that are positively associated with success of groups at self-organizing the provision of public goods, self-organizing avoidance of tragedies of the commons, and self-managing common property resources (CPRs). Hence, the use of coercive schemes or other governmental catalyzed schemes is not required to get a workable solution.

Ostrom's analysis is germane to our case of locating features of the underlying social, cultural, and ecological context that might allow our community of noncooperative Nash players to do better. Of course, the literature on supergames and dynamic games suggests that small discounting of the future utilities allows threat strategies to be designed that will support a self-enforcing agreement to manage the system at the optimal level.


Intuitively, the noncooperative Nash equilibrium is returned to and played forever if anyone is caught loading more than the globally optimal loading. It is beyond the scope of this chapter to get into the issues raised by the literature on dynamic commons games (Dutta and Radner 1999). We turn to the less formal approach of Ostrom (1990) and Ostrom et al. (1994). Here is a list of Ostrom's (1990) conditions that are positively associated with successful self-evolution of institutions for managing a common property resource like a lake that give social payoff better than noncooperative equilibrium:

• Most appropriators share a common judgment that they will be harmed if they do not adopt an alternative rule.

• Most appropriators will be affected in similar ways by the proposed rule changes.

• Most appropriators highly value the continuation activities from this CPR; in other words, they have low discount rates.

• Appropriators face relatively low information, transformation, and enforcement costs.

• Most appropriators share generalized norms of reciprocity and trust that can be used as initial social capital.

• The group appropriating from the CPR is relatively small and stable.

The word appropriator refers to a user of a CPR but could equally apply to self-organized communal users of a commonly shared resource like the watershed of a lake and the lake itself. In this case, enforcement costs refer to detection and policing of shirkers. Ostrom (1990) lists many case studies to document the importance of these conditions, which she has numbered in order of importance (1 is the most important) for successful self-organization of workable cooperation.

Second, we used the concept of open-loop dynamic Nash noncooperative equilibrium above. Dechert (1978, 1999) has produced a handy method for computing open-loop Nash equilibria that applies to our setting. He produces an optimal control problem whose solution is a Nash equilibrium. For our case, his "as if" control problem is the sum of the loading utilities across the players minus the cost for one player (recall that the cost is the same for all players). Hence (generically), we have an odd number of steady-state equilibria, and in the case of one or three steady-state equilibria, the above taxonomy of case 1 and case 2 with three subcases applies.

Dechert and Brock (1999) devised a very rapid algorithm4 to compute solutions to a discrete time version of our lake problem and, using similar assumptions, showed the following. First, for two-player games in Nash noncooperative open-loop equilibrium, overloading relative to the social optimum loading at each level of x is surprisingly small until initial x is past the point of inflection of g(x). This is the level of x where positive feedback

288 BROCK,~ER,PERRTINGS

effects are triggered in the ecosystem dynamics. However, for three or more players there is an abrupt change in the level of shirking even before the inflection point of g(x) is reached. Second, Dechert sets a constant tax at the level necessary to make the social optimal steady state the steady-state solution to the dynamic game·. They also numerically show that the dynamic game solution converges to the social optimum steady state even up to one hundred players.


The lake case shows evidence of hysteresis in the state dynamics, implying that a system that flips from one state to another at some value of the control may require a very different value of the control to return it to the original state. Because such systems can flip suddenly from one state to another as a result of particular events, it is difficult for any regulatory agency to observe the signals of an impending change in time to take action to avert it. In a managed system, the dynamics of the resource are revealed through the response of the state variables to the controls. The closer the system is brought to the boundaries of the stability domain, the higher the risk of an unanticipated irreversible or only slowly reversible change as the system flips from a higher productivity state to a lower productivity state. The same phenomenon makes it difficult to devise a decentralized regulatory system involving taxes or charges. Once the system has flipped to an undesirable state, taxes or user fees would have to be such as to drive runoff well below the original levels and to hold them there in order to overcome the hysteretic effect.

Systems ranging from coral reefs to semiarid savannas have been observed to behave in very similar ways. From a regulatory perspective the problem is precisely that bifurcation points may not be seen before they are reached. The observable level of environmental quality does not generally offer a reliable indicator of the system's relative position with respect to thresholds. Moreover, the conditions under which ecosystems respond to increasing stress without suffering an irreversible or near irreversible loss appear to be fairly restrictive.

Finally, it is worth noting that modeling the interactive decisions of human agents and other species poses special problems. Human decision makers do have the capacity to be forward looking. In this they seem to differ from other species. More important, they are capable of social learning and possess institutional memory (e.g., libraries). This is obvious, but it is hard to write down differential equations in ways that differentiate human interactions from the interactions of other species. In economics, we typically capture that difference in the level of sophistication agents are assumed to have in their expectations of the future and their strategic behavior. For example, in rational expectations models we deal with systems of ordinary differential equations where we solve the system "forwards"; whereas in other sciences, including mathematical biology, we deal with systems where we solve the ordinary differential equations "backwards." It may be argued


that the economic approach is a benchmark way of incorporating the relative ingenuity of humans. The appropriate level of complexity of forwardlooking behavior and the level of strategic interactions among human beings relative to animal and plant community systems demand a correspondingly more sophisticated modeling approach than game theoretic concepts. Economic applications of game theory, for example, exploit more levels of iterated common knowledge than do the biological and evolutionary applications. The biological and evolutionary applications also do not exploit the degree of farsightedness as does the rational expectations literature. It can be argued that this is entirely appropriate given the level of sophisticated cognition contained in human beings relative to other species.

Notes

1. A second definition of resilience refers to the properties of the system near some stable equilibrium (in the neighborhood of a stable focus or node) and defines the resilience of a system to be a measure of the speed of its return to equilibrium following perturbation (Pimm 1984). The two measures are related.

2. Technically, in the special case where P is both irreducible and aperiodic, the system will have a unique globally stable equilibrium-only one stability domain. In this case, the transition probabilities of the system may be said to be equivalent to Holling's resilience measures. In the more general case where Pis reducible, the state space may be partitioned into classes corresponding to multiple equilibria. It follows that a sufficient condition for a system to be infinitely resilient is that P is irreducible. If Pis reducible, (1) the system may, in the limit, occupy any one of a finite number of closed classes; (2) it is sensitive to initial conditions; and (3) it is path dependent (the key properties of complex systems generally). In this case, the limiting transition probabilities of the chain depend on the initial state, i. The future evolution of the system depends on where it starts.

3. Maler, Xepapadeas, and De Zeeuw (1999) offer a full simulation of the lake model with both open-loop and feedback equilibria for a differential game on the use of the lake.

4. The upwind Gauss-Seidel can be used because the problem has a one-dimensional state variable, is recursive, and has a monotone optimal policy function. These features may be exploited to yield a very fast computational algorithm.

Part IV

Linking Theory to Practice

CHAPTER 11

RESILIENT RANGELANDS

ADAPTATION IN COMPLEX SYSTEMS

Brian Walker and Nick Abel

R angelands are the vast tracts between deserts and the agricultural zones where rainfall is generally too low or unreliable for cropping (Huntley and Walker 1982) and where people make their livelihood

from pastoralism. Species composition and structure of rangeland vegetation vary according to rainfall and soil type, but at the simplest level they consist of a grass layer (a mixture of perennial and annual grasses plus a variety of forbs) and, in most cases, a woody plant layer of trees, shrubs, or both. The perennial grasses and the shrubs vary in their palatability to livestock. Large herbivores can include wildlife as well as livestock (cattle, sheep, goats, and camels), consuming grass, woody plant leaves, or both.

Old World rangelands have a long history of human use, and their present structure and composition have been sculpted over many hundreds and even thousands of years of pastoral activities. But over the past hundred years or so, on all continents, soil and vegetation systems have experienced major changes associated with increased stocking levels and reduced seasonal movements. Disease control, fencing, and the establishment of water points have made this possible. Changes common to all countries include a loss of high fodder-quality perennial grasses and their replacement by annual grasses or unpalatable perennials, lowered primary production through soil erosion, and, depending on soil type, an increase in woody plants. Changes can be episodic or continuous. In some cases (e.g., loss of soil through erosion), the changes are irreversible on a human, or management, time scale. In others, they represent alternate stable states-once a change has occurred, it is often difficult or very slow to reverse (Westoby et al. 1989; Walker 1993).

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The rangelands are host to some 800 million people (World Resources Institute 1998), about 360 million cattle, over 600 million sheep and goats, and a very large array of native biota (FAO 1999). Since climates do not favor high-input agriculture, production depends on the functioning of soilvegetation systems with relatively little support from fossil fuels or chemicals. People, livestock, and landscape processes are thus tightly interconnected. Their mutual welfare is subject to the influence of four main external drivers--climate, land alienation by immigrants, livestock prices, and regional investment. The proportion of rangeland contribution to GDP has been declining steadily in all developed countries for the past hundred years (Walker 1995), and, for most landowners, so have the terms of trade for livestock production. In developing countries, increases in the numbers of humans and stock, and encroachment on rangelands by farmers, have reduced pastoral mobility and increased dependency on drought relief. In general, the political influence of rangeland peoples declined during the twentieth century. Access to and management of land are thus often influenced strongly by policies and institutions established in remote cities. These often detract from the resilience of rangeland regions.

What follows in this chapter is based on our experience and on rangelands literature, supplemented by insights gained from four regional case studies carried out for the Resilience Network, by researchers from those regions. A comparative analysis will be published shortly. The regions are: the Western Division of New South Wales, Australia; the Great Plains of the United States; the Southeastern lowveld of Zimbabwe; and the Victoria River Downs region of the Northern Territory, Australia.

This chapter is an exploration of the concept of resilience in rangeland regions. Our aim is t9 develop a general framework for analyzing the sustainability of such systems and for developing practical ways of enhancing it. In keeping with the thrust of this book, by sustainability we mean ecological, economic, and social sustainability.

Rangeland Dynamics-Resilience, Panarchy, and Pulses

We take our concept of resilience from Chapter 2, where it is defined as the capacity of a system "to experience disturbance and still maintain its ongoing functions and controls. A measure of resilience is the magnitude of disturbance that can be experienced without the system flipping into another state or stability domain."

Resilience

The concept of resilience needs to be considered in relation to the purpose for which the land is being used (Tongway and Ludwig 1997a, 1997b). For exmple, a chenopod shrubland that supports sheep can be transformed to grassland by grazing pressure without apparent loss of wool production (Wilson et al. 1969). The processes essential for wool production (a supply of

11. RESILIENT RANGELANDS-ADAPTATION IN COMPLEX SYSTEMS 295

forage at requisite levels of quality and quantity, and the maintenance of ground cover and nutrient cycles) have, by definition, been maintained. We stress that not all apparently deleterious changes have such benign consequences for production, but we use this example to make the point that had the same tract of land been scheduled for the conservation of native species, the change would have been seen as a loss of function. Identification of essential processes therefore depends on the purpose of the analysis. Lack of progress in the "range degradation" debate is a warning to those attempting to evaluate resilience without specifying purpose. That debate degenerated into what were ostensibly technical disputes over data, when in fact the disagreements were about differences in values. Such disagreements are best understood and resolved by evaluating resilience for each purpose separately (Behnke and Abel 1996). The notion of general resilience-that is, ecosystems that are resilient in the face of any and all disturbances for all purposes (production, species diversity, aesthetic value, and so on)-is not achievable, and the quest for it clouds understanding.

Another aspect of resilience in rangelands that needs clarification is the issue of resistance versus resilience and what we mean by disturbance. The level of resilience increases with (and is equated to) the breadth of the domain of attraction of the essential variables and processes (D. Ludwig et al. 1997; Chapter 7). In sandy rangelands 80 percent or more of the biomass is underground, and so no matter how much grazing pressure is applied at any one time, the maximum disturbance is about 20 percent. (Note that grazing, fire, and drought are pressures that may or may not cause a disturbance.) Strictly speaking, this system is resistant to change more than it is resilient following change. But if we define the variable of interest as aboveground grass biomass, then it can suffer virtually 100 percent disturbance and recover; the system is highly resilient owing to the important process of recovery from underground storage. Rangelands on black cracking clays persist with little variation in production and species composition because soil structure is self-reorganizing under a regime of heavy grazing and trampling. Whichever way it is defined, these systems can be subjected to great disturbance without changing state because critical functions in the clay country are likely to persist under the livestock pressure that induces the changes in the grass biomass. Resistance and resilience are connected, and to avoid misunderstandings we need to focus on the important outcome-the persistence of a system state (the ranges of state variables and the associated controls on system function) in the face of external pressure.

Specification of time scale must accompany any discussion of resilience. Without it, disagreements will arise between a geomorphologist, who thinks the last ice age has just ended, and an economist, who thinks thirty years is a long run (Abel et al. 2000). Time scale also affects the classification of system components as variables or parameters. Topography is a parameter in models of landscape function, but over millennia it is a variable. For our purposes we use a time horizon of two centuries (a period that will encompass many

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major disturbances) as the standard for determining the relative speed of variables, and for distinguishing between variables and parameters. A highly resilient rangeland system would then be one that persisted through two centuries of perturbation with no loss of biophysical function (e.g., rainfalluse efficiency by vegetation) or socioeconomic function (e.g., generation and distribution of wealth). During this time it is likely to have passed through multiple states, and may have passed through several stages of the adaptive cycle. Also during this time the key processes, although they have probably fluctuated, have persisted. Alternatively, a nonresilient system would be one where the key processes deteriorated (in terms of human purpose) and did not recover within our arbitrary period.

The Adaptive Cycle

The adaptive cycle is offered (Chapter 2) as a way of understanding change and resilience in complex systems. The metaphor of the adaptive cycle, with its phases of exploitation, conservation, release, and reorganization, provides a strong framework that underpins our interpretation of rangelands. We digress, however, from the general case of a three-dimensional figure-eight dynamic over time. The heuristic value of this metaphor does not extend, and indeed is not expected to extend, to application in particular systems. In the early versions of the adaptive cycle, the first two axes of "capital" and "connectedness" are positively related. In attempting to apply it to the rangeland case studies, difficulties arose particularly in regard to the notion of an increase in the capital axis during the change from creative destruction to reorganization and then, again, during its subsequent decline from reorganization to exploitative phases. This conceptual difficulty remains however the axis is defined, and so we have used the valuable notion of the four phases of the cycle without reference to particular axes. It is necessary, however, to make some comments on the third axis, resilience, the understanding of · which is one of our major objectives.

In Figure 2-2 resilience is seen as waxing and waning according to the stages of the cycle. During the transition from Q through a to r, adaptive options are opened that can potentially maintain system resilience, and we concur with this general proposition. It is useful to think of the Q phase as one with no well-defined system state and the a phase as the time when a betterdefined and perhaps new basin of attraction comes into being. Resilience is shown in Figure 2-2 as being at a maximum in the r phase. It is associated with the start of increasing connectivity, the rapid accumulation of capital, and a more determinate path. Our observations of rangeland ecosystems suggest perhaps that resilience is at a maximum somewhere between r and K, as the system moves from rapid growth to consolidation. The rigid hierarchy of a late K phase prevents adaptation to disturbances, resilience declines, and eventually the structure disintegrates again. Adaptations developed during previous phases of collapse and recovery are then available for another recovery.

11. RESILIENT RANGELANDs-ADAPTATION IN COMPLEX SYSTEMS 297

Systems appear to pass through the various phases of the adaptive cycle and thereby maintain resilience. The system is at risk to change into an undesirable state, especially during the transition from Q to a. If a system is managed to remain in the K phase, components will not develop adaptations to disturbances, the diversity of species will decline, and the system will be more likely to collapse eventually and switch to an unwanted state despite management efforts. Passage through the somewhat risky and unproductive phases of collapse, reorganization, and recovery is therefore the price paid for maintaining resilience. Seen this way, resilience is a property that emerges from the cycle as a whole, and the magnitudes of the levels attributed to within-cycle phases are (in a hierarchical sense) determined by the resilience properties of the system that arise as the system passes through the complete cycle.

AI> Chapter 2 stresses, the adaptive cycle is predicted to operate at a range of scales and at different rates, and is intended to be relevant to both biophysical and human subsystems. We expect it to apply well to biophysical changes on rangelands at a landscape scale, as the following example illustrates. Late in the K phase resources are appropriated by a few dominant species. Following a major disturbance (fire, or a major drought plus heavy grazing), these resources are released rapidly as plants and animals die, or are burned and oxidized. Leaching and runoff may cause some net loss of nutrients, but the remainder becomes available to species suppressed by competition during the K phase. Seeds from soil seed banks germinate or are imported by wind or animals. This reorganization is crucial for the generation of new structures that are better suited to the changed environmental conditions that follow the disturbance and collapse of the K phase. Vegetative cover then increases as r proceeds. In the absence of at least moderate grazing pressure, primary productivity slows as moribund plant material accumulates, and a late K phase is reestablished, until another disturbance restarts the cycle. Disturbance and recovery phases cause system components to adapt, either by learning (smart pastoralists) or by selection (other pastoralists go bankrupt; drought-sensitive grasses increase or decline depending on climate and grazing), and system resilience can be enhanced.

Pulses

A variant of the adaptive cycle metaphor for repetitive change that provides a useful insight into the dynamics of semiarid rangelands is the "triggertransfer-reserve-pulse" (TTRP) framework suggested by J. Ludwig et al. (1997). Ludwig et al. developed the framework out of analyses of the western New South Wales rangelands, where episodic rainfall events result in pulses of activity following periods of quiescence. A rainfall event above some minimum level acts as a trigger, leading to a transfer of materials across the landscape (water, soil, nutrients, plant propagules, litter). These are captured by, for example, patches and bands of vegetation, fallen trees, or run-on sites,

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to form "reserves." Pulses of plant growth and animal and microbial activity occur within the spatially discrete reserves. The same sort of pattern can be generated by wind as a driver of the TTRP. The repeated sequences of TTRP events give rise to a very spatially heterogeneous system, each rangeland having a characteristic spatial scale at which overall resource capture, rain-use efficiency, and primary productivity are maximized (Noy-Meir 1973). Disruption of the spatial pattern (e.g., through heavy grazing) leads to varying degrees of "landscape dysfunction" 0. Ludwig et al. 1997). The changes that occur through time conform to a state-and-transition model of Westoby et al. (1989), which suggests that a rangeland can be in one of a number of states and that the transition from one state to another is determined by a particular set of conditions. Not all transitions are possible, and some are irreversible.

It is tempting to extend the TTRP biophysical model to encompass social and economic triggers. For example, during a phase in a business cycle when savings may be abundant in an economy, the investment in an "undeveloped" rangeland region can be considered as a pulse that enables an increase in water points, transport and communication systems, abattoirs, services, and human population. It produces a pulse of production, some of which is reinvested, some exported. If compatible industries and supporting services are not developed, the economy is "leaky," is not self-sustaining, and is subject to booms and busts. However, we lack sufficient empirical evidence to explore this further, and for now leave the TTRP metaphor as applied to the biophysical component of rangelands.

Although the adaptive cycle is expected to apply in ecological, social, and economic systems, the socioeconomic pattern is unlikely to be congruent with the biophysical pattern in scale or rate of change. The biophysical dynamics occur at a local or landscape spatial scale and on a temporal scale of one or two decades. It is difficult to generalize these fine-scale cycles into a cycle for a rangeland region or beyond, unless land and land use are unusually uniform. We predict, though, that cycles occur in socioeconomic systems at scales spanning households, organizations (Chapter 13), regions, states, and nations. The period of these cycles may range from a human generation, in the case of the household, to centuries. We explore the underlying dynamics of these emergent patterns further by examining changes at three spatial scales, using examples from the three regional case studies and elsewhere.

We conclude this commentary on resilience by noting that the adaptive cycle fits in well with several other interpretations of how social and natural systems work, including Boserup (1965) and Tainter (1988). Mao Tse-tung's notion of episodic revolution to prevent accumulation of power by elites, as well as democratic voting systems and anti-monopoly legislation, is also congruent with the adaptive cycle. We note that applying the notion does not always lead to expected results.

11. RESILIENT RANGELANDS-ADAPTATION IN COMPLEX SYSTEMS 2 99

Changes in Rangelands at Different Scales

Examined in sufficient detail, changes in any system constitute a continuum from those at a point scale to those at the largest scale, that of the entire system. Although this continuum is found in rangelands, two spatial scales are appropriate-the local and the regional-for consideration of resilience and human management. We address those two scales in this section. Later, in our general model of rangelands as complex adaptive systems, we discuss a third scale that applies to the largest areas, those of states, nations, and larger units.

Local-Scale Changes

At the paddock and property scale, changes between alternative states are variations of the outcomes of interacting grass and woody dynamics. Often, an initial (and undesirable) change is from perennial to annual grasses, or from palatable to unpalatable perennial grasses. In some cases it is accompanied by a change from an open, grassy rangeland to a woody thicket. In each case, the change can represent either a flip from one stability domain to another, or a return path that is so slow that from a manager's point of view it may as well be an alternative stable state.

Change to a wooded state does not occur in all rangelands, but where it does, the transition can come as an unwelcome surprise to the manager. It comes about through a combination of sustained grazing pressure and lack of fire. The grazing pressure reduces the competitive effect of grasses on shrubs, leading to increasing woody plant biomass, and it also reduces the amount of fuel for a fire. Periods of drought with high stock numbers bring about death of perennial grasses, leading to reduced grass cover. When followed by a high-rainfall season, this leads to a profusion of new woody plants (woody seedling establishment is strongly inhibited by a vigorous grass layer). If, at this point, all livestock are removed, enough grass growth can still occur to enable an effective fire, killing the new woody plants, reducing established ones, and keeping the system in a grassy state. However, if grazing pressure is maintained, there comes a point in the increasing woodyto-grass biomass ratio when, even if all livestock are then removed, the competitive effect of the woody plants prevents the buildup of sufficient grass fuel to carry a fire. The system then stays in the woody state until woody plants begin to die, opening up the system for increased grass growth and the reintroduction of fire. This can take thirty or forty years.

Earlier we cited an example in which a change from chenopod shrubland to grassland through grazing pressure did not cause a loss of resilience. However, that is a feature of that particular rangeland; not all rangelands behave in such a way. In those where grass species composition can change from palatable to unpalatable species, the change can lead to loss in animal production (Tainton 1981; Laycock 1991). In such rangelands a change in grass species that is not followed by a recovery of the original composition after the disturbance does indeed constitute a loss in resilience of the

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system-for the purpose of livestock production. An experimental investigation of change in grass species composition in such a rangeland in northern Australia (Walker et al. 1997) showed that there were limits to its resilience. It was able to recover fully its original composition following induced changes in plant species and in percentage grass basal cover, up to a particular threshold of disturbance. Species composition and percent basal cover trended strongly back to the original composition-a set of species that is competitively superior in terms of accessing water, nutrients, and light. Those species were mostly leafy, palatable perennials. The experiment tracked the changes in grass species composition and cover following cessation of the disturbance (removal of grass tufts). \Vhere grass basal cover was reduced by more than half (which frequentlly occurs in heavily grazed rangelands), and where the most palatable species were the first to go, then, in the competitive race to recovery, full basal cover was achieved before the original composition could be achieved. "Full" cover is the amount of grass that can be maintained by the annual rainfall. The less competitive, coarser, unpalatable grasses were able to expand faster through tillering, and new pioneer species arrived faster via seeds, than the more competitive palatable grasses were able to recover via new seedling development. A conclusion of the study was that the community would stay in that state until the established perennial grass tufts died and allowed new plants to establish. Death of established perennial grasses in rangelands most often occurs during a severe drought under conditions of heavy grazing (Hodgkinson 199 5).

The TTRP (trigger-transfer-reserve-pulse) framework introduced earlier emphasizes the pulsed pattern of system behavior that is characteristic of rangelands. In a well-functioning landscape, water and nutrient flows and cycles are controlled by the patchiness of the vegetation. Perennial grasses play a key role in promoting infiltration. The scale of patchiness can be coarsened by the loss of perennial grasses under grazing pressure. If that occurs, infiltration rate declines, the length of fetch increases, and the landscape becomes more leaky, losing water and nutrients to the wider system. The decline in fertility further reduces grass density, infiltration, and nutrient retention. Slow recovery can follow destocking. It can be hastened by management intervention to reestablish the dimensions of the spatial pattern (Ludwig and Tongway 1996).

There are differences among landscapes in the vulnerability of landscape function to grazing and drought (Tongway and Hindley 1999). Figure 11-1 supports Holling and Gunderson (Chapter 2) in their view that a measure of resilience is the magnitude of disturbance that can be experienced before a system flips to another state.

The feedback mechanisms that maintain a rangeland in zone I (Figure 11-1) vary between landscape types. Caughley (1987) offers an example. The rate of increase of red and gray kangaroos in western New South Wales depends on the availability of forage, the production of which depends on highly variable rainfall. The standing biomass of forage depends on rainfall

t c: 0 g .2 Q) c..

~ "'0 c: .!2


A robust landscape B fragile landscape

I I

III

stress/disturbance - stress/disturbance -

Figure 11-1. (A) The functional response of a robust landscape to increasing levels of stress and/or disturbance, according to Tongway and Hindley (1999). Zones I, II, and III represent three functional states, which could be empirically linked with an appropriate state and transition model (Westoby et al. 1989). (B) The functional response of a fragile landscape to increasing levels of stress and/or disturbance.

in the previous six months, and the rate of consumption. The rate at which kangaroos can consume forage is a function of grass biomass. The rate of grass growth slows as peak biomass is approached (feedback one). When forage is abundant, kangaroos are satiated, but when forage biomass falls below a threshold, the rate of intake falls below maintenance levels, and fecundity declines (feedback two). Together these feedback mechanisms prevent kangaroos from becoming so numerous that they destroy their food supply, or so scarce that they cannot recover after drought. Caughley describes this system as centripetal-it varies widely, because it is driven by erratic rainfall, but the feedback mechanisms resulting from interactions between grass and kangaroos enable it to persist.

Regional-Scale Changes

The four regions that provided our case studies differ in climate, vegetation, livestock and wildlife type and numbers, human population density, age structure, culture, institutions (including property rights), and economic status. Historically, all four regions were occupied by indigenous peoples who made significant use of native wildlife. They were displaced by settlers of European origin. Pastoral histories of the regions since commercial livestock operations began to show strong similarities. Each appears to have gone through four stages:

1. Initial, extensive, seminomadic movements, where management was opportunistic. Human and livestock densities were low. People were self-reliant. Strong feedback occurred from the ecological subsystem to the household economy, but linkages were being established to the global economy.

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2. Strong connection of the local to the global economic system. Much investment from overseas. Development of technology, especially water points, wire fences, disease control, and livestock breeding and husbandry. During this stage, natural capital, in the form of well-functioning landscape processes, was converted rapidly to economic capital. Livestock production was a significant and important component of gross domestic production. Pastoralists became politically powerful. Government support and subsidies developed, and there was public investment in infrastructure and disease control.

3. Rangeland problems emerged. Some were ecological, such as shrub encroachment, loss of perennial grasses, soil erosion, and animal pests. Others were economic, including declining terms of trade, other industries assuming greater economic importance, and increasing subsidization of pastoralists due to disproportionate political influence.

4. A change in attitude toward the rangelands occurred on the part of people in the cities and in government. Conservation and indigenous people's rights and aspirations were seen as important. Some withdrawal of subsidies and the development of multiple or alternative uses began.

No single biophysical state characterizes a region, because spatial heterogeneity is high. At this scale social and economic changes come strongly into play, and the adaptive cycle may help explain them. For example, the pastoral industry in western New South Wales, Australia, began with few institutions affecting land use, a seminomadic form of production, and the displacement of Aboriginal hunter-gatherers. Initially, land was occupied illegally or purchased speculatively. This period equates to an alpha or early r phase of the adaptive cycle. Subsequently, capital was invested heavily as the industry sought to satisfy strong demand from newly established gold mines-clearly similar to the r stage in our cycle. Land rights were established formally under pastoral leasehold. The coincidence of drought and recession at the end of the nineteenth century, the consequent collapse of pastoralism, and extensive land degradation stimulated the establishment of a new bureaucracy to regulate pastoralists. This equates to a shift from K, through Q to a. During the first half of the twentieth century, governments attempted to increase the density of settlements on rangelands by subdividing extensive holdings, thus fostering a new r phase. Widespread degradation was reported, and the policy was eventually reversed to one of property amalgamation. By the 1980s, complex land tenure and management institutions were established to provide drought relief, regulate wool prices, and provide elaborate infrastructure and services. This period equates to a late K phase in the adaptive cycle. During the 1990s, declining wool prices, changes in government subsidies for "drought relief," the decline of services, and


changes in rural population age structure combined to effect an Q phase that is currently in progress. There has been a decline in the number of properties that are occupied and replacement of sheep by goats or cattle. Capital is being withdrawn from the wool industry, and the number of associated organizations is dwindling, while the potential for a crisis (Q) is in the offing. The outbreak of ovine Johne's disease has placed additional stresses on pastoralists. The significance of these events for resilience is that an economic disturbance obliged enterprises to adapt. Our theory predicts an increase in the resilience of pastoralism to future economic shocks.

In southeast Zimbabwe, biophysical dynamics are similar to those in New South Wales, but the socioeconomic pattern is different. The cattle industry developed over some sixty to seventy years. In the early 1980s, livestock prices and general terms of trade for the cattle industry were down when a major, multiyear regional drought occurred. More than 90 percent of cattle died, but a high proportion of the remaining native ungulates survived. Fallowing the financial demise of most of the commercial livestock ranches (an Q phase), the resulting reorganization led to an amalgamation of cattle ranches into jointly operated wildlife conservancies. The new r phase of the adaptive cycle consequently has a diversity of wild and domestic herbivores (the commercial product) with different uses and dependencies on the vegetation. As with the return to large holdings in New South Wales, the socioeconomic system is structured at a larger spatial scale, allowing for greater use of spatial heterogeneity in the biophysical system. Both these features are believed to contribute to resilience. As we write, a new national-scale disturbance is affecting the region (government-supported occupation of the conservancies by adjacent subsistence farmers) while it is still in an early r phase. This will bring about a new reorganization in the system with unknown consequences for the trajectory of the region and its resilience. Given that the system is still in a very early r phase following the last reorganization, another disturbance is unlikely to result in any gains in resilience. It raises the question of what consequences might arise from the reverse of management/policy that maintains the K phase beyond adaptation into maladaptation; i.e., what are the consequences for resilience of policy/management that subjects the system to too frequent disturbances, preventing it from developing from exploitation and rapid growth into the accumulation and consolidation part of the r to K dynamic. We suggest that such policy/management would lead to declining productivity and declining resilience, owing to the inability of the system to submit novel, potentially adaptive structures to the selection process of competitive consolidation. (A similar interpretation may apply to attempts to deliberately manage the adaptive cycle-such as Mao Tse-tung's managed revolutions.)

Overview of Regional Changes

The histories of all four of the rangeland regions we have considered, as livestock producing systems, are very brief. Considered as integrated

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ecological-social-economic systems they have barely completed one cycle of learning, and are thus naive. The adaptive cycle is a useful concept in both biophysical and socioeconomic systems, with the proviso that cyclicity in the sense of recurring similar states is unlikely to occur. Most rangeland regions in the world are undergoing major changes in human populations, culture, relationships to the global economy, institutions, infrastructure, and technology. Many of these changes are likely to be irreversible. Extinction of species, invasions by or introductions of new species, movement of soils, lowered water tables (as in the Great Plains), establishment of infrastructure, and introduction of new technologies are examples of "ratchets" that prevent return to earlier states. Moreover, disturbances arrive in sequences that are rarely repeated, so that each period of time is associated with a particular sequence and particular consequences for biophysical and socioeconomic systems. The history of each rangeland region is thus an idiosyncratic progression of physical and institutional conditions driven by an underlying cycle of disturbance and reorganization. This suggested to us the usefulness of viewing rangeland regions as complex adaptive systems (CAS). We outline a general framework for this in the next section.

Rangelands as Complex Adaptive Systems

Rangeland regions quality as complex systems that exhibit resilience dynamics, a nested hierarchical structure, cross-scale interactions, nonlinear processes, fast and slow variables, lagged responses, and components that adapt to disturbances (Levin 1998; O'Neill et al. 1986; Pahl-Wostl 1995; Holling and Sanderson 1996). The application of these concepts to a simple general framework for rangelands is summarized in Figures 11-2, 11-3, and 11-4, which

key

locus of human

intervention

affected by intervention

mjl11~ncr ongimllrs from: this scale other scale

0 0 D D

variables perceived as important are shown in bold

Figure 11-2. Complex adaptive system framework for national-scale processes affecting rangelands.


-local ~"'\ lan~us/

Figure 11-3. Complex adaptive system framework for regional-scale processes affecting rangelands. (See Figure 11-2 for key.)

Figure 11-4. Complex adaptive system framework for local-scale processes affecting rangelands. ("Land use" is the enterprise selected; "management" is the way it is managed. See Figure 11-2 for key to other variables.)

show three scales. The scales are interconnected. For brevity we will omit discussion of the state level in a federal system and subsume it within the national scale. Variables that influence system resilience are those likely to be affected by human adaptations and are identified in these three figures.

The framework simplifies and structures key relationships (Abel 1999). Variables in the framework, such as economy, resource-use pattern, land-

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scape function and attributes, and household economy, are "black boxes," each comprising complex processes that cannot be expressed with requisite simplicity. The slow variables at world and national scales are climate change, human demography, and perhaps human culture. We do not consider them further, though their effects will surely be felt in the rangelands. World commodity prices are rapidly fluctuating variables that send waves of profit and loss through national, regional, and household economies, causing short-term disturbances. New technologies arrive episodically to affect those economies in nonlinear ways. They are also affected by long-term trends in the world economy that affect investment, costs of production, and revenues (Walker 1995). These effects on the national economy influence the behavior of participants in political processes in ways that affect range use and management at regional and local scales. This is why we present next what might seem a digression into political economy.

Godden (1997) uses modified public choice theory to explain in neoclassical economic terms how political processes affect policies, institutions, and ultimately land use and management in a pluralistic society. He treats a democratic political system as a highly imperfect market in which participants attempt to maximize their utility. Participants are voters, political parties, bureaucracies, and interest groups, including industries, firms, the media, and groups of citizens pursuing a particular interest-pastoralists, for example. The aim of politicians is to be elected or re-elected. Representative democracy is assumed. Political parties offer bundles of policies and institutional changes in the political market. The design of the bundles is based on the expected net return to the party in terms of political support. The bundles are designed to win at least 50 percent plus one of votes in 50 percent plus one of electorates (unimodal, continuous, and symmetrical frequency distribution assumed). However, political support is provided not only through votes. Information is a key resource, and party organizations are needed. Both need funds, which are provided by interest groups that calculate the likely returns to their members in terms of favorable policies and institutions. The set of existing institutions, such as laws, established by similar processes in the past, constrains the behavior of all current participants in the process, because of broadly shared views about the rule of law and respect for the constitution. Thus the market is not free. An example is legislation to constrain the sources, levels, and uses of party funding.

Pure democracies do not exist. The extent to which a system is democratic is affected by, among other things, the number of political parties competing for votes, constituency boundaries, ethnic allegiances of voters, and levels of coercion. Moreover, votes are just one way of expressing political influence. We believe that the principles outlined by Godden (1997) and Magee et al. (1989) are generally applicable even under undemocratic regimes, because to get and keep power a government still has to make trade-offs if it is to secure support from other groups. The trade-offs are just less apparent when the political market is secretive, so hypotheses are harder to test.

11. RESILIENT RANGELANDS-ADAPTATION IN COMPLEX SYSTEMS 3 07

The significance for rangeland regions of these political processes is this: Institutions and policies emerging from the bargaining process at the national level (Figure 11-2) affect the regional economy, society, and environment (Figure 11-3). The distribution and quality of infrastructure and services, land tenure, drought relief, tax arrangements, wildlife policies, and laws affect patterns of land and water management and use, which in tum impact the regional economy, population, and the condition of its land and water resources. Pastoralists' priorities, and their voting patterns, are influenced by their perceptions of these impacts, and can bring about changes in policies, institutions, or governments through marginal changes in voting patterns. Meanwhile, they are constrained by policies and institutions established in the past. Godden calls the influence of landholders on the formation of policies and institutions "farming the government," in contrast with the top-down government of farms through policies and institutions. The impacts of policies and institutions at local scales (Figure 11-4) occur in our framework through the response of household economies to changes in costs and prices, infrastructure, and services. Households are assumed to react by reallocating labor, capital, and land, or changing land management, thus affecting landscape function. Examples are changing stocking densities or type of animal, including a shift from livestock to wildlife harvesting, or getting off-farm work. They may also react as members of interest groups by shifting their political allegiance, thus impacting government priorities (Figure.11-2).

These interactions and adaptations are occurring within a system that is in any case changing through shifts in culture, investment, population change, the adoption of new technologies, losses of species, outbreaks of pests and human and animal diseases, and resource degradation. Meanwhile, institutions are becoming more complex as humans respond to problems arising from these changes. As Tainter (1988) points out, human societies tend to address problems through elaboration of organizations rather than through simplification or change of functions. This is because political costs are imposed by those affected negatively by simplification and functional change, and such changes do not attract many votes. Thus the long-term trend in rangeland institutions is toward increasing complexity: more and bigger agencies, often in competition or conflict, administering a growing complexity of laws that are often mutually incompatible.

The problem is made worse by the tendency of agencies to adopt a "command and control" approach. Gunderson and others (1995a) write of the dysfunctional management arising from the application by governmental agencies of militaristic command and control approaches to ecosystems. The rangelands are, with significant exceptions such as U.S. federal lands, mainly managed by families, clans, tribes, and companies, not bureaucracies. Pastoralists are well known for their individualism, and direct command and control management cannot be invoked in most cases as an explanation of loss of resilience. However, agencies do make policy, and policies and legislation do tend to be constructed, in the mode of command and control, as if land is

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uniform, and stable equilibria desirable and achievable. The concept of resilience as a product of the adaptive cycle is alien to this mind-set. Hence major efforts must be directed to engage policy makers and change their perceptions.

In Figures 11-2 through 11-4 we identify where human perceptions, or mental models, are crucial in their effects on system structure and behavior. A mental model of a complex system is an abstraction that cannot reflect the actual behavior of the system with total accuracy. Yet it is necessarily our mental models that affect investment patterns, for example, and investments can change economic structure and the path of economic development irreversibly. Mental models of the status and trend of an ecological system can affect policies and institutions, investment and resource-use patterns and management, regardless of the "actual" status and trend. Changes in mental models can thus drive change even if not preceded by changes in the actual status or trend of the systems being perceived. Rigid mental models can prevent positive adaptations, or they can "flip" to become drivers of beneficial change (Abel et al. 1998). A paradigm shift occurs when this happens at a disciplinary level (Kuhn 1962). Brock and Durlauf (1999) have modeled the aggregation of researchers into alternative stable states, each characterized by a particular dominant model. The model can be extended to stock market behavior.

Variables to which humans must adapt operate at different rates. The success of adaptation is related to that rate. Humans adapt well to changes in fast variables such as grass growth, animal numbers, stock prices, and interest rates. We are less successful in adapting to variables of intermediate rate, such as human population increase, the spread of an insidious disease such as Hrv, slow soil loss, or progressive increases in woody cover. Humans are least successful in adapting to slow variables, such as climate change, the depreciation of infrastructure, or the depletion of an aquifer. At least part of the explanation lies in the length of a human generation and the duration of human institutional or cultural memory compared with the rate of the variable. Current emphasis by policy makers is understandably upon fast variables and the short term. A CAS framework emphasizes "lurking surprises" that await future generations (Chapter 2; Brock and Hommes 1998). However, as these examples illustrate, solutions to long-term problems often lie in collective actions (Brock, pers. comm.), which carry transaction and other costs (Ostrom 1990). CAS models may help in the estimation of the net benefits of such actions.

Another source of surprises, not necessarily due to slow variables, arises from nonlinear relationships (Brock 1988). An example from the New South Wales rangelands is the sudden withdrawal of the wool floor-price scheme that had previously given partial protection to pastoralists from major price decreases. Nonlinearity is probably the main reason development of theory and models of complex systems is difficult. In the case studies, we are attempting quantification of change in resilience. It will be useful for promoting the understanding of resilience by comparison between case studies. However, it can only be retrospective. For the concept of resilience


to be useful for management of resource-use systems, it needs to focus on identifying the signs of loss of resilience and alerting users to impending change (Abel et al. 2000).

In Figures 11-2 through 11-4 and our discussion, we have not identified adaptations as contributing to or detracting from resilience. It is not possible to do that without specifying time and spatial scales, for an adaptation that enhances system resilience in the short run may detract from it in the long term-drought relief, for example, may keep pastoralists in business but at the expense of landscape function. With this reservation, in the next section we list some factors that may or may not be shown to enhance or diminish resilience in the four case studies.

Determinants of Ecological Resilience

There are biological and physical attributes of rangeland regions that confer resilience for human purposes. Some are independent of human adaptation, but humans can adapt to take advantage of these attributes. Examples from the case studies include:

• climates that have periods of higher rainfall that allow vegetation processes to recover from the disturbances of drought and grazing;

• rainfall with low erosivity and soils with low erodibility;

• • •

•

•

•

•

•

•

soils that maintain infiltration rates under grazing pressure;

soils such as self-mulching clays that resist degradation;

juxtaposition of soils with differing abilities to accept and store rainfall, enabling vegetation on some soils to survive through periods of sparse rainfall and on others to grow well under conditions of higher rainfall;

landscapes that have sufficient relief to allow water and wind to concentrate nutrients and water in fertile patches, but where slopes are below the critical threshold (around 5 percent) above which soil loss increases exponentially; topography that provides "memory"-for example, where the shape of the landscape determines water and nutrient flows so that over time a similar vegetation structure will redevelop after disturbance;

soils that maintain the stability of vegetation-a heavy clay soil or one with a relatively shallow mineral pan, which will not allow shrubs to encroach;

strong vegetation control over water and nutrient flows, maintaining spatial pattern and retaining resources under fluctuating rainfall;

plant species with high root-to-shoot ratios and high seed production and dispersal;

plant communities with high species richness within functional types (groups) of species, ensuring a variety of responses to differing environmental disturbances;

310 VVALKER,ABEL

• high genetic variability within species, again ensuring a diversity of response capability;

• banks of seeds and other propagules that retain memory and influence restructuring during the a. phase; and

• mixed grazer-browser herbivore communities, preventing wide fluctuation in ratios of woody to grass biomass as rainfall and animal biomass fluctuate.

These examples of biophysical attributes that enable pastoralists to adapt are the background to Tables 11-1 through 11-3.1n the tables we give exam-

Table 11-1. Local-Scale Adaptations and Resilience within Four Rangeland Regions

Factor Adaptation and Possible Effect on Resilience

Biota [ +] Drought-adapted forage species and herbivores. [ + l Mixed grazer and browser animal populations increase forage and marketing options, reduce drought risk, and slow shrub encroachment.

Diversity-spatial [ + l Access to a mix of complementary land systems at a fine scale, e.g., river channels with heavy soils amid uplands with lighter soils, provides reliable fodder supply at a local scale. [ +] Access to grazing in different climatic zone provides reliable fodder supply at a regional scale, e.g., ownership of land in other places.

Diversify- [ + j Diverse enterprises linked to different markets and requiring different production weather conditions reduce risk. strategies

Energy sources [+/-]A range of energy sources (human labor, horses, oxen, fossil fuels) widens resource-use opportunities.

External resources [+]Access to off-farm jobs and investments.

Mental models [ + l Smart buying and selling strategies based on accurate perceptions of landscape function and economic system. [-] Rate of learning is slower than the rate of degradation. [-] Reluctance to use fire enables shrubs to increase to the detriment of grazing animals. [ -] Short memory of past disturbances means mistakes are repeated.

Population [-] Aging households are less able to adapt. structure [ +] A relatively large workforce with a mix of sexes and ages expands

adaptive opportunities.

Savings [ +] Savings increase economic options.

Scale [+]A larger land holding yields economies of scale, lower debt and more savings, better credit rating, and more options in responding to disturbances.

Technology [ -] Paddock layout poorly related to land system boundaries limits production levels and adaptation. [ -] Establishment of permanent water on land systems not adapted to continuous grazing results in deterioration of landscape function.

Note: Whether contribution to resilience is in fact positive [+]or negative [ -] is subject to time-scale and spatial boundaries.


Table 11-2. Regional-Scale Adaptations and Resilience within Four Rangeland Regions


Climate [ +] Having access to a region with spatially variable climate enables survival through! mobility.

Diversity-spatial [ +] Having access to diverse land systems at regional scales that offer a range of opportunities in time and space.

Mental models [ -] Having short cultural memory prevents accumulation of understanding.

Population structure [ + l Having a balance age structure enhances capacity to respond to disturbances and opportunities. [ -] HIV kills economically active age group in Zimbabwe. [-]Aging pastoral population in Australian and U.S. rangelands.

Services, infrastructure [-]Immovable infrastructure reduces flexibility, e.g., fixed v. earlier mobile abattoirs, promoting continuation of unadaptive practices. [ +] High level of services encourages capable people to stay and innovate. [ + l Road network permits the pursuit of protein and energy across the region. [ +] Communication network assists spread of ideas.

Social support networks [ +] Reciprocal obligations called upon in crises.

Note: Whether contribution to resilience is in fact positive [ +] or negative [ -] is subject to time-scale and spatial boundaries.

Table 11-3. National- and State-Scale Adaptations and Resilience within Four Rangeland Regions


External resources [ +/-] Loans and grants to assist recovery after a disturbance can foster dependency instead of adaptation.

Institutions and policies [-]Inflexible institutions and policies are insensttive to feedback. [+]Weather- and price-sens~ive tax policy spreads benefits and costs across years. [ + l Community structures and projects that bring innovative approaches and outside resources. [ +] Remoteness of policy making from its local consequences leads to inability to learn.

Mental models [-]Simplified mental models held by agencies stress temporal equilibrium and command and control rather than adaptation to uncertainty. [ -] Short organizational memories lead to repetition of past mistakes.

Savings [ +] Public savings enable recovery from disturbances but can foster dependency.

Technology [ +/-] Fence wire, firearms, motorbikes, helicopters, bore drills, and other technologies extend adaptive opportun~ies but can enable overexploitation.

Note: Whether contribution to resilience is in fact positive [+]or negative [ -] is subject to time-scale and spatial boundaries.

312 VVALKER,ABEL

pies of the kinds of adaptations that pastoralists and governments might make in response to disturbances. There are many similarities with the findings of Folke and others (1998). Whether or not pastoralists and governments adapt in these or other ways in the four regions under study will be apparent when the case studies are completed. We have speculated in the tables about the effects these adaptations might have on system resilience, but without rigorous specification of time and spatial boundaries.


Our knowledge of the rangeland literature, and a preview of the four rangeland regions, suggests that rangelands behave as complex adaptive systems. Their inherent complexity is amenable to simplification. Key processes, and the limited number of variables that control them, can be identified at national and state, regional, and local scales in a hierarchical structure. Social, economic, and biophysical change and competition provide the selection processes that lead to adaptation in components and evolution of the system as a whole.

At local scales the pattern of the adaptive cycle is evident in many instances in the biophysical dynamics. Ecological succession leads to an accumulation of biomass and nutrients in the dominant species. Suppression of adaptive opportunities through competitive exclusion eventually leads to "creative destruction," freeing nutrients for reinvestment in a range of species. The reorganization phase allows a new combination of species to become established. The new combination is potentially better adapted to the environmental conditions that followed the disturbance. The process repeats itself. Resilience is maintained through these repetitions.

There is also evidence of accumulating rigidity in social structures that is predicted to give rise to major changes in policies, institutions, and land use. Examples are the current changes in western New South Wales and the Great Plains. This rigidity contrasts with the low connectivity and associated flexibility characteristic of the current early stage of development of the cattle industry in the Australian Northern Territory. The dynamics of social and biophysical components are not, however, congruent, nor do they occur at the same time and spatial scales. Together with the effects of nonlinearity, multiple interactions, and variation in the sequences of disturbances, this makes prediction of future states of the rangeland system difficult.

Significant aspects of the dynamics of rangelands occur as irreversible changes resulting in an ever changing progression of the system (extinction and introductions of species, loss of soil, new technologies, new infrastructure, and so forth). In all four regions, though decadal-scale ecological cycles are apparent, the combined social and biophysical dynamics involving commercial livestock production have barely achieved the equivalent of one adaptive cycle, and there has therefore been little chance for development of understanding and behavior through learning and adaptive system dynamics.


One strength of the adaptive cycle framework is the distinction it makes between fast and slow variables. As has been found in other major humannature systems, people in the rangelands respond to changes in the fast variables. But many of the problems emerging in rangelands stem from a lack of recognition of the potential effects of changes in slow variables (woody plant development, declining aquifers, long-term trends in markets and investment, and cumulative changes in infrastructure). Changes in the slow variables may not be recognized simply because their rate is imperceptible to humans. Often, however, they are not recognized because they do not fit into the current mental models of managers and policy makers. Even when perceived, the costs of the collective responses often needed to address such changes may inhibit action. People consequently introduce increasingly complex and maladaptive practices and policies to nurture short-term welfare. By the time efforts to keep the fast variables within desired limits are either no longer worthwhile or no longer possible, it may be too late to avoid a major system change. The command-and-control model of agencies and managers tends to reinforce this social inability to adapt in time to a changing environment, and unwelcome surprises have occurred in rangeland regions.

Conceiving of rangelands as complex adaptive systems, and understanding the components of resilience together with possible adaptations to these components, casts pastoralists, researchers, and policy makers in a new light. They are not external controllers of a biophysical system, but adaptive agents within an integrated social-economic-ecological system. The concept should help them identify the windows of opportunity when intervention is likely to be successful. A first attempt to develop a CAS model of such a rangeland system (Janssen et al. 2000) provides some interesting and counterintuitive insights into the value of this approach.

The late K phase of the adaptive cycle is a time when a little leverage is likely to result in a large but often unpredictable change. The a phase is an opportunity to guide the course of investment and the structure of the developing system. The concept operationalizes the state-and-transition approach to management. The future is regarded as an oncoming stream of opportunities and hazards, and the objective of management is to learn enough about the combined dynamics of the system to be able to avoid the hazards and to seize the opportunities (Westoby et al. 1989). Adaptive management and policy making in rangelands require humans to promote resilience through learning and institutional design, and meanwhile to keep a close eye on the slow variables-biophysical and social-across the key scales, and expect the unexpected.

Acknowledgments

We thank Buz Brock, David Tongway, Charles Perrings, Don Ludwig, Doug Cocks, and Mike Austin for their helpful comments and criticisms and Jenny Langridge for her technical help in preparing the manuscript.

CHAPTER 12

SURPRISES AND SUSTAINABILITY:

CYCLES OF RENEWAL IN THE

EVERGLADES

Lance H. Gunderson, C. S. Holling, and Garry D. Peterson

She needs wide-open spaces-room to make the big mistakes. -Dixie Chicks

I n this chapter we evaluate some of the theoretical propositions of ecosystem dynamics and resilience set forth in Chapter 2. We will not attempt to rigorously refute hypotheses or propositions, but rather search for patterns

of similarity and important areas of disagreement. We attempt to establish the utility of using the four-phase and panarchy heuristics from ecological systems to interpret dynamics of a coupled ecological and social systems.

We will do this by drawing from lessons learned from the Everglades, and where appropriate, from a wider set of case studies of regional development and resource management. Those systems, especially the Everglades development and attempts at sustainability, involve technology-based, bureaucratic approaches to large-scale ecosystem management in developed areas (Gunderson et al. 1995a; Johnson et al. 1999).

There are growing hints that limitations to these models could be usefully explored and perhaps the theory expanded by applying it to specific examples of development linking people, nature, and regional economies. Rather than forcing an ecological model on social systems, therefore, our hope was more to expose its inadequacies and to perhaps expand its generality. We attempt this in the next section, using the management history of the Everglades to provide a focus.

Interpreting the Management History of the Everglades

Water management in the Everglades during the twentieth century was characterized by four distinct eras (Light et al. 1995). The first of these four eras of water management began in 1903 with efforts to dig canals to drain

315

316 GUNDERSON, HOLLING, PETERSON

the system for development and agriculture in a strategy labeled Cut 'n Try (Light et al. 1995). The second and most prominent era involved implementation of the massive federal and state public works project (1948-70) that created levees, canals, pumps, and operational guidelines in order to prevent flood damage, in an era dubbed Turning Green Lines to Red (Light et al. 1995). The third era (No Easy Answers 1971-82 (Light et al. 1995)) attempted to restructure the existing management agency into a new, system-wide management agency to deal with water shortages in addition to flood problems. The most recent era (Restoring the Everglades 1983-present (Light et al. 1995)) is characterized by attempts to restore the natural values of the system.

These four management eras illustrate four separate iterations of ari adaptive cycle. Each era is characterized by a slow period of capital accumulation, followed by a perceived crisis and reformation. New eras occurred when the system made an evolutionary leap into radically new stability regimes, or de novo system configuration (represented by a different set of boxes in Figure 12-1). The crisis that created the first management era in

Restoration

Figure 12-1. Time course of water management in the Everglades as represented by successive iterations of the adaptive cycle. The initial management era is shown in the top set of boxes, the last one in the bottom set.

12. CYCLES OF RENEWAL IN TilE EVERGLADES 317

1903 was a flood. This Cut 'n Try management era reflected a development from the r through K phases of system development, where increasing structure in the form of canals reflected an attempt to control the system.

Precipitated by the flood of 1947, the system underwent a major reconfiguration as it entered the second management period. During this period, the large bureaucracy-driven plan (called the Central and Southern Florida Flood Control Project) was implemented. The plan called for massive construction of levees, canals, pumps, and weirs to stabilize water levels and control flooding. The system developed, during the 1950s and 1960s, as the canals and levees were being built. A series of floods and droughts resulted in changes to the management rules, or to how water was partitioned among the user groups and institutional roles. The first cycle occurred when the droughts of the 1960s resulted in a guaranteed water delivery to Everglades Park.

The third management era was rooted in the drought of 1971. Although another cycle began, little new physical capital was created; rather, the focus was on creating new forms of social capital. This time it was an institutional reconfiguration that resulted in the formation of the South Florida Water Management District and was characterized by the label No Easy Answers.

The latest era, also called Save Our Everglades, reflects another cycle of the system, and was brought about by crises in the early 1980s. The coincidence of high rainfall and fear of pollution in Lake Okeechobee resulted in a reconfiguration that included modified regulation schedules in the water conservation areas and a rainfall-based formula for delivering water to the Park. The last major cycle occurred with the settlement of a water quality lawsuit. It has triggered yet another major cycle focusing initially on water treatment areas that are to minimize impacts of water movement from agricultural to natural areas.

The four-phase heuristic, therefore, provides a framework for explaining a complex history: periods of slow growth and policy implementation, and crisis followed by minor and major reformation when an entirely new system configuration is noticed. That pattern is followed in many of the cases listed in Berkes and Folke (1998) and in Johnson et al. (1999). The heuristic has been used in other historical interpretations as well; business organizational development and renewal (Hurst 1995). Perhaps the adaptive cycle construct is useful because at its foundation, the model is essentially a tautology of birth, growth and maturation, death, and renewal that must apply to any living system and perhaps to non-living ones as well. But viewed as such, the issue becomes one not of inappropriate transfer from one field to another, but of universal applicability-e.g., finding no situation where it does not apply.

The critical feature of the model that can distinguish among different systems, however, lies in the phase of renewal, the a phase of Figure 2-1. Renewal can simply mean the endless repetition of the same initializing condition for the four-phase cycle. That was what was implied in the initial


application of the model to ecosystems. For human systems, however, that would mean that humanity was tied to a rack of determinism doomed to repeat the lessons of history with no option for individual will. And certainly much of the history of development, including the Everglades, is just that. How else could Marchetti (1987) so consistently describe the development of various technologies with a simple logistic curve that depicts a transition from the r to the K phase?

In order to explore those similarities and differences between ecological and social systems further, it is useful now to determine whether related concepts and theories, particularly those with an empirical base, have been developed in the social sciences.

Theories of Social Change

We can usefully identify three classes of social theories of change. The first is the life-cycle representation so common to many fields and to the logistic formulation that Marchetti (1987) uses to such good purpose. These lifecycle/logistic representations imply growth to some sustained plateau, with senescent elements replaced from some unknown pool. In ecology that was the foundation for Clement's model of ecosystem succession described earlier in Chapter 2. In organizational theory, that is the foundation for representing the time course for products, processes, and organizations (Kimberly et al. 1980). In economics, however, new expansions of theory expose the abrupt nature of the flip from one mature product to a competing one (Arthur 1990), much as we describe here for the shift from the K to the n phase. Mature products are seen as capturing a market and, for a time, freezing out superior, competitive innovations because of increasing returns to scale.

The second class of social change theories contrasts gradualist life-cycle models with those that model revolutionary change. Gersick (1991) has reviewed these using another biological theory as a template for describing change in complex systems. This is Eldredge and Gould's (1972) view of biological evolution as proceeding by punctuated equilibria rather than by gradual incremental change. The fossil record suggests that species lineages persist for long periods in essentially the same form or equilibrium, and that new species arise abruptly in sudden adaptive explosions of rapid change. That representation is consistent with the behavior generated by the fourphase cycle but aggregates the four into two stages--one prolonged period of gradual change and one of rapid transformation. The theory emerged as a description of the fossil record, with explanations for the sudden changes ranging from the consequences of external disturbances (e.g., planetesimal impacts on the earth) to internal senescent/reorganization sequences.

Similar representations have been proposed in the social sciences. For example, in the philosophy of science, Kuhn (1962) distinguished the alternations between long periods of normal science and sudden scientific

12. CYCLES OF RENEWAL IN TIIE EVERGLADES 319

revolutions leading to a paradigm shift. Abernathy and Utterback (1982) distinguish gradual from radical innovation sequences in industry. Friesen and Miller's (1984) theory of organizational adaptation contrasts periods of momentum with those of revolution, and Levinson (1978) sees individual human development as periods of stability alternating with abrupt rapid transitions.

Such theories identify so-called deep structures that provide the sustaining rules for the gradual incremental changes that occur throughout the "equilibrium" periods. Revolutions are seen as brief periods when a system's deep structure collapses to become subsequently reformed around new strategies, power, and alignments.

We earlier described related entities when we applied the four-phase cycle to ecosystems. For example, stands of even-aged trees are the slow structural variables that for long periods can provide the context for dynamic interactions among fast variables such as needles, insect defoliators, and their predators. The even-aged tree stands have a speed, or turnover rate, of approximately one hundred years and a spatial grain of tens of meters. The fast variables have a turnover rate of a year and a spatial grain of centimeterstwo orders of magnitude faster and smaller. For long periods the budworm is controlled by predators at low densities, allowing trees to slowly grow to maturity. The "revolution" occurs when the control of budworm collapses because growth of the forest-the slow, structural variable-dilutes the effects of predation, and an outbreak of the insect-the fast variable-is generated that kills trees over large areas.

But this, as in the case of the social revolutionary change theories, is more a description of a phenomenon than an explanation of its causes. Recognizing the different variables that control each of the four phases deepens understanding of the dynamics. There are some detailed differences between these social revolutionary change theories and the four-phase adaptive cycle, but the fundamental difference is that the boom-and-bust dynamic, and the opportunities that at times are constrained and at other times opened, emerges from the interaction among variables that characterize and control each of the four phases. The behavior emerges from the way control shifts from r, to K, to Q, to a to a new or repeated r set. We will expand that description of the variables and processes involved in that shifting control in a moment, but before doing so, one final set of social theories of change needs to be described, because these theories come close to and deepen the insights of the four-phase adaptive cycle.

These deeper theories explicitly recognize the four-phase properties of complex evolving systems and the tensions they generate to produce stages of growth and transformation. For example, the Austrian economist Schumpeter (1950) saw socioeconomic transformations proceeding such that market forces controlled the r phase of innovation; institutional hierarchies, monopolism, and social rigidity controlled the K phase of consolidation; forces of "creative destruction" triggered the release or Q phase; and technological invention determined the source for a phase transformation at a.


Such theories of revolutionary change provide insight by recognizing the paradox of the creative destruction or Q phase and the uncertainty of the a phase. There is obviously a destructive element to the collapse of a company or to the occurrence of an intensive fire in a mature forest. But there is also a creative element, because previously tightly bound capital is released-money, skills, and knowledge in a business sector; organized carbon and nutrients in a forest. In contrast, the renewal or a phase lies behind a "veil of ignorance" by reason of its inherently unpredictable nature. Schumpeter's designation of capitalism as a "perennial gale of creative destruction" highlights precisely the same paradox in ecosystems at the transition from consolidation, or K phase, to release, or Q phase.

An even more specific typology comes from cultural anthropology in the works of Douglas (1978), Thompson (1983), and Thompson et al. (1990). Four explicit types of individuals or institutions are identified, and they are organized within two axes very similar to the ones in Figure 2.1. The r phase is designated as the entrepreneur, the K phase is the caste or bureaucracy, the Q phase the sects, and the a phase the ineffectual individual (Douglas 1978; Thompson 1983). The insights provided by their descriptions of sects resonate with attributes of the release processes that we describe for ecosystems. The sects are described as being small and tightly organized, often around a charismatic leader with a strong, singular ideological purpose. Their power emerges only occasionally when their tenacious allegiance to internal rules and purpose intersects with the vulnerability of a mature and rigid bureaucracy. This captures their role in triggering release and, for us, has been particularly helpful in illuminating our understanding of the role of the more extreme types of environmental activists in the earlier analyses of the case studies.

Their description of the critical a phase, however, only partly captures our description of the ecological analogue. They do see the dissociated nature of the elements of the a phase, describing them as atomized individuals with no control over their own destiny, caught by whatever winds of change are generated by the other players. But in ecological systems, that phase provides a repository of the capital that has accumulated during earlier phases of growth and maturation-r to K. Its dissociated, weakly connected state is the very attribute that makes unexpected combinations of associations possible and individuals most influential. It is the flywheel of the whole system, whose properties determine whether there is a repetition of past cycles, collapse of those cycles, or the emergence of a new system that is distinguished by its novelty.

Increasing attention is being paid to the micro-scale dynamics that drive collapse and reorganization. A number of people have focused on the simulation of the interaction of groups of heterogeneous agents. Brock and Hommes (1997) use a group of agents interacting in a simple market who have to pay to maintain a record of the past and to acquire information. These agents can choose the model they use to predict future behavior of the

12. CYCLES OF RENEWAL IN THE EVERGLADES 3 21

system. They can choose to use either a cheap, short-term, naive model of the future or an expensive, sophisticated, long-term model. \Vhen these agents interact, if the entire population of agents uses the expensive model, then the system is stable. However, in this stable situation the cheap model performs just as well as the expensive model, so due to its lower costs, agents begin to switch to using the cheap model rather than the expensive model. As an increasing number of agents use the cheap model, the system becomes unstable and begins to fluctuate. As fluctuations increase in size, the expensive model outperforms the cheap model, and agents begin to switch back to it. Brock and Hommes demonstrate that these dynamics and consequendy both the prices in the market and the agents' strategies vary chaotically. These dynamics are similar to the four-phase cycle. The expensive strategies achieve an equilibrium, then agents gradually switch to the cheap model, which in turn gradually decreases the stability of the equilibrium until it becomes unstable, and the system begins to fluctuate widely, until it is rapidly stabilized by agents again adopting the expensive model.

This work is similar to work Karl Sigmund (1993) has done on populations of strategies playing the prisoner's dilemma. If the population can evolve, or agents can learn over time, they discover that with noisy communication, a similar set of dynamics occurs over time. A retaliatory strategy establishes an equilibrium that is then exploited by a more profitable naive strategy. However, as this naive strategy increases in frequency, the population as a whole becomes vulnerable to parasitical strategies, which causes the retaliatory strategies to rise to prominence once again. Lindgren (1991) produced a more diverse set of strategies that also gave rise to complex periods of relative stasis punctuated by periods of rapid change and reorganization. Leimar (1997) has shown that there are many strategies that can produce such cyclical behavior.

Janssen (1998) has used a population of agents that hold different models of the world/climate system to model response to climate change. His work demonstrates that a disturbance of shock to the climate system often causes the disintegration of a dominant model. If a system contains noise, often agents will persist with a model, due to the lack of any clear signal that it is inaccurate. Disturbances can provide such a signal, indicating to agents that their model does not correspond to reality. Often disturbances provide an opportunity for learning, resulting in models that better match reality; however, they can also produce at least temporary dominance by inaccurate models.

All these models offer interesting views of micro-processes that could produce four-phase dynamics. However, empirical evidence suggests that more complicated cross-scale dynamics occur, at least in social systems. For example, Alfred Chandler (1977) argues in Visible Hand that in response to the 1873 stock market crash, U.S. business managers learned and reorganized their corporations. They took advantage of new economies of scale and developed large, integrated companies, which could survive disruptions in credit availability. These large companies could successfully plan and manage


their corporations since groups and individuals outside the company were relatively unorganized. The large corporations were not affected by shortterm fluctuations in markets because their size allowed them to persist. However, this arrangement depended on the ability of large numbers of U.S. residents to fall back on subsistence farming during economic downturns. As ·more and more corporations were established, it became more difficult for corporations to predict and control their dealings with large corporations or government. The success of the corporate model depended upon there being a large segment of society that was not corporate; as that proportion decreased, corporations became more vulnerable to the business cycle. The success of this corporate system resulted in people moving from farms to towns and cities, leaving an increasing number of people unable to revert to a subsistence livelihood. In this changed situation, corporate policies, which formerly worked, failed, as firing of workers decreased purchases and initiated a downward spiral. Indeed, corporatization methods resulted in a bigger crash when it did arrive in 1929.

What this suggests theoretically is that people can organize institutions to avoid crashes or oscillations (such as climatic variation in the Everglades), but that focus on the spatial and/or temporal scale of the oscillations leaves these new institutions vulnerable to larger and smaller dynamics.

The a. phase is the phase that is least understood because of its inherendy unpredictable nature. The only treatment we have encountered that gives it some specificity is from the body of chaos theory (Stewart 1989). One of the key points of chaos theory is that slight changes in initial conditions can generate a great complexity of behavior and unpredictable outcomes. A favorite example comes from a simple model of the atmosphere developed by Lorenz (Stewart 1989), which showed that slight departures from initial conditions of weather lead to widely divergent futures. The behavior that results looks random, although within a bounded domain, and yet is completely deterministic and inherendy unpredictable. Lorenz named this the Butterfly Effect, dramatizing the phenomenon with an analogy in which a butterfly flapping its wings in Beijing now can change storm patterns in Florida next month.

Many examples of chaotic behavior have been identified or proposed in physical, biological, and social systems. As with any new theory that is partial but gives fresh insight, chaos theory has generated an exuberant search for other examples, driven by the yearning for universality. Is healthy brain function chaotic and unhealthy functioning stable? Does heart function have chaotic patterns? Planetary orbits? But for ecosystems, at least, the question should not be whether they are chaotic, but under what conditions they are chaotic and under what conditions they are not. .

For long periods, ecosystems develop growing connectivity and predictability as they progress through the r to the K sequence. During this transition, the conditions that generate chaotic behavior are unlikely because of growing regulatory processes functioning within wide stability

12. CYCLES OF RENEWAL IN THE EVERGLADES 323

domains. But those same conditions also gradually produce a brittleness that sets the condition for the release, or creative destruction phase. That then leads to the conditions for chaotic behavior during the brief period when the cycle achieves the weakly connected state of the a phase. It is this organization that allows, in Kauffman's (1993) terms, systems to exploit the edge of chaos where adaptive opportunity lies. But the window for that opportunity opens briefly, in comparison with the longer period of accumulating capital.

To summarize, at times system behavior is determined by the r-strategists-pioneers, entrepreneurs, and opportunists. They set the conditions for control to shift to the K-strategists-to the effective competitors and consolidators of position and power. Resilience is reduced, controls intensified, and the system can become an accident waiting to happen. As the shift to the Q phase occurs, the slow, extensive variables lose their control of system behavior; fast variables assume control and suddenly release the capital that was stored and sequestered in tightly organized form. This capital then becomes dissociated in the a phase, where a new set of variables, processes, and random events slow the leakage of capital out of the system, mobilize it in accessible forms, and precipitate possible unexpected associations between previously independent variables. The a phase is the one with the greatest uncertainty-both of risk and of opportunity.

Resilience in Social Systems

In this section, we examine the property of resilience in social systems. As described in Chapter 2, resilience is defined sensu Holling (1973b) as the amount of disturbance that a system can absorb without changing stability domains. But does that property of systems extend to social ones as well? We begin with some modest answers in the remainder of this chapter. Other contributors to this volume will embellish and extend those responses. We structure our arguments about resilience in social systems in the following three paragraphs. In the first, we discuss how social systems (primarily those linked to ecological systems) respond to disturbances and whether they (social systems) appear as multiple or alternative stable states. In the second section we discuss how social systems renew both themselves and ecological components through building adaptive capacity. We end with a section on the role of novelty in social systems, a property we suspect is greater than in ecological ones. We begin with the recognition of alternative states or organizational patterns in social systems.

In the preceding section, we argued that resource management institutions go through similar phases of the adaptive cycle. That heuristic is useful to depict founding, maturation, crisis, and reformation of institutions. The history of water management in the Everglades was used as an example to illustrate how ecological crises led to new configurations of water management institutions. Each of the alternative institutional configurations


(or water management eras) can be thought of as an alternative stability domain of the social system. In each of the evolutionary transitions unforeseen ecological variation exceeded the resilience of the social system, resulting in a new configuration (Table 12-1). The flood of 1903 resulted in an institutional configuration called the Flood Control District, in which a board of trustees oversaw and funded the digging of canals and levees in the system. In 194 7, the new social structure was called the Central and Southern Florida Flood Control District and reflected a partnership of federal, state, and local governments to build and manage the hydrologic infrastructure. In 1971, a drought created yet another institutional arrangement, the South Florida Water Management District, with a new set of management objectives-flood control and water supply. A similar reconfiguration was made in 1983, with the creation of an informal meshing group-the Everglades Coalition-aimed at coordinating governmental and nongovernmental organizations to seek resolution of chronic environmental ISSUeS.

Social organizations linked to resource systems can respond to environmental crises in a number of ways. Many organizations focus on the renewal and novelty, while others focus on buffering themselves against change. Long-standing (and in some sense successful) social systems that deal with natural resources focus on mechanisms that buffer disturbances or attempt to minimize the magnitude of perturbations (Berkes and Folke 1998). Yet in a wide range of systems, that approach appears to prevent crises from overwhelming the adaptive capacity of the social system (Folke, Berkes, and Colding 1998). Folke, Berkes, and Colding (1998) argue that this distinction is one of scale matching-that is, by managing disturbances at an appropriate scale, some stability of social institutions is achieved. Other institutional re-

Table 12-l.lnstitutional Reconfigurations in Response to Ecological Crises in the Everglades

Crisis (year) Institution Created Following Crisis

Flood (1903) Everglades Drainage District

Flood (1947) Central and Southern Florida Flood Control District

Drought (1971) South Florida Water Management District

Flood in park (1983) Everglades Coalition

Lawsuit (1989) Federal Restudy Committee, Governor's Commission for Sustainable South Florida


sponses deal with environmental fluctuations or crises through a shifting set of rules, and other mechanisms for when alternative rule sets are invoked. These shifting rule sets often involve incipient institutions, where new entities come into play along with new rule sets. Such is the case with U.S.-based emergency management agencies, or in traditional societies' "sleeping territorialities" (Folke, Berkes, and Golding 1998). This pattern is similar to the role of species diversity in ecological resilience (Peterson et al. 1998; Walker et al. 1999).

A unique property of human systems in response to uncertainty is the generation of new types of social structures. Novelty is key in responding to surprises or crises. Humans are unique in that they create novelty that transforms the future over multiple decades to centuries. Natural evolutionary processes cause the same magnitude of transformation over time spans of millennia. Examples are the creation of new types and arrangements of management institutions after resource crises in the Everglades (Light et al. 1995), the Columbia River Basin (Lee 1993), and the Baltic Sea Q"ansson and Velner 1995). In technologies it is invention and adaptations that transform the future (Arthur et al. 1997).

One interpretation of this institutional creation is that these institutions are set up to resolve types of uncertainties. They provide a venue in which some technical and social uncertainties can be resolved (Chapter 6; Lee 1993).

Yet there are many situations where the institutions constantly struggle with resolving those uncertainties; and those with high r institutional inertia can be described as unable to reinvent themselves and adapt to changing conditions. Many agencies appear incapable of generating either novel solutions or policies to solve chronic resource problems; one of the few mechanisms for change is an ecological crisis, as appeared true in the Everglades (Light et al. 1995; Gunderson 1999).

One reason that management institutions have such high moments of inertia is that they utilize (directly or indirectly) ambiguities and uncertainties of resource issues to maintain a status quo. With a pragmatic focus on policy implementation, most agencies seem to have a twofold strategy that is aimed at reinforcing the status quo: prove that extant policies are correct, and don't act until confident of what to do next. Many agencies focus on implementation, without realizing either that narrow implementation schemes often subvert policy intent, or that implementation is an organic process that changes over time and reveals the failure of policy, not its success (Gunderson et al. 1995b). One example of this is the implementation of the legislation that guaranteed a minimum water delivery to Everglades National Park in 1970. The intent of the law was to ensure that the park got a minimum amount of water each year-at least 350,000 acre feet/year. Instead, over the next decade, the park received the legislated amount, regardless of ambient rainfall or storage within the system.

Another source of bureaucratic inertia is the power of vested interest groups, particularly those that have political and social sway over agencies.


While science uses uncertainty to drive the engine of inquiry, vested interest groups use and foster uncertainty to maintain a status quo policy. There are many examples-take the actions of sugar farmers in the Everglades following claims that nutrient runoff was changing the structure and function of pristine areas in the Everglades. Prominent scientists were hired to generate alternative hypotheses (other than those that involved phosphorus), which for a while stopped any movement toward resolving that crisis. Similar results of disinformation campaigns have been chronicled for health, climate change, and biodiversity issues (Ehrlich and Ehrlich 1996). Vested interests are not the only groups that generate or defend pet hypotheses. Agency scientists often generate policy recommendations that are politically correct in the sense of gaining what they view as a favorable policy. These examples further highlight the point that science is a highly social process, with lots of tacit and implicit factors influencing and shaping an "objective" process.

Our exploration of possible similarities and differences between actual ecological and social systems and between theories of change developed in each field has led us to better formalize two features that distinguish our arguments. One has to do with the adaptive character of the opportunity that is opened by the destruction and renewal phases (Q to a) of the four-phase cycle. The other has to do with the nested nature of the elements that comprise complex ecological or social systems. We will deal with each in turn in the next section, where we attempt a theoretical synthesis.

Linking Theories of Ecologic and Social Dynamics

In contrast to existing theories of social change, the four-phase adaptive cycle emphasizes a loop from hierarchical consolidation in the K phase to two phases of destruction and reorganization where innovation and chance assume a dominant role. That reorganization phase occurs when a rare and unexpected intervention or event can shape new futures as an act of creating opportunity. The tight organization and hierarchical control of the K phase, which precludes alternatives, is broken because of maturing brittleness that often intersects with external events that provide the proximate trigger for the change. The resulting loss of control leads to the release of the accumulated capital (nutrients and organized carbon in ecosystems; money, skills, contacts, and experience in organizations) and to its decay or dissociation into constituent elements in the a phase. At this stage the system becomes ill defined and loosely coupled. The system is in a paradoxical phase; it is in a state most likely to collapse or be transformed by innovation. High risks are matched by great opportunity. In human systems, it is the stage where the individual, for good or ill, has the greatest potential for influencing the future. The disassociated nature of the a phase is the very condition that makes either good or bad outcome possible.

As an example, the adaptive model can help describe the dynamics of resource management institutions (Figure 12-2). Most bureaucratic


institutions are set up to carry out some set of policies, and indeed spend most of their time and resources implementing those policies and monitoring key indicators in the ecosystem. The Corps of Engineers and the South Florida Water Management District are those bureaucracies in the Everglades. Inevitably, crises arise and are usually dramatized by some outside activists claiming that existing policy is no longer viable. The reformation phase involves a temporary group, often outside the institution itself, whose members informally develop alternatives for formally empowered decision makers. Just as the activists are the agents of release (the spruce budworms of institutions), the temporary groups (or individuals) essentially create the future in the way alternative policies are designed and presented.

Note that as the system cycles through all of its four phases, although control shifts from one set of variables, processes, and events to another set, all variables and processes other than the ones controlling at the moment are present in all phases and function in either a maintenance or a "holding" pattern. For example, pioneer species or entrepreneurs are present during the consolidation phases when conditions are inimical to them; some trees and bureaucrats (or at least the seeds and saplings of each) persist through the release and reorganization sequence; soil processes function throughout all phases. It is that functional diversity that keeps critical actors in the wings or in a supporting role, while the lead shifts for a period to others. The four-phase cycle has helped make sense of the case studies we have explored, the actors involved, and the role they play. This is summarized in Table 12-2.

Although we see fundamental similarity between adaptive ecological and adaptive human systems, we propose that the human ones have much greater powers for both rigidity and novelty. The ability of the bureaucracy of a gov-

t

policy alternatives

policy plan

policy implementation

policy failure

connectedness -

Figure 12-2. Generalized adaptive cycle applied to resource management policy. Note that the diagram is a specific rendition of the four-phase dynamic, and various groups facilitate the transitions among phases of policy development, implementation, and failure.


Table 12-2. Attributes of Human Groups That Dominate Activities in Four Phases of the Adaptive Cycle as It Applies to Resource Management Policy

Characteristics Phase of Adaptive Cycle

r-+K K-+Q Q-+a a-+newstate

Group type bureaucracy loyal heretics reformers higher level (government) decision body

Group type NGOs activists epistemic visionary (collective) community new leader

Policy implementing destroying framing resolution activity new options transformation

Science and science affirms science science science is policy policy invalidates integrates and politically relationship policy assesses expedient

Type of science monitoring rejecting sorting among expert single multiple testimony hypothesis hypotheses

Strategy "Doing as before "Creating a "Unlearning "Inventing but more" crisis" yesterday" tomorrow"

Response to ignoring and forcing change creating compromising change denying change new futures or reconciling

Guiding vision stability anarchy reconstruction reconfiguration of myths

Source: Modified from Gunderson et al. 1995b.

Note: The listed group dominates during the phase indicated, but is present and functioning in other phases as well. This table represents a centrist view of primarily North American institutions.

ernment agency to control information and resist change seems to show a level of individual and group ingenuity and persistence that reflects conscious control by dedicated and intelligent individuals. And certainly some empires and some institutions have long endured in their same basic form. But that observation might simply reflect the frustration the authors have experienced in dealing with inflexible bureaucracies. Alternatively, the possibility exists that the locus and speed of the adaptive cycle can be changed by conscious design so that renewal occurs internally while overall structure is maintained.


In the Everglades, there are examples of institutions that are reinvented (as suggested by the adaptive cycle). Examples include the creation of the South Florida Water Management District in 1971 and the Everglades Coalition in 1983. Yet there are also examples of long-lasting or apparently resilient institutions (such as the National Park Service and the Corps of Engineers). These institutions appear to keep the novelty of the reorganization phase and the consolidation of the r phase in some kind of working relationship. This appears to be done by periodically changing leadership. Some bureaucracies remain responsive and adaptive over long periods of time. These seem to be the ones that allow for deviants to continue to express alternate views within the organization and wherein those at the strategic apex remain aware and informed about the innovations. The Catholic Church is a good example of this: at critical points popes have recognized potential "heresies"-instead of attempting to suppress them-negotiated with the heretics to incorporate innovation into church practice while maintaining overall structure. Similar examples exist in the Everglades; a scientist with the Water Management District was shunned within his organization in the 1980s when he brought to light the issues of nutrient-induced vegetation change. Yet with a change in leadership a few years later, he became chief ecologist for the district and led the ongoing ecosystem restoration efforts. Chapter 13 provides a wonderful treatment of similar dynamics in another setting.

As indicated in Chapter 2, there are exceptions to the adaptive cycle, which are particularly germane to large, bureaucratic institutions. At times, the upward flow of information inside a bureaucracy is so curtailed that leaders do not hear dissenting views, or the leadership is so intolerant of dissenters that they expel them (as in the Protestant Reformation). Perhaps the lack of dissenting information is one condition that would precede collapse or reorganization within the bureaucracy. One unanswered question is whether social systems can get "trapped" in one of the phases of the adaptive cycle. This is suggested in Chapter 2, as poverty or hierarchy traps. Hints that social systems become trapped in a crisis or reorganization phase are what Kai Erikson (1995) has described as chronic disaster. But for theresilient systems and agencies, the key seems to be managing for change, not against it. We continue this argument into the next section, where crossscale interactions are discussed.

Panarchies and Ecosystem Politics

In Chapter 3 of this volume the concept of panarchy is introduced as a construct for combining features of the adaptive cycle with processes that interact across scales. The history of water management in the Everglades can help highlight some aspects of the panarchy model, especially around up (revolt) and down (remember) scale processes.


In each of the four cycles of management eras in the Everglades in the twentieth century, an ecological crisis was key in precipitating the transition between eras. The crises during the first two eras were floods associated with excessive rainfall. The third crisis was associated with a drought year intersecting with a burgeoning human population. The most recent crisis was associated primarily with nutrient movement across an oligotrophic landscape, the result of earlier land-use transformation.

In all of these situations the crisis was the result of broader processes interacting with local vulnerabilities. In the first three cases, the crises were created by variations in larger-scale processes. The droughts and floods in the Everglades are linked to ENSO (El Niiio Southern Oscillation) fluctuations-this coupling between sea surface temperatures in the southern Pacific and atmospheric flows can dramatically influence how much rain falls on southern Florida. Yet the variation in rainfall was intersecting with changing local situations-incipient agriculture in 1903, human development along the eastern coastal ridge in 1947 and 1971. A similar model can be used to explain the most recent, nutrient-induced, crisis, where changing soil nutrient levels reduced the resilience of the native vegetation to deal with variability in droughts, fires, or freezes (Davis 1994). So as the resilience was exceeded in each of these cases, the ensuing crisis created cross-scale reactions that cascaded and increased the scale of impact.

All of these crises brought into question the efficacy of water management policy at the scale of the Everglades ecosystem. The flooding in 1903 and 194 7 was noted primarily in the developed areas south of Lake Okeechobee and along the coastal ridge. The drought of 1971 impacted urbanized portions of the historical eastern Everglades. The nutrient-induced vegetation shifts occurred in local regions near canals of water conservation areas one and two. Yet these local impacts cascaded to the spatial extent of the Everglades, in large part due to the perception that the policies of water management operated at the scale of the hydrologic system. Hence these crises would have to be resolved at the scale of the hydrologic systemwhere policies were operating.

The cascading or upscaling of crises at one level to a larger level is described as revolt processes in the panarchy model. The positive feedbacks of the temporal processes intersecting with the broader-scale connections create this rapid upscaling (Figure 12-3). In these Everglades crises, the social constructs seem to amplify and resonate with deep-seated myths or beliefs about the system. For example, the myth of a "fragile" Everglades ecosystem (similar to the myth of nature anarchic, Chapter 1) appears to be reinforced by media stories. It is that interaction, the propagation of the social constructs, that seems to provide the upscaling phenomenon. For example, a picture of a drowning cow was used in 194 7 to depict the struggle to deal with a devastating flood and the need to recover from this unexpected natural disaster. In 1983, reports and pictures of the algae bloom in Lake ·Okeechobee provided evidence of the continued environmental degradation


Figure 12-3. Panarchy of a linked ecological-social system. This figure depicts three scales of structures and processes as they interact in the Everglades. Processes that propagate upscale are labeled "revolt" and include the social contagion associated with an ecological surprise. Those processes create access to larger-scale resources that cascade downscale (labeled "remember"). In the Everglades, often federal capital (money, expertise, and values) is used to modify policy and adapt or modify the ecological surprise.

of the Everglades. The mass media of newspapers and television seem to provide the matrix or grid by which these events cascade across scale and are spread to wider audiences. So perhaps one of the keys as to why these ecological crises, and not others, played key roles in crystallizing deep social changes is that they were rapidly shifted to larger scales and created a larger arena in which alternative futures were determined. But there is something missing, which we can only identify for future research, as to what makes these larger systems vulnerable at some times and not at others. Another difficulty in this analysis is that the revolt and remember events in social systems are more difficult to pinpoint due to unclear bounds of social structures across scales.

If indeed these identified crises were events of revolt, the resulting solutions to the crises were also linked to broader and larger-scale structures and processes. T hat is, as the crises were scaled to larger-scale venues, suddenly the resources at those larger scales became available for solutions at the local or smaller scale. This is clearly the case in all of the crises in the Everglades since the 1920s, when federal resources or capital were brought to bear to resolve the local surprise. In 1947, it was tax dollars, and the expertise of the Army Corps of Engineers, that enabled the central and southern Florida flood control plan to be implemented. Local or state resources would clearly not have been able to accomplish such a massive undertaking. In a sense, a


set of plausible alternative futures was discarded by the imposition of federal resources. The federal capital was accessed again in the 1970s, 1980s, and 1990s, but these times it was in the form of negative incentive, in terms of constraining the options for management available to the state water managers. The constraints were in the form of mandating water quality concerns and water supply needs for federally held properties in the Everglades. So the remembrance process-tapping into capital at larger scales-was critical in both creating and confining options for renewal in Everglades policy following smaller-scale crises that revealed the inadequacies of that policy.


In this chapter, we have used the heuristics of the adaptive cycle (Holling 1992; Chapter 2) and panarchy (Gunderson et al. 1995a; Chapter 3) to examine some similarities between ecological and social systems. We used a case history from the Everglades to illustrate linkages between ecological and social systems. In most of these systems the linked or composite system followed the four phases of the adaptive cycle. As new institutions (social rules, norms, and structures) matured, they became more and more vulnerable to disturbances or perturbations from the outside. In some cases, those disturbances were part of unforeseen or nonrecorded variation in key processes of the ecological system. In other cases, the effects of those disturbances were exaggerated by previous management actions, leading to an increased vulnerability of the social system. This is apparently the case in the history of many technologically based systems-including wetland systems such as the Everglades, where water level is the key management target.

Other similarities exist between ecological and social systems, in the back loop (renewal and reorganization phases) of the adaptive cycle. Many social systems focus on buffering mechanisms to maintain their resilience. Those institutions actively pursue ecosystem management actions to mitigate impacts of disturbances and maintain their stability through tolerable perturbations. Other institutional settings show a remarkable ability to reinvent themselves or create totally new solutions.

CHAPTER 13

THE DEVIL IN THE DYNAMICS:

ADAPTIVE MANAGEMENT ON THE

FRoNT LINEs

Frances Westley

Once upon a time, everything seemed fixed and solid. Now everything in the universe has begun to slide under our foet: mountains,

continents, life, and even matter itself To make future progress science must peel away all the coverings of apparent stability in the world.

- Teilhard de Chardin

T his chapter focuses squarely on the management aspect of adaptive management. Much work has been done exploring, describing, and modeling the ecosystem dynamics. The phases of Holling's four-box

cycle have been charted and analyzed in ecosystems around the world, and our understanding of the complex and unpredictable aspects of those dynamics has increased as a result. Less work has been done on exploring, describing, and modeling the social system dynamics and their interaction with the adaptive environmental cycles. However, here too, steady progress has been made. Gunderson, Holling, and Light (1995a) explored the interactions between management approaches and ecological crisis and renewal from a historical perspective. Holling and Sanderson (1996) have been developing a political ecological approach; Berkes and Folke (1994, 1998) with colleagues have been documenting and exploring the role of traditional ecological knowledge (TEK) in maintaining ecological resilience. For the most part, however, these studies have focused on the macro level, in order to apprehend the slow variables (institutions, laws, and cultures) and the ways in which particular management practices embedded in institutions support or undermine ecological resilience.

This chapter will take a micro-level perspective, in an effort to complement the work done from the institutional perspectives. It will focus on the case of one manager, Evan Karel, and a series of resource management challenges in which he tried to manage adaptively. Our concern is at the level of the individual decision maker, and at the level of the relationships in which he is embedded and seeks to work. We will explore how the larger institutional forces affect the individual decision maker, and to what extent the

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relationships that the manager forges within his or her own organization, and across the social system in which that organization is embedded, form a complex, adaptive system, acting as a response network, to provide the manager with social resources for dealing with crisis and surprise at the ecosystem level. To put it another way, this chapter is an exploration of what Gunderson, Holling, and Light (1995a) describe as the key to the reality of the adaptive management of complexity: "that individuals and small groups of individuals exert extraordinary influence by performing certain distinct roles within and outside institutions." This chapter seeks to explore in more detail the nature of these roles and the decisions and actions of the individuals that shape them.l

Adaptive Management and Managerial Decision Making

In his ground breaking study of managerial work, Henry Mintzberg revealed the fragmented and chaotic world of most managers' work lives, far removed from the planning, controlling, and coordinating that the literature said was an accurate description of the manager's job (Mintzberg 1973). Since then a number of thinkers and writers about managerial decision making have challenged the idea that it is a rational or even logical process. Political pressures often intervene (Allison 1971), and contextual dynamics introduce uncertainty and surprise (March and Heath 1994). Complexity arid incomplete information result in behavior. Action demands a logic very different from analysis (Brunnson 1982), and in general decision points can be determined only retroactively through a process of sense making (Weick 1995). Nonetheless, the challenge of choice for key system actors remains a critical one for understanding the human system response to ecosystem dynamics.

Evidence from the historical cases of ecosystem management suggests that certain management regimes, clusters of beliefs, and practices dominate for periods as long as twenty years, their erosion precipitated only by ecosystem crisis (Gunderson et al. 1995a). This rigidity has in part been attributed to a mechanistic and reductionistic worldview, a command and control approach to management, and a commodification of nature (Holling, Berkes, and Falke 1998). In recent years, proponents of adaptive management have suggested alternatives to the approach, based on a systems perspective, the interaction between social and natural dynamics (Holling 1978; Walters 1986; Lee 1993). But what would this mean for a manager and individual decision maker, particularly one who is working within the more traditional management regimes? Institutional theory suggests that institutions do change, but only through major crisis (such as that explored in Gunderson, Holling, and Light 1995a) or through a gradual shift in perspective of a critical mass or group within the organization (Greenwood and Hinings 1996). This chapter explores the choices and decisions of a manager working within the context of an agency in a time of institutional change.

The descriptions that follow are based on extensive, in-depth interviews with a single manager. As such, the accounts are clearly biased. No effort was

13. ADAPTIVE MANAGEMENT ON THE FRONT LINES 3 3 5

made to introduce other perspectives, and therefore the accounts of the events in each case must be viewed as subjective. Our focus here is on the sense-making process of a key actor who attempts to manage a complex, adaptive system. The account searches for depth of understanding as opposed to breadth and hopes to provide a rich enough description of the process to allow for comparison with other accounts of individual managers. In that sense it is offered as a complement to the more system-level approaches of other chapters.

Evan Karel: An Adaptive Manager

Evan Karel grew up on the shores of Great Lake, during the collapse of the walleye fishery, and this event, for him and for those he knew, had a powerful, shaping effect. Walleyes for the fishermen of that lake had an almost religious significance. Their demise was part of an ecological collapse that was experienced as a tremendous loss by Karel in the formative years of childhood:

We were avid anglers and as a little kid, I remember going out at night and hanging a lantern out the end of the pier and the emerald shiners would come up to the light and the walleyes would follow them up. We'd bait our hooks with emerald shiners, and you'd catch walleyes as fast as you could throw your line in the lake. And then the mayflies ... [l]n late May and early June the lake flies, we called them Canadian soldiers, would be a foot deep on the streets and the street sweepers would be out at night sweeping them up. And in 1953 the lake stagnated, went anaerobic on the bottom, and they all died and they never came back.

When Evan was growing up, there were more than forty-five commercial fisheries on the lake, and by the time he was a teenager, there were only two. His grandfather, who was a naturalist and avid angler, kept "putting it in perspective. He told me how there used to be cisco and whitefish in the lake. We were shanty Irish, and when I was a kid a big deal was to have a baked whitefish. But by the time I was in junior high school there were no more whitefish. I lived through these changes. I remember my grandfather saying that nobody gave a damn and industry was going to develop the shoreline and mills were going to dump pollution into the lake and all the fish were going to be gone in his lifetime ... and they were."

This experience shaped Evan's values in a number of important ways. He developed a keen sense of conservation, a proletarian suspicion of the rich and powerful (his other grandfather was a union leader), a faith in the people, a love of fish, a fascination with chemistry and its relation to lakes, and a systems view of their dynamics. These values had a strong impact on his ideas about resource management and his decision making.

His early family experiences also prepared him well for both conflict and collaboration. His stepfather, in particular, a firm, fair man, was tough but

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just. The rules in Evan's household were clear. You shared, you weren't selfish. You tested the line, you paid:

My stepfather was a very value-driven, principled man. He was judgmental but could also be tolerant of people very unlike himself. He had a terrible temper. I remember when I was about ten or twelve, he told me to take the trash to the town dump before he came home for lunch. I didn't, and I talked back to him sarcastically when he said, "There's going to be hell to pay when I get home tonight if this trash is not gone." I responded, "There is always hell when you're around," and he picked me up by the collar and shook me. Then he threw me out the locked screen door and said, "When you can do that to me, then you can talk back." Then, surprised at how far I sailed through the door, he silently checked to see if I was all right. Confirming that, he repeated that the trash better be gone when I get home and fix the latch on the screen door, too. He was not unfair, and the rules were clear. He expressed a steady, undemonstrative love, behind the conflict. This is perhaps why I'm not afraid of conflict.

Evan went to college at Central State University, earning a degree in aquatic zoology. He had started in chemistry but then switched to limnology because he got a job as a biochemist on a boat one summer. He then received a master's degree in limnology, researching the translocation of copper through walleye lakes. His first job was as a research assistant at the Natural Resources Institute at Central State University, and then he went on to join the Central State Department of Natural Resources, in inland fisheries research, in 1963. During his time at Central State, he began to get involved in projects and studies with very active citizen involvement. He was particularly impressed by a study he participated in on the socioeconomics of Central State fishermen:

This had a great impact on me. What struck me as interesting was that up 'til then I'd seen the greedy side of the sports fisherman, people who only wanted more and more fish without limit. We had worked with several insurance companies to develop some value questions at the end, which were designed so that you couldn't just lie. They detected subtle connects to the angler's deep beliefs and values. It turned out that these people were really connected to the environment. So my attitude toward anglers evolved from seeing anglers as only interested in exploiting the resource to one that recognized that anglers are really good people, who care deeply for the resource, not just greedy exploiters of it. The other thing that the questionnaire demonstrated was how much they valued the aesthetics of fishing on clean water. They needed to catch fish, but almost as important was the idea that they might catch one. This made me


realize from a management perspective, that you had to manage more than fish, you had to deal with the experience, the expectations, and values.

Evan brought this holistic perspective, systemic from the point of view of both ecosystems and social systems, to the variety of challenges that he faced in his career. In 1974 he joined the Northern Department of Natural Resources (DNR) as regional fisheries supervisor. In 1986 he was promoted to director of the Bureau of Fisheries Management, in 1987 to administrator of the Division of Resource Management, and in 1997 he became director of the Bureau of Integrated Science Services, where he is today. What follows are a number of case stories, representing both challenges and successes, that describe his style and philosophy of management. Contained in these stories is evidence of four adaptive management strategies, which combine to present a singular picture of the challenges of managing complex adaptive systems.

Managing Complexity

Few managers are so clearly confronted with the need to deal with complex adaptive systems as the natural resource manager within a given ecosystem. It is questionable in fact whether any individual or group can manage such systems, which are characterized by high levels of diversity, continuous change and learning, and complex interconnections that render them unpredictable. Rather, images of agency in such systems tend toward improvisation (Weick 1998), story telling (Gardner 1995), humor (Weick and Westley 1999), discovering of harmony (Coveney and Highfield 1990), and sense making (Weick 1995). It would appear that managing in such systems is about self-reference (understanding and maintaining commitment to core values or competencies); emergent pattern recognition; and openness to diversity, change, and new information (Wheatley 1992).

Karel's career, like all careers of excellent managers, is a tapestry of successes into which are woven a few spectacular failures, by his own admission. For Karel, managing complexity means "never taking action with just one objective in mind." He approached most problems with multiple objectives, four of which are recurrent enough to provide conceptual focus: managing through, managing out, managing in, and managing up.

Managing through refers to Karel's commitment to a scientific approach to management. His own training and experience convinced him of the value of a scientific approach, which treated management interventions as experiments to learn from, as opposed to solutions to be implemented. This drove his own passion to treat ecosystems from a truly systemic point of view. One of his objectives was always to diffuse such an experimental and scientific attitude through his department and among the stakeholders with whom he dealt.

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Managing out refers to the commitment to involve external groups or stakeholders in management processes and decisions: "Much of what we [managers] do actually gets in the way. We need to get people out into the community. In the past when a manager got too close to the communities we would say they have 'gone local.' Well, in fact we should all 'go local.' But when I say that, people get very anxious. We should be defining important principles but not micromanaging the details. Leadership in this context is generating the communication and mediating between groups."

Managing in refers to the need to also manage position and influence within the department or organization. Moving through a career, Karel occupied various formal roles within his own organization. Depending on his position and the particular personalities with whom he was involved, maintaining internal support for his experiments and his external stakeholder activities was more or less difficult and required different kinds of skills.

Managing up refers to the need to take into account the larger political context in which Karel's career and strategies unfolded. Even success at the agency level could be easily undercut at the level of the state legislature in Northern. Unless actions taken at the community, organizational, or scientific level were considered from the point of view of the larger political arena, much excellent effort could be ended with the slash of a pen.

It is helpful to envision these four kinds of strategies as four balls, which the effective manager seeking to harness complexity must juggle (Figure 13-1).

bureaucr ............. . process

political process

adaptive, sciencebased

process

community process

Figure 13-1. Four processes represented as four balls, which an effective manager who seeks to harness complexity must juggle simultaneously.


Depending on his or her values and skills as well as his or her formal position and contextual factors, it is easy to drop one or more balls. Extending the metaphor, surprise may act like a sudden wind, looping a ball into a new dynamic, or like a sudden shift in terrain, whiCh causes the juggler to lose his footing and his balance. The trick is to keep the eye on these four balls and somehow, with peripheral vision, adjust to those surprises as they unfold, or, even better, use them like the good golfer or tennis player uses the wind. The key, however, is that, as Karel puts it, "The devil is not just in the details, it's in the dynamics." In complex, adaptive systems, disequilibrium and surprise are the rule, and failure is as instructive as success. We will review a number of these cases, as seen through Karel's eyes, with a particular focus on the patterns that emerged as Karel's to try to determine the role each of these strategies and their interactions played in both generating and responding to surprise.

The Salmon-Raising Experiment

This particular case is one that Karel remembers vividly, as it occurred during the first few weeks of his move to Northern State. Considerable tension had been building up over the issue of snagging salmon. Snagging, a practice whereby hooks were dragged through a salmon run to catch fish, had become popular in the lake. Native Americans had used the practice to harvest fish, as had pioneers and other foragers, but by the turn of the twentieth century it had been deemed unecological as well as unsportsmanlike, and DNR wardens worked hard to stamp it out by arresting poachers. Now the solution to a new problem,. stocking Pacific salmon to curb the invasion of alewife into the lake, had brought snagging back into vogue in Northern State, at least by some groups.

The problem started when salmon was stocked in the lake in an effort to deal with the alewife problem. After swimming upriver to lay their eggs, the stock salmon would die, polluting the rivers and shores of the lake. Central State had responded to this crisis by allowing snagging. Northern had maintained a ban on the practice, however, and either hauled away the dead fish or let them lie. Although anglers could still catch some fish with artificial lures and bait, their success was much less that that of a skilled snagger. Many Northern anglers favored the more liberal snagging regulations-e.g., being allowed to keep foul-hooked or accidentally snagged salmon-but others strongly opposed this practice. The continued ban and a fairly evenly divided public caused much tension among various groups that held strongly different positions on snagging and foul hooking. Karel explains:

One segment of the angling community felt that these fish were going to die and would be wasted anyhow, so you should take them by any method. They felt that these were stocked fish that don't reproduce ... you aren't killing spawners. These were the "get them out of the stream proponents." They were sportsmen (the sports-

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men were divided), local community folks that didn't want the dead fish (sanitation people, local government people), and the tourist interests (attract fishermen and keep the lakes clean). Then there were the skeptical and undecided ... they would occasionally foul-hook [snag] fish "accidentally" and felt this should be allowed. Then there would be some more conservative types: conservation wardens, most fishery managers, members of the fishing community who remembered how hard it was to stop poachers from snagging walleye and northern pike and feared it would train a whole new generation of poachers and that it was not ethical. Northern's outdoorsmen have always had high ethical standards.

The opinions of these groups were extraordinarily strong and polarized ... they would get into physical fights on the piers. The intensity of this debate was incredible ... some were laying ladders against chain-link fences to snag ... others would come with trailers and can salmon all weekend. The further you got from the lake, the stronger the feeling was. It was almost a simple black and white, wrong and right. The majority of the angling community was against it.

The first week on the job, Karel was asked by his boss to give a talk on salmon hatcheries to a meeting of an important fishing club in one of the regions under his jurisdiction. Karel knew he was walking into an ambush: the week before all of the officers of the club had been arrested on the grounds of intent to snag. He had read all the background materials and knew that the warden who had done the arrests was on shaky ground, as the law did not in fact include a penalty for intent to snag.

I had read all the citations, and I thought the wardens had really screwed up. They had gotten overambitious. These anglers were kind of taunting the wardens. The rules said you couldn't snag ... nothing about intent to snag. The guys were snagging but not catching fish, so they came up with this jargon about intent to snag. I could win the club by saying these were pig-headed wardens, they were wrong, so go to court; but I figured that's going to really build a good relationship with the wardens ... and they have a network critical to the fisheries programs' success. So I figured the only thing I could do was to say I wasn't there, I don't know what you were doing.

Fresh out of a university research position and new to the job, Karel decided to take his wife along. They arrived before dinner and enjoyed a cordial meal. However, Karel noticed that one of the officers who had been arrested kept heading back to the bar, and he figured there was going to be trouble. After dinner he stood up to begin his speech on the state salmon fisheries, and as soon as he put up the first slide, the officer, quite drunk by now, was on his feet:

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As soon as I started talking, --- was up moaning about "You know what happened to us? What's your opinion on snagging?" The president of the club, instead of shutting him up, said to me, "Just answer the question, and then we'll be able to get on with it." So I answered, "I don't think snagging is appropriate in this case," and he said, "Well, the wardens must agree with you because I got arrested." Then I just started with "I wasn't there ... I don't know what happened. If you think you were wrongfully arrested, you should go to court." "Well, what do you think your warden was trying to do?" "I wasn't there." He kept taunting me and finally he started swearing, and then one of the women shouted out, "Sit down,---" He ignored her, and then the president said, "Sit down, ---, he's told you what he can." Finally, two big guys came over and sort of stood next to him, and he sat down. I finished my speech and afterwards, the president came over and said, "I think --- wants to buy you a drink."

Interestingly, this trial by fire seems to have established Karel's credibility with the group. The fact that he didn't lose his temper despite the fact that he was taunted impressed them. ("I have a terrible temper, so I don't lose it. In those situations I really separate my person from my role.") As a result, after the talk, the club members approached him with a second frustration. In a nearby area, salmon were being reared in a salmon pond, partly to produce fish for the big regional salmon fishing competition. This pond had never been a big success, but the fishing club had wanted to try it. They had heard from friends in a major nearby city that fishing groups were raising salmon in cages in the harbor, and they wanted to try it in their own salmon pond. The local fish manager was against it, and they were aggrieved. Every year salmon had been dying in the pond, and the fish manager wanted to get rid of it.

This was a bit of a dilemma for Karel: "So here I was ... I'd met this fish manager once, and now I'm his boss and they're after me to get him to change his attitude." Karel promised to look into it, but when he got back to the office, he found that the situation was even more complicated than he had realized:

Now it just happened that the chairman of the critical committee of the legislature was from this town [where the fishing club was based]. He was a very aggressive legislator and liked to throw his weight around. So when I got back to the office and told the director what had happened, he said, "You've got to do something for these people, because we're going into a budget year and the secretary will be calling me if the chairman isn't happy. Make him happy and try not to get the fish managers unhappy when you do it."

Karel's response to this dilemma was to set up the first adaptive experiment in Northern State. He personally agreed with the local fish manager

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that raising salmon in cages was a bad idea, but he felt that the only way to convince the local fishermen of that was to let them try it. So he organized a group of fishermen interested in the project, convinced the fish managers to join the discussion, and set up an experiment. The local fish manager remained unconvinced and hostile. He knew it wouldn't work and felt it was a waste of his time, but Karel was determined that he would be involved. ("I said I would be project leader, but he needed to get involved because it was his ward.") Karel also brought in some salmon culture experts and laid out a plan with an evaluation component that was to run for three years.

The first year we raised them we had all the problems we anticipated. I'd drive from the city and sit all night with these guys watching the fish getting sick. The second year the water had been warmed by rain and it was killing them, so we chained all the floating cages together and towed them out into the lake with a chartered boat, but this was a big group thing. These people were steady. There was someone watching those fish twenty-four hours a day between April and July. They were really invested. This was their experiment.

When it came time for the third year's experiment, I sat down and said what do you want to do? And they said, you really think it won't work? I said, well, I don't think so, but I promised you three years, so that's ·what you're going to get. They said, "We're spending a lot of time killing fish." "Well, that's what Ron [the fish manager] was telling us." "Maybe we ought not to do it this year." And so the experiment was abandoned.

We didn't kill everything. Of about fifty thousand fish fingerlings, about twenty...:five hundred got stocked out in the lake. Our goal had been fifteen thousand. But we had built a real collaboration. Elsewhere in the state anglers and managers were at war over size limits and other issues, and down in our district we were working together with anglers and had turned around potentially serious conflict and built a successful collaboration with local anglers.

Karel felt that this experiment not only represented a particularly notable success, but also embodied a number of key values, which he worked for throughout his career. Most notably this was a case of managing out. As he said, his efforts to involve the citizens of the community in an experiment that he felt was bound to fail, and at the risk of alienating his network of fish managers (who were essential to him), were based on his profound feeling that the power of government really does reside with the people: "We may be right in terms of what we know technically, but unless it's right for the people we're doing it to or for, it will be a failure, because they'll reject it."

This value perspective, as noted earlier, was an enduring disposition, or self-referent, as Wheatley (1992) would call it. According to complexity


theory, such dispositions allow for greater flexibility in dealing with change, chaos, and diversity. In this case, this value orientation allowed Karel to economically juggle managing out and managing through. For him, making it right for the people meant employing a form of citizen science. He believed (and still believes) that if citizens can become engaged in the science itself, there is a much greater potential for building a relationship between the professionals managing the resource and the local populations. When managers, viewing themselves as scientists, also become "priestlike, seeing themselves above the people," a major breakdown in communication occurs. The sword cuts both ways-citizens get angry at what appear to them to be arbitrary decisions (even when those decisions are based on good scientific judgment, as in the case described above), and meanwhile the professionals feel pressured to "do things we just couldn't do without extraordinary outlays of energy and resources." From Karel's point of view, the answer lies in building a bridge through understanding. For him, the bridge was founded on his fundamentally democratic belief in the capacity and right of citizens: "My hope was that if we could explain what the system was capable of, the people were smart enough to get us into the options that were viable."

This confidence was rewarded, as we have seen, in the citizens' decision to suspend the salmon-raising experiment after two years. It would also be rewarded in many other situations that Karel sought to address over the years (most notably in the Lake Algonquin case described below). Most important, this case illustrates how a manager intent on adaptive management can attempt to apply good experimental science while building stakeholder commitment to policy. The two balls are juggled in such a way that their rhythms are synchronized, creating a dynamic that assists the juggler.

Similarly, the reference to the state legislator and the director's political concern with keeping people happy so the politician would be happy hints at another synergy, between managing out and managing up, which we will consider in the next cases.

The Spruce Lake Case

From Karel's point of view, "We couldn't get anything through the bureaucracy, unless I got the people we were doing it for demanding it." As employees of a government department, managers in Natural Resources were subject to pressure from above, from elected politicians in the state legislature, in particular. One of the things that Karel learned, however, was that by managing out, he could find the clout to successfully keep the managing up process alive. This image of managing politics by going directly to the people was an outgrowth of the proletarian values of his parents and grandparents.

These values combined with Karel's love of lakes to fuel his determination to secure fishermen access to all lakes in Northern State. Northern State law and agency policy supported reasonable boat access to all lakes.

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The laws had been established with logging companies in mind, but for many lakeshore owners in Northern, reasonable boating represented a threat to their privacy and to their property values. The department created a lakeuse task force to deal with this issue and over a period of fifteen years finally managed to reach a consensus with the Association of Lake Owners that access would be assured on 85-90 percent of the lakes. This took tremendous patience on the part of all managers: "We went in and got options and had big wars with the local communities, but we tried, as well, to understand local property owner's needs, and when we were able to do that and follow through, they kind of became allies."

In one community in particular, Beech Lake, the lake owners were mostly factory workers who had bought property on the lake when it was cheap. They objected to DNR's campaign to get access to their lakeshore because nearby Spruce Lake was locked up by very rich, influential landowners, and no lake access seemed possible since lakeshore owners there were politically connected and powerful. Karel promised that he and other managers would go after access there, too, if the Beech Lake Association worked with him. He was true to his word. The Spruce Lake Association wouldn't even talk to the DNR managers, but Karel fell into a more direct approach:

State law provides that all approved subdivison plots on lakeshores have sixty feet of public access on a lake, but on Spruce Lake, they had turned it into this little park ... but they had no parking within about two miles of the site. One time I came with a friend in my canoe and we were carrying the canoe all across the park and the town constable came to issue a citation to me. We told him we wanted to create a test case since we hadn't been able to get anyone to deal with this access issue. He leaves. Next the town chairman comes down and tells us off.

Things didn't move quickly in terms of obtaining access there until some time later when Karel came back to the Spruce Lake area to give a talk to the local garden club. It was a routine talk, aimed at grandmothers, warning them about contaminants in fish and how much of lake fish it was safe to feed to children. He always threw in his pitch about lake access and how important it was to provide it. At the end of this talk, he was approached by a woman who congratulated him on the talk and then pointed out that there was no access to Spruce Lake.

"My husband is chairman of Town Board, and I don't think this is right," she said. I said, "I've met your husband." She said, "You're a very nice boy." Next thing I get a call from the chairman saying, "You've been talking to my wife. I'd like to talk with you." So I found some prairie plants and took them to her house, and that started negotiations. It took about several years, but between her ha-


ranguing him and our managers taking easements or buying access on other lakes, we finally got the town to provide proper access.

This method of mobilizing forces in his favor in order to get the job done was played out on a much larger scale in the numerous conflicts that have confronted DNR in the years that Karel has worked there. For Karel, when such conflicts arise, the most important element is to get the groups talking to each other, as opposed to both groups pressuring the DNR. The goal is to "feed those people back and forth to one another":

Basically you've developed a process by which you manage conflict with a process which creates cross-fertilization. I've always tried not to be at the apex of the triangle, but rather to get the groups that want to triangulate on the agency and get us to referee their debate, to hold their debate and not even serve as a mediator, but more as a facilitator of the discussion and in most cases they ask our opinion. It's almost an intuitive political process.

Ultimately, by building bridges with particular individuals and groups in the community, an adaptive manager builds up a constituency whose energy can be tapped to manage up, and affect the larger-scale political processes that shape legislation. This image of political influence is very different from lobbying or insider connections. Yet relationships with key individuals represent vertical social capital that is critical to mobilize when conflict erupts in a domain (Chapter 8). For Karel this is often a very personal and particular process of introducing key individuals to each other. ("I'd like you people to talk, because I know Andy and I know Joe, and although I get angry with them about some things, they're pretty good people.") As he builds loyalty across the system, his ability to make these connections (to bridge structural holes, as Burt [1992, 1997] would say) increases. Managing out and managing up have close and useful affinities. So do managing up and managing through, as the next case illustrates.

The Lake Algonquin Case-Managing All Balls at Once

In the case of Lake Algonquin, Karel had the opportunity to put many of his ideas about adaptive management to the test. One of the largest lakes in the state, its proximity to the state capital and to several important research institutions gave it a high visibility. While the lake had been the subject of intensive study and management since the 1940s, it was nonetheless suffering from eutrophication due to agricultural runoff as well as pollution from the municipalities on its shore. Karel saw promise in a trophic cascade approach to managing Lake Algonquin (Carpenter et al. 1985). This approach used the introduction of carnivorous fish species such as the walleye to reduce the number of fish that eat zooplankton, thereby allowing the zooplankton population to increase and consume more of the algae that polluted the lake. It appealed to Karel on several points:

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• from a water management point of view, it dealt with the pollution problem and so would increase water transparency in the lake;

• from a fisheries management point of view, it would increase the number of walleyes in the lake, which would be attractive to the anglers;

• from a political point of view, new funds just opened through the Federal Aid for Fisheries Restoration Program made such a large experiment viable;

• from an agency point of view, it represented a chance to disseminate internally an emerging set of tools (including modeling and evaluation techniques) developing within the scientific world.

Karel therefore saw the project as a chance to keep all the balls in the air at the same time and create strategic synergies. He insisted that the project be run in the state capital:

I said that if I was going to do something that important, I wanted to do it in the capital. I had a political constituency there, because I had large numbers of groups that were trying to raise and stock walleye, and they would work with us on it. My feeling was that if I could demonstrate it in the state capital, where the politicians and the population are, that people would begin to think in terms of systems. And the other thing was that it was a lot easier to get a legislator out on Lake Algonquin than on a lake up north. You're balancing goals: your own interests, educating an active, university community, capitalizing on the momentum already present in the demand for walleye.

The chief opposition to the project came from inside the DNR. Two dynamics presented obstacles and had to do with turf issues: control of scientific data and control of capital resources, both fish and operational funds. One of the DNR scientists who was particularly interested in the recovery process had been quietly gathering plankton and chemistry data on Lake Algonquin for the past ten or fifteen years (against the advice of his supervisor, who thought it was a waste of time). The result was a "pivotal, incredible data set." When Karel realized this, he spread the word, much to the scientist's annoyance; he didn't want to share his data:

I brought him in and told him I was going to take the data, as it was state data, and I was going to kick him off the project, unless he figured out a way to get along with the other scientists. Then I went to the others and told them to figure it out. One of them went out of his way to act as a mentor and bring him along. It was a classic case of scientific paranoia ... he thought people were going to steal data.

The second, even more difficult internal barrier was the other staff in the DNR. While a number were very interested, many, including Karel's subsequent chief of fisheries, hated the idea. To launch the project required moving fish from northern districts south, which wasn't popular with the


other fish managers. "Every fish manager has groups who want walleye, and I said you're not going to get a lot of walleye for the next few years." For this and other reasons, Karel's superiors tried to talk him out of it, over a period of six months. The feeling was that "there was too much of our political capital tied up in one high risk investment." Ultimately, Karel needed to sell the idea to the secretary:

I went to the secretary, who was a pretty savvy biologist himself, a brilliant man and who was generally prepared to take risks. I sat down and said to him, "What I'd really like is to take this chance. We could spend up to 30 million dollars and produce only scientific results. I don't know if we'll produce any management solutions right away, maybe in three or four generations, but not right away. But I can produce a world-class walleye fishery in the state capital that will make a lot of people happy. So there will be short-term returns. The downside is that we won't allow them to catch them because we need to keep them in the system. So we've got to sell catch and release. He said, "I understand what you're saying, but how are you going to sell that politically?"

The secretary indicated that Karel's next step was to sell the idea to the politicians and policy makers. The policy-making body at this time was a citizen board elected from all over the state. Karel knew the chairman of this board fairly well, as they were fishing buddies, and after "endless discussion while fishing (and over brandy in the shack)," Karel convinced him that it was worth a try. Support from other board members was forthcoming, as at that period the board had an unusually experimental and dynamic composition: "The board members we had at that time were literally brilliant people. One of them had completely revamped a major failed network of companies, another was an investment banker who understood probabilistic issues and would go for the edge, and another had worked with air pollution. They all understood that what we had been doing was kind of a fa~ade ... feeding people's needs without addressing underlying problems."

With a green light from both the DNR and the policy board, Karel went ahead. The results were in themselves surprising. Enormous amounts of scientific learning occurred, and a lasting link was made between the university and the department, but the walleye fishery never flourished to the required degree. Two reasons have been given for this. The first was biological: "The lake didn't want to grow walleyes. It wanted to grow small-mouth bass. So that's one miscalculation we made. It didn't have the spawning habitat ... the community structure wasn't there. It was so disturbed that we couldn't see that before we started. We had a huge die-off of cisco as a fortuitous event, and that reduced the planktivore population. So the concept of the cascade was correct."

The biggest miscalculation, however, was social. The project was launched to much media fanfare, and despite the fact that the DNR had set a fairly restrictive bag level, the interest in the project resulted in a sevenfold

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increase in anglers, attracted by the enhanced opportunity of catching walleyes. This kind of phenomenon has been labeled the Paradox of Enhancement, "the rapidly rising public expectations that exceed the capacity of the resource ... common to enhancement programs" Gohnson and Staggs 1992). This effectively intervened to counteract the experiment.

From Karel's viewpoint, this can effectively be viewed as a failure of managing out. "While the project was begun with considerable support from local groups, as it progressed, communication between scientists and citizens broke down. Key to this breakdown was a turnover in personnel, both in the agency and in the conservation groups that had partnered the original initiative; but there was also a drift away from the partnership itself:

We had a huge people turnover. The life cycle of a conservation group is about four years, and if you don't reinvest in terms of bringing the next generation along, it's almost a new game. In order to have made this work, we needed to have gone to the anglers earlier. If we had done it as an education effort, most of the anglers would have come in on our side. We initiated the project with a massive involvement of anglers, but as we moved through the cycle, they fell out of the process. Our field people saw this as an imposition. It's a hell of a lot more work to use volunteers than to do it ourselves. In 1992 or 3 we started losing the citizen ownership ... they said the hell with raising fish, I'm going fishing. People move, people get busy with other things. Two key postdocs left. I moved out, and the guy behind me was reluctant ...

Karel's own promotion to director of fisheries and then to division administrator also placed him in a new relationship to managing through and managing out. While the move up allowed him to continue to hold the project together through clout, it also brought its own distractions. A legal battle over Native American spearing rights and political maneuvering around stocking practices, among other things, pulled his attention and energy elsewhere: "I didn't invest my personal time and energy in debriefing ... in making it come back as a more adaptive system ... in closing the loop with local groups."

Finally, the reluctant managers within DNR, those who had resented the project in the first place, took the first opportunity to close the project down. According to Karel, "We didn't get one year beyond what we said we were going to do ... as soon as we met the five-year stocking commitment, boy, they slammed the door."

The Lake Algonquin experiment really exhibits the tensions and challenges of keeping all four balls in the air at the same time. "While the cases of salmon hatcheries and Spruce Lake illustrate the synergies that the adaptive manager can discover between some of these dynamics, the Lake Algonquin case also illustrates some of the inherent contradictions.

First of all, it illustrates how precarious it is to try to manage through while managing up and out. While the project was sold on the basis of al-


lowing for good science, an enhanced and therefore politically attractive fishery, citizen involvement, and agency showcasing, in fact, each of these goals in turn kept being threatened. Controlling the experiment would have meant insisting on a catch-and-release policy, which was hard to sell politically. Good science might have indicated that the lake was unsuitable for growing walleyes, which would not have been welcome politically. Compromises between managing through and managing up were made, despite the apparent synergies.

More significant, however, were the tensions between managing up and through and managing out and in. The science itself was so exciting and involving that those who owned the experiment were distracted from maintaining the citizen involvement. In an odd way, the experiment resulted in something of a social cascade, equivalent to the trophic cascade. Karel's ability to manage up, never more apparent than in his championing of this initiative, may have led (in part) to his being moved up within his organization. Removing him from intensive interactions with other stakeholder groups, however, meant that those relationships fell victim to at best reluctant debutantes and at worse antagonists who failed to follow through on a project they had never supported. As those relationships died, citizens drifted away, and the crucial communication concerning the effects of the increased angling never occurred.

As Karel put it, from an adaptive management perspective, the second loop of double-loop learning never happened. It appears then, that managing up is sometimes in tension with both managing out and managing in. Politically, the profile of the agency and Karel's career advancement resulted from the project. Scientifically, much good data were collected. But the managing out and managing in balls were dropped, with the result that the social system did not adapt as fully as all had hoped.

Overall, it is perhaps the managing in relationship that threatens the really good adaptive manager. As he or she turns attention to managing up or out (and at times even through), "back home" support is weakened, and sometimes opposition is mounted. In addition, the further managers move up the hierarchy, the more cut off they are from citizens, grassroots groups, and other constituencies. This can result in unpleasant surprises, as we will explore in the final two cases.

The Deer Hunting and Spearing Cases

In the years after being promoted to a research director position, Karel encountered several frustrating cases in which surprise was a clear element. As in the case of Lake Algonquin, the surprises stemmed from the social system, but they served, nonetheless, to weaken Karel's immediate ability to manage adaptively. The first concerned the deer hunting quota system, the second the game fish quota system.

Deer hunting in Northern State is something of a sacred pursuit, and the issue of hunting quotas has always been controversial. Whether to hunt

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bucks or does and how many to take and how many to leave are hotly debated. On one side of the controversy is the Conservation Congress, a statewide body whose mission is to give advice to the Department ofNatural Resources. Every year, each county elects three people to this congress; it then sets up study committees for different areas of concern. Historically, this group had been in opposition to the DNR, challenging its formulas for determining harvest levels. Over a fairly long period of time DNR biologists had come up with a formula for calculating population based on harvest data. While in Karel's opinion "it was almost impossible to calculate this realistically," the biologists found it very predictive.

However, the model was counterintuitive and difficult for the citizen groups to understand. This had fueled the conflict. The public had also realized that the formula, as a tool, limited their ability to modify and make policy, as it didn't allow for negotiations around nonbiological or social issues. Feeling ran high on both sides.

When Karel got involved in the situation, the debate appeared to be about no more than 2 percent of the deer. He therefore saw it as a trivial issue from a biological point of view and felt it should be approached as a social issue. Using his established collaborative approach, he recommended the creation of a task force, a joint committee of citizens (farmers, hunters, insurance reps, and animal rights people), including representatives from the conservation committee and the scientists. However, as DNR scientists insisted on playing an expert role, he insisted that they act only as observers.

The process was a lengthy one that created considerable resentment among the DNR scientists. However, the results proved again that given enough information and training, citizens could come up with recommendations that were biologically sound on the one hand and politically sensitive on the other. Karel was pleased.

Others were not so pleased. The biologists complained that they had been disempowered. They did not see the value of consensus. Karel, who was not a wildlife manager, had aggravated them by challenging their model. They further resented the extra work that the task force created. The conservation committee was also not pleased, as it felt that the lines of authority had been messed up by the creation of the task force. Karel had succeeded only in turning the opponents into a coalition against his initiative. The upshot was that both groups complained to the Natural Resources Board, whose secretary called Karel on the carpet.

In looking back, Karel questioned why he had failed to secure support in his own organization. He attributed it to two factors. The first was that he was "diffused." "In my new position I had seven other programs to worry about." The second was "a touch of arrogance ... had it been nonscientists, I would have taken the time to try to understand them. As it was, I felt frustrated and impatient. I felt I was the expert, and I wanted them to understand me."

The spearing issue also occurred after his promotion to director. A recent court victory had made it possible for Native Americans to spear fish,

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as they had traditionally. Spearing had always evoked violent reactions from sports fishermen. They would call Native Americans they saw spearing derogatory names and would throw rocks at their boats. From the point of view of the DNR, the change induced a crisis. The spearing was a threat to the fishery, as Karel saw it. He needed to go to court and convince a federal judge to protect the fishery.

Karel went about collecting the data to demonstrate the stress that the spearing created from a fishery viewpoint. He had hoped to go direcdy to informal negotiations with the two groups, but once the challenge reached the courts, that kind of compromise was difficult. The result was that he had to reduce the bag limits of the sports fishermen, an unpopular and controversial move. The governor's response was to stock more fish, which the DNR scientists felt would further damage the stock. Karel was involved in an intense process of managing up. He was sent to represent his department in court, where a lengthy batde ensued. In addition, he had to go personally to the governor's home to defend the actions of the department and to encourage the governor to protect the fish stocks and follow management advice. In the process he found himself in an adversary role vis-a-vis both the governor and the tribes. A delay was all he could achieve.

Both these cases represented frustrations for Karel when compared with the salmon hatcheries or Lake Algonquin projects. Interestingly, they both also occurred after his promotion. In the first case, the surprise came in the DNR resistance to Karel's task force initiative, even when it proved successful at building consensus. The managing out and managing in dynamics proved to be in tension. However, it could also be argued, as Karel himself argues, that in his promotion he was pulled into an arena where managing up became the critical issue and that he had to delegate the work of building the bridges to others. The result was that the ball of managing in was dropped.

In the second case, the particular dynamics of managing up become apparent. Scientific principles came into direct contact with political will and legal decisions. The surprise here came when a change in the legal system that was not anticipated by the DNR simultaneously increased conflict between the two groups (Native Americans and fishermen) and made building informal bridges unlikely. In his new position, Karel continued to use his skill at managing up, but managing through became more of a defensive position than scientific inquiry. Managing in and managing out became more difficult and problematic.


First and foremost the description of these cases indicates the incredible complexity of managing adaptively when seen from the individual's perspective. Second, when seen from the wider system perspective, it suggests that the unit of analysis for understanding adaptive management is the problem domain, not the organization, or even the institution. Third, it suggests that

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successful adaptive management involves an understanding of how the dynamics of overlapping problem domains interact to allow for action and successful response (corridors of movement) on the one hand and resistance and inaction (congestion of corridors) on another. We will deal with each one of these in turn.

The Adaptive Manager as Decision Maker

What is adaptive decision making, and how does it differ from rational decision making? While rational decision processes are closely linked to logical choice and to rule following, they are not necessarily intelligent. On the other hand, traditional knowledge may be a poor match for present contexts. The key to improving adaptiveness in the individual decision maker is to strengthen the match between decisions and the demands of the decision environment (March and Heath 1994). In the case of adaptive management of natural resources, there are at least four decision environments: the ecosystem, the political system, the organizational system, and the interorganizational system. In addition to the sheer complexity of environments is added what March and Heath refer to as adaptive inefficiencies: temporal lags between decision and environment; responding to local as opposed to global feedback; the historical path of previous events and decisions; unpredictable diffusion of decision impacts across multiple concurrent problem domains; mutual adaptation of environment and decision maker; and the way individual decision makers are linked to others in "ecologies of adaptation" (March 1994).

Given the level of this complexity, we agree with March's (1994) conclusion that "the efficiency of any decision process is sensitive to the relation between the rate of exploratory variation reflected by the practice and the rate of change in the environment." Under circumstances of such complexity, no practice or approach is in itself adaptive; no philosophy will work as an idee fixe. Rather, the adaptive manager is perhaps best equated with the knight-errant, perhaps even Don Quixote, who said, "For a knight-errant to make himself foolish for a reason warrants neither credit nor thanks; the point is to be foolish without justification" (Cervantes).

In practical terms, therefore, this means one needs to eschew a best practices approach to adaptive management in favor of an approach that focuses on goals, values, aptitudes, and skills. Among the lessons or insights about adaptive management that emerge from these cases are the following:

• To manage adaptively requires strong values as opposed to rational analysis. In the case of Karel, as we have noted, he grew up with a love of science and a respect for people in almost equal measures. He loved lakes and held a strong conservation ethic. He valued collaboration and wasn't afraid of conflict. Throughout these cases he adhered to those principles: trying to build collaborations, encouraging citizen science to build bridges between

•

•

13. ADAPTIVE MANAGEMENT ON THE FRONT LINES 3 53

scientists and citizens, working toward conserving resources based on scientific estimates. In themselves the values were not unusual. What was unusual was the tenacity with which Karel used them as a guide across a wide variety of situations. He showed huge patience when it came to identifying the stake each group had in the problem and in bringing different stakeholders together to negotiate, always using the language of science to build common ground. With commitment to these core values giving his management style an enduring identity, he was able to be adaptive or responsive to the particular constellation of interests and energies each case represented.

To manage adaptively and respond to complexity, it is necessary to juggle multiple strategies and goals (Figure 13-1). The danger is to become too focused, a danger that might be labeled as the peril of simplicity. The adaptive manager must have aptitudes for being a scientist, collaborator, politician, and agency manager simultaneously. Managing in avoids the peril of forgetting roots and the importance of back home commitment for any domain decision and action. Managing out by building social capital across different organizations and groups avoids the perils of going it alone and, ironically, of going too quickly. Managing up helps to avoid surprises coming from the wide political system (such as the change in the spearing regulations) and ultimately the peril of becoming locked in political confrontations that make flexible responses to different situations nearly impossible. Managing through science is the anchor that ties all decisions to ecosystem dynamics. A commitment to good science avoids the peril of becoming drawn into political infighting or going local to such an extent that special interests begin to prevail over larger system goals.

To manage adaptively requires strong control of emotions, little fear of conflict, and great humility. Karel mentioned when discussing the deer hunt controversy that he had perhaps been affected by a touch of arrogance and that he had grown angry at the DNR people because they hadn't understood what he was trying to do with the task force: "I thought in this case I was the expert and they should listen to me." It would appear that humility is a mind-set that opens the individual to what is going around him or her and pride, the overestimation of oneself and one's immediate environment that acts as a barrier to new information, even when it is crucial for organizational survival (Deutsch 1966). Flight crew members who think they can rely on themselves in emergencies generally do badly when the emergency, which requires a team response, actually occurs (Weick and Westley 1999). Heedfulness, staying open and responsive to those around you

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and to what is happening, is a key feature in dealing with rapid change, crisis, and the unexpected (Weick 1995).

Of course, this kind of receptiveness and humility is impossible if an individual is blinded by fear or anger. Karel noted that emotional control was an important aspect of his behavior in cases where he felt he had managed adaptively. In the salmon hatcheries episode, for example, he kept his temper firmly intact by separating his role (which was being attacked) from himself. In this he was following March's first rule of adaptive decision making: "Treat the self as a hypothesis ... treat decision making less as a process of deduction or negotiation and more as a process of gently upsetting preconceptions of what is desirable or appropriate" (March 1994). By not taking himself too seriously, by admitting that despite his formal position he had no answers and knew very little, Karel began a process of trust building that gently shifted both the DNR and the salmon fishermen away from their normal positions and toward a ground on which they could begin to collaborate. Emotional control and humility working together are key qualities of the adaptive manager.

• In order to manage adaptively, the manager needs to capitalize on the energy and movement of others. The experience of managing in complex adaptive systems is more similar to catching waves or looking for emergent corridors for action than pulling strings or working levers. The historical moment is hence important. It is possible to look at the successes and failures of Karel's management approach from the point of view of same process, different context. In particular, in comparing the salmon hatcheries case with the deer hunting case, we can see him responding in similar ways to two different but similar problems. In both cases there was conflict between the DNR and sports groups, and in both cases Karel insisted on involving the citizens in scientific explorations and decisions. In the first case, however, the opponents were of lower status than he was and under his direct control. In the second, he was dealing with peers who were not under his direct control. Although Karel was promoted largely on the basis of his successful management of citizen groups and his success with citizen and normal science, it appears that the closer he came to the top, the farther he was from the environment. It is as if organizations really should be drawn as circles rather than triangles, with the apex represented by an inner circle, most removed and disconnected from the environment.

Perhaps more important the domain around the salmon hatcheries was not as organized as that of the deer hunting issue and so the motivation to collaborate was higher (Figure 13-2). In the latter case a recognized body, the Conservation Congress, had


A B

political

organizational

interorganizational

individual

beginning of project end of project

Figure 13-2. Use of separate adaptive cycles to depict phases of issues as interpreted in four systems-political, organizational, interorganizational, and individual. Managers, actions, and solutions must account for the dynamics of these systems.

long adopted a position critical of the DNR scientists and their tools for estimating deer populations. The DNR scientists on the other hand had worked long to develop an effective, although counterintuitive, means for making those estimations. Motivation to collaborate had been reduced by the rigid stances of both organizations and a clear delineation of roles. While the task force created apparent collaboration, it was a forced one from the point of view of the DNR scientists. They complained to the Natural Resource Council that the process had disempowered them. The result was that Karel was sanctioned, which brings us to the consideration of what this case reveals about what it means for the adaptive manager to manage the social system holistically.

The Social System as Problem Domain

The above cases show that the social system that the adaptive manager seeks to manage does not correspond to a single institution or even to a single organization. Rather, it is the problem domain, the system of actors brought together by their stake in a particular problem, that is the relevant unit of analysis.

This system has both vertical and horizontal dimensions. The horizontal dimension contains three nested social systems: the political system, the organizational system, and the stakeholder or interorganizational system. Each of these has its own dynamic on which the adaptive manager can seek to capitalize. For example, the political, when it is in a creative destruction or renewal phase, may make resources available for adaptive experiments. This

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occurred in the case of the Lake Algonquin project when changes in the legislation opened up resources for ecosystem restoration. At other times, because of changes in the political climate, such resources will not be available. The adaptive manager, managing up, can spot these phase shifts and take advantage of them (Figure 13-2).

Similarly, the organizational system can cycle between risk taking and conservation. This is partially a result of cascade effects from the political system, but it is also an effect of the particular personalities in decisionmaking positions. Even when the organizational system is in the conservation, or K, phase, the backing of a particular individual, for example, can allow for the adaptive manager to create innovative and adaptive programs. And a failure to manage in may result in a blockage of resources for the adaptive manager even in the exploitation phase of the adaptive cycle. This was probably what happened in the deer hunting case, where the DNR scientists, having developed and consolidated a good set of tools for management, were unwilling to respond to initiatives stemming from the interorganizational, or stakeholder, system.

At the interorganizational, or stakeholder, system level we can see the most direct effects of the individual manager such as Karel. Around a particular issue or problem domain, the release phase represents a state of disorganization, in which the different stakeholders may not even know that they have a problem. For example, in the snagging case described here, it was the dead and dying salmon that alerted stakeholders such as tourism and fishing groups to the issue and put the problem of snagging on the public agenda. At the renewal stage, groups with conflicting and perhaps illformulated ideas may come into conflict. The adaptive manager works hard to bring them into dialogue and to find common ground. At the exploitation phase, the groups have found common ground and appear to be prepared to exchange information and even resources to solve the problem. From that point on, the stakeholder system can either move forward to a more formal, consolidated organizational form (such as a Conservation Congress or other elected referent organization), or dissipate as it did in the Lake Algonquin case, with stakeholders losing interest and moving on to other things.

The enterprising adaptive manager, successfully juggling all four balls, can strategically use the dynamics of these three nested systems to find windows of opportunity (or corridors of indifference) through which he or she can drive scientific, adaptive initiatives. The best example of this in these cases is that of Lake Algonquin. Here Karel explicitly identified such a window of possibility. The political system was in a phase of releasing resources for ecosystem renewal. The organizational system, though not so receptive and clearly entering into a conservation phase, could be managed because it contained risk-taking individuals both on the conservation board and within the organization. The interorganizational/stakeholder system was in an exploitation phase (with a responsive network, open to and keen on citizen science). All three hierarchical systems, although not in the

13. ADAPTIVE MANAGEMENT ON THE FRONf LINES 357

identical phases, were aligned for action on the part of the manager (Figure 13-2).

Of course, such moments are rare and transitory. The kaleidoscope shifts, the window disappears. In the case of Lake Algonquin, while the political system remained relatively open, the organizational system, due to the departure of key personalities (including Karel because of his promotion) slammed the door on the project. And the stakeholder system, on an even shorter cycle (which Karel identified as five years), simply dissipated. New people had taken up positions in the involved organizations, and without ongoing networking the original interested citizens drifted off to more compelling pursuits (Figure 13-2).

Overall, however, these cases indicate that not one of these subsystems considered alone can help to reveal the interactive dynamics of social system and ecosystem that confront the adaptive manager. Rather, the entire network of interacting individuals and organizations at all three levels represents the social system. It is clear, therefore, that to manage adaptively is a question of creating the right links, at the right time, around the right issues to create a responsive system. As noted above, it is not a question of identifying best practices or institutional arrangements.

The Devil is in the Dynamics-The Problem of Contagion and Cascades

As if the dynamics described above were not complicated enough, these cases suggest two further levels of complexity. The first is that all three social systems are structured by meanings, not just by rules, roles, and resources.

The same system and therefore the same people not only respond differently to the same issue at different times, but also respond differently to different issues occurring at the same time. So, for example, the agency may be in the conservation phase around the deer hunting issue but in the renewal stage when it comes to lake management issues. A stakeholder group may be in the conservation phase when it comes to lake access, in the exploitation phase when it comes to stocking, and in the renewal stage when it comes to hatcheries. The adaptive manager not only must deal with three nested systems, cycling at different rates through adaptive cycles in his decisions and actions, but also must recognize that the same systems will be in different phases when it comes to different issues.

An interesting lateral or horizontal dynamic may then occur, as a result of the manager's need to handle multiple projects simultaneously. The three cases of failure or unanticipated surprise described above seemed to occur after Karel's promotion. At one point, describing the failure to close the loop with the stakeholders and educate them further as to the scientific experiment in the Algonquin project, Karel noted that he was distracted, as he had six or seven other issues to deal with. One of these was the spear-hunting problem; another was the ongoing issue of the deer hunt. Above we suggested that

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perhaps promotion in an organization makes it more difficult to manage out. Another more holistic explanation is that the dynamics of one problem domain cascaded or spread to affect the dynamics of adjacent problem domains coexisting in time, through the agency of the adaptive manager.

The fact that Karel was distracted by dealing with the spearing case meant that his attention was drawn into a problem domain in which unanticipated legislation had created a legal standoff in the political system (gridlock characteristic of a domain in the late exploitation or early conservation phase). Simultaneously, it created an organizational crisis (characteristic of the late conservation or early release phase) in the agency. These two nested dynamics made it impossible for Karel to use his normal managing out techniques and bring the parties together to begin a renewal process. More interesting in terms of the point we are making here, however, was that they simultaneously caused Karel to drop the managing out ball in the Lake Algonquin case, resulting in the dissipation of stakeholder support and understanding. The dynamic from one problem domain spread to infect another. In the reverse direction, Karel's success in the salmon case created a renewed and reorganized group of citizen scientists that helped move the Lake Algonquin experiment quickly into the exploitation stage (Figure 13-3).

We are now dealing with levels of complexity that would be difficult to handle strategically or deliberately. Indeed, the dynamic of contagion or horizontal cascade resembles what March (1994) identified as the garbage-can model of decision making: "In important ways, decision processes build on ... temporal categories, combining people problems and solutions in terms of their simultaneity. Problems and solutions are attached to choices,

Spearing Lake Algonquin

political

organizational

5.dissipation of support

interorganizational

Figure 13-3. Example of how the dynamics of one problem domain can spread to those of another. The success in the salmon case created a renewed and reorganized group of citizen scientists that helped move the Lake Algonquin experiment quickly into the exploitation stage.


and thus to each other, not because of any means-ends linkage but because of their temporal proximity." Under these circumstances, it is clear that Karel was not acting strategically, as he was when he searched for action synergies and corridors of indifference across the vertical systems. Rather, he was at the mercy of interactive dynamics that made deliberate action almost impossible. This perhaps is the chief source of surprise for the adaptive manager: the unanticipated consequences of contagion between problem domains that coexist temporally. If this is true, it may be that surprise, from the perspective of the manager, occurs more frequently at higher hierarchical positions, when the manager becomes responsible for more projects simultaneously. This is a paradox of resilience: more hierarchical control, less system control.

Questions for Future Research

We started this chapter with the statement that it described the case of a single manager. As such the questions and issues we have raised are purely exploratory. More studies of practitioners seeking to manage adaptively in complex situations are needed to put some flesh on these bones. The story of Evan Karel as manager raises some tantalizing questions, however, about decision making and its role in adaptive management:

• Are strong values, emotional control, and interpersonal skills critical to adaptive managers? If so, are such characteristics essential across time and social place?

• Is the juggling of four strategies (managing up, in, through, and out) as important in all cases as in those described here? Do successful cases of adaptive management combine all four strategies in single initiatives?

• Are the best adaptive managers those who are in the closest immediate contact with both the physical environment and the stakeholder environment? Is moving up the same as moving in, making it difficult for managers who climb too high to manage adaptively? Or does it mean they need to switch roles, to become supporters and anchors for frontline managers? Do we need to search for adaptive management teams as opposed to individuals?

• What is the critical structuring force behind adaptive cycles in social systems? If a social system can be in one phase on one issue and simultaneously in another around a second problem, what does it mean to talk about management regimes or institutions that are more responsive or adaptive to ecosystem dynamics and hence more resilient? Is enduring, self-referent identity, in the form of consistent value orientation, the critical factor? How is that ensured in organizations where the individual managers turn over rapidly?

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• If contagion or horizontal cascades are occurring across problem domains that the manager seeks to manage at the same time, is his or her agency the critical link? Are other factors at work to combine or recombine elements and induce phases across problem domains, which overlap in time (and social space)?

• Is the horizontal cascading/contagion dynamic described here similar to or different from the cross-scale interactions in panarchies?

Note

1. The names of people and places have been deliberately changed in the interests of protecting confidentiality.

CHAPTER 14

PLANNING FOR RESILIENCE:

SCENARIOS, SURPRISES, AND

BRANCH POINTS

Gilberta C. Gallopin

0 f all the environmental policy concepts to emerge in the last twenty years, none is more compelling than that of sustainability. The concept was put on the international policy agenda by the

Brundtland Commission by formulating the classic definition of sustainable development, namely, development that "seeks to meet the needs and aspirations of the present without compromising the ability to meet those of the future" (World Commission on Environment and Development [WCED] 1987). The same goal has guided other international policy endeavors, notably the Earth Summit in 1992 and the climate negotiations that began in Kyoto in 1997. The introduction of these concepts has raised the important question of whether humanity at the global scale is currently on a sustainable or an unsustainable path.

On the one hand, the world is now moving through a period of extraordinary turbulence, reflecting the genesis and intensification of deep economic, social, political, and cultural changes associated with the current technologic-economic revolution. In addition, the speed and magnitude of global change, the increasing connectedness of the social and natural systems at the planetary level, and the growing complexity of societies and their impacts upon the biosphere result in a high level of uncertainty and unpredictability. These changes pose new threats but also new opportunities for humankind.

On the other hand, the current trends are seen to be unsustainable for both ecological and social systems. The need to reverse these trends was officially recognized at the Earth Summit in June 1992. However, a new direction has not yet been clearly defined because discussions and recom-

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mendations that would help define those directions are still very compartmentalized. As mentioned elsewhere in this volume (Chapters 1 and 2), the very success of classical compartmentalized approaches tends to aggravate environmental and developmental problems.

Ultimately, the major obstacles to sustainable development can be reduced to three basic categories: willingness, understanding, and capacity. The first and major obstacle has been described as a lack of political will to implement those changes that are glaringly necessary. Asymmetric power structures, vested interests, and conceptions by humankind that emphasize antagonism, competition, and individualism over cooperation and solidarity lie at the heart of this obstacle. Even in cases where political will is present, another obstacle is the lack of understanding of the behavior of complex systems. This lack of understanding results often in a failure to address the relevant linkages within and between systems and across scales. Compartmentalized perceptions of reality and a scientific tradition and training that are still largely reductionist impair the development of understanding. Inadequate institutions, lack of financial resources, unskilled human resources, weak infrastructure, plain poverty, and other limitations contribute to the third obstacle: insufficient capacity to perform the actions and changes needed, affecting notably (but not exclusively) the developing world.

In seeking sustainable development, we must overcome the obstacles of lack of understanding, unwillingness to change, and lack of adaptive capacity. New ideas and approaches to overcome these obstacles will be required to produce appropriate actions and changes (Figure 14-1 ). The intersection of those domains with the domain of what is physically possible completes the characterization of the feasibility space (Figure 14-2).

Figure 14-1. The three pillars of decision making for sustainable development. Intersections of these characteristics determine types of actions taken.

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physically possible

Figure 14-2. Intersection between physical feasibility and decision processes. The capacity to do what is physically impossible cannot exist (by definition). Understanding generally allows for realization of what is and is not possible, although in some cases, people are willing to implement actions that are not possible (because they violate physical laws or constraints).

The quest for sustainable development poses new, deep challenges to the ways we define problems, identify solutions, and implement actions. The ideas and results presented in this book have a direct bearing on the issue of sustainability. They may have significance far beyond the scope of the management of natural resources; they may provide crucial insights for the survival and progress of humankind.

In light of that quest to seek understanding of sustainability, this chapter focuses on exploring the challenges of sustainable development at a very broad level and with a long time horizon. That exploration will be done through evaluating and analyzing a set of scenarios of plausible futures. The description of these scenarios draws upon previous work (Gallopin et al. 1997; Gallopin and Raskin 1998; Raskin et al. 1998). The exploration of long-term futures of the global socioecological system (Gallopin et al. 1989) is a highly subjective and tentative enterprise. Hence, prediction in the classical positivist sense is simply not applicable. The merits of such exercise, as discussed later, lie in potential insights that could be gained in terms of illuminating alternatives and decisions to be made, and perhaps providing new understanding by asking novel questions.

Exploring the Future

There is no question that the contradiction between the modern world's imperative toward growth and Earth's finite resources will ultimately be resolved in some way. The only question is how that will unfold. Will it be

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through enlightened management? Or will it involve economic and environmental catastrophe? Or will some other path be taken? No one can predict these outcomes with any certainty. Projections that might be valid over the short term may lose their validity as the time horizon increases from months or years to decades or even generations.

Fundamental uncertainty is introduced both by our limited understanding of human and ecological processes and by the intrinsic indeterminism of complex dynamic systems. Moreover, social futures will depend on human choices that are yet to be made (Gallopfn et al. 1997). The complexity of the interactions and problems is quickly increasing. This is due to a number of factors:

• Ontological changes. Human-induced changes in the nature of the real world are proceeding at unprecedented rates, resulting in growing connectedness and interdependence at many levels. The molecules of carbon dioxide emitted by fossil fuel burning (mostly in the north) join the molecules of carbon dioxide produced by deforestation (mostly in the south) to force global climate change. An economic crisis in Asia reverberates across the global economic system thereby affecting faraway countries.

• Epistemological changes. Changes in our understanding of the world include the modern scientific awareness of the behavior of complex systems. This new understanding emphasizes that unpredictability and surprise may be woven into the fabric of reality, not only at the microscopic level (shown by Heisenberg's uncertainty principle) but also at the macroscopic level, as abundantly illustrated in this book.

• Changes in the nature of decision making. In many parts of the world, a more participatory style of decision making is becoming widespread, superseding technocratic and authoritarian styles. Additional criteria, such as the environment, human rights, gender, and even animal rights, are being considered in decision making. The emergence of new social actors such as the nongovernmental organizations (NGOs) and transnational companies (TNCs) leads to an increase in the number of dimensions used to define issues, problems, and solutions and hence to higher complexity.

All of these changes indicate that the world at the beginning of the twenty-first century is a fundamentally different world. This has led to recognition of the need for a new "social contract for science" not from the fringes but rather from the mainstream scientific establishment (Lubchenco 1998). The imperative in this new contract is to focus on linkages among social, political, economic, physical, biological, chemical, and geological systems. Dynamic, cross-systemic explanations are replacing static, compartmentalized, and reductionist models and approaches Gasanoff et al. 1997).


The Scenario Approach

One way to gain insights into an uncertain future is to construct scenarios. This technique has been used since the 1970s to bring issues of environment and development to the attention of both scientists and policy makers. This chapter explores a range of long-term scenarios that could unfold from the forces that will drive the world system in the twenty-first century. The scenarios are based on those developed by an international and interdisciplinary group of fifteen development professionals called the Global Scenario Group (Gallopin et al. 1997; Raskin et al. 1998). Members of this group all have long experience in scenario and policy analysis at global and regional levels. This scan of the future illuminates the perils and possibilities before us and, more important, helps to clarify the changes in policies and values that may be required for a transition to sustainability during coming decades.

A scenario is essentially a story about the future. It indicates what the future may be like, as well as how events might unfold. Unlike projections and forecasts, which tend to be more quantitative and more limited in their assumptions, scenarios are logical narratives dealing with possibly farreaching changes (Kahn and Wiener 1967; Schwartz 1991; Cole 1981; Miles 1981; Godet 1987). A scenario includes a possible course of events leading to a resulting state or image of the future world. "While an image is like a picture or a snapshot of the future situation, the scenario includes the image plus a history of developments that would lead to the image. Originally (Kahn and Wiener 1967), a scenario was defined as a hypothetical sequence of events constructed for the purpose of focusing attention on causal processes and decision points. The importance of considering scenarios as courses of events is twofold: (1) they can direct attention to the unfolding of alternatives, and (2) they can identify branching points at which human actions can significantly affect the future.

Scenario analysis offers us a uniquely valuable way to ponder critical issues. Scenarios also clarify alternative worldviews and values, challenge conventional thinking, and encourage debate. The scenario approach can provide a common framework for diverse stakeholders to map and address critical concerns and identify alternatives, as well as a forum for discussion and debate.

Methodological Elements

Anatomy of Scenarios

Scenario building goes through several steps. These begin with characterizing the current situation to define an issue or problem. Next, critical dimensions, driving forces, strategic invariant elements, and critical uncertainties are defined. These steps allow for assessing logics of the system and, finally, developing images of the future. These elements are described in the following paragraphs.

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The development of scenarios generally begins with the characterization of the current situation. This includes the identification of a focal issue to be analyzed, or a critical decision that must be made.

An important step is represented by the definition of the critical dimensions describing the scenario. Together, they define the multidimensional space within which scenarios can be mapped or constructed. Dimensions do not necessarily imply definite causal assumptions. Rather, they are defined in terms of their relevance. Examples of possible dimensions are economic growth, social progress, environmental quality, and level of conflict. These dimensions underpin descriptors and attributes of the images of the future.

A critical step is the identification of the major driving forces. These forces represent key factors, trends, or processes that influence the situation, focal issue, or decisions. They propel the system forward and determine outcomes. Some of these forces are invariant over multiple scenarios. Examples of invariant forces include slowly changing phenomena such as human population growth or building of physical infrastructure. Other forces include processes already in the pipeline (e.g., cohorts who are recently born, most of whom are destined to become members of the teenage population of the next fifteen years), inevitable collisions, and constrained situations (Schwartz 1991).

Some of these driving forces are a source of critical uncertainties. Unknown starting points, unknown effects, and unknown interactions among forces may fundamentally alter the course of events.

The different building blocks are then put together in the form of a narrative, showing how the world would change from one time to another. The current state, driving forces, strategic invariants, and critical uncertainties form the backbone of these scenarios. In addition, all scenarios unfold according to an internal logic that links the elements into a coherent plot. Challenges of developing scenarios are to identify a plot that best captures the dynamics of the situation and to communicate the point effectively. The same set of driving forces might, of course, behave in a variety of different ways, according to different possible plots. Scenarios explore a short number of those alternatives, based on the plots (or combinations of plots) that are most worth considering. The end point of a scenario is an image of the future.

The construction and interpretation of a scenario will be influenced by the beliefs and theoretical assumptions of the analyst. The account of the mechanisms leading to alternative scenarios and judgment of the efficacy of alternative actions is guided by one's worldview, although this is rarely made explicit. However, the writings of Herrera et al. (1976), Miles (1981), and Rotmans and De Vries (1997) are exceptions in that they make the worldview explicit. Though always difficult, critical reflection and explication of the philosophical predisposition informing a scenario are essential aspects of scenario description and documentation.

The critical ingredients to scenario analysis were generically developed in this section on methodology. The next section describes a number of key factors or trends that are expected to critically influence the global future.


Critical Trends

A number of conditions and processes now under way act as basic drivers of change. The momentum built into these drivers strongly influences the near-term evolution of the global system and reduces the likelihood, and even the possibility, of many long-range scenarios. While current trends are not inevitably persistent, they certainly condition the initial direction of economic, social, and environmental change and may strongly influence even the long-term future. It is important to recognize, however, that ultimately these processes are themselves influenced by social, economic, and environmental conditions; they are human processes that can and do change as social attitudes and expectations adjust to altered circumstances. Population growth, economic growth, technological change, changes in governance, equity trends, resource depletion, and environmental change are among these important drivers, and each is discussed below.

Population Growth

The human population is growing by about 900 million per decade-the largest absolute increase in human history. Nearly all of this growth is occurring in developing countries. Developing countries are also experiencing a surge of urbanization; 90 percent of their population growth is in urban areas. Among nations with industrial and transitional economies, only the United States is experiencing any significant population growth. This is due in part to continued in-migration. Most other industrial and transitional nations have stable or declining populations.

Rapid population growth appears unavoidable for at least the next two or three decades. This growth is a consequence of: (1) increased life expectancies in most countries, and (2) large proportions of the population in developing regions still reaching childbearing age. Population patterns after about 2025 will depend on a number of factors; the most important is fertility, which shows very large regional variations.

Most population projections assume a gradual decline in fertility rates. The UN medium projection, for example, assumes that all countries reach replacement rate fertility by the year 2050 (United Nations 2000). If no decline occurs, then population growth could be significantly higher than projected. At the same time, if childbearing attitudes and behavior were to change rapidly, or if mortality rates were to rise precipitously, global population growth could be less rapid than standard projections.

Economic Growth

The global pattern of economic activity, now concentrated in industrial countries, is likely to change. Economic growth rates in many developing regions are higher than those in the present industrial countries, presaging their growing role in the world's economy. Average per capita incomes in de-

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vel oping regions, however, are likely to remain far below those in the present industrial countries.

Global consumption of energy and raw materials is also rising. Indeed, although per capita consumption of raw materials and energy has generally reached a plateau in the industrial countries in recent decades, the developing countries' needs for basic infrastructure-and their still growing populations-mean that energy and materials consumption is likely to continue to rise rapidly for some time. Per capita levels of energy and materials consumption in these countries remain much lower than in developed countries. These historic gaps will persist over the next decades, closing only gradually with economic growth in the nonindustrialized countries. Overall, rising consumption of natural resources is likely to result in more environmental degradation, especially in developing countries, although increasing efficiency in the use of energy and materials could partially offset that trend.

Technological Change

We live in a period of unprecedented technological innovation, led by information technology. This revolution in information technology is far from complete. The social effects of this technology, which may include a significant global impact on industrial organization and the structure of economic activity, employment patterns, and lifestyles, are only beginning to be widely experienced. But it already is clear that they may be profound, displacing some forms of human intellectual activity (in much the same way that the industrial revolution displaced some forms of human physical activity) while enabling others, possibly including far more complex forms of social organization.

In addition, the biotechnology revolution (the ability to manipulate genetic information and the biochemical mechanisms of living organisms) and the materials revolution (the ability to craft new materials at the molecular level) are both gathering momentum. Their full technological impact is uncertain and lies some decades in the future. Intense competition in the global marketplace provides incentive for rapid introduction and worldwide diffusion of new technologies. The technological ground rules are likely to change significantly over the next half century with the potential for immense impacts through employment displacement, lifestyle change, and the globalization of culture.

Decentralization of Authority

Currently, there is a strong trend toward decentralization of authority and greater individual autonomy. On an individual level, this trend is noticeable in increased emphasis on "rights" -human rights, women's rights, and so on. In the private sector, this trend is reflected in the form of flat corporate structures and decentralized decision making. The rise of entities that have no formal authority structure, such as the Internet, follows this trend. In the public sector, the trend is noticeable in the spread of democratic govern-


ments, in the devolution of governmental authority to smaller and more local units, and in the rise of separatist movements.

These trends and the rise of many new actors-from citizen groups to global corporations-make governance an increasingly complex process. Global capital markets, for example, are not under the control of any government and can destabilize even strong currencies. Global communications networks from CNN to cellular satellite phones to the Internet convey information that is increasingly difficult for even determined governments to control. The growing strength of these global private-sector entities is in marked contrast to the continuing weakness of global governance institutions. On local and national levels, the growing number and influence of nongovernmental organizations and citizen groups is in part due to their ability to provide information and services that governments do not or cannot provide.

Equity Trends

A worrisome trend is the growing economic polarization between rich and poor both within and among countries. In the United States, for example, the distribution of family incomes has widened over the past twenty years. Families in the lower half of the income distribution have actually lost ground in constant dollars, while those in the upper 20 percent have done very well, many becoming extravagantly wealthy. The income gap between developing and developed countries has increased over the same period. For example, the gap between average per capita income in Japan and in China has doubled, while that between the United States and China has increased by 30 percent, despite rapid economic growth in China. An increasing percentage of global income and wealth accrues to the richest 20 percent of the world's population. In the last three decades, the share of global income by the poorest 20 percent of the population of the Earth decreased from 2.3 percent to 1.4 percent, while that of richest 20 percent increased from 70 percent to 85 percent. That doubled the ratio of the shares of richest to poorest-from 30:1 to 61:1 (UNDP 1996). Standard economic projections suggest that inequity is likely to become more extreme in coming decades within countries, as welfare policies are weakened, and among countries, where only very feeble international mechanisms exist for wealth transfer. Widening equity gaps within a society may threaten social stability; widening gaps among nations motivate illegal immigration and social tension, complicating attempts to forge joint solutions to global problems.

Resource Depletion

The most accessible and high-grade nonrenewable resources, such as minerals and energy sources, are being depleted. Growing global demands eventually will require more efficient use of these resources and the development of substitutes, a continuing challenge to technology. The challenge of

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needs (concerns with limits to growth notwithstanding) was met over the past twenty years because known reserves (economically exploitable resources) were higher than expected and real commodity prices were lower. Nevertheless, current trends could lead to shortages and dislocations in strategic materials over the next decades-for example, a reemergence of oil shortages and, with it, the potentially explosive geopolitics of oil (Raskin and Margolis 1995).

Of even greater concern is the depletion and degradation of renewable resources such as fresh water, arable land, fisheries, and forests in many parts of the world. One reason is that resources are harvested at rates greater than they can be replenished. Also, damage to the natural systems that sustain renewable resources is increasing through the clearing of forests, degradation of fertile soils, exhaustion of fisheries, and damage to watersheds and estuaries. This trend has been dominated historically by the overexploitation of resources to service industrialization, but it has been linked also to the growing populations of impoverished people who depend on and are forced to overuse these resources for lack of other options. Depleted resources, in turn, undermine economies. Thus, this environmental trend is closely linked to the problem of poverty.

Pollution and Global Environmental Change

Rapidly growing urban areas in developing countries are subjecting larger and larger populations to urban pollution hazards, from shortages of both clean drinking water and sanitation to increased exposure to air pollution and toxic materials. Despite significant progress, the number of people who lack access to clean drinking water is still growing (\Vorld Resource Institute 1998). Most mega-cities in developing countries fail to meet World Health Organization standards for air quality. Both trends are significant threats to human health.

On a global scale, worldwide emissions of carbon dioxide-a major greenhouse gas-are rising rapidly, a consequence of growing use of coal, oil, and natural gas. Projections of future energy demand suggest that energy use will double by the year 2020, for example, and that carbon dioxide emissions will rise by nearly the same amount, reflecting continuing high levels of energy use in developed countries and growing industrialization in many developing countries. The implication of this trend is that, far from stabilizing atmospheric levels of carbon dioxide, the world is accelerating the threat of global climate change.

Other Trends

Other social and environmental trends may also prove significant over the long term. Rapid shifts are occurring in labor markets. In developed countries a transition is occuring from manual to knowledge-based jobs, which generates considerable labor displacement and structural unemployment. In


developing countries the transition is from agricultural labor to urban manufacturing and service jobs. Other trends include the increase in violence in all societies, and the potential threat to human fertility and cognitive abilities from chronic exposures to toxic materials. At the same time, literacy is improving, incomes and health are generally improving (although not for everyone), and the risk of global war is apparently declining, although numerous regional-scale conflicts persist.

Developing Alternative Futures

These trends generate a set of outlooks on the future that can be described as surrealistic if not schizophrenic. For example, in 1992 some one hundred heads of state at the Earth Summit at Rio de Janeiro declared the current trends as unsustainable. These same trends were heralded as a triumphal ascent by the World Bank and other international financial organizations. On one hand, there seems to be a general "official" belief in a single global future, involving only marginal variations around a central theme of economics. Most of these discussions about the long-term future focus on the issues of economic competitiveness and financial gains. Both human needs and human development appear to have become nonissues. On the other hand, a number of indicators suggest the possibility of ruptures in the historical trajectory. These indicators suggest global, negative environmental trends (UNEP 1999) and an increasing inequality between and within nations (UNDP 1999).

Considering the speed, magnitude, and pervasiveness of the changes in our times and the fast increase in connectedness and complexity, as discussed before, the only certainty may be that things will indeed change very much and that the future world trajectory will not be a projection from past trends. The increase in complexity and connectedness (especially nonevolved and nonplanned connectedness) might lead, as a number of results from different areas suggest, to decreased stability, increased vulnerability, and a sharp increase in the costs of errors.

In this context, the scenario group focused on identification of possible breaking points and on qualitatively different future trajectories rather than attempting to refine a description of a single trajectory. For detailed information regarding the causal sequence of the interacting factors that generated each of these scenarios, the reader is directed to the reports of the Global Scenario Group (Gallopin et al. 1997; Gallopin and Raskin 1998; Raskin et al. 1998). Their analyses led to the following conclusions:

•

•

Alternative, qualitatively different scenarios are possible for the global system in the next thirty to fifty years.

Some scenarios imply a gradual unfolding from the present situation, without ruptures or discontinuities. These include continuation of practices as before (business as usual) or a scenario of policy reform, in which strong polices for sustainability are im-

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•

plemented, but continuity in institutions and values is assumed, as well as rapid economic growth and global convergence to the northern standards.

However, other scenarios that imply a rupture in the historical trends, or a breakdown of institutions or economy, are equally or more likely than the gradual transition scenarios. These include negative scenarios that foresee a generalized breakdown of civilization or a polarized world composed of elite countries and groups and an excluded, impoverished majority. Other, more positive scenarios include a sustainable, humane, and equitable global civilization or a small-is-beautiful scenario, essentially localist. Both require fundamental changes in societal values and new socioeconomic arrangements.

These findings are embellished in the following section, where the scenarios are described.

Unfolding of the Scenarios

With the backdrop provided by the analysis of current trends, alternative scenarios have been derived, all unfolding from the same set of driving forces. These driving forces cluster into six sets, as described below.

• The economic and geopolitical forces include the end of the Cold War, a universal expansion of the capitalistic system, and an acceleration of globalization.

• A second cluster is associated with social issues, particularly poverty and national and international inequity.

• The demographic forces include population growth that is concentrated in poor countries and regions, and changing age structures related to youth-dominated populations in poor areas and an aging structure in the rich areas.

• The environmental set of driving forces refers to increasing environmental stress, widespread ecosystem disturbance, and an increase in global ecological interdependence.

• A technological cluster assumes the continuation of the technological revolution, the expansion of global information and communications, and the private control of technological innovation and diffusion and of its benefits.

• Changes in global governance are only significantly operational in the Great Transition scenarios. These include the continued proliferation of nongovernmental organizations, the strengthening of a civil society, and reinvigoration of the United Nations system.


Based on these driving forces, a number of scenarios were developed that group into three categories (Figure 14-3). The first group is called Conventional Worlds, and its scenarios do not deviate sharply from the present. The second set of scenarios is dubbed Barbarization Worlds and contemplates the possibility of deterioration in civilization, as problems overwhelm the coping capacity of both markets and policies. The final group includes scenarios that are identified as Great Transition worlds. The transition worlds incorporate visionary solutions to the sustainability challenge, including fundamental changes in the prevailing values as well as novel socioeconomic arrangements. Descriptions of each of these groups are developed in the following sections.

Conventional Worlds

The Conventional Worlds scenarios include a continuation of generally extant processes and forces. The values and socioeconomic arrangements of the industrial era continue to evolve without major discontinuities. Competitive markets and private investment remain the engines of economic growth and wealth allocation. The globalization of product and labor markets continues apace, catalyzed by free trade agreements, unregulated flows of capital, and advances in information technology. The nation-state remains the dominant unit of governance, while transnational corporations dominate an increasingly borderless economy. The consumption patterns and production practices of the developing regions converge toward those of

Figure 14-3. Transitions among global futures. Arrows indicate transitions. A particular world may continue into the future indefinitely (self loop), or it may change into another kind of world (straight arrows). Dotted arrows indicate possible but unlikely transitions.

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the highly industrialized countries. Consumerism and possessive individualism prevail. Primary motives that underlie human behavior develop a consumer culture that permeates all societies via electronic media. This leads to a reduction in cultural diversity, despite fundamentalist, ethnic, and nationalist backlashes.

Two variants of Conventional Worlds have been identified: the Reference Scenario and the Policy Reform Scenario. The Reference Scenario can also be described as business as usual because it assumes that current trends and policies (or lack thereof) are maintained and that development follows a midrange course (as assumed in many analyses). The Policy Reform Scenario maintains the essential assumptions of the Conventional World paradigm. But in contrast to the Reference Scenario, Policy Reform assumes the emergence of a public consensus and strong political will for taking action to ensure a successful transition to a sustainable future. In this context, an integrated set of initiatives is crafted and implemented, including economic reform, regulatory instruments, social programs, and technology development. This scenario is based on recommendations of the Brundtland Commission (WCED 1987).

Reference Scenario

In the Reference Scenario (Figure 14-4), a number of changes occur. Population increases from about 6 billion today to about 10 billion by the year 2050, with the growth occurring primarily in developing regions. The world economy grows from about $33 trillion in 1995 (in PPP: purchase power parity units) to about $145 trillion in 2050 and continues growing thereafter. The economies of developing countries grow more rapidly than

Figure 14-4. Stylized depiction of the Reference Scenario.


those of the wealthy Organization for Economic Cooperation and Development (OECD) countries. Consequently, the OECD countries' share of world output decreases. Incomes in the two groups of countries gradually converge; the ratio between the average gross domestic product (GDP) per capita in the OECD countries decreases with respect to that in other regions. However, the absolute difference increases from an average of $17,000 per capita in 1995 to $47,000 per capita by 2050 as incomes soar in rich countries. The structural shift in economic activity from industry to services continues. In particular, the share of materials-intensive industries eventually decreases everywhere, consistent with recent trends in the industrialized countries. The spread of new technology leads to more efficient use of energy and water, growing utilization of renewable energy resources, and cleaner industrial processes. Although energy and water use grow far less rapidly than GDP due to the structural and technological changes described above, pressure on resources and the environment increases as the greater scale of human activity overwhelms these resource efficiency improvements.

Several types of destabilizing risks can be identified in this scenario. First, the cumulative loads on Earth's biogeochemical cycles and ecosystems could well exceed natural assimilative capacities. One of the reasons is the sharp increase in emissions of carbon dioxide, feeding global climate change. Habitat destruction, biodiversity loss, and the accumulation of toxic chemicals in the environment continue.

Second, heightened pressure on natural resources could lead to economic and social disruptions or even conflicts. Unless major reserves of oil are discovered, oil would become scarce over the next several decades, so that prices would rise and oil would again become a major theme in international affairs. Water pollution and the growing demand for fresh water would increasingly stress renewable water resources, threaten aquatic ecosystems, and generate discord over the allocation of fresh water within and between countries. Agricultural output would need to increase to more than double current yields by 2050 in order to feed a richer and larger population. This agricultural conversion would likely involve further conversion of forests and wetlands, create more pollution of soils and water systems, and continue the degradation and loss of arable land due to unsustainable farming practices. Unfavorable climate alterations would further complicate many of these issues.

Third, social and geopolitical stresses would threaten socioeconomic sustainability. The persistence of poverty on a large scale and the continued inequality between and within nations (exacerbated by environmental degradation and resource constraints) would undermine social cohesion, stimulate migration, and put stress on international security systems. Breakdowns in sociopolitical stability could, in tum, provide the breeding conditions for authoritarianism; the flaring of regional, ethnic, and religious conflicts; and the suppression of democratic institutions. These changes set the stage for the types of scenarios described later under the cataclysmic set.

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Depending on one's philosophical predisposition, the risks inherent in this variant of the Conventional Worlds scenario will be weighed very differently. Free-market optimists will tend to downgrade the environmental and social concerns, trusting in market adaptations and human ingenuity to provide timely solutions. Less ideological observers might simply believe that muddling through is less dangerous than well-intentioned but wrong-headed. policy activism. Pessimists, distrusting the adequacy of automatic market mechanisms, would fear that business as usual would endanger, perhaps catastrophically, the long-range health of social and ecological systems.

The Reference Scenario results in increased environmental and socioeconomic stress and a large loss of resilience. Events that were absorbed in the past would be capable of overwhelming the system, resulting in a global crisis interlacing environmental, social, and economic factors. A vicious, selfreinforcing circle involving economic instability, inefficiency, social and military conflict, and ecological stress could become established. A number of disturbances can be envisioned that would exceed a diminished resilience of the global system and lead to a global crisis. These triggers include: a major drought (either natural or because of climate change), a new disease epidemic, an extended famine, and perhaps a regional economic crisis. The impact of these events might initially occur at the regional level, but triggers in other regions could aggregate and initiate a global crisis.

Policy Reform Scenario

Population is assumed to grow slower in the Policy Reform Scenario (Figure 14-5) than the Reference Scenario for two reasons. First, the improved con-

Figure 14-5. Stylized depiction of the Policy Reform Scenario.

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clition of the lives of the very poor are assumed to be associated with declines in fertility rates as living standards improve, access to education increases, and women become more empowered. Second, active family planning is assumed to reduce the number of unwanted pregnancies.

Two critical elements in achieving the poverty reduction targets in the Policy Reform Scenario increase equity. First, greater equity is sought between regions, aided by strategies that lead to more rapid economic growth in developing and transitional regions and faster convergence toward OECD levels of development. Second, greater equity within regions and countries is incorporated as "growth with equity" becomes the prevailing philosophy in national development strategies.

Another specific social sustainability goal is to reduce the number of people who remain hungry in 2050 to one-fourth of current levels. To meet these hunger targets, national equity must improve. This would be the case even if one were to posit a very high average economic growth to 2050. With the levels of inequality assumed in the Reference Scenario, GDP in developing regions would have to increase more than 6 percent per year to meet the sustainability target. Such high growth is implausible and implies an immense expansion in the scale of the world economy (by a factor of 15) with correspondingly greater strains on environmental systems.

In the Policy Reform Scenario, social targets are met through a combination of effects. These include a relatively strong regional economic growth, significant but gradual motion toward international equity, and maintaining national equity at close to current levels. The scenario satisfies the hunger reduction target. As a result of differential income growth, international equity (the ratio of non-OECD to OECD incomes) increases from 0.15 in 1995 to 0.36 in 2050. Though the gap between rich and poor nations is far from closed, the level of international equity is twice that of the Reference Scenario.

The challenges are thus to simultaneously foster world economic growth, development convergence, and greater national equity, while remaining within environmental sustainability goals.

The sustainability target for climate change is set as an upper limit on global temperature change of 0.1 oc per decade. This implies a cumulative carbon dioxide emission allowance of the order of 700 GtC (billion tons of carbon) between 1990 and 2100. In order to meet that target, an acceptable global agreement must include realistic reduction goals for industrialized countries while allowing some emission increases from developing countries as economies converge in the course of the twenty-first century. Achieving these carbon emission constraints requires a significant improvement in the way energy is used and produced. Consequently, the Policy Reform Scenario incorporates a variety of measures for achieving energy intensity improvements. These improvements are well within the bounds of what is considered to be technically and economically feasible during the period from now until 2025. The second prong of a sustainable energy strategy is

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switching fuels, with a greater use of renewable energy sources and use of natural gas as a near-term bridge fuel.

The challenge of simultaneously meeting all sustainability criteria is formidable. Increased agricultural requirements must be met under multiple constraints. Forest and habitat protection reduces the scope of expansion for agricultural land. Decreasing the level of stress on water resources in waterscarce areas will limit the use of irrigated water. Reducing land degradation and chemical inputs to agriculture will require the adoption of more sustainable farming practices, while maintaining yield increases.

Abating freshwater degradation and scarcity is exceedingly difficult. In the Policy Reform Scenario, the transition to the environmentally sustainable use of water resources is in some ways at cross purposes with efforts to meet the social goals. The requirements for water services increase as a result of efforts to deliver household water services to the poor and the rapid expansion of developing country economies. The major features underlying these results include efficiency improvement and water resource expansion to partly mitigate the increasing demand for water. These factors combine to keep national freshwater withdrawal requirements within the sustainability targets.

Targets for sustainable resource use are partially addressed through strategies that reduce the intensity of fossil fuel extraction, that control soil erosion, and others. But much more would be required to manage the level of material inputs to industrial economies and the environmental impacts of waste loads. Dematerialization at the required level requires efforts across several dimensions Q"ackson 1993). At the facility level, processes can become cleaner and far more efficient in their use of natural resources. At the commodity level, product lifetimes can be lengthened, thereby reducing the discard rate and the need for virgin materials. At the system level, streams of materials once thought of as waste can become resources through the reuse of products, remanufacturing of components, and recycling of materials. Ultimately, detailed consideration must be given to the system impacts of alternative processes and materials through life-cycle analysis that considers both the direct and indirect impacts of alternatives. In addition, aggregate reduction in the intensity of resource use will be aided by shifts in the composition of the economy from resource-intensive to knowledge-intensive sectors. In the end, reducing the throughput of materials to lie within tolerable sustainability boundaries may require a moderation in consumerist lifestyles. However, this would carry us beyond the premises of the Policy Reform Scenario.

Scanning across the various elements that constitute the Policy Reform Scenario, the complex and multidimensional character of a policy-driven sustainability transition becomes apparent. Moreover, the tension between two assumptions in the scenario will not be easily reconciled. The continuity of dominant values and institutions appears at odds with the goals of greater equity, a reduction in poverty, and protection of the climate and environment. At the very least, a comprehensive action agenda will be needed to begin to bend the curve of development toward sustainability.

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\Vhat will happen if the global and national systems prove unable, or unwilling, to confront the stresses and challenges associated with both Conventional Worlds scenarios? Will too little be done, or will it be too late? Most people (including many policy makers) would be tempted to assume that policy can always catch up with history. If too little is done now, there is always the possibility to take stronger actions in the future, and thus the problem will be solved. The trouble with this notion is that it assumes an infinitely forgiving world, ignoring irreversible processes and the possibility of structural reorganizations in the global system that lead to situations that are, in practical terms, irrevocable.

Failure to address adequately the challenges posed by these scenarios could result in incremental worsening of the global situation, but also in more dramatic transformations. Even if some countries or groups do better than others, the resulting asymmetry could lead to vicious circles in which even winners are losers. These possibilities are explored in the Barbarization scenarios.

Barbarization Scenarios

As in the Conventional Worlds scenarios, the Barbarization scenarios are driven by an ascendancy of global economic forces; however, humanity is unable to manage the resulting change. Conventional institutions ultimately unravel. The number of people living in poverty increases, while the gap between rich and poor grows (both within and among countries). To make matters worse, social concern is radically downgraded as governments gradually lose relevance and power relative to large multinational corporations and global market forces. At the same time, development aid diminishes, being limited primarily to disaster relief.

A number of other consequences follow from a growing disparity in income. Inundated by global media and tourism, millions of people in underdeveloped regions become resentful of the immense differences in lifestyles between rich and poor. The poor become convinced that they have been cheated out of development and that their options have been preempted by the wealthy.

With rapid population growth in the poorer regions, a large international youth culture emerges. Numbering in the billions, teenagers around the world share remarkably similar expectations and attitudes (Schwartz 1991). Consumerist and nihilist tendencies are reinforced by entertainment programs and advertising that reach every comer of the earth. But these young people ultimately discover that the tantalizing visions of "McWorld" are largely unattainable (Barber 1995). This leads to mass migrations into areas of prosperity within poor countries and as waves of legal and illegal immigration to rich countries.

Despite improvements in the richest countries, global environmental conditions continue to worsen. The unfettered expansion of market-based economies leads to an increase in industrial activity and subsequent pollution. Rapid urbanization displaces natural ecosystems and increases stress on

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local environments. Deepening rural poverty accelerates soil degradation and deforestation. As fresh water becomes increasingly scarce, conflicts over water emerge among countries that share rivers. Already brittle marine fisheries collapse under the additional pressure, depriving a billion people of their primary source of protein. Climate change causes hardship for subsistence farmers in many regions. Famine becomes more frequent and more severe in Africa and elsewhere, while the response capacity of relief agencies declines. Mortality rates increase as a result of the growing environmental degradation, which aids the emergence of new diseases and the resurgence of old ones (Miller 1989).

Social tensions become more widespread and intense because of growing socioeconomic inequalities; increased morbidity; and reduced access to water, grazing land, and other natural resources. International discord mounts due to widening disparities between regions, growing economic competition, and a progressive decline in development assistance. People in rich countries increasingly fear that their well-being is being threatened by factors they associate with poor countries, including migration, terrorism, disease, and global environmental degradation. At the same time, a new type of have-not emerges as a significant factor in rich countries, namely, the educated but long-term unemployed.

Long-standing ethnic and religious differences, politically motivated terrorism, struggles over scarce natural resources, competing nationalisms, and commercial conflicts all increase the incidence of violent confrontations. By and large, military actions take the form of multiple small-scale engagements rather than major wars. At the same time, civil order progressively breaks down, as a kind of criminal anarchy prevails in many areas (Kaplan 1994). These developments take an increasing toll on economic growth as more resources are diverted to security and intemational investment in troubled regions plummets. In areas of prolonged conflict, both environmental protection and the maintenance of infrastructure are neglected, reversing decades of progress.

Politically, a jagged pattem of city-states and nebulous regional formations emerges. Some formerly prosperous industrial countries join the ranks of the impoverished. Economic development ceases, technological progress stagnates except for efforts to provide better security for the privileged, and no individual country is able to assume a leadership role.

Barbarization can lead to two basic outcomes. These outcomes depend on the degree to which the prevailing power structures (govemments, transnational corporations, international organizations, and armed forces) manage to maintain some sense of order. These two outcomes are designated as a Breakdown Scenario and a Fortress World Scenario (see Figure 14-3).

Breakdown Scenario

The Breakdown Scenario (Figure 14-6) results from an inability to stem the tide of violence flowing from disaffected individuals, terrorist organ-

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Figure 14-6. Stylized depiction of the Breakdown Scenario.

izations, ethnic and religious groups, economic factions, and organized criminals. Civil order largely breaks down, leading to a general collapse of social, cultural, and political institutions along with the market economy. Many regions experience a return to tribal or feudal social structures. Although population continues to grow for some time in the poorer regions (in a vicious cycle of poverty and high birth rates), it eventually decreases as mortality rates surge in response to the economic decline, infrastructural collapse, and degradation of the resource base. In a bitter irony, equity increases because everyone is poorer. If such a breakdown were to occur, it could persist for many decades before evolution to a higher level was again possible.

Fortress World Scenario

In the Fortress World variant of the Barbarization scenarios (Figure 14-7), powerful regional and international entities manage to impose some form of authoritarian order on the populace at large. In this variant, the well-off elite flourish in protected enclaves (mostly in the historically rich countries), while the majority remains mired in poverty and denied basic human rights. To preserve their access to the goods and services provided by the environment, the elites place large areas under protected status and exclude the poor from them. The elite place strategic reserves of fossil fuels, minerals, fresh water, and germ plasm diversity under military control. Pollution is kept low within the fortress by means of increased efficiency, recycling, and external dumping. Outside the fortress, environmental conditions deteriorate dramatically. Although the system embodied in the Fortress World variant would probably contain the seeds of its own destruction, it could last for

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Figure 14-7. Stylized depiction of the Fortress Scenario.

decades if it were able to control popular unrest. Only an uprising by the outside majority could threaten it, and even then their success would probably hinge on fissures in the alliance of dominant groups.

Not all alternatives to the Conventional Worlds scenarios are gloomy. Indeed, it is possible to conceive of a scenario in which we would transcend the industrial culture of the present without descending into chaos as described in the Barbarization group. These alternative, positive views are described in the next section as Great Transitions scenarios.

Great Transitions

In contrast to the dismal, pessimistic worlds presented in the previous section, two positive worlds were developed as variants of the Great Transitions scenarios. These are described as an ecocommunalism world and a new sustainability paradigm (see Figure 14-3).

Ecocommunalism

In the Ecocommunalism variant (Figure 14-8), a network of largely selfsufficient communities replaces the huge, highly interdependent institutions of the modern world. In this "small is beautiful" and biocentric vision, an ethic of voluntary simplicity and local autonomy dominates. Material consumption levels fall in wealthy areas as a craft economy rises to complement production from small-scale and locally owned facilities and farms while outside economic links are minimal. Population contracts and urban centers gradually give way to town- and village-scale settlements. Proximity to nature becomes highly valued as a spiritual bond that unifies each commu-


Figure 14-8. Stylized depiction of the Ecocommunalism Scenario.

nity. However, it is very difficult to imagine a viable trajectory leading from the current situation to Ecocommunalism. Much more plausibly, Ecocommunalism could arise as a recovery path from the Breakdown Scenario or, possibly but less likely, as a societal decision to move from the New Sustainability Paradigm Scenario to Ecocommunalism.

New Sustainability Paradigm

The New Sustainability Paradigm (Figure 14-9) balances the cosmopolitanism of a global outlook with a strong sense of community, egalitarianism, and environmentalism. Most people feel a strong affiliation with a global family as well as with their own regional and local communities. Governance systems, economic relations, and culture reflect this new multilevel perspective. The consumerism of the Conventional Worlds scenarios gives way to an emphasis on qualitative goals such as education, leisure, arts, nature experience, service, and spiritual pursuits. The· flow of energy and materials through the economy is radically reduced in wealthier areas through efficient technologies, lower material-consumption lifestyles, and the widespread use of renewable resources. Poorer regions rapidly converge toward this revised concept of development. Values, institutions, and the very notion of the good life would indeed undergo a great transition.

How might the New Sustainability Paradigm emerge? A possible unfolding is that during the next few decades, the biosphere is widely perceived to be threatened by cumulative environmental stresses. There is growing evidence that both ecosystems and human health will suffer serious harm as certain related problems reach critical levels (examples include global warming, acidification, disease, and toxification).

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Figure 14-9. Stylized depiction of the New Sustainability Paradigm Scenario.

New insights from the science of complexity lead to greater awareness of the risks of massive, irreversible changes in the climate and life-support systems. An example of this type of possible change or flip is the disruption of major ocean currents due to global warming. Warmer sea surface temperatures lead to more evaporation and increased salinity, thus hampering the downwelling necessary for currents to flow. Scientists already have evidence of frequent, large, abrupt (on the order of a few decades), and global cooling episodes during the last glacial period owing to sudden shifts in the operation of ocean currents (see Broecker 1997). Other insights from the science of complexity include the discovery of chaotic behavior in deterministic nonlinear systems, the possibility of self-organization in complex systems, and the existence of irreducible unpredictability in the evolution of complex systems (Nicolis and Prigogine 1989; Waldrop 1992). And of course, the present book focuses on many of these same issues.

A new intemational polity emerges to solve a worsening social polarization and conflict and a widespread feeling that life has lost much of its meaning. A conviction grows that reliance on the profit motive to guide the economy has been environmentally and socially cosdy and that govemment has become too weak. Disenchantment with the consumerist lifestyle mushrooms, gradually affecting all groups but particularly the young. The values of simplicity, tranquility, and community begin to displace those of consumerism, competition, and individualism. Many people opt to work (and eam) less to free up time for study, art, relationships, personal and spiritual growth, and myriad hobbies, crafts, sports, and other pastimes.

These processes slowly coalesce into a worldwide ferment of untold millions searching for new ideals, meaning, and forms of social existence. Some


tum toward esoteric sects, but they are in the minority. Young people around the world discover a collective identity in a new idealism that is directed toward creating a planetary community. The Internet becomes an important forum for this new consciousness, helping to forge a sense of unity. Global meetings and festivals explore the new values of equity, human rights, and the environment. Spiritual and aesthetic exploration occurs through a global network of civic groups that politically organizes to promote freedom and plurality. Eventually, many communities and some regions opt for alternative lifestyles and economic practices. Some stress high-technology solutions, others prefer frugality, and still others adopt a utopian vision that small is beautiful, emphasizing the protection of the wilderness and a mystical relationship with nature. Gradually, a federation of diverse global constituencies emerges. In reaction to trends of homogenization and manipulation, this group conducts collective discussions about the destiny of humankind.

At this point, the tension between the forces of conventional development (or Barbarization) and a new planetary consciousness has reached the critical moment. Progressive reconstruction overcomes all resistance. Equity and sustainability, rather than economic growth per se, become the goals of development. Material simplicity becomes the preferred lifestyle, while ostentatious consumption is viewed as primitive and a sign of bad taste. Interestingly, some transnational corporations accept (or even advocate) general limits on growth as part of the new business ethic of ecoefficiency. Others resist change, but under popular pressure governments and corporations begin negotiations for a new planetary deal. This includes international agreements on the redistribution of wealth in the context of reduced material consumption in the rich countries. Income transfers are tied to developing countries' voluntarily reducing family size and meeting globally agreed upon environmental targets. New technologies for sustainability flourish as public preferences and prices shift.

Complementing the above changes, a new metropolitan vision inspires the redesign of urban neighborhoods. Integrated settlement patterns place home, work, shops, and leisure activities in closer proximity. Dependence on the automobile is radically reduced. A sense of community is reestablished through increased connectedness. The basis for this renaissance of diverse and secure communities is the elimination of the urban underclass, the ubiquitous signal of social distress during the previous era. For many people, the town-within-the-city provides the ideal balance of a human scale and access to cosmopolitan culture.

Small towns also become popular as communication and information technologies increasingly allow for the decentralization of activities. The migration from rural to urban areas begins to reverse as many people opt for the lower stress level and increased contact with nature offered by smaller communities. A new spirit of community is reinforced by more self-reliant production patterns (including decentralized renewable energy systems) and pride in local environments. The mall culture fades as new urban and rural

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alternatives underscore the sterility, hidden costs, and isolation of suburbia. In the new economy, markets still play a major role in achieving effi

ciencies in the production and allocation of goods and services, but the aggregate level of economic activity is constrained by social, cultural, and environmental goals. In addition, the time horizon for economic decisions is lengthened to decades to take meaningful account of ecological processes. A variety of mechanisms are used to enforce these principles, including a new tax system that discourages environmental impacts. Patterns of consumption and regulation adhere to the polluter-pays principle. Antisocial corporate behavior is further discouraged by thorough public disclosure of key information. Well-designed environmental, economic, and social indicators measure the effectiveness of policies, giving the public an informed basis for seeking change.

Experiments with alternative forms of governance proliferate from local to global scales. Regions and communities have considerable control over their own affairs, being constrained only by the impacts of those decisions on others. For example, local energy systems vary greatly, but all of them meet per capita greenhouse gas emissions guidelines set by global agreements. Similarly, local water management is compatible with ecosystem goals for the entire watershed from which water is drawn. Global governance is based on a federation of regions that effectively fosters cooperation, security, and environmental health through a rejuvenated United Nations and a truly global civil service. A fully interactive Internet offers powerful new channels for communication, education, and the democratic process to undercut any reappearance of authoritarianism. The politics of diversity through global unity has found its natural medium.

Conflicts are resolved through negotiation, collaboration, and consensus. Armies are abolished and defense systems dismantled. The massive peace dividend is used to speed the transition to sustainability and to eradicate the last vestiges of poverty. Economic development continues indefinitely, but it is mostly concentrated in the low-material-use realm of services, culture, art, sports, and research. A labor-intensive, craft economy rises spontaneously on the platform of the high-technology base, thereby providing a rewarding outlet for creative expression and a dizzying diversity of highly aesthetic and treasured goods. A pervasive exhilaration about pioneering a socially and environmentally superior way of life becomes a powerful attracting force in its own right, a self-fulfilling prophecy that is able to draw the present to itself. Humanity has at last reached the end of its childhood.

Transitions among Alternative Futures

The descriptions above depict a broader range of alternative futures for humankind than those discussed in current international and national energy and development fora. In addition, as shown in Figure 14-3, multiple transi-


tions among these alternative world futures may exist (Gallopin 1997). Broader range and multiple transitions create great uncertainty for analysts and policy makers due to the wide range of possible outcomes. One outcome may arise from a business-as-usual extension of the current world that can persist into the future without major discontinuities. However, large disruptions resulting from current trends and problems (as well as from novel phenomena such as climate change) may kick the global trajectory into alternative paths. These alternatives include the dubious possibility of bouncing back into a future described in the Conventional Worlds.

This analysis provides a starting point to resolve these uncertainties of both images of the future and transitions among them. These discussions of policies for sustainable development suggest that the historical world trajectory may be entering a crisis phase with qualitatively different resolutions. This would make current policies obsolete, irrelevant, or even counterproductive. Adopting the position that Conventional Worlds represent the only possible (or likely) path into the future may be suicidal. Another, but less obvious conclusion, is that to keep the world system from falling into undesirable trajectories (e.g., Barbarization) is at least as important for sustainability as concentrating on policies that attempt to optimize the state of affairs within one trajectory. Not only are the kinds of actions important, but the nature, intensity, timing, and interactions of actions are also critical. Indicators that provide an early warning of looming transitions between scenarios must be identified, as well as the recognition of policies that increase the likelihood of synergies with other policies.

Embryonic signs of all of these scenarios are with us today. Which ones will grow and eventually dominate and which ones will whither away is still unclear. The future will depend on human choices made within the constraints posed by material and thermodynamic laws. The explicit consideration of the variety of possible futures for the global system, hopefully, may illuminate the choice process.

Global Scenarios and the Adaptive Cycle

This section will compare and contrast the alternative scenarios presented in the previous section with the concepts presented in Chapter 2 on understanding complex systems dynamics. An initial question to guide this comparison posits whether the unfolding of the alternative scenarios is compatible with the adaptive cycle. If so, what kind of new questions, uncertainties, and research are suggested? To begin to address these questions implies an examination of the adaptive cycle metaphor at a broad level, one of human civilization and the biosphere. However, this exploration must be done without forcing an analysis to fit the adaptive cycle hypothesis. Stripped to the bare essentials, the adaptive cycle in its basic formulation is based on the following fundamental assumptions:

388 GALLOPfN

1. Capital may exist in (at least) an active, free form, which is available for doing work and building structure (kinetic capital), or in a bounded, stored, sequestered form that is not available as a usable resource to the system until it is released (potential capital). The two forms are interconvertible; total capital is finite.

2. Increasing connectedness in the system implies:

• strengthening of the negative feedback (regulatory) couplings between elements of the system;

• increased transformation of kinetic capital into sequestered, potential capital;

• increased dominance by a few elements and relations;

• increasing efficiency in dealing with the known and expected;

• increasing autonomy of the system regarding its environment or external forces.

3. There is a direct relation between connectedness and rigidity and fragility, leading to vulnerability and unexpected conditions, Therefore, sooner or later, an overconnected system will collapse.

4. The collapse liberates free capital that is utilized by the elements that triggered the collapse, and it involves the replacement of strong negative feedback interactions between elements by transient strong positive interactions. This destructive process stops when kinetic capital is used up.

5. This is followed by a fast decline in total connectedness, an unraveling of the couplings between elements.

6. Remaining capital is sequestered into the remains (debris) of the old structure, and it is not immediately available. It takes time for the liberation of capital from potential to kinetic.

7. The phase with low connectedness and capital becoming freely available (alpha) is when the seeds of new organization can appear, under strong influence of chance events.

8. Once kinetic capital is abundant, one of two things happens. Either the beginning of the exploitation phase (perhaps with novel elements and organization) signals that the cycle repeats itself, or the system flips into a fundamentally different configuration, exiting to a different adaptive cycle.

While the scenarios were derived independently of the adaptive cycle metaphor, it is instructive to reinterpret them in its light, in order to explore how far the analogy can be carried out, and to examine what kinds of new questions may arise.

The Reference Scenario clearly depicts a trajectory that can be assimilated to the progression from the exploitation phase (r) to the conservation


phase (K). Through the evolution of civilization, and particularly since the industrial revolution, the connectedness of the global system has been growing, and an increasing amount of resources is used to operate and maintain the system. Capital (in the sense described earlier) has moved from freely available to bound, having been sequestered or controlled by the dominant socioecological system. Kinetic capital has been reduced not only through this process of conversion into potential capital, but also through destruction and misuse. This increase in connectedness and the sequestering of resources needs no further elaboration, as it has been abundandy documented. As discussed under the scenario, there are good reasons to argue that overall resilience is being lost, as resources are being depleted, connectedness is increasing, and the total size of the human component is increasing. Figure 14-10 (upper left) maps the Reference Scenario into the adaptive cycle. The description of the scenario also highlights an additional source of vulnerability: the increasing inner tensions due to the inequities seemingly associated with the dominant elements and relations in the global system. These are not simply related to connectedness; they are better interpreted as a disharmony or conflict between subsystems.

The Policy Reform Scenario, interpreted according to the adaptive cycle metaphor, is essentially an attempt to maintain the global system at the peak of the conservation phase (Figure 14-10, upper right) through moderating and regulating resource consumption and utilization and reducing the internal tensions due to inequity. If successful, the global system would be kept poised at a situation in which most of the capital is in its potential form, efficiency of resource use is very high, and connectedness is also very high. In

reference policy reform~

fortress world breakdown

~\.®

ecocommunalism

Figure 14-10. Scenarios as mapped on the adaptive cycle. Conventional futures are minor deviations from current cycle. Barbarized futures represent traps and pathologies that deviate from the adaptive cycle. The transition worlds represent leaps to new cycle configurations.

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the adaptive cycle interpretation, this would turn the system into an "accident waiting to happen." As defined originally, Policy Reform maintains continuity with the present situation. No qualitative changes in institutions or human values occur. In the adaptive cycle interpretation, the policy reforms defining the scenario will not eliminate the risk of a collapse of the system, but only postpone it (and possibly increase its likelihood), as efficiency, resource throughput, and connectedness increase even if internal inequity tensions are reduced.

The Barbarization scenarios imply drastic discontinuities and reduction of connectedness (Figure 14-10, middle). As the level of civilization recedes, bounded capital becomes available to a different set of social actors (revolters) at least in the form of vacated buildings and available debris from a more advanced technology.

In the Breakdown variant, a full unraveling of institutions and other couplings occurs. Civilization collapses, and all capital either disappears or is transformed into available kinetic capital. After the reorganization phase, a new cycle of exploitation would take place (beyond the time horizon contemplated in the scenarios). It might be also possible that this scenario goes to a poverty trap of resilient degradation (Figure 3-12).

The Fortress World variant can be interpreted as an attempt to maintain the global system from falling into the omega phase. Part of the capital is freed by excluding the majority of population from the "bubbles of wealth," and connectedness is reduced by simplifying the system. However, internal tensions due to inequity are immense, and this scenario is inherently unstable. A collapse of this scenario would likely lead to the Breakdown variant. Alternatively, the Fortress World could come to be a part of the trajectories from exploitation to conservation following reorganization from the Barbarization scenario. This would not be terribly different from the historical path already trodden by humankind (e.g., the colonial era) and would be consistent with a repetition of the same adaptive cycle.

As has been remarked before, it is difficult to imagine a trajectory leading from the current world situation directly to an Ecocommunalism future. Too much is committed, and Ecocommunalism would imply a voluntary dismantling of much of what has been built by civilization, as well as strong reductions not only in population growth, but also in population size. However, Ecocommunalism could very well arise through the reorganization phase following the generalized collapse associated with the end of a Barbarization scenario. Ecocommunalism, viewed from the adaptive cycle paradigm, could well signify an attempt to stay in between the exploitation and the conservation phases, avoiding overconnectedness and total use and sequestering of capital, allowing for redundancy, diversity, and slack (Figure 14-10, bottom left).

Perhaps the most intriguing scenario interpreted from an adaptive cycle perspective is the scenario of the New Sustainability Paradigm. This scenario could emerge out of a new understanding of the sustainability


challenge (and of solutions to it) or as a reaction to some catastrophic event or visible threat. This scenario does not arise as a recovery from a release and reorganization phase, but it is posited to evolve from basically the current situation through a drastic institutional reorganization and a revival of basic human values.

The scenario implies a strong decrease of the pressure upon resources and the environment (through lifestyle changes, stabilization of population growth, and dematerialization of the economy) and a strong reduction in social sources of tension and instability. Material capital would still be mostly in its potential form, but cultural and spiritual capital would be liberated and utilized, thus shifting the balance toward kinetic capital. It is this nonmaterial capital that makes possible the fundamental societal reorganization envisaged in this scenario. Connectedness would still be relatively high but would be reduced through decentralization and establishment of diversified self-organizing units at different scales. In other worlds, connectedness is high but qualitatively different from the rigid couplings associated with the culmination of the conservation phase.

The release of nonmaterial (cultural or spiritual) capital and the fundamental reorganization of socioecological systems are the root of the distinction between the New Sustainability Paradigm and the Policy Reform Scenario. The New Sustainability Paradigm would start a new cycle from exploitation to conservation, but one based on the utilization of cultural and spiritual capital, as well as material and technological capital. It is not possible to anticipate what kind of civilization could evolve from this. The feasibility of this scenario rests on two qualitative transformations: one is the opening of a different kind of capital based on the forces of solidarity at the level of the human species; this is a nonmaterial, nonexhaustible capital. The second qualitative transformation is from rigid connectedness to flexible, self-organizing connectedness. This could be triggered by the conviction that human civilization has passed a critical threshold of complexity, that emerging ultra-complex socioecological systems could never be managed in a centralized form, and that they require a new style of governance that is based on decentralized, cooperating networks. New theories that convincingly show that the existence of large inequities threatens the functioning and even the survival of the governance network might also contribute.

Large reorganization without large reduction of connectedness is difficult to fit within one adaptive cycle. Furthermore, as just discussed, the major difference between the New Sustainability Paradigm and all the other scenarios is the qualitatively different nature of the utilized capital. If the scenario were based on an additional injection of material capital, one could interpret that as a movement of the system backwards within the exploitation-to-conservation loop. Within this framework, the system would eventually come back to the current situation.

The notion that its type of capital and type of connectedness are qualitatively different than those of all other scenarios suggest that the New

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Sustainability Paradigm represents an escape of the global system toward another, qualitatively different, adaptive cycle (Figure 14-10, bottom right). This analysis is admittedly metaphoric. However, it leads to posing important strategic questions:

• How much reorganization can the global system go through without a dramatic reduction in connectedness?

• How much release of capital into its kinetic form does the system need to be able to reorganize?

Finally, if the adaptive cycle metaphor is applicable to the global system, and if it is true that adaptive systems must operate sequentially between the phases that maximize production and accumulation and the phases that maximize invention and reassortment:

• Is a New Sustainability Paradigm at all possible?

• Or, worse, are the Barbarization scenarios inevitable?

• Given that the global system includes conscious actors, is there a healthy strategy to maintain an advanced level of civilization while keeping adaptive fitness?

• Is the deliberate setting of a smaller adaptive cycle (Figure 14-11) one such strategy?

Figure 14-11. Does the path to sustainability imply adaptive cycles that are smaller, shorter, and more manageable?

The answers to these questions are crucial to planning for resilience as an approach to seeking sustainable futures.

PartV

Summary and Synthesis

CHAPTER15 DISCOVERIES FOR

SUSTAINABLE FuTuREs

C. S. Holling, Stephen R. Carpenter, William A. Brock, and Lance H. Gunderson

I give you fifteen (oops), I mean ten commandments. -Mel Brooks, "History of the World"

I n the course of the project that led to this volume, we identified twelve conclusions (Table 15-1) in our search for theories for sustainable futures. Those conclusions are reviewed in this section. That is followed

with a final section resolving the paradoxes presented in Chapter 1:

• If collapse and instabilities characterize systems of people and nature, then why are we still here?

• Why have we fallen into a trap where expertise is thought to be the only way to manage uncertainties inherent in these complex systems?

1. Abrupt shifts among a multiplicity of very different stable domains are plausible in regional ecosystems, some economic systems, and some political systems.

After more than twenty-five years of ecological research since these features were described for ecosystems (Lewontin 1969; Holling 1973b), it is now evident that alternate and alternating stable states arise in a wide variety of ecosystems, such as lakes, marine fisheries, benthic systems, wedands, forests, savannas, and rangelands. The most convincing cases of multiple states are based on the synthesis of several lines of evidence and usually include long-term observations, experimentation, understanding of causation, and comparative studies of many sites. Because of the heavy demands placed on the data, evidence for multiple states will be equivocal in situations where extensive research has not been possible. However, even small probabilities of multiple states (on the order of 10 percent) have powerful

395

396 HOLLING, CARPENTER, BROCK, GUNDERSON

Table 15-1. Summary Findings from the Assessment of Resilience in Ecosystems, Economies, and Institutions

Summary Statement Conclusion

Multistable states are common in 1. Abrupt shifts among a multiplicity of very many systems. different stable domains are plausible in

regional ecosystems, some economic systems, and some political systems.

The adaptive cycle is the fundamental 2. An adaptive cycle that aggregates unit of dynamic change. resources and periodically restructures to

create opportunities for innovation is a fundamental unit for understanding complex systems from cells to ecosystems to societies to cultures.

Not all adaptive cycles are the same, 3. Variants to the adaptive cycle are present and some are maladaptive. in different systems. These include physical

systems with no internal storage, ecosystems strongly influenced by external pulses, and human systems with foresight and adaptive methods to stabilize variability. Some are maladaptive and trigger poverty and rigidity traps.

Sustainability requires both change 4. Sustainability is maintained by relationships and persistence. among a nested set of adaptive cycles

arranged as a dynamic hierarchy in space and time-the panarchy.

Self-organization provides the arena 5. Self-organization of ecological systems for change. establishes the arena for evolutionary change;

self-organization of human institutional patterns establishes the arena for future sustainable opportunity.

Three types of learning can be identified. 6. Panarchies identity three types of change-incremental, lurching, and transforming-each of which can generate a correspondingly different kind of learning.

The world is lumpy. 7. Attributes of biological and human entities form clumped patterns that reflect panarchical organization, create diversity, and contribute to resilience and sustainability.

15. DISCOVERIES FOR SUSTAINABLE FUTURES 397

Summary Statement Conclusion

Functional diversity builds resilience. 8. Functional groups across size classes of organisms maintain ecosystem resilience.

Tractability comes from a "Rule of Hand." 9. Being as simple as possible, but no simpler than necessary leads to the "Rule of Hand." Understanding a panarchy and its adaptive cycle requires a model of a least 3-5 key interacting components, 3 qualitatively different speeds, and nonlinear causation. Vulnerability and resilience of the system change with the slow variables. Spatial contagion and biotic legacies generate self-organized patterns over scales in space and time.

Systems of humans and nature can 10. Linked ecological, economic, and social behave differently than their parts. systems exhibit emergent behavior. The

behavior is a result of strong connectivity between the human and ecological components and the presence of nonlinearity and complexity, as suggested in the "Rule of Hand."

Management must cope with surprise 11. Managing complex systems requires and unpredictability. confronting multiple uncertainties. These can

arise from technical consideration, such as model structure or analytic framework. The examples suggest that as much complexity exists in the social dimensions as in the ecological ones, and that managers must juggle shifting objectives.

Adaptive management outperforms 12. Slow variables, multistable behaviors, and other management approaches. stochasticity cause active adaptive

management to outperform optimization approaches that seek stable targets.

leverage in decision analyses where the costs of sliding into an undesirable state are severe.

Alternative stable domains in ecological systems become evident when shifts occur in the control of key ecosystem processes and structure. These controls can be switched by a variety of human activities. One such switch is associated with a decrease in disturbance variability, such as the change in


timing and magnitude of forest pest outbreaks following attempts to control insect populations (Clark et al. 1979; Ludwig et al. 1978). In other cases, it is acceleration of the rates of change of "slow" variables that results in a loss of resilience or shrinkage of a stability domain. The increased rates of nutrient addition to many freshwater lakes and wetlands, for example, have resulted in abrupt flips in the composition and structure of organisms (Carpenter 2000; Scheffer 1998; Chapters 7 and 8). In other ecosystems, it is the change in abundance of key structuring organisms that results in the shift in stability domains. Examples of these include overfishing that leads to changes in trophic structure in coral reefs (McClanahan et al. 1996; Done 1992) or freshwater lakes (Carpenter and Kitchell 1993; Scheffer 1998). Another example is the loss of drought-tolerant species in semiarid rangelands (Walker 1988; Chapter 11), which leads to conversion from grasslands to shrublands. In all of these cases, economic and social systems must manage and adapt to these abrupt shifts in ecological state.

Some economic and social systems also exhibit multiple states. Convergence clubs in economic growth theory are an example (Barro 1997). Multiple states arise in pluralist politics when different units of government, scaled in proportion to the problems they are purported to solve, lead to different outcomes and different organizational frameworks (Chapter 6).

2. An adaptive cycle that aggregates resources and periodically restructures to create opportunities for innovation is a fundamental unit for understanding complex systems from cells to ecosystems to societies to cultures.

Three properties seem to shape the response of the ecosystems, agencies and people in the case examples of regional development and ecosystem management. These properties include the accumulation of potential, the degree of connectedness of elements, and the resilience of the system (Chapter 2). The interaction among these properties creates the four phases of the adaptive cycle (r, K, n, and a, Figures 2-1 and 2-2).

One property is the potential acquired from the accretion or accumulation of resources, inventions, and mutations-biological, ecological, social, or economic. In ecosystems, potential can be measured, in part, by production of biomass or nutrients accumulated as a consequence of ecosystem successional dynamics (Carpenter, Ludwig, and Brock 1999; Chapter 7). Social or cultural potential can be represented by the character of human relationships-friendships, mutual respect, and trust among people and between people and institutions of governance (Chapters 5 and 13). In the economy, potential can be represented by the economic capital provided by usable knowledge and skills that are available and accessible (Chapter 10).

The second property is connectedness. It represents the strength of internal connections that mediate and regulate the influences between inside processes and the outside world-essentially, the degree of control that a system can exert over exogenous variability. An organism, ecosystem, organ-

15. DISCOVERIES FOR SUSTAINABLE FuTURES 399

ization, or economic sector with high connectedness is little influenced by external variability; its operation and fate are determined by internal regulatory processes that mediate or control variability. It could be measured near equilibrium stability-of speed of return after a small disturbance, for example. Or, less technically, it could be measured by the intensity of control by direct human activity (Carpenter, Brock, and Hansen 1999).

The third property is resilience, or its opposite, vulnerability. We use resilience in its ecosystem sense (Holling 1973b, 1996) to represent the capacity of a system to experience disturbance and still maintain its ongoing functions and controls. A measure of resilience is the magnitude of disturbance that can be experienced without the system flipping into another state or stability domain (Chapters 7 and 11).

These three properties shape a dynamic of change. An example of a linked ecosystem/economic model where output is expressed by changes in all three properties is shown in Chapter 7, Figure 7-7, and in Chapter 9. Potential sets limits to what is possible. Connectedness determines the degree to which a system can control its own destiny. Resilience determines how vulnerable it is to external disturbance that can exceed or break that control.

This was the foundation for the description of an adaptive cycle (Holling 1986, 1992; Figures 2-1, 2-2), where periods of slow accumulation of resource or of environmental or social potential are interrupted by sudden and rapid reorganization of that potential. It is at that moment that experiment and novelty can appear. The consequences can be simply a repetition of the previous cycle, or they can be the initiation of a novel new pattern of accumulation, or they can be the precipitation of a collapse into a degraded state.

The adaptive cycle in its most general form is a metaphor that has some relevance to a number of systems (Table 15-2). But it should not be read as a rigid, predetermined path and trajectory, for ecosystems at least, let alone economies and organizations. What are suggested are waxing and waning tendencies, with various degrees of predictability at different stages. All actors and species can be present throughout-pioneers, consolidators, mavericks, revolutionaries, and leaders. Their role and significance change as their actions create the cycle. The four phases of the cycle can overlap, but the most distinct separation is between K and Q. That is the shift that occurs as a stability region collapses, or as a disturbance moves variables into another stability domain. But even the most predictable sequence, from r to K, can be diverted by extreme or episodic events.

The phases of the cycle are also useful to understanding the practice of resource management. Local and traditional systems have developed a rich variety of practices (in many cultures and geographic areas) that interpret and respond to feedback from these complex ecosystems. They include practices that mimic disturbance at lower scales of the panarchy and that nurture sources of renewal. Instead of removing or eliminating disturbance altogether, local and traditional adaptations seem to accept perturbations as an intrinsic part of ecosystem dynamics and focus instead on "putting the brakes

Table 15-2. Some Examples of the Four Phases of the Adaptive Cycle (see Figures 2-1 and 2-2)

System Type Phase of Adaptive Cycle Reference

r K Q a

Ecosystems exploitation conservation release reorganization Holling 1986; Chapter 2

Economies market, monopoly, creative invention Schumpeter 1950 entrepreneur hierarchy destruction

Organizations adhocracies bureaucracy catalysts, visionary Westley 1995; routinization heretics Chapter 13

Institutions markets hierarchies sects isolates Thompson 1983; Chapters 6 and 9

Individuals sensation thinking intuition feeling Jung,asin Mann et al. 1976


on release" by managing the magnitude and frequency of release. These practices are in contrast to bureaucratic, western systems, where the focus is on eliminating disturbance through stabilization of key variables. This inevitably leads to management crises. Navigation through these crises by western approaches is problematic, messy, and contingent (Chapters 6, 11, 12, and 13). This theme is developed later.

3. Variants to the adaptive cycle are present in different systems. These include physical systems with no internal storage, ecosystems strongly influenced by external pulses, and human systems with foresight and adaptive methods to stabilize variability. Some are maladaptive and trigger poverty and rigidity traps.

In the course of using the adaptive cycle as an organizing metaphor for understanding system dynamics, researchers found the behavior of a number of systems to represent variants from the general pattern (Chapter 2). The exceptions are related to differences in the ability of a system to create novelty and to deal internally with external variation. These exceptions fall into at least four categories: (a) physical systems with no ability to create novelty, (b) living systems with evolved components that adapt passively to variation, (c) living systems with evolved components that manage variation actively over some ranges of scale, and (d) human systems with foresight and intentionality that both control variability and create novelty. These four are expanded on below:

a. Physical systems lack chance inventions and mutations and therefore limit their potential for evolutionary change. In these systems there is little or no accumulation of novel potential (e.g., mutation, inventions, or exotics) that can subsequently act to transform the system response. Examples include tectonic plate dynamics and Bak's (1996) sandpile experiments. Each system exhibits periods of instability and reorganization, but novelty and mutation are not created and rearranged to the same degree as in living systems.

b. Living systems can have different strategies for dealing with variability, depending on whether their environment is controllable or predictable. If it is neither, the strategy is to live passively with variability by individuals evolving extensive adaptations to variability. Such ecosystems have an adaptive cycle largely restricted to the r and alpha phases. Pelagic, open water communities and eroded semiarid savannas exposed to rare and unpredictable episodes of rain are examples.

c. Living systems that can control variability, over some scales, show the full cycle of the four phases, of growth, rigidity, collapse, and reorganization. Examples are productive temperate ecosystems and large bureaucratic organizations.


d. Human systems with foresight and intentionality can uniquely manage variability creatively, in order to minimize or prevent instabilities and retain flexibility. Forward expectation markets that deal with resource scarcity are one such example. Another example includes large organizations that attempt to maintain creativity by converting organization-wide boom-and-bust cycles to smaller, internal learning cycles. In such human systems we might identify ways to anticipate and manipulate variability creatively, and escape the apparent inevitability of the adaptive cycle and its prediction of rigidity leading to crisis.

4. Sustainability is maintained by relationships among a nested set of adaptive cycles arranged as a dynamic hierarchy in space and timethe panarchy.

The panarchy is a nested set of interacting adaptive cycles arranged in a hierarchy across scales in space and time. It represents the dynamic interplay between the processes and structures that sustain relationships on the one hand, and those that accumulate potential on the other. The concept is sufficiently new that precise insights and prescriptions are just beginning to be made. Many of the alternative stable states mentioned above are situations in which panarchies are transformed, either because productive novelty cascades up the levels or because destructive catastrophes cascade down.

The adaptive cycle is the engine that periodically generates the variability and novelty on which experimentation and change depend. As a consequence of the adaptive cycle and its periodic but transient phases of creative destruction (.Q phase) and renewal (a phase), each level of a system's structure and processes can be reorganized. This reshuffling allows the possibility of new system configurations and opportunities from the incorporation of exotic and entirely novel entrants that accumulated in earlier phases.

For organisms, those novel entrants are mutated genes, or for some bacteria, exotic genes transferred occasionally between species. For ecosystems, the novel entrants are exotic species, or species "in the wings" waiting for more appropriate conditions. For economic systems, those novel entrants are inventions, creative ideas and people that emerge in the earlier phase of growth where they were constrained from further development of their potential. The adaptive cycle explicitly introduces a slow period of growth in which mutations, invasions, and inventions can accumulate, followed by a brief period of rearrangements. It is a periodic process that can occur within each hierarchical level, in a way that partially isolates the resulting experiments, reducing the risk to the integrity of the whole structure.

Novelty can be generated, tested, and selected in the constituent adaptive cycles of the panarchy and can then spread to other levels. Many times, the source of novelty lies not so much in de novo entities like inventions,


mutations, and exotics but in novel, unpredictable combinations of those with existing components that can suddenly establish new domains of influence, opening an entirely new set of adaptive pathways. Examples include the sixty-year wave of technological innovation initiated in the nineteenth century and the Internet in the later part of the twentieth century. These were triggered not simply by single new inventions (e.g., the steam engine or the personal computer), but by the context of a whole economy and society that had accumulated a set of rigidities and novelties that precipitated, synergized, and directed a transformation (Schumpeter 1950; Fischer 1996).

Levi-Strauss (1962) coined the word bricolage for this process of recombining existing elements and new mutations and inventions to form something novel that solves a newly emerged problem or creates new opportunity. The adaptive cycle accumulates those elements as potential and then, for transient moments, rearranges them for subsequent testing in changing circumstances. Those of consequence can nucleate new opportunity and accumulate further potential. If that accumulated potential exceeds a threshold, it can cascade upward in the panarchy and create new panarchicallevels.

Such transformations are qualitatively different from the incremental changes that occur during the growth phase of the adaptive cycle. They are also qualitatively different from the potentially more extreme changes and frozen accidents that can occur during the more revolutionary shift in the adaptive cycle from creative destruction (Q) to renewal (a). They cascade and transform the whole hierarchy and its constituent adaptive cycles. They are panarchical transformations. Such transformations and the panarchies that create them provide a robust theoretical foundation for sustainability.

The organization and functions we now see embracing biological, ecological, and human systems are therefore these that contain a nested set of the four-phase adaptive cycles, arranged in a dynamic hierarchy, in which opportunities for periodic reshuffling within levels create novel adaptive opening and the simple interactions across levels conserves the ability to test, propagate or smother those opportunities (Chapter 3). What distinguishes the biological, ecological, and human systems from one another is the way inventions are accumulated and transferred over time-through genes, selforganized patterns, oro communication.

Panarchies succinctly summarize the property that we define as sustainability. The fast, small levels invent, ef{periment, and test; the slower, larger levels stabilize and conserve accumulated memory of past successful, surviving experiments. The whole panarchy is both creative and conserving. The interactions between cycles in a panarchy combine change with continuity (Chapter 3, Figure 3-5). That clarifies the meaning of sustainable development. Sustainability is the capacity to create, test, and maintain adaptive capability. Development is the process of creating, testing, and maintaining opportunity. The phrase that combines the two, sustainable development, rather than being an oxymoron, represents a logical partnership.


5. Self-organization of ecological systems establishes the arena for evolutionary change; self-organization of human institutional patterns establishes the arena for future systainable opportunity.

The Ecological Theater and the Evolutionary Play, the evocative tide of a book of essays by G. E. Hutchinson (1965), captures the notions that ecological context influences the course of natural selection and that the results can further reinforce the ecological context. Selective pressures come also from aspects of the physical-chemical environment, such as geomorphology, hydrology, biogeochemistry, and climate. Evolution, in tum, shapes ecosystems because ecological systems are self-organized from evolved components, as Levin (1999) describes in Fragile Dominion. Those self-organized components include some suites of organisms that create patterns and are reinforced by those patterns (Holling 1992). Others act as "ecological engineers," altering the physical structure and especially the biogeochemistry of ecosystems Oones and Lawton 1995). Thus the interplay of evolution, ecology, and the physical-chemical environment is an intricate dance, in which configuration and control change eternally. Humans develop selforganized patterns more intensively and over much larger ranges of scale than other organisms do. We conjecture that those self-organized patterns are as important for evolution as Darwinian natural selection, and as important for sustainable development as the market.

The panarchy is created by these self-organizing processes within the constraints set by physical laws. The phases within the constituent adaptive cycles are highly dynamic and variable, the adaptive cycle itself is less so, and the full panarchy is highly conservative. The resulting ecological panarchy is a template sustained by living processes and reinforcing those same processes. Different sets of those processes function at different scales of ranges, producing a sustained, conservative pattern of eddies of productivity and opportunity across scales.

6. Panarchies identify three types of change-incremental, lurching, and transformational-each of which can generate a correspondingly different kind of learning.

Incremental change and learning. This type of change occurs in the predictable development phase or from the r to K phases of the adaptive cycle (Figure 2-1). During these phases, models or schemas are assumed to be correct, and learning is characterized by collecting data or information to update these models. This type of learning is similar to the single-loop learning of Argyris and Schon (1978). In bureaucratically dominated resource systems, the activity of learning is carried out largely by self-referential professionals or technocrats who primarily view dealing with this type of change and learning as problem solving (Chapter 13).

15. DISCOVERIES FOR SUSTAINABLE FuTuREs 405

Abrupt change and spasmodic learning. This type of change is episodic, discontinuous, and surprising. It is created by slow-fast dynamics that reveal the inadequacies of the underlying model or schema structure. It is the change described by transitions from the conservation phase (K) through the creative destruction (Q) and renewal (a.) phases of the adaptive cycle. This can be manifested as an environmental crisis, where policy failure is undeniable (Gunderson et al. 1995a) and results from an environmental cognitive dissonance. In this case, the learning is described as double loop, in which the underlying model or schema is questioned and rejected (Argyris and Schon 1978). This is also characterized as problem reformation. In bureaucratic resource systems, this type of learning is facilitated by outside groups or charismatic integrators.

Transformational/earning. This is the most dramatic type of change and requires the deepest type of learning. Cross-scale or novelty surprises characterize this type of change and are related to interaction between different sets of labile variables. In these cases, learning involves solving problems of identifying problem domains among sets of wicked and complex variables (Chapter 13). Transformational learning involves several levels in a panarchy, not simply one level. This is also described as evolutionary learning (Parson and Glark 1995), in which not just new models or schema are developed, but also new paradigmatic structures (sensu Kuhn 1962).

7. Attributes of biological and human entities form clumped ·patterns that reflect panarchical organization, create diversity, and contribute to resilience and systainability.

Ecological, economic, and human systems can exhibit scale invariant properties that can be fit to continuous functions. The current emphasis on power laws in complex systems research provides examples. But, in addition, in ecosystems, the pattern of morphological and geometric attributes of entities along those continua exhibit clumped structures (Holling 1992; Chapter 3). Moreover we show that such clumped structures of attributes are associated with a range of impacts of considerable ecological and evolutionary consequences. Among plants, these include species performing critical ecosystem functions (Walker et al. 1999). Among animals, these include species that are the indicators and creators of change: those that are endangered, invasive, nomadic, and migratory (Allen et al. 1999). This structure and its associated species diversity determine resilience.

Panarchies of living systems, social as well as ecological, provide a discontinuous template in space and time that entrains attributes of variables into a number of distinct lumps. By lumps we not only mean the discrete aggregates that Krugman (1996) describes for human settlements. There are such discrete aggregates in ecosystems-some very obvious, like individual organisms; some more amorphous, like plant associations and ecosystems themselves. But in addition, we mean that attributes of size, speed, and function of those discrete aggregates should be distributed in a lumpy manner.

406 HOLLING, CARPENTER; BROCK, GUNDERSON

Those attributes could be periodicity of fluctuations, size of objects at different scales on a landscape, scales of decision processes of animals and humans, or morphological and functional attributes of animals and plants.

Evidence of these lumpy patterns has been found in the morphology of organisms for a number of taxa (mammals, birds, insects, herpetofauna, and plants) in a number of different ecosystems in dry and wet regions, cold and hot ones, in lakes, on land, and in the benthos. The patterns are very conservative and persistent, changing only under extreme disturbances. We propose that they reflect the conservative, sustaining nature of ecological panarchies.

There are two reasons an ecosystem or landscape would create a lumpy template. One is the discontinuous nature of the processes that form different levels of the panarchy. Those are the processes that create a disjunct separation of scales among key structuring variables. The other is the nature of the adaptive cycle itself at each level of the panarchy. The phases of the cycle are distinct, and the shift in controls from one to another is abrupt, because the processes controlling the shifts are nonlinear and the behavior multistable. Each phase creates its own distinct conditions, which in tum define distinct attributes of size and speed of aggregates that control the phase or are adapted to its conditions. K-species and firms tend to be big and slow; r-species and firms tend to be small and fast. We are not saying that the four phases of a cycle entrain four lumps, though it would be fun to develop a test of that hypothesis. We are saying that the combination of panarchylevel discontinuities and adaptive cycle discontinuities will generate a number of lumps, the number defined by the resolution of the observations and the range of scales tested. Panarchies form a lumpy template that entrains the same lumpy attributes in organisms that create or are part of them.

We conjecture that some social and economic systems will exhibit the same structures. Barro (1997), for example, groups countries into economic lumps called convergence clubs. Countries within a given club have economic growth performances that tend to converge. These patterns of growth performance across countries appear to be structured by movement toward a long-term "target" rate of growth for each country, where the long-term target is determined by slow and medium time scale variables. Slow processes of governance establish the degree of flexibility, trust, and freedom of institutional and political structures. Medium-speed processes set the general level of public physical infrastructure and education.

We hypothesize that these clumped structures can concentrate opportunity and potential and maintain resilience and adaptive capability across scales.

8. Functional groups across size classes of organisms maintain ecosystem resilience.

Biodiversity contributes resilience to the functioning of an ecosystem. Its functions arise from the interaction of a diverse set of biotic and physical processes that control net carbon assimilation and transpiration, water ex-


traction from various soil layers, nutrient cycling and retention, herbivory, and predation. Chemical and physical processes interact with processes mediated by critical species in the biota.

These species can be divided into functional groups based on differences in their ecological functions. The different functions of plants are represented by attributes such as nitrogen-fixing capacity, rooting depth, water-use efficiency, and litter decomposition rate. For animals they are represented by trophic status, body mass, and foraging class.

Adequate performance of ecosystem function depends on having all the necessary functional groups (the full array, or diversity, of functional groups) present. The persistence of ecosystem function over time (i.e., the resilience of ecosystem function) depends on the diversity of species within functional groups. There are two important forms of diversity within functional groups: one providing functional compensation within a narrow range of scales and one providing functional reinforcement across a wide range of scales (Chapter 3).

Functional compensation within a narrow range of scales occurs when species perform a similar function but have different environmental sensitivities. For example, if one species of nitrogen fixer is greatly reduced in abundance, or eliminated by a disease or an extreme temperature, other nitrogen-fixing species that are resistant to the disease or have different temperature responses are able to substitute for it. A study of functional attribute diversity of an Australian rangeland (Walker et al. 1999) revealed that the most abundant plant species were far apart from each other in plant-attribute space (i.e., they perform different functions). However, among the less abundant species, at least one species was functionally very similar to each abundant species. Furthermore, on a site that had been heavily grazed, dominant species that had been eliminated were replaced by a functionally similar species that was less abundant on a lightly grazed site. It is an example of resilience achieved from functional compensation within a scale range.

Important generalizations concerning the role of biodiversity have recently been developed using ecolpgical experiments in laboratory and field settings (Naeem et al. 1994; Tilman 1996; Tilman et al. 1996; Kassen et al. 2000). However, the size of enclosures and quadrats in these experiments was, for practical necessity, small relative to the full range of processes represented by ecological panarchies, i.e., centimeters to a few meters and days to a few years, rather than meters to hundreds of kilometers and months to centuries (Figures 3-2 and 3-3). The conclusions of these experiments therefore exclusively concern functional compensation within a (small) scale range. In contrast, conclusions concerning the tole of biodiversity across scales comes from regional scale studies of ecosystems, where modeling, process understanding, and management interventions combine to allow analysis of larger parts of the panarchy and the multiple scales they represent. Those studies reveal an additional and different role for biodiversity in providing crossscale reinforcement.


Cross-scale functional reinforcement occurs when species perform similar ecological functions but at very different scales. It allows function to persist despite environmental variation and endogenous cycles, because of overlapping reinforcement of their effects. For example, small birds that glean individual larvae from conifer needles usually maintain regulation of forest insect pest populations (like those of the spruce budworm) over long periods at low levels in young forests. However, if budworm populations increase, birds of progressively larger body sizes begin to prey on them (Holling 1988; Peterson et al. 1998). This cross-scale functional reinforcement is able to maintain effective predation on budworm population over a much larger range of budworm population densities than would a set of foragers that operate at only the same scales.

In addition, the cross-scale functional reinforcement of ecological function enhances the ability of an ecosystem to reestablish itself following disturbance. Species that operate at a small scale may survive a larger disturbance by continuing to persist in the interstices of a disturbed area. Large animals are able to avoid a smaller-scale disturbance. For example, dispersal of palm seeds can occur in a deforested patch of forest, in the absence of dispersers within a patch, if large animals pass through the patch, bringing seeds from the surrounding forest (Peterson et al. 1998). Decreases in crossscale functional reinforcement, therefore, will likely reduce the ability of ecosystems to recover from disturbance.

These two effects of diversity do-not represent the effects of redundancy in the replicated sense that an engineer might apply it to achieve engineering reliability. Rather, for functional compensation, each species in the same scale range has a similar function but different responses to unanticipated environmental change. If the ecosystem were a theater, the species within such a narrow scale range would be like multiple stand-in actors prepared to replace each other in the event of unexpected external surprises and crises. For cross-scale functional reinforcement, species in different scale ranges can also engage in similar or related ecosystem functions, but, because of their different sizes, they differ in the scale and degree of their influence. In our ecosystem theater, species in different scale ranges are like actors waiting in the wings to facilitate a change in pace or plot when needed. The withinscale and between-scale diversity produces an overlapping reinforcement of function that is remarkably robust. We call it imbricated redundancy.

Such imbricated redundancy of species in ecosystems is a critically important mechanism for ensuring resilience of ecosystem function. It is a serious error to assume that minor species are indeed "passenger" species that can afford to be lost. It may be difficult to detect the importance of species if they are providing compensation or performing a function over broad spatial or time scales. The importance of such species may be detected only when they are needed, following a disruption. In addition, the ability of ecosystems to recover from disturbance may be decreased by the loss of species, especially those that operate over large scales. In the absence


of those species, ecosystems may reorganize in response to formerly tolerable disturbances

9. Being as simple as possible but no simpler than necessary leads to the "rule of hand."

The minimal complexity needed to understand a panarchy and its adaptive cycles (Chapter 2) requires:

• three to five key interacting components

• three qualitatively different speeds of variables

• nonlinear causation and multistable behavior

• vulnerability and resilience that change with slow variables

• creation of structures by biota and reinforcements of biota from structure

• spatially contagious processes to generate self-organized patterns

Anyone contemplating a theory of sustainability confronts a vast diversity of entities, variables, and processes that could be included in models. The art of modeling is to suppress detail; focus attention on broad, crosscutting phenomena; and develop powerful, general, and testable ideas. It does not matter that the model will ultimately prove to be inadequate, or at best reliable within only a limited domain. Rather, we derive insight and ideas from the learning process associated with building, evaluating, discarding, and revising models. As noted by George Box (1976), "All models are wrong, but some are useful."

We chose to begin with models that were simple, but not too simple. We added detail only grudgingly, as the dissonance between models and the systems we sought to understand became unacceptably large. As examples of models that are too simple, consider the models of the disciplines we sought to integrate. Ecological models do not generally include the forward-looking behaviors of large numbers of interacting agents, which are central to economic and social models. Socioeconomic models do not generally address the multiscale hierarchies of ecological systems and are just beginning to consider the difficulties posed by slow social and ecological variables. By definition, the individual disciplinary models are too limited, although they offer useful component models and mathematics for building the more integrated models that we seek.

As one foundation for appropriately simple models, we have the ecologists' rule of hand. It is not a mathematical theorem; rather, it is a loose guideline for developing ecological and linked ecological and economic models that are complex enough to generate revealing and testable predictions, but simple enough to understand. Such models have at least three, and perhaps as many as five, interacting components (hence "rule of hand"; a rule of thumb would have only one component). The components of the model


exhibit at least three quite different (by around tenfold) turnover times. Some of the linkages among components are nonlinear in the state variables (not necessarily in the parameters). The slow variables create a stability landscape with multiple attractors. Because the slow variables are dynamic and have feedbacks with the fast variables, the stability landscape is itself dynamic, so transitions among attractors are possible.

How do we decide that a model is overly simple and that additional detail is needed? We ask whether the model can address the phenomena we seek to understand and explain the facts known to us. If not, the model must be discarded or revised. Early in the project we discovered that the rule of hand was necessary for ecosystem models but too simple for linked ecosystem, economic, and decision models (see Chapters 3 and 7). In those situations it was necessary to both include three speeds of key variables and consider the emergence of economic patterns from individual actions of many forward-looking agents. These agents must cope with difficulties of learning about the slowly changing variables in ecological systems. And these agents act within dynamical structures of social legitimation, domination, and signification, themselves functioning at three speeds (Chapter 13).

The pathway to our next generation of models involved several experiments (Carpenter, Brock, and Hanson 1999). In each, we combined a simplified, three-speed ecological model with a model of forward-looking, boundedly rational agents engaged in economic and political activity. This sequence of experiments led to the models represented in this book (Chapters 7, 8, and 9). These models include an ecological system consistent with the rule of hand. In addition, we model a technologically based management agency based on examples known from many democracies. The agency gathers data about the ecosystem and economic system. The information is used in a decision-making process that promulgates regulations and incentives designed to guide the actions of many individual agents. Each of the agents has some effect on the ecosystem, which the agents share. Although the agents are individualistic, they are cocreating a common environment. Their actions are guided by learning about this environment and the behavior of the agency and external markets. An individual's actions are based on his or her forecasts of how various choices will affect his or her well-being.

While this family of models has proven to be a rich source of insight and ideas (Chapters 7, 8, 9, and 10), we have a growing list of interesting problems that they cannot address. As we grow dissatisfied with the limited domain of the existing models, we will enter a new adaptive cycle of our own research, creating the next generation of models. Some challenges for the future are as follows: (1) How is the self-organization of ecological systems, and the adaptive landscape for evolution, affected by social activity? (2) How do different social systems affect the potential for evolutionary change and the origins of biotic novelty? (3) How does the interaction of ecological learning with social dynamics affect resilience? Some approaches to forecast-


ing encourage cautious experiments that lead to adaptive change and sustained or growing resilience. Others lead to risky behavior or social rigidification, which may shrink resilience or cause collapse. These raise fundamental questions about the interactions of science and society related to foresight and sustainability.

10. Linked ecological, economic, and social systems exhibit emergent behavior.

Integrated systems exhibit emergent behavior if they have strong connectivity between the human and ecological components, and if they have key characteristics of nonlinearity and complexity. Those key characteristics of the socioeconomic system include many individual, boundedly rational agents or institutions making decisions (using formal or informal rules) and learning about a world they cocreate (Carpenter, Ludwig, and Brock 1999; Janssen and Carpenter 1999). Those key characteristics for ecosystems are described in the rule of hand above.

A common pattern in the dynamics of these linked systems is that stabilization of one or more of the subsystems inevitably leads to instabilities or collapse of the whole. This is manifest as a loss of resilience in the ecological components or loss of adaptive capacity in the human components. Perhaps ecological collapses, and the subsequent need to innovate, create, reorganize, and rebuild, are a likely, maybe even inevitable, consequence of human interactions with nature.

Ultimately, the risk of collapse under apparently optimal management traces to slow variables that are mistakenly assumed to be static, a broad probability distribution of uncertainties, shortsightedness due to discounting of the future, and losses of social flexibility and ecological resilience. Therefore, institutions that counter these trends may help ameliorate the risk or severity of collapse. Such forward-looking policies can be introduced during the backloop (Q to a. tor phases) of the adaptive cycle. In these phases, relatively simple models can serve as the focus for activities designed to evoke insight, creative debate, and cooperative learning. The models are heuristic devices that simulate reality, give insight into possible human choice mechanisms and their interactions with ecosystems, and for the practitioner provide a chance to explore implications of possible interventions.

Carpenter, Brock, and Ludwig (1999) indicate situations where even if the ecological components of the system are known perfectly, environmental stochasticity and changing human dynamics contribute to destabilization, collapse, and renewal. This suggests that what is needed is not just research on the disciplinary components of these systems, but rather a broader, integrative view that helps develop understanding as much as analysis. Other contributors to this volume begin to suggest how people deal with such fundamental uncertainties.


11. Managing complex systems requires confronting multiple uncertainties.

We began this volume with a series of paradoxes or contrasts that arose from mostly western, bureaucratic approaches to resource management. In the process of resolving those paradoxes (which is done in more detail below), we looked to a variety of alternative viewpoints to see how people manage to manage these complicated systems. Those perspectives were from analysts of traditional approaches to management (Chapter 5), political systems (Chapter 6), social systems (Chapter 8), and economics (Chapter 10) and from experiences of practitioners (Chapters 11, 12, and 13). A common thread that wove through these chapters surmised that these systems are so difficult to manage because of the multiple sources of uncertainty that confront any practitioner. These range from concepts of how people begin to understand and monitor the ecosystem, to the myriad of ways people confront, test, and resolve those uncertainties from myths to institutions, and the complexities of action in the linked system.

Local and traditional practices confront uncertainties of resource dynamics through a number of mechanisms. Berkes and Falke (Chapter 5) argue that traditional approaches are based on a dynamic concept of nature that manages with environmental variation rather than against it. Traditional approaches use long time series of local observation and institutional memory to deal with infrequent (at least on a human time scale) and little experienced environmental fluctuations. This experience accrues in "traditional knowledge" that is culturally transmitted and evolves through adaptive processes. Knowledge carriers, such as elders, play a crucial role in this institutional memory of ecosystem change. So do myths and rituals, by helping people remember the rules and interpret environmental signals. Qualitative monitoring is key to testing this traditional knowledge base.

The development of an array of political institutions and settings appears key to managing certain types of uncertainties, yet the contributors paint a messy, mixed picture of success. As stressed by Pritchard and Sanderson (Chapter 6), there is no magic or singular fix to the design of institutions and implementation of policies to cope with the types of ecologic dynamics mentioned earlier. Indeed, the authors point out the weakness of approaches that are based on administrative rationality, market rationality, pluralist politics or communitarianism in dealing with these "wicked problems." Notwithstanding the optimism of economics to optimize and prescribe policies under a small set of preconditions (Chapter 10), these authors emphasize the dynamic or fluid nature of policies and institutions, as captured by their metaphor of politics as a "floating crap game." These authors discuss the failure, invention, creation, or reinvention of institutions as a common pattern and suggest participatory pluralist approaches to help bridge differences between local knowledge and broader-scale issues and perspectives. They caution about the difficulties of implementa-


tion in a context of there being no best approach, a theme reinforced by other contributors.

In the one example from the front line, a scientifically based western resource manager, complications at a micro level only compound these complexities (Chapter 13). Uncertainties arise from the dynamics of multiple social groups within which the manager must operate. The political arena, the organizational objectives, and the stakeholder (or interorganizational) preferences all must be juggled with the implementation of a science-based approach. None of these subsystems can be considered alone to help understand the interactive dynamics of social system and ecosyste)TI that confront the adaptive manager. Rather, the entire network of interacting individuals and organizations at all three levels represents the social system. It is clear, therefore, that to manage adaptively is a question of creating the right links, at the right time, around the right issues to create a responsive system. As noted above, it is not a question of identifying best practices or institutional arrangements.

All of the contributors who reported on the successes and limitations from a practitioner's perspective discuss an approach that was deliberately developed to confront and resolve the uncertainties of resource issues through a process called adaptive management (Holling 1978; Walters 1986; Lee 1993; Gunderson et al. 1995a). Some conclusions about the efficacy of this approach are described in the final section.

12. Slow variables, multistable behaviors, and stochasticity cause active adaptive management to outperform optimization approaches that seek stable targets.

Walters (1997), Johnson et al. (1999), Gunderson (1999a), and the cases in this volume have stressed the practical difficulties that humans face in attempting to manage ecosystems. The multiple scales of variables, cross-scale, and nonlinear interactions generate the multistable behaviors in ecosystem dynamics. The surprises generated by this multistable behavior create a range of problems for management. Some of the difficulties are caused by lack of knowledge of the dynamical system (which may possess a very large state space and be nonstationary, as well as not being known). Other difficulties are caused by measurement error in state variables even if they can be properly identified. For example, Carpenter assessed the key sets of variables in one of the most studied and measured ecosystems in the world (Lake Mendota) and concluded that it is impossible to determine a priori when a change of state will occur. This is due to the interaction between slowly changing variables that influence the vulnerability of the system, and the faster, stochastic variables, as described in Chapters 2, 3, and 7. All of these compound the difficulties of managing, much less managing adaptively.

Adaptive management in its early form focused on confronting the uncertainty of resource dynamics through actions designed for learning (Holling 1978; Walters 1986). This has evolved from a problem of testing a single hypothesis about the system to sorting among multiple hypotheses,


each of which may have different social and management implications (Gunderson 1999a). Other layers of complexity arise from having inadequate monitoring or data to put these hypotheses at risk (Walters 1997). Ludwig (1995) considers harvesting strategies under increasing layers of uncertainty and shows that increasing uncertainty generally leads to increasing caution in harvesting and a strengthened precautionary principle. The challenges posed have a technical dimension and a social dimension. First the technical one.

The technical challenge has two parts as well: the first is to develop a framework that will allow for a process of formulating testable hypotheses; and the second is to choose among multiple hypotheses. The types of models of complex adaptive systems presented in Chapters 7, 8, 9, and 11 appear to be a useful framework for the problem of formulating hypotheses. These have a long history of use in the process of adaptive assessment (Walters 1997). The process of making the model is much more important than the model itself (Walters 1986). The technical challenges of sorting among competing hypotheses were not addressed in this volume. The reader is directed to Walters (1986), Hilborn and Mangel (1998), and an extensive literature in the statistics area, as well as the area of optimal adaptive control in systems with unknown parameters.

The second area is the social arena. The types of organizational complexity raised by Westley (Chapter 13) and of political pathologies (Chapter 6) generate barriers for adoption of adaptive management in western, bureaucratic agencies. Adaptive management has been socially challenged through practices such as the self-serving interest of management agencies, self-interested career concerns and "greed" among scientific experts, and disinformation campaigns by opposing sides who exploit the uncertainty of multiple hypotheses for other gains (Walters 1997).

All of the conclusions presented provide a lurch or transformation in our understanding. Yet they also point out gaps in our knowledge. Suggestions of important avenues to explore to fill those gaps are presented in the next chapter. Before we attempt a synthesis, we present another set of discoveries in the final section, where we address and resolve paradoxes raised in Chapter 1.

Resolving Paradoxes of Sustainability

We began this volume with two paradoxes that provided key insights into the puzzle of sustainability. One involves the duality of success and failures in regional systems of humans and nature, and the second involves how scientific-based approaches appear to have created a competency trap.

Sustainable Regional Resource Development

Worldwide, people are struggling to manage large-scale resource systems. Many are failing, as shown by the numerous resource systems that exist in a constant or recurring state of crisis (Ludwig et al. 1993). In the Florida


Everglades, for example, agricultural interests, environmentalists, and urban residents contest with one another for control over clean water (Light et al. 1995). In the U. S. Pacific Northwest, various advocates of salmon argue over the appropriate use of the Columbia River with those who prefer cheap hydroelectric power (Lee 1993; Volkman and McConnaha 1993). The nations surrounding the Baltic Sea struggle with issues of governance as the fish populations and water quality of the sea decline G ansson and Velner 1995). In these cases, resource management has taken a pathological form in which the complexity of the issues, institutional inertia, and uncertainty leads to a state of institutional gridlock, when inaction causes ecological issues to be ignored and existing policies and relationships to be continued.

Paradoxically, this failure arises from the success of initial management actions. Managers of natural resource systems are often successful at rapidly achieving a set of narrowly defined goals. Unfortunately, this success encourages people to build up a dependence on its continuation while simultaneously eroding the ecological support that it requires. This leads to a state in which ecological change is increasingly undesirable to the people dependent on the natural resource and more difficult to avoid. This management pathology leads to unwanted changes in nature, a loss of ecological resilience, conservative management policies, and loss of trust in management agencies.

When shifts occur between alternative states or conditions, it is usually signaled as a resource crisis. That is, a crisis occurs when an ecosystem behaves in a surprising manner or when observations of a system are qualitatively different from people's expectations of that system. Such surprises occur when variation in broad-scale processes (such as a hurricane or extreme drought) intersects with internal changes in an ecosystem due to human alteration. Examples include woody invasion of semiarid rangelands (Walker 1981), algae blooms in freshwater lakes (Carpenter and Leavitt 1991), and shifts in vegetation due to nutrient enrichment in the freshwater Everglades (Davis 1994). With each of these shifts in stability domains chronicled as crisis, understanding how and why people chose to react is key to managing for resilience.

When faced with shifting stability domains and corresponding crises, management options fall into one of three general classes of response. The first is to do nothing and wait to see if the system will return to some acceptable state while sacrificing lost benefits of the undesirable state. The second option is to actively manage the system and try to return the system to a desirable stability domain. The third option is to admit that the system is irreversibly changed, and hence the only strategy is to adapt to the new, altered system.

The resilience of the ecological system provides "insurance" within which managers can affordably fail and learn while applying policies and practices. The social equivalent of ecological resilience, or human adaptive capacity, resides in the ability to confront uncertainty.and develop understanding of


what contributes to loss of ecological resilience. Effective responses are those that identify sources of flexibility, as well as development of actions that are structured for learning and allow for the generation of novelty.

The explanation of the paradox of "the pathology of regional development and renewable resource management" is that natural systems have great resilience because of diversity within functions and across scales, and because humans can learn. Therefore, bad regional policy and management can typically be corrected, but at great and often increasing costs. The resilience is not infinite, and learning proceeds by costly lurches because of nonlinearities. Variability that maintains renewal capacity is the source of sustainable change, not the enemy of it. The key question for future work is how we can implement ways to expand human opportunity, sustain resilience, and facilitate human learning.

Developing New Expertise

Our book has stressed the "paradox of the expert," the tendency of experts to become rigid in their view and closed to potentially useful alternatives. Recent work in theoretical economics suggests some potentially useful speculation on this matter. Smith and Sorensen (2000) tell the following story, which can be adapted to experts in charge of managing an ecosystem. Imagine a series of experts, each of which must make sequential decisions about the true model of an ecosystem under scrutiny, all in the preordained order given by the research history on that ecosystem. Since the true model is an objective scientific matter, all these scientists would want to end up making the same decision about which model in a set of candidate models is the true model.

Smith and Sorensen study the problem of how rational individuals sequentially learn from the actions of others through a Bayesian approach of sorting among hypotheses rather than rejecting a single hypothesis. The main problem is the possible emergence of an incorrect herd or, in our context, settling on the wrong model. This unhappy result can occur even in the presence of a lot of information. This is so because each expert must condition his choice of model on his own research investigation, as well as the published work of all experts before him, but he cannot observe the rich, hidden investigative experiences of each of those previous experts. He can infer something about previous scientists' experiences by the way they write up their results and other indicators. These indicators provide information about the strength of the signal that each scientist received during his research as well as his observed reported beliefs about the true model.

This story is essentially an information cascade story. There has been a burst of research in economics on information cascade phenomena in social learning situations, with special emphasis on the possibility of incorrect herding on the wrong conclusion. Incorrect herding among scientists can lead to a situation where experts become rigid and closed to potentially fruit-


ful ideas because, based on their collective investigative history, the right way has become established. Information cascade theory gives a potentially useful set of methods to make such vague notions more precise with the useful byproduct of giving us more precise methods of identifying situations where this potential danger looms large. ·

Notice that unlike the Brock and Durlauf (1999) peer-pressure scientist model, there are no external reference group pressure effects. Adding realistic peer-pressure effects in a group of experts referencing each other in their commonly learned culture, as well as realistic positional incentives where each expert tries to bolsters her credibility by reference to other experts, will strengthen the incorrect informational herd effect. A lesson that we learn from briefly considering the Brock and Durlauf, Smith and Sorensen (2000) theories applied to cultures of informational experts is how easy natural incentives can end up trapping a community of experts into a bad basin of attraction that is perversely resilient to the introduction of potentially useful alternative points of view. This observation might strengthen the case for building institutional frameworks where members of the lay public, even though they are not experts, interact in a mutually informative setting where each gets to speak her piece and each gets to question any expert in a nonintimidating, mutually open, and supportive framework. This process might act something like "simulated annealing," where the "shaking" effect might help keep the system from getting trapped in an inferior basin of attraction, but the emergence of potential consensus slows the shaking at an appropriate rate so that the best basin of attraction is found and eventually maintained.

In many cases of regional resource management, the trap of the expert is offset by tolerance of a diversity of ideas and hereticism. That is, in the cycles of surprise and renewal (Gunderson et al. 1995a; Berkes and Folke 1998; Johnson et al. 1999) that characterize large-scale resource systems, generally a small set of players help lead the social scientific dynamics in such a way that new ideas are injected, new policies are developed to correct failures of the past, and new ways of attempting to understand complex dynamics are developed. Those reformations are generally a result of past experience, research that somehow becomes integrated and crystallized at key times (Gunderson 1999a; Chapter 13).

The explanation of the paradox of "the trap of the expert" is that existing theory and practice for linked systems of nature, economies, and people are too partial and fragmented among ecology, economics, and social science. Well-intentioned recommendations of the expert therefore can often be so partial that they become ammunition for powerful vested interests to distort public information and policy. The key question for future work is how to develop and implement integrated understanding, policies, and actions among scientists, economic and public interest groups, and citizens so that a self-correcting market for knowledge and action develops.

CHAPTER16

TOWARD AN INTEGRATIVE SYNTHESIS

RalfYorque, Brian Walker, C. S. Holling, Lance H. Gunderson, Carl Falke, Stephen R. Carpenter, and William A. Brock

Two roads diverged in a wood, and II took the one less traveled by,

And that has made all the difference. -Robert Frost

I n this synthesis chapter, we build upon Robert Frost's imagery of divergent roads as a metaphor for seeking sustainable futures. Clearly, there are many feasible futures, some sustainable and others not. We describe

multiple pathways, as there are many options for sustainability. Those paths are characterized by previous definitions of sustainability, including maintaining options for future generations, and having an anthropocentric origin (WCED 1987; Clark and Munn 1986). We suggest that the roads less traveled are defined by an emergent approach, one based on what we have learned from the past and how we translate that knowledge into action. That is, the question is not whether to seek sustainable futures, but rather how to seek sustainable futures. We argue that, because of the inherent complexities involved in attempting to answer such a question, the most pragmatic approach is one based on learning our way to sustainable futures, rather than planning our way.

Seeking sustainable futures is based on linking grounded theory with thoughtful practice. In that sense, practice informs theory as much as theory informs practice. We oudine that grounded theory in this chapter in three sections. In the first section, we develop a common conceptual framework based on blending concepts from various disciplines. We follow that with a section that describes a set of common themes that have emerged. We conclude with a set of guidelines that describes the steps of putting these synthetic themes at risk. We begin with the description of that integrative theory.

419

420 YORQUE, WALKER, HOLLING, GUNDERSON, FOLKE, CARPENTER, BROCK

Integrating Ecological, Economic, and Social Theory

The objective of this volume is to develop a nascent theory of regional sustainability that integrates ecologic, economic, and social dynamics. At the level of practice, all regional sustainability projects involve some combination of resource conservation, business development, and community empowerment objectives. These tripartite objectives have been depicted as vertices of a triangle (Munasinghe and Shearer 1995; Figure 16-1). Extreme domination by any one of these components will lead to failure, as sustainability objectives become overly narrow or dismissive of other components. The triangular model is an overly simplistic representation of the relationships among these components but provides a starting point for moving from theory to practice. We argue in this book that due to the complexities of relationships, multiple solutions, and inevitable surprising outcomes, there is no fixed optimal strategy, or mixture of strategies, for seeking sustainability. Instead, any optimal path follows an ever changing landscape, contoured by the dynamics of slow ecological and social variables. Thus, sustainability entails continual learning and adaptation. The purpose of this book is to envision an integrative theory for the process of sustainability, a theory that evokes provocative and testable questions.

Pragmatic regional managers might deny that their programs rest upon theory. Nevertheless, it is quite clear that actions are firmly, if subliminally, grounded in theories of ecology and evolution, economics and free markets, and social and institutional dynamics (Figure 16-2). These theories developed in separate disciplines with distinctive habits of mind. Each set of theories can point to remarkable successes within its own domain. Each set of theories is necessary, yet insufficient, to develop a theory of sustainability.

conservation and environment

Figure 16-1. Any regional project for development or sustainability has a mixture of conservation, business development, and community empowerment objectives. Any given project can be represented as a point within the triangle. A project that was completely dominated by a single approach would lie at a vertex of the triangle-100 percent conservation, or 100 percent business development, or 100 percent community empowerment.

16. TOWARD AN INTEGRATIVE SYNTHESIS 421

conservation and environment

comm"n;ty ~·<%~ empowerme~ t' ~ bos;ne,. ac tee development

I

I 1 ecology & 1 1

1 evolution 1 1

~--·t·;~ scoence ~ econom;cs s?cial 1"Theory :

Figure 16-2. The vertices of the triangle of practice (Figure 16-1) rest on theoretical foundations shown by the lower triangle, even though the connection of practice to theory is subliminal and sometimes forgotten. We seek an integrative theory for the lower triangle, which can evoke answerable questions to guide practice.

Academics encounter many incentives to maintain the purity of this discipline, and few incentives for integration. In this book, we have assumed that integration is essential to solve the sustainability problem. Moreover, we assume that the integrated theory can be discovered. "In the beginning, there was the universe; from the Middle Ages on, there have been academic disciplines to study it" (Daily and Ehrlich 1999). We attempt to go back to the beginning and discover a more inclusive approach.

Modeling Complex Systems

In building bridges to connect ecological, economic, and general social theories, especially from the modeling perspective (Figure 16-3), we begin with three abstract mathematical metaphors: (1) infinite-horizon optimal-control models of stochastic dynamical systems; (2) threshold cellular automata subject to outside forcing (e.g., sandpile models [Bak 1996; Scheinkman and Woodford 1994; Vespignani and Zapperi 1998]); and (3) coupled hierarchies of stochastic dynamical systems with control variables, in which the state variables display a discontinuous distribution of activities with respect to temporal and spatial scales.

In order to treat both time and space in a serious way, the state spaces of all these mathematical metaphors are sometimes assumed to be very large. Elements of all three of these classes of mathematical metaphors have been used in building complex systems models that have been applied in ecology (Levin 1998), economics (Arthur, Durlauf, and Lane 1997), and general social sciences such as political science and quantitative sociology (Axelrod 1997). These three categories provide a useful organization for exposition. The discussion here is related to articles such as those Hartvigsen, Kinzig, and Peterson (1998), Levin (1998), and Brock (1999a, 2000), but here more stress will be placed on the temporal and spatial smoothing characteristics of


social system .

• individualists coi:reating a common environment ··

• learning and choosing • sell-organizing

• slow and last variables • muhiple possible states • se~·organized from

evolved components

ecosystem

... · · ·· · ·· ··. : ~~7~~~ g~ ~~~ic

sustainable system

• integration of disciplines • systemic understanding o awareness of

cross-scale dynamics

models • flexible or rigid

Figure 16-3. General structure of the simulation models of people and nature introduced in Chapters 7 and 9 o

optimization models used in economics contrasted with the other two broad classes of models.

Despite explosive scientific progress, the dynamics of complex ecological and socioeconomic systems are difficult to understand and predict. Yet our ability to anticipate and change the future depends largely on our ability to comprehend complexity. Both ecologicaf and social systems share characteristics of complexity such as the absence of a global controller, hierarchical organization, dispersed interaction, ongoing creation of novelty, selection, and adaptation (Arthur et al. 1997; Holland 1995; Hartvigsen et al. 1998; Levin 1998; Milne 1998). Dynamics depend on history and have multiple possible outcomes. Therefore, a fundamental question is the degree to which system properties are environmentally determined versus the degree to which they are self-organized (Levin 1998, 1999)0

The nonlinear mathematics introduced to explore this question offers compelling generality, but an integration with empirical evidence remains fragmentary and incomplete. In both ecology and social science, for example, linear tools dominate empirical research. Linear methods almost always prove superior to nonlinear ones for practical analysis and policy implementation. Yet a recent review concludes that "evidence for nonlinearity is strong, but it is not clear where exactly this nonlinearity is coming from" (Dechert and Hommes 2000). There is evidence of important nonlinearities in alternate states of ecosystems (Carpenter 2000), spatial patterning of ecosystems (Milne 1998), and clumped or discontinuous size structure of ecological communities (Holling 1992). Current paradigms seem to fall into two clusters: one characterized by gradual reversible change described by adaptations of linear methods; the other embracing surprises, hysteresis, and irreversibilities that imply fundamental nonlinearities. For the practicing scientist, the question is when does the weight of evidence indicate that

16. TOWARD AN INTEGRATIVE SYNTIIESIS 423

complexity-based approaches add significant value for understanding or forecasting the system? For the policy analyst, the question is when do plausible nonlinearities create risks and opportunities that have low (but nontrivial) posterior probabilities yet extreme utilities?

The explanation and prediction of regional ecosystems motivate a substantial body of ecological research. Ecologists are attracted to this problem because it is important and difficult yet appears tractable and ultimately solvable. Great progress has occurred, and several significant advances occur each year in this active area of research. Approaches to spatial pattern dynamics range from stylized analytic models to richly detailed simulation studies (Tilman and Kareiva 1997). Key insights often emerge from juxtaposition of contrasting styles of spatial models (Ives et al. 1998; Pacala and Rees 1998; Levin, Grenfell et al. 1998). Many ecological theorists now emphasize discrimination among alternative models through quantitative, data-rich analyses (Hilborn and Mangel 1998; Kendall et al. 1998).

The detection and prediction of thresholds and breakpoints in ecosystems remain a significant challenge. Evidence for various forms of nonlinearities derives from the integration of multiple lines of evidence and large data sets (Milne 1998; Carpenter 2000; Kendall et al. 1998). Massive changes in ecosystems are rare events, however. Multiple observations of phase changes are needed to fit models and build predictive capability. This takes a long time. Tremendous progress in weather forecasting has derived from frequent observation and spatially synoptic data. New information to update and improve weather models arrives every day. In contrast, one year may be the smallest meaningful time step for fitting ecosystem models. This is why ecologists emphasize the importance of long-term data and devise proxy records using tree rings and lake sediments. Nevertheless, ecological data are often insufficient to detect or forecast potentially important thresholds. Predictions of linear models generally outperform nonlinear ones, except in cases where strong and tested causal understanding exists, long time series are available, and perturbations (either natural or deliberate) reveal nonlinear ecosystem processes (Clark et al. 1979; Carpenter et al. 1994).

In order to make policy-relevant forecasts, therefore, ecologists must measure the probabilities that should be attached to several different structural models, some with smooth· dynamics and others that include potentially important thresholds. These probabilities are not easy to measure. For regional forecasting, the quantification of uncertainty is as important as the creation of plausible alternative models and the development of point predictions. In the next sections, three modeling frameworks are presented to begin to approach these difficult problems.

Infinite-Horizon Stochastic Optimal-Control Models

In economics and game theory, ecological models can be combined with variations on optimal-control models to represent optimal planning goals of individual agents. Infinite horizons are sometimes used to approximate

424 YORQUE, WALKER, HOLLING, GUNDERSON, FOLKE, CARPENrER, BROCK

agents whose time span of planning is long relative to time scales of variables of interest. These agents are then coupled in various kinds of interactive settings to determine entities such as prices and quantities by some kind of equilibration.

An important theme that often occurs is the smoothing role of optimal control when goals are in the form of a discounted sum of rewards and reward functions have some regularity properties such as concavity. The attempt by each agent to do something like equate marginal rewards across time can create equilibria (provided the future is discounted lightly enough) that are ergodic Markov processes (Brock 1998). The ubiquity of Markov process equilibria with unique limiting ergodic properties playing the role of a unique stable state whose basin of attraction is the whole space, in turn, can be approximated by linear methods. In important subfields of economics, linear methods are favored because evidence for nonlinearity is scant, and intertemporal smoothing caused by long-lived agents optimizing their goals is thought to be approximated by linear models (Brock 2000). In ecology there are important subfields where linear methodology is also dominant (Carpenter 2000).

This book is concerned with nonlinear phenomena in ecology, economics, and science in general. We are especially concerned with policy relevant cases where the application of linear methodology is not only mistaken but also harmful. Many examples of alternative stable states in ecology are presented by Carpenter (2000) and in Chapter 2. Obviously, if an economic sector is coupled onto any of these systems and we manage such a system using linear methodology, things may go badly wrong. Nonlinear methods based on the assumption of a unique stable state can also produce disastrous results. Carpenter, Ludwig, and Brock (1999) in an optimization setting and Carpenter, Brock, and Hanson (1999) in an agent-based evolutive setting developed examples for the case of coping with human-induced eutrophication of lakes (Chapter 7). This brings us to two leading types of nonlinearity we wish to discuss: self-organized criticality and cross-scale interactions.

Poised States and Self-Organized Criticality

Bak (1996) discusses models organized around the metaphor of a sandpile with sand raining down upon it. As the pile builds up, it self-organizes to a critical level (self-organized criticality, SOC) where slides of.varying durations and sizes occur. The durations and sizes of the slides tend to be distributed as power laws at SOC. These models appear to be attractive because the power law distributions seem to be quite robust to details of particular models, and, on the face of it, no outside tuning parameter (e.g., temperature in interacting particle systems models such as Ising models of magnetism) appears to be needed to attain criticality (of course, the pile must be "fed" with sand). There is an enormous literature on these models, and we are glossing over many important details and qualifications. The main point of interest here is that power laws are commonly observed in. many sci-

16. TOWARD AN INTEGRATIVE SYNTIIESIS 425

ences, including ecology and economics, and sandpile-like endogenous buildup of critical or poised states with episodic releases may give rise to these power laws. See Maslov and Zhang (1995); Nagler, Hauert, and Schuster (1999); and Vespignani and Zapperi (1998) for some results on these models.

Sandpile mathematical models represent one attempt to capture recurrent buildup, release, and renewal phenomena represented in the adaptive cycle (Chapter 2). Avalanches represent release, and buildup is represented by the accumulation (adding of additional sand) to another avalanche. E. Anderson (1996) stresses homeostatic phenomena in general as a feature of complex systems. That, and the nested speeds, is what gives the panarchy representation such conservative properties (Chapter 3).

Notice that the buildup, release, and renewal pattern of sand slides looks somewhat like the adaptive cycle discussed in Chapter 2. For example, as sand continues to drop, the pile remains inert until a sand slide starts. Connectedness might be represented as the number of neighboring grains just at critical angle with respect to each other. Clearly, this measure would tend to increase since the time the last slide finished. At the point where a grain exceeds the critical angle, a slide starts, and its size will be roughly proportional to a measure of the number of neighboring grains just at critical angle. The number of grains just at critical angle could be viewed as a type of capital or potential in the adaptive cycle (Figures 2-1, 2-2). The resilience measure is initially high and slowly changes (no grains move as grains continue to be added, though sensitivity to outside disturbances increases until one exceeds the critical angle relative to its neighbors to start a slide) until resilience collapses and a slide begins. Of course, much more work is needed to develop this analogy.

Panarchies: Hierarchical Dynamical Systems

Another attempt to capture recurrent buildup, release, and renewal behavior and, at the same time, to simplify complexity is by looking for evidence of clustering and discontinuities in spatial and temporal data (via spatial and temporal spectral analysis) and studying structuring processes that produce such patterns (Holling 1992; Chapter 3).

This attempt to simplify complexity by application of spectral analysis in time and space in order to identify clumps of high spectral power is analogous to the application of spectral analysis in macroeconomics to locate regions of high spectral power. See, for example, Sargent (1987), where frequencies (e.g., business cycle frequencies) are sought at which high spectral coherence occurs across variables of interest (e.g., output, price level, unemployment in business cycle analysis). In ecology, spectral analysis and related methods are used to identify structural clumps in space or time (Levin et al. 1993; Powell and Steele 1995). Trophic cascades in lakes, for example, produce clumps of variance at time scales that correspond to the life cycle of the fish at the apex of the food web (Carpenter 1988; Carpenter and Leavitt


1991). The small number of dominant turnover times evident in aquatic food webs enables ecologists to extract considerable insight and ·predictive power by fitting relatively simple structural models (lves et al. 1999).

Another point we wish to make here is that evidence from some ecological case studies suggests that slow-moving variables may entrain the faster-moving processes into lumpy distributions or convergence classes. Averaging over these slow-moving heterogeneities may produce the appearance of a power law distribution and mask important structure.

In hierarchical dynamical systems of slow/fast variables, the structuring of temporal variables resembles a directed tree in graph theory. Spectral analysis can be used to identify gaps in the tree for purposes of conceptual simplification. The idea is that variables lower on the tree are slaves to those above. Variables higher (i.e., slower) in the hierarchy serve as potential bifurcation parameters for variables that are lower (i.e., faster).

The hierarchy approach to complex systems research can be viewed as Herbert Simon views it (discussed in Chapter 3). Simon argues that many complex systems have a hierarchical structure where a Marshallian type of partial equilibrium analysis might be applicable. However, that type of approximation is not good in a dynamical setting poised at a critical state such as a sandpile or an interacting particle system with tuning parameters set at the edge of a phase transition. That is one of the essential features of the panarchy that turns hierarchies into dynamic structures. Individual levels do have such nonlinear multistable properties. Critical connections between levels can stabilize (the remembrance arrow of Figure 3-5) and destabilize (the revolt arrow of Figure 3-5). In the hierarchical dynamical systems stressed here, linear methodology at one spatial or temporal scale may work poorly, especially when there is a hard loss of stability (sensu Ludwig, Walker, and Holling 1997).

Coping with Uncertainty

In this section, we examine two emergent themes that address the ability of humans to cope with the uncertainty inherent in these complex systems. The first theme involves the unique property of social systems to engage in forward-looking behavior, and the types of institutions in certain systems that are developed to deal with anticipated uncertainty. We end with a section on practices that deal with deeper, more fundamental unknowability and ways to navigate these uncertainties.

Human Foresight Potential

An attempt to differentiate human systems from ecosystems might start with the relatively high degree of foresight potential exhibited by human agents in contrast to other agents such as plant and animal agents (Chapter 4). Human agents also have the capacity to design institutions (such as futures and derivatives markets) that not only improve resource allocation but also

16. TOWARD AN lNTEGRATIVE SYNTIIESIS 427

impound individual information and transmit it in the form of publicly observable aggregators such as market prices (Grossman 1989). The design of markets and supporting institutions to augment their role in transmitting information as allocating resources echoes Sober and Wilson's (1998) stress on group-level functional organization. Sober and Wilson emphasize that our species "seems special when it comes to group-level functional organization." Of course, other species such as the social insects (ants, bees, termites, and others) can organize magnificent structures, but none of these species has futures markets. Great differences exist between the complexity of structures created by humans and that created by other species. The human ability to design forward-looking institutions such as asset markets is related to dramatic technical differences in modeling strategies between economics and other sciences. The differences involve levels of strategic sophistication (e.g., compare the depth of strategic reasoning typically assumed in game theoretic approaches in biology with that assumed in game theoretic approaches to human systems) or even the mechanics of solving differential equations and other dynamical systems models.

For example, human forward-looking cognitive behavior emphasizes solving dynamical systems models forward, as do rational expectation modelers, rather than solving them backward, as do physicists and other natural scientists. Optimal control theory is applied in both economics and natural science, but the model setup in each is fundamentally different. The difference we are attempting to emphasize here is best explained with a simple example (Box 16-1).

This apparendy simple difference between mathematical approaches turns out to be a fundamental divide between economics and ecology, which scientists must bridge thoughtfully in order to conduct interdisciplinary work. Indeed, the failure of scientists to appreciate this difference in models underlies some prominent disagreements between ecologists and economists (Box 16-2).

Let us elaborate the contrast between backward- and forward-looking models by stating the criticisms that adherents of one approach would cast upon the other. According to the backwards-looking modeler (BLM), the forward-looking modeler's (FLM's) assumption of a jump to a target assumes too much. How can the target for equation 16.1D, for example, be known in advance, without careful fitting of models like 16.1 C to extensive historical data? Even if appropriate data exist and statistics are done rigorously, surprises may occur. Learning about ecosystems is a slow process. Assimilating information about slowly changing variables is the key to resilience and avoidance of surprise. To natural scientists, the assumptions of rational expectations models seem grossly oversimplified at best, arrogant and misguided at worst. Efforts to predict events or phenomena with complex, diffuse, and regional impacts, such as acid rain, energy supply and consumption, the behavior of radioactive waste in a geological repository, and global climate change, have rarely contributed to the resolution of policy debates


Box 16-l. Backward- and Forward-Looking Models

W. A. Brock and S. R. Carpenter

In order to illustrate our distinction between backward- and forwardlooking models, here we introduce a simple example. Consider an asset market where people can put their money into a bond and receive R one period later versus putting their money into a stock at price p(t) today and receive q(t + 1) = p(t + 1) + y(t + 1) next period. We will assume that people have common expectations on y(t + 1), and we will set y(t + 1) = y, constant in time, in order to concentrate on the problem of forecasting price.

The problem is to form an expectation on q(t + 1) at date t, before q(t + 1) is observed. Worse yet, the expected dividends price p(t + 1) depends on dividends in future periods. If all market participants have common expectations on q(t + 1), call them q(t + l;t); then arbitrage forces equation 1A to hold.

Rp(t) = q(t + 1;t) (lA)

Solving lA "backward" corresponds to putting p(t + l;t) = p(t - I)

to obtain the equation 1 B,

Rp(t) = p(t - l) + y (I B)

Or equivalently

p(t) = b(p) = [p(t- I)+ y]/R (I C)

Under this model, the equilibrium price p* = yj(R- l ) is stable if R > l.

In the natural sciences, where material explanations are often based on the notion that the past is key to the future, it is natural to form backward-looking predictors of p(t) like equation I C. Such predictors may use weighted averages of past prices and other historical information but still predict the future from observed past behavior.

Economists, in contrast, wish to model the capacity of agents to anticipate and even change the future. Thus they emphasize forward-looking reasoning, which in the simple case of perfect foresight amounts to setting p(t + l;t) = p(t + 1). T his leads to solving equation A forward to obtain

Rp(t) = p(t + l) + y (10)

Or equivalently

p(t + 1) = /(p) = Rp(t) - y (I E)


Under this model, the equilibrium price p* = yj(R- l) is the same as before, but the equilibrium is unstable if R > 1. For this reason, economists tend to posit that the solution of equation 1 E "jumps" top*.

Let us look at the difference between treating price as a forward-looking, jump variable or a backward-looking historical adjustment variable as in 1C. For a dramatic example, imagine that it is announced today that at future time T > l , y will be replaced by 2y from timeT to forever. Solution 1C would stick at p* until T and then converge up to 2p* after T. Solution 1E would jump today in such a way that it would be at 2p* exactly at date T when the dividend process changes from y to 2y . In a situation like this where there is a commonly understood announced change to take place in the future at T, economists argue that a forward-looking solution like equation 1E is a much more reasonable model of human behavior than any backward-looking model, such as 1 C. Variations on theories like equation 1 C when risk and uncertainty are present are typically called rational expectations theories. Turnovsky (1995) discusses jump variables and basic issues in rational expectations modeling. Even sophisticated ecosystem modelers do not consider rational expectations models, although optimization models have a long history in evolutionary ecology. Roughgarden (1997) presents an optimal foraging model of a sentient lizard and quips that it might be smarter than a university dean!

and have often contributed to political gridlock. This experience in part reflects the intrinsic scientific challenges of prediction, but it also derives from the complex scientific and policy context within which the predictive research takes place (Sarewitz and Pielke 2000). In other words, in situations of high complexity and uncertainty, where there are no appropriate markets like the ones assumed in Box 16-1, a market of power among vested interests emerges. In addition, nonlinear dynamics of coupled ecological-social systems can produce abrupt changes that thwart the smoothing features invoked in Box 16-1.

In contrast, the FLM argues that the BLM's assumptions about human nature are too simplistic. While acknowledging that the assumptions of some rational expectations models are too extreme to capture actual human behavior, the FLM notes that the low-level cognitive behavior represented in the BLM is far too simplistic. The FLM invokes an egocentric self-consistency rule: Do not put any human behavior into your model that you would not be proud to call your own. An economic critique of BLM is elaborated in Box 16-2.


Box 16-2. Failure ofNaive Backward-Expectation Models

WA. Brock

Problems in the management of ecosystems by humans should generate a search for frictions in the formation of collective social action. In modeling, this changes the focus away from market-based approaches toward approaches that focus on collective action processes. In relatively frictionless systems like financial markets in highly developed countries, there's almost zero evidence for exploitable patterns in stock prices over time and across securities. This is an equilibrium type of property that would not be generated by backward-looking differential equations or probabilistic cellular automaton (PCA) models. But it is consistent with forward-looking rational expectation types of models. In these models the differential equations are solved forward to reflect the rationality of an expectational equilibrium.

A famous example of how one type of systems modeling was discredited by economists and other critics was the Club of Rome debate of the 1970s. Models by Meadows et a!. ( 1972) forecasted the emergence of overshoots and crashes as humans were projected to run down natural resource stocks. Some critics have stressed the poor forecasting record of similar-looking doomsday modelers from the past and (in some cases) the extremely damaging policies that have been implemented based on such work. Solow (1973) presented a withering critique of the systems-based modeling of Meadows et a!., who ignored forward-looking behaviors. That is, these types of models did not include elements such as futures markets, where strategic buying and holding of commodities could play a major role in transmitting perceived future scarcities into current prices that, in turn, help to induce conservation.

Indeed, related critics of systems-based modeling might argue that a focus on the potential instability of backward-looking systems modeling draws attention away from the real problem in environmental management: Getting the accounting system corrected to reflect the full cost of production and consumption and getting the incentive system corrected so that all who create costs bear those own costs. For many economists this incentive gap is the real environmental tragedy. For them more good can be done by convincing governments to implement reforms such as green ac-

16. TOWARD AN lN:rEGRATIVE SYNTIIESIS 431

counting and imposing green taxes to gain revenue as well as realigning incentives to stop private interests and government departments from exploiting the environment and off-loading the costs onto everyone else.

Even if there are slow-moving variables in the real world that might lead to a collapse in economic quantities, one must find the force that blunts the very large incentive for people to find this variable, measure it, forecast the coming collapse better than the rest of the market, and take a position to profit from it. But what one market participant can do, all can do, thereby transmitting information to the market as a whole.

The dynamics-structuring role of this kind of self-interested behavior in pursuit of profit acting to transmit information from the future to the present to the human system as a whole is a major difference between the dynamics of human systems and the dynamics of ecosystems.

To reiterate the main complaint of critics against adaptive systems modeling is purporting to model human behavior but ignoring essential human behaviors such as forward-looking expectational behavior and ignoring forward-looking institutions and incentives such as forward markets and futures markets as well as ignoring incentives for human agents to store now the cheap resources and sell them in the future when they are expensive and scarce. Such strong incentives generate signals (high prices today) to conserve and, perhaps, steer the system away from instability.

Indeed, basic results in economic theory suggest that such forces can be powerful in removing instabilities in systems that are unstable when such forces are ignored. Of course, one should not exaggerate the stabilizing properties of asset markets, especially when crashes and blowoffs abound. The point being made here is that such markets are useful in transmitting and aggregating information, and market prices, being forward-looking informational jump variables (Grossman 1989; Turnovsky 1995), absorb instabilities in economic systems in a much different way than natural systems do. Furthermore, the intertemporal consumption- and production-smoothing services provided by a large array of markets help build a type of resilience into the economic system. Brock (1988) discusses possible frictions in the real world that may blunt this usual result.


In our view, both BLM and FLM are useful in certain contexts and overly simplistic for other goals. The criticisms of both BLM and FLM center on the caricatures of learning embedded in the two models. Learning about natural systems builds on inferences and syntheses that are more complex and insightful than those in the BLM. Yet humanity's foresight about natural systems is far weaker than assumed by the FLM, allowing the exercise of power to override market efficiencies and triggering surprises that thwart the smoothing function of forward expectations. Each caricature has useful elements of truth, but the learning needed for sustainability is more complex than its representation in either class of models. This book searches for a model of learning that will enable creation of alternative futures. Such learning will integrate multiple strands of information in ways that lead one to avoid certain blind alleys and cliff edges. It also involves creation of ideas and options that never existed before. It requires experimental probing with an acceptance of the risk of mistakes. Learning about natural systems takes time, social flexibility, and enough ecological resilience to make the system tolerant of our explorations.

Navigating Uncertainty

Successful ecosystem management requires monitoring and institutional capacity to respond to environmental feedback (Gunderson et al. 1995a; Folke, Berkes, and Colding 1998). Urbanization and many aspects of globalization that tighten intersystem linkages, hierarchies, and interdependencies between local resource users and the wider society, through the market, political control, and social networks, tend to distance resource users from their dependence on life-support ecosystems, disconnect the production from consumption, and disconnect the production of knowledge from its application (Folke et al. 1998). The tightening of processes of globalization weakens the tightening of societal feedback loops to ecosystem dynamics essential for sustaining and building adaptive capacity and for securing the flow of critical ecosystem services.

What are the implications of a human-dominated planet for the disturbance panorama, for ecosystem resilience, and for human health? Several studies have shown how increasingly nested human activities are changing disturbance panoramas by (1) actively suppressing or removing disturbance, (2) transforming pulse events into persistent disturbance or even chronic stress, and (3) introducing new disturbances (Holling and Meffe 1996; Nystrom et al. 2000). The intensity, severity, duration, spatial distribution, and frequency of disturbances are altered. Combinations of those changes lead to new synergistic effects or so-called compounded perturbations that in many aspects are new to organisms and ecosystem dynamics (Paine et al. 1998).

The main part of Earth's surface has been modified by human activities, and recently at a much faster pace than earlier in human history. In the process of globalization there is a tendency to simplify ecosystems for pro-

16. TOWARD AN INTEGRATIVE SYNTHESIS 4 3 3

duction of resources to be traded on global markets. Simplification causes loss of resilience. Ecosystems with reduced resilience may still maintain function and generate services-i.e., may seem to be in good shape. But when they are faced with an additional disturbance, a critical threshold may be reached as a consequence of loss of resilience, and the system may slide into another stability domain where a large-scale degradation may occur, a pattern observed in many ecosystems (Nystrom et al. 2000).

A disturbance that earlier triggered a dynamic development of the system may under circumstances of lowered resilience become an obstacle to development. Losses of resilience through impacts on the landscape and seascape will exacerbate the effects of changed disturbance panoramas, compounded perturbations, and increase the likelihood of shifts into other stability domains. Shifts from one stability domain to another may be irreversible (at least during the time span of a human generation), cause losses of essential ecosystem services, and affect socioeconomic progress and human health.

In a world with inexhaustible ecological resilience, it wouldn't be a major issue to expand, urbanize, globalize, and disconnect from environmental feedback. The basic support to socioeconomic development would be there anyhow, and new solutions and innovations like biotechnology and information technology could easily cope with environmental problems and constraints when they appear. In a world with reduced resilience, variability will most likely increase, uncertainty will be larger, and surprises will be more common (Gunderson et al. 1995a; Paine et al. 1998). It will be more difficult to predict and direct society toward prosperous development. Ecosystem reorganization and recovery after disturbance can no longer be taken for granted. Clearly, conventional approaches will not suffice to cope with a spectrum of potentially catastrophic and irreversible environmental problems (Levin, Barrett et al. 1998).

Therefore, resilience, the capacity to renew and reorganize after disturbance, has to be actively managed. Risk spreading and insurance strategies in ecosystem management for building resilience should become part of policies for social and economic development (Costanza et al. 1999). Societies need a variety of ways to assess the changing state of ecosystem resilience not only at local scales as is routinely done via environmental impact assessment, but at numerous scales and across systems. The sustainability transition requires an active redirection of devastating mismatches between societal dynamics and ecosystem dynamics. Restoring and managing ecosystem resilience for enhancing the adaptive capacity to respond to change should be a key component of the sustainability transition.

Accelerating Learning through Actively Adaptive Networking

\Vith these issues in mind, we suggest that learning our way into sustainability can best be done by a two-step process: (1) build on the theoretical


understanding presented in this volume, and (2) test and apply the theories in a series of regional case studies. The development of the theoretical work on integrating across disciplines and scales will feed on the results emerging from the case studies. And the case studies will attempt to develop frameworks for achieving integrated social, economic, and ecological sustainability, by applying the theory in an adaptive management context.

We suggest that transition to sustainability should transform the way regional development and resource management is done in regions that are experiencing crisis and change. The word region implies different things to different people. Here it is considered at the scale of a catchment or subcatchment, the scale at which ecosystems and people are tightly interconnected. This is the scale where ecosystem managers attempt to balance the constraints on their efforts to maximize their own welfare, imposed, on the one hand, by social rules, government regulations, and markets, and on the other by the responses of ecosystems to their management actions. All over the world, from the poorest and most degraded to the richest and most productive, dozens of such regions involve many millions of people in the throes of crisis-driven change; and in most there seems to be a dearth of effective policies and actions to bring them onto a trajectory of social and ecological sustainability.

According to the theories advanced in this volume, these are all regions that either are moving toward or are in the reorganization (the a.) phase of the adaptive cycle. That is the phase with the greatest uncertainty, inequities, and resource conflicts. It is where the poor are most vulnerable and where the ranks of the impoverished can grow. But it is also the phase where windows can be opened for novel and creative solutions.

Worldwide, the pattern of dealing with the emerging problems (pests, loss of fertility, soil erosion, price changes, migration of people to the cities) has been to tackle them separately, as they have become evident, using a variety of positive and negative incentives, subsidies, and technological innovations (credit, change in crop type, marketing, new pesticides, etc.).

As the problems have grown in magnitude, there has been a growing recognition that they are multiscale problems, involving interactions across different spatial components of regions, often involving whole river basins including the river systems themselves and the estuaries or lakes into which the rivers flow. Much of the failure of effort and investment in agricultural restoration and regional plans is because they involved the wrong scales and were done at times when change was unlikely to be effected, and because the individual problems were tackled as separate entities.

Research is needed, therefore, to develop the necessary level of understanding of the dynamics of these complex systems required for long-term sustainability. In line with the real needs of the regions and with the increasing emphasis on corporate and government requirements for triple bottom-line accounting, sustainability is taken to include ecological, economic, and social sustainability.


In conjunction with this proposed set of regional case studies, future research should include crosscutting projects on the politics of resilience, on the dynamics and resilience of social and institutional structures, and an overarching project on modeling complex systems to ensure that the regional studies learn from and inform each other and that the synergies in this process are maximized. This component involves the development of integrative models that combine realistically nonlinear ecosystems, with assemblages of people who make individualistic decisions about a world they cocreate even as they try to understand it, with technologically based management agencies. The prototypes of these models yield behavior that is totally unexpected from theories of ecology, social organization, or optimal economic decision theory alone. Yet the models (e.g., Chapter 7) seem to mimic the observed behavior of many case studies of ecosystem management examined so far. We suggest projects that would explore processes of adaptive learning through workshops with practitioners, using strategic models codesigned with practitioners.

To complement this research agenda, a strong communications program is needed, involving dialogues with industry and a series of colloquia involving scientists and policy makers. One such opportunity exists with online journals (such as Conservation Ecology, www.consecol.org) as a medium for supporting outreach activities, along with promoting understanding and adoption of an emerging theory in science and in resource-use policies. The success of such a communications effort could be judged by the degree to which it changes the way people, industry, and agencies think about the dynamics of regional agroecosystems and the necessary components for their integrated (ecological, economic, and social) sustainability.

Posing Questions

We live on a human-dominated planet. Disturbance regimes and ecosystem resilience are altered at a faster rate and at larger scales than previously in human history, and the patterns and processes of self-organization are modified. True uncertainty and surprise will increase. There will be a paradigm shift from approaches emphasizing optimal solution and control over limited temporal and spatial scales toward approaches emphasizing cross-scale interactions and living with true uncertainty and surprise. The emphasis should be on flexible institutions and human organizations that can build adaptive capacity in synergy with ecosystem dynamics and reward systems that respond to feedback. In order to begin to address these complexities that confront us, we pose a number of key questions, grouped in three categories: (1) pursuing understanding of complex adaptive systems, (2) developing new myths that allow us to test that understanding, and (3) designing new ways to manage with change rather than against it. Key questions are posed in each of these categories.


Seeking Understanding of Complex Systems

In this volume we have stressed integrative theories. Key unknowns lie in the development of theories to address self-organization (how structure and process interact to create emergent, persistent patterns) and to address evolutionary change in joined systems of people and nature. Perhaps addressing these questions will prove as important to understanding systems of people and nature as Darwinian natural selection is to biology. In order to develop such a theory, we suggest the following questions:

• How are self-organized patterns created and sustained in ecosystems and on landscapes at different scales, from meters and months to thousands of kilometers and millennia?

• How do such patterns, the processes that produce them, and species adaptation sustain critical ecological functions across those scales?

• How can we understand the role of diversity in allowing and modulating adaptability in a wide range of settings, from biodiversity and evolution to the diversity of ideas and its influence on human adaptability to changing circumstances?

• How does the interaction between social, economic, and ecological processes interact to change those patterns?

• "What are the critical structuring forces behind adaptive cycles in social systems?

• How do we address multiple equilibria in historical, social, and political systems through the context of paths not taken, lessons not learned, and decisions not made?

Developing New Myths

Many of our popular and scientific ideas are based on a static view of the world and the place of humans in it. Some views of sustainability have this static quality. In contrast, the evolutionary basis of our biological insight stresses adaptation and response to changing conditions. Our present system of economic values is based on a static view, and it is heavily influenced by wealth and power distributions of the status quo. Our view of sustainability stresses adaptability and learning through thoughtful probing.

In order to help probe uncertainties about sustainability, we pose the following questions in order to help future learning:

• How much societal redundancy is required to sustain the capacity to adapt in a flexible way to unpredictable change?

• How do we develop adaptive capacity in a world of rapidly changing information, technology, and the homogeneity created by globalization?

• How can we fundamentally change the basis of popular and scientific ideas to reflect evolutionary, adaptive, and responsive perspectives?


• Why do intelligent, knowledgeable people organized in sophisticated societies degrade their life-support systems to the point of ecological and economic disaster? And how can such catastrophic degradation be prevented?

Managing for Change

Good solutions to the problem of leaving a decent environmental future for our children and their descendents will depend on our ability to design institutions (and incentives within them) that work as well in producing environmental prosperity as the institutions of free markets and private property (and incentives within them) have worked in producing material prosperity. We ask the folliwng questions to better understand how those institutions may be structured:

• How can we build the social flexibility needed to facilitate experimentation and reorganization following ecological crises?

• What are the pathways and prospects for adaptive institutional solutions with the ability to restore, sustain, and enhance the sources of self-organization and resilience in landscapes and seascapes increasingly dominated by humans?

• How can we formulate and estimate mechanisms that determine the patterns of emergence of social control mechanisms for dealing with emergent environmental problems, and how can we create policies to increase the speed of emergence and the efficiency of learning?

• Can we develop and exploit analogues of virtual engineering to conduct policy experiments that would allow a mix of material and environmental prosperity that truly increases human well-being?

• How can we confront the multiple paradoxes of politics? What is the resolution between stability created by bureaucracy and resilience created by innovation? How is power channeled across levels of political organization when openness, violence, knowledge, and the social construction of authority are all instruments of transference?

• How can we implement forms of management based on learning? How do we overcome difficulties at the personal level (multiple and shifting problem domains), and difficulties faced by large, bureaucratic institutions? How do we blend traditional and other forms of knowledge with scientifically based ones?

An End and a Beginning

We began this chapter and this volume on the premise that sustainable futures are inherently unpredictable. This unpredictability arises from partial


theories and constructs that arise from disciplinary perspectives and from policies and actions that arise from these partial approaches. It isn't that there are too few theories for these linked ecological-economic-social systems. There are too many. And they are all (or mostly) correct-correct but seriously incomplete. Their use generates long-term problems in satisfying short-term objectives.

In our quest for theory, we therefore tried to integrate existing theory and develop novel extensions of that integration. The goal was to achieve a requisite level of simplicity, just complex enough to capture and explain the behaviors we see and the policies and investments we need in order to provide opportunity and sustain it. We attempted to explain discrete patterns in space and time, discontinous structures, crises, and surprises generated by management, and how novelty and innovation are suppressed or are entrained. For prescriptive purposes we also sought adaptive ways to deal with surprise and the unpredictable. We concentrated on adaptive approaches that recognize uncertainty and unpredictability and do not smother opportunity, in contrast to control approaches that presume that knowledge is sufficient and the consequences of policy implementation are predictable.

In the years since the Berlin wall fell, there has been a cascade of global and regional transformational change-biophysical, economic, and political. These "gales of change" suggest that the window for constructive change has opened at several scales. It is a time when conditions of the backloop of the adaptive cycle (Figure 2-1) dominate. Under those conditions, the prescription for facilitating constructive change includes the following approaches:

• Identify and reduce destructive constraints and inhibitions on change, such as perverse subsidies.

• Protect and preserve the accumulated experience on which change will be based.

• Stimulate innovation in a variety of safe-to-fail experiments that probe possible directions, in a way that is low in costs for people's careers and organizations' budgets.

• Encourage new foundations for renewal that build and sustain the capacity of people, economies, and nature for dealing with change.

• Encourage new foundations to consolidate and expand understanding of change.

These prescriptions describe a positive and constructive approach to seeking sustainable futures. But much remains, in terms of developing and testing theory and methods that help accelerate learning about feasible, sustainable paths. We hope this volume contributes to discovering such a future.

APPENDIX A.

A MODEL FOR ECOSYSTEMS WITH

ALTERNATIVE STABLE STATES

I n order to analyze how the socioeconomic system interacts with ecosystem dynamics, it is useful to capture the basic properties of the catastrophic response of ecosystems in a simple mathematical model.

Although, as argued, on a high level of abstraction lakes and dry lands have some common properties, the actual mechanisms involved are really quite different. Therefore, it is not possible to formulate a model that faithfully reflects the mechanisms operating in lakes, deserts, and other catastrophically responding ecosystems. Instead, we propose the following very simple model, which captures the catastrophic properties in a rather abstract way, describing the change over time of an "unwanted" ecosystem property x (equation A-1).

dxjdt =a- bx + rf(x) (A-1)

The parameter a represents stress imposed by human use, which promotes x. The remainder of the equation describes the internal dynamics: parameter b represents the rate at which x decays in the system, whereas r is the rate at which x recovers again as a function (f) of x. For lakes one can think of x as nutrients suspended in phytoplankton and causing turbidity, of a as nutrient loading, of b as nutrient removal rate, and of r as internal nutrient recycling. For dry lands one may think of x as barren soil, of a as vegetation destruction, of b as recolonization of barren soil by plants, and of r as erosion by wind and runoff. This specific equation has also been proposed to mimic the dynamics of nutrient-loaded deep lakes (Carpenter, Ludwig, and Brock 1999).

439

440 APPENDIX A

For r = 0, the model has a single equilibrium at x = ajb. The last term, however, can cause the existence of alternative stable states-for instance, if f(x) is a function that increases steeply at a threshold (h), as in the case of the hill function: f(x) = xP j(xP + hP), where the exponent p determines the steepness of the switch occurring around h. Notice that equation A-1 can have multiple stable states only if the maximum r f (x) > b. ·

APPENDIX B.

OPTIMIZING SOCIAL UTILITY FROM

LAKE USE

S uppose the lake is affected by N Affectors, and each Affector i loads a(i) nutrients into the lake. Then the dynamics of the lake in response to the Affectors' action can be characterized by substituting a with

A= Sum[a(i)} in equation A-1. Now let the Affector utility be given by

UA = Sum[u(a(i), i], (A-2)

and the utility to Enjoyers be given by

UE = Sum[v(x, k)], (A-3)

where u, v are concave functions, and where u is assumed to increase in a(i), and vis assumed to decrease in x. Carpenter et al. (1998) treat this problem in some detail.

In the "normative" case, where the future is weighted equal to the present (i.e., there is no discounting), we would optimize welfare by solving the problem

Maximize {UA + UE}, (A-4)

subject to the constraint that the ecosystem equilibrium state responds to the stress imposed by the total load A imposed by the Affectors:

dxjdt = 0 =A- bx + rf(x). (A-5)

441

442 APPENDIX B

Figure 8-7 captures the solution to this kind of problem for the special case in which all Affectors and all Enjoyers are identical. In the figure we plotted the value of the objective A-4 on the vertical axis, A on the stress axis, and a desirable aspect of ecosystem state such as vegetation biomass on the third axis. Note that, since x represents an unwanted aspect (e.g., turbidity or barren soil), x would increase from left to right along the ecosystem state axis.

In the special case where there are N identical Affectors and M identical Enjoyers, with utilities u(a), v(x), respectively, problems A-4 and A-5 become

Maximize {Nu(a) + Mv(x)} (A-4')

subject to

dxjdt = 0 = Na- bx + rf(x). (A-5')

One can now imagine a management authority (rational social planner) who defines the public interest as the total sum of Affector and Enjoyer utility as defined above. Suppose now that the authority does not know the rules by which the ecosystem behaves (the setS= {(a, x)!O = Na- bx + rf(x)}). It may then operate in an iterative way, simply responding to short-term changes in utility perceived by Affectors and Enjoyers in its attempts to regulate N a so as to increase Nu(a) + Mv(x). For instance, if the authority starts at a very low level of "a" and gradually increases "a," continuously trading off the measured willingness of Affectors to pay against the measured value of quality loss from the Enjoyers, it will eventually reach a point indicated as optimum on Figure 8-7B.

APPENDIX C.

TAX AS A WAY TO DIRECT SOCIETY

F ollowing Magee et al. (1989), let a tax T on loadings be proposed as the regulatory instrument. The idea behind tax as an incentive is that given the tax rate, T, Affectors will choose their loading a in such a

way that they maximize their individual net benefit. Thus they solve:

Maximize {u(a)- Ta}, (A-6)

which causes each Affector to choose a(T) to solve (Figure 8-8),

u'(a) = T, (A-7)

where u' (a) is the derivative of u with respect to a and we assume that there is a unique solution to A-7 for each positive T. If a* is the social optimum from problems A-4 and A-5, then we can choose T = T* such that A-7 yields the choice a = a*. This is the simplest story told in decentralized regulation of the negative externalities spilling over from the Affectors onto the Enjoyers.

Turn now to a slighdy different type of tax-setting scheme that will serve as a foundation for the political economy to be discussed below. Suppose a tax T is levied on Affectors' activities and the proceeds a(T)T are redistributed in a lump sum to the Affectors in such a way that equation A-7 holds. This can happen, for example, when there are a large number of Affectors and each ignores his action's impact on the total tax take. For each T, social welfare W (T) is then given as

W(T) = Nu(a(T)) + Mv(x(T)), (A-8)

443

444 APPENDIX C

where the ecosystem state experienced by the Enjoyers for given tax level, x(T), is found by solving the ecosystems equilibrium condition:

0 = Na(T)- bx + rf(x). (A-9)

Now in the case where there is only one global welfare optimum (which is often not the case, as argued), we can adjust T in the direction of increasing welfare by the hill-climbing procedure

dT jdt = W'(T) = Nu'a'(T) + Mv'(x)x'(T) = [(b- rf')u' + v' M]x', (A-10)

where' denotes derivative. Hence, we see that at a rest point of A-10 we have

0 = [(b- rf')u' + v'M], (A-ll)

provided that x'(T) is not zero, which we assume. Notice that, indeed, A-ll is the first-order necessary condition for a maximum for the welfare problem A-4' and A-5' above. Thus, such an iterative tax-setting procedure may result in reaching the welfare optimum. We shall think of A-10 as a model for a regulator (a RASP) who is guided by normative analysis. That is, the regulator adapts the instrument T toward the direction of increased welfare, where all interests are equally weighted.

APPENDIX D. COLLECTIVE ACTION PROBLEMS

AND THEIR EFFECT ON

POLITICAL POWER

Political pressure supply functions may be derived as Nash equilibria from a non-cooperative game model as outlined above, following Magee et al. (1989). These analyses suggest that the resources invested

by an individual to exert political pressure depend on the expected effectiveness of the individual contribution multiplied by the interest at stake:

x(T) = [A/N + B]{U(O)- U(T)}, (A-12)

y(T) = [C/M + D]{V(T)- V(O)}, (A-13)

where x(T) and y(T) represent the pressure from individual Affectors and Enjoyers, respectively, against and in favor of raising the pollution tax from 0 toT.

U (T) = Affectors' utility= u(a(T)) - a(T)T, (A-14)

which is assumed to fall as T increases from zero. And

V(T) = Enjoyers' utility= v(x(T)) + (1/M)(Na(T)T),

where x(T) solves A-9 for a = a(T). In this model the terms [A/ N + B] and [C / M +D) represent the power

of mustering collective effort of the Affectors and Enjoyers, respectively (Figure 8-9). The coefficients C and D for the Enjoyers (likewise A and B for the Affectors) capture Mancur Olson's notions of perceived effectiveness and noticeability, respectively (Magee et al. 1989). The perceived effectiveness

445

446 APPENDIX D

(C) depends on the strength of beliefs on the power of the sum of contributions to move policy in the direction desired by the Enjoyers. The size of C would tend to increase the greater the merit of the Enjoyers' case. Notice that the free-rider effect is captured by the term C j M, so that if each Enjoyer does not feel "noticeable" (i.e., D = 0), then the contribution of each, y(T), will fall to zero as the number ofEnjoyers (M) increases. Notice, however, that when D is zero, the total contribution is C; so depending on how C depends on M, this may rise with M or fall with M when D iszero.

Suppose that there is a regulator who continuously adjusts the pollution tax T in such a way that he equalizes the marginal pressures from the different interest groups, i.e.,

dT jdt = Y'(T)- X'(T), (A-15)

where Y(T) = My(T) + Na(T)T equals total pressure supplied by Enjoyers in favor of the tax move from zero to T and X(T) = Nx(T) equals the Affectors' pressure against the move. Notice that we have assumed that the proceeds of the taxes Na(T)T effectively go to the Enjoyers. The conditions for a rest point of A-15 are identical to the first-order conditions for a maximum of the weighted sum

(A+ BN)u(a) + (C + DM)v(x), subject to (a, x) inS. (A-15')

Thus, we need the power terms (A/ N +B) equal to (C j M +D) in order for the system to deliver the same marginal conditions as maximization of the social objective (JS.S) Nu(a) + Mv(x), subject to (a, x) inS. Any difference in power at mustering political pressure results in a deviation of the realized situation from the welfare optimum discussed in the section on normative economics.

Generalizations to this simple model can be made to accommodate other, more realistic, distribution formulas for the proceeds of the taxes. Indeed, one can imagine designing the distribution scheme to mobilize support for the program. For example, in practice, it is common to observe a few Affectors causing most of the problem. This suggests that a redistribution scheme might be designed to mobilize most of the Affectors (who would like to run cleaner operations if they could afford it) against these few "dirty players."

The graphical models that show how the welfare of society could be maximized (Figure 8-7) can be modified to produce graphs that show where political power between Affectors and Enjoyers will be in balance (Figure 8-10). To see this, first consider the precise meaning of the figure in terms of our models. If one plots the ordered pair (Nu', Mv') on the surface of Figure 8-7B at each point (a, x) in the floor of the diagram, one gets the "flux" of local utility. That is, if one moves in the direction (da, dx) at (a, x), the flux of incremental social welfare is given by Nu'da + Mv'dx = (Nu', Mv').(da, dx), where "." denotes vector dot product. Thus, welfare locally increases when Nu'da + Mv'dx = (Nu', Mv').(da, dx) > 0 for a proposed policy move

APPENDIX D 44 7

(da, dx). Since each level of a needs to be a steady-state x(a) of the ecosystem, we must restrain proposed differential policy movements (da, dx) to be compatible with the ecosystem equilibrium set S, i.e.,

0 = da- bdx + f'(x(a))dx(a). (A-16)

In other words, the system guided by our RASP will move uphill in the direction of increasing social welfare (the plane) following the ecosystem equilibrium state (graph).

Now consider the pair of socially optimal utility directional "arrows" (N u' M v'). Politics distorts these arrows by changing them into ([A+ BfN]Nu', [C + DfM]Mv'). A "political force graph" would thus be obtained by plotting ([A+ B/N]Nu + [C + DfM]Mv) rather than (Nu + Mv) as the objective function. This implies that differences in political power will tilt the depicted welfare plane, downweighting the interests of the less powerful group. Since, in the most egregious cases, there are typically a small number of highly organized large Affectors, and a large number of tiny diffuse Enjoyers, we have C and D approximately zero, so the objective function increases with stress (a) imposed by Affectors but becomes almost independent on the ecosystem state (x). Thus the "hillclimbing" political system will myopically move to higher stress levels, as it simply keeps looking for incremental moves da, dx(a)) such that ([A+ BfN]Nu', [C + D/M]Mv').(da, dx(a)) approximately equal to ([A+ BfN]Nu', O.Mv').(da, dx(a)) =([A+ B/N]Nu')da > 0, and a just keeps tending to increase (Figure 8-lOB).

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LIST OF CONTRIBUTORS

Nick Abel Sustainable Ecosystems Commonwealth Scientific and Industrial Research Organization GPO Box284 Canberra, ACT, 2601, Australia

Fikret Berkes Natural Resources Institute University of Manitoba Winnipeg, Manitoba, Canada R3T 2N2

William A. Brock Department of Economics University of Wisconsin 1180 Observatory Drive Madison, WI 53706, USA

Pille Bunnell President, Life Works 2366 West 18th Avenue Vancouver, BC, Canada V 6L lAS

489

490 LIST OF CONTRIBUTORS

Stephen R. Carpenter Center for Limnology University of Wisconsin 680 N. Park Street Madison, WI 53706, USA

Carl Folke Department of Systems Ecology Stockholm University S-106 91 Stockholm, Sweden

Gilberto C. Gallopin Division of Environment and Human Settlements Economic Commission for Latin America and the Caribbean Casilla 179 D, Santiago, Chile

Lance H. Gunderson Department of Environmental Studies Emory University 1715 N. Decatur Road Adanta, GA 30322, USA

C. S. (Buzz) Holling 16871 Sturgis Circle Cedar Key, FL 3262 5, USA

Milena Holmgren Forestry Ecology and Silviculture Group Department of Environmental Sciences Wageningen Agricultural University P.O. Box 342, 6700 AH Wageningen The Netherlands

Marco Janssen Department of Spatial Economics Free University De Boelelaan 1105 1081 HV Amsterdam The Netherlands

Don Ludwig 1909 Southmere Crescent E. Surrey, BC, Canada, V4A 6Z1

Karl-GOran Maler The Beijer Institute Box 50005 S-10405 Stockholm, Sweden

Charles Perrings

LIST OF CONTRIBUTORS 491

Department of Environmental Economics and Environmental Management University of York Heslington, York Y01 5DD Great Britain

Garry Peterson Center for Limnology University of Wisconsin 680 N. Park Street Madison, WI 53706, USA

Lowell Pritchard Jr. Department of Environmental Studies Emory University 1715 N. Decatur Road Adanta, GA 30322, USA

Steven Sanderson Wildlife Conservation Society 2300 Southern Boulevard Bronx, NY 10460, USA

Marten Scheffer Aquatic Ecology and Water Quality Management Group Wageningen Agricultural University P.O. Box 8080, NL-6700 DD, Wageningen The Netherlands

492 LIST OF CONTRIBUTORS

Brian Walker Sustainable Ecosystems Commonwealth Scientific and Industrial Research Organization GPO Box284 Canberra, ACT, 2601, Australia

Frances Westley Faculty of Management McGill University 1001 Sherbrooke Street West Montreal, PQ, Canada H3A 1 G5

INDEX

Abel, Nick, 169, 293-313 Abernathy, W., 319 Abstraction, human ability of, 108-10 Achterhuis, H., 249 Adams, Robert, 92 Adaptive change, search for theory of, see

Search for a theory of adaptive change

Adaptive management, 91, 158, 163-64, 333-60

case study of an adaptive manager, see Adaptive manager, case study of an

citizen involvement in, 167-68 discounting and, 191-92 managerial decision making, 3 34 micro-level perspective of, 333-60, 413 outperforming optimization approaches,

413-14 possible responses to crises, 141-45 rational decision making distinguished

from, 352 see also Resource management; individual

examples, e.g. Everglades, water management in the

Adaptive manager, case study of an, 3 34-60, 413

adaptive manager and decision maker, 352-55

background of the manager, 3 3 5-3 7 deer hunting and spearing cases, 349-51,

358 future research, questions for, 359-60 Lake Algonquin case, 345-49, 356, 357,

358 managing complexity, 337-39 managing in, 338, 349, 351 managing out, 338, 342, 343, 348-49,

358 managing through, 337, 343, 348-49,

351 managing up, 338, 343-45, 348-49, 351 problem of contagion and cascades,

357-59 salmon-raising experiment, case of,

338-43, 358 social system as problem domain, 355-57 Spruce Lake case, 343-45 summary and conclusions, 352-59

Adaptive renewal cycle, 124-25, 206-207

493

Adaptive renewal cycle (continued) concept of, 31-32 conclusions about, 398-401 front-loop and back-loop stages, 47-49 local knowledge and, see Local

knowledge and ecosystem dynamics maladaptive consequences and, 60-61 phases of, see specific phases, e.g.

Conservation (K) phase; Exploitation (r) phase

potential and connectedness, 33-40, 49-50, 51-52, 398-99

rangeland case study and, 296-97 resilience and, see Resilience and the

adaptive cycle scenarios for global future and, 387-92 similarities and differences between

ecosystems and social systems and, see Social systems and ecosystems, similarities and differences between, adaptive cycle and

testing the limits of the metaphor, 49-52 Administrative rationalism, 156-64 Administrative science models, 224 Agenda framing as source of political power,

155, 161 Agrawal, Arun, 169 Agricultural revolution, 91 Agriculture:

subsidization of, 118 and water quality model, 181-84, 185,

191-92 Alcorn,]. B., 129, 132, 133, 135, 141, 144,

152 Allen, C. R., 83, 405 Allen, T. F. H., 72 Allison, G., 334 Alpha phase (a), see Reorganization (a)

phase Alternative stable states, 36-38, 42, 202-203,

205,433 discoveries about, 395-98 model for ecosystems with, 439-40 ofree&, 36-37,398 of shallow lakes, 36, 199, 200, 398

Altieri, M.A., 132 Alvarez, L. W., 92 Anarchy, 96 Anderson, E. N., 141, 425

494 INDEX

Anderson, J. L., 113 Anderson, P., 14, 243 Argyris, C., 404 Arid ecosystems, vegetation degradation in,

200-202 Army Corps of Engineers, 331 Arrow, K., ix, 14, 19, 21, 169 Arthur, W B., 13, 14, 44, 119, 318, 325, 421,

422 Ashman, D., 230 Aski:i meetings, ix Atkinson, G., 270 AT&T, 56-58 Austin, Mike, 313 Australia, extinctions in, 38, 39 Australian aborigines, fire management

practices of, 13 0-31 Axelrod, R., 178, 421

Baak, S., 211 Bacharach, P., 227 Backwards-looking models, 113-15, 427-32 Bak, Per, 51, 62, 78, 401, 421, 424 Baranzini, A., 243 Baratz, M. S., 227 Barbarization scenarios, 379-82, 390 Barber, B., 379 Baron, J. N., 34, 59 Barrett, S., 21, 31, 271, 433 Barriers and Bridges in the Renewal of

&osystems and Institutions (Gunderson), 59

Barro, R., 75, 87, 398, 406 Baskerville, Gordon, 76, 91 Bates, D. G., 138, 139 Beets,]., 173 Behnke, R. H., Jr., 149, 295 Beijer Institute, Swedish Academy of

Sciences, ix Bell System, 56-58 Bennett, E. M., 186 Berger, P., 110, 228 Berkes, Fikret, 49, 75, 96, 97-98, 121-46,

158,231,243,317,324,325,333, 334, 412, 417

Billiard ball model of political power, 154 Billmoria, D., 228 Biodiversity, 406-409 Biomanipulation, 205 Bioscience, xxi Blindow, 1., 36 Body mass of animals and lumps, 79-83 Boreal forest, decision panarchies in, 86, 87 Bormann, F. H., 41,44 Boserup, E., 298 Bosse!, H., 249 Bougon, M., 230 Boulding, K. E., 226 Bourdieu, P., 231

Box, George, 408 Boyd, R., 130 Brand, Stewarr, 76 Braude!, Fernand, 92 Breakdown Scenario, 380-81, 390 Bricolage, 86, 403

defined,89 Brock, Buz, 313 Brock, William A., 7, 29, 47, 48, 49, 50, 56,

71, 78,88,99,103-19,173-93, 195-240,261-89,308,320,321, 395-417,419-38,439

Broecker, W S., 384 Bromley, D., 225 Brooks, Harvey, 13 Brosius,]., 150 Brown, D., 261 Brown, J. H., 79 Brown, L. D., 227, 230, 231, 232 Brundtland Commission, 12, 269-70, 361 Brunnson, N., 334 Bureaucracies, 59, 156-64, 326-29

see also Adaptive management; Resource management

Burt, R. S., 228, 231, 345 Burton, M., 34 Butterfly Effect, 322

Canfield, D. E., 198 Caribou management, Cree Indian system

for, 127-28, 129, 139-41, 144-45 Carpenter, Stephen R., 26, 29, 48, 49, 50,

51, 69, 71, 79, 80, 99, 103-19, 112, 150,173-93,216,260,275,279,345, 395-417,398,419-38,422,439

Casagrandi, R., 206 Cascading, problem for adaptive manager of,

358-59 Caste systems, 96, 97-98 Catastrophe folds, 207-208 Cattle grazing, 5, 6 Caughley, G., 300, 301 CCPP (Communize the Cost, Privatize the

Profit) phenomenon, 219-20 Cellular automata, 243, 421 Cerny, P. G., 169 Chambers, P. A., 198 Chandler, Alfred, 321-22 Change:

as episodic, 36 potential for, see Potential and

connectedness, the adaptive renewal cycle and

types of, and corresponding types of learning, 404-405

see also Adaptive renewal cycle Chaos theory, 322-23 Chatwin, B., 112 Chavas, J., 210, 211

Chichilnisky, G., 213 Children and lee (Stanley), 112 Chile, evergreen shrublands of, 236-37 Christensen, D. L., 91, 179 Citizen science, 158, 167-68 Clark, C., 68, 174, 176, 398,423 Clark, W. C., 30, 77, 405, 419 Clarke, J. N., 189 Clementsian succession, 18-19, 318 Climate change, global, 162, 188, 245, 321,

370 changing perspectives on, integrated

models and, 251-60 conclusions, 259-60 in the face of climate surprises,

256-60 utopias and dystopias of climate

change-oriented futures, 253-56 views of different stakeholders,

252-53 forest ecosystem responses to, 106-107 Kyoto negotiations, 361 lack of property rights and, 268

Climate and human evolution, 112 Clock of the Long Nuw, The (Brand), 7 6 Club of Rome debate, 114,242,430 Coase, R., 268 Coase theorem, 268 Cocks, Doug, 313 Cod resource collapse in Newfoundland,

142-43 Cogan grass, 82-83 Colding, J., 122, 133, 141, 324, 325, 432 Cole, S., 365 Coleman,]. S., 231 Collapse, learning, and renewal, 173-93

agriculture and water quality model, 181-84, 185, 191-92

experimentation and adaptation, 190-92 lake fishery model, 179-81 maximum sustainable yield (MSY) and

fisheries, 174-78 perfect science and prevention of,

184-87 social factors in destabilization of

ecological-socioeconomic systems, 188-90

summary and conclusions, 192-93 uncertain science and bounded

rationality, 188 Collective action problems, 220-23, 445-47 Collins, R., 228, 229 Command and control resource

management, 117-18, 307-308 Common, M., 271 Commoner, B., 117 Communication, human, 100, 108-10 Communitarian discourse, 157-58, 159,

160-61 Complex ecological-economic systems, 243

INDEX 495

Complex ecological-economic systems (continued)

characteristic elements of, 243-45 Complex systems, example of adaptation in,

see Rangelands, resilient Connectedness, 35, 50, 398-99 Connell, J. H., 29, 44 Conseroation &ology, 91, 435 Conservation (K) phase, 33-34, 35, 52,

56-59,297,320,323,326,399 institutional dynamics and, 250 local knowledge and, 125-29 models of, 173-74 shift from exploitation to, 43-44 shift to omega [0] phase, 45

Consumer sovereignty, principle of, 262-63 Contagion, problem for adaptive manager

of, 358-59 Conte, R., 243 Conventional Worlds scenarios, 3 7 3-79 Convexity, 264-65

see also Economic analysis of nonlinear dynamic systems, convexity and resource allocation mechanisms

Conway, G. R., 272 Cooperrider, D., 228 Coral reefs, 53, 13 3 Corporations:

adaptive cycles of, 40, 56-60 renewal and change in, 95

Corps of Engineers, 327, 329 Costanza, R., 61, 243, 433 Cottingham, K. L., 275 Coveney, 3 3 7 Cree Indians, 96, 144

caribou management system, 127-28, 129, 139-41, 144-45

Crenson, M.A., 155 Crosby, A. W., 96 Crosson, M., 108 Cultural anthropology, 320 Cultural change, 110-11, 117 Cultural theory, 246-47, 252-53

characteristics of cultural perspectives, 247-49

Daily, G. C., 421 Dale, V. H., 144, 187 Dasgupta, P., 96, 268, 270 Davis, A., 132 Davis, S. M., 46, 415 DeAngelis, D. L., 28 Dechert, W. D., 281, 285, 287, 288, 422 Decision panarchies, 85-87 Deer hunting and spearing cases, 349-51,

358 Democracy(ies), 191, 306

diffusing episodes of creative destruction, 95

496 INDEX

Democracy, DialoffUe and Environmental Disputes (W"tlliams and Matheny), 162

Denevan, W. M., 131 Density-dependent threshold, 270 Desertification, 201 Deutsch, K., 353 de Vries, H.]. M., 249, 250, 252, 253, 366 Dewatripont, M., 188, 189 De Zeeuw, A., 289 Diamond,}. M., 89, 116, 243 Discounting, 191-92 Discoveries for sustainable futures, 395-417

on adaptive cycle as fundamental unit for understanding complex systems, 398-401

on adaptive management outperforming other managerial approaches, 413-14

on alternate and alternative stable states, 395-98

on functional diversity and resilience, 406-409

on integrated systems exhibiting emergent behavior, 410

onlumps,405-406 on managing complex systems, 412-13 on panarchies, 402-403

types of change and learning, 404-405

on self-organization, 404 summary of, 396-97

on tractability coming from a "Rule of Hand," 409-11

on variants on the adaptive cycle, 401-402

Disturbances, 202-203 Dobson, A., 38 Done, T.]., 37, 398 Donnellan, A., 230 Donovan, S.K.,92 Doran, J., 243 Domer, D., 113, 173 Douglas, Mary, 13 Douglas, M. S., 66, 246, 320 Doveri, F., 208, 210 Downing,}. A., 27, 28 Dryzek, John, 154, 156, 164 Dublin, H. 1l.,29, 39,201 Duffield, C., 128 Duggins, D. 0., 37 Durlauf, S. N., 13, 14, 88, 184, 188, 308,

417,421 Dutta, P., 287 Dystopias, 249

global climate change models and, 255-56

Earth Summit of 1992, 361, 371 Easley, D., 191 Easter Island, collapse of civilization of, 116

Ecocommunalism, 382-83, 390 Ecological memory, 135, 137 Ecological Theater and the Evolutionary Play

(Hutchinson), 404 Ecology:

integrating ecological, economic, and social theory, 420-21

partial truths of, 9, 19 Economic analysis of nonlinear dynamic

systems, 261-89 convexity and resource allocation

mechanisms, 262-69 continuity, 266-67 convexity, 264-66 property rights, 267-69 welfare economics theorems, 263-64

equity, sustainability, and resilience, 269-75

lakes, dynamics of, 275-88 conclusions, 288 dynamic analysis, 280-86 the lake model, 276-78 as metaphor for other ecological

problems, 275-76 small numbers case, 286-88 steady-state economics, 278-80

overview, 261-62 summary and conclusions, 288-89

Economics, 318 discounting, 191-92 dynamic interaction of societies and

ecosystems and, 211-25 normative economics, maximum

utility and, 212-19, 441-42 positive economics, 219-25

efficient market hypothesis, 241 integrating ecological, social, and

economic theory, 420-21 models of human behavior, 243 partial truths of, 8-9, 19 pure market model of, 19

Ecosystem resilience, see Resilience, concept of, Resilience and the adaptive cycle, definitions of resilience

Ecosystems: dimensions of, 105-107 distinctions between social systems and,

see Social systems and ecosystems, similarities and differences between

dynamic interaction of societies and, see Societies and ecosystems, dynamic interaction of

local knowledge and dynamics of, see Local knowledge and ecosystem dynamics

Efficient market hypothesis, 241 Ehrlich, A., 188,326 Ehrlich, P., 113, 188, 326, 421

Elders, transmission of ecological and social knowledge in traditional societies by, 138-41, 144

Eldredge, N., Ill, 318 Elephants, fire, and savanna, 38 Elliott,]. E., 34 Elmqvist, T., 135 Elster,]., 157 Elton, C. S., 271 Emery, F., 226, 227 Emission permits market, 286 Engineering resilience, 27, 28-30, 202, 203 Equilibria of ecosystems, multiple, 26 Equity trends, global, 369 Erikson, K. T., lOS, 109, 329 Estes,]. A., 37 Eutrophication of shallow lakes, see Lakes,

shallow, eutrophication of Evans, D., 56, 57, 78, 219, 285, 286 Evans, P., 231 Everglades, 38, 61, 64--66

water management in the, 315-18, 323-26,329-32,415

Everglades Coalition, 324, 329 Evolution:

climate and human, 112 conservativeness of, 89 partial truths of theory of, lO

Exotic species, see Invasive species Expert, paradox of the, 7, 416-17 Exploitation (r) phase, 33-34, 35, 38, 52,

56-60, 320, 323 institutional dynamics and, 250 local knowledge and, 125-29 models of, 173-74 shift to conservation phase ftom, 43-44

Extinctions: in Australia, 38, 39 loss of stable state and, 38, 39 Pleistocene era, 38 recovery of biodiversity ftom mass, 92

FAO, 294 Feichtinger, G., 2ll Ferguson, T. A., 129, 131 Feyerabend, P., 123 Fiering, M., 29 Finlayson, A. C., 142 Fire:

elephants, and savanna, 38 in Northern Florida, 37-38 small, frequent, as management practice,

207 suppression of forest fires, 13 3 traditional use of fire management,

130-31 Firth, Raymond, 138-39 Fischer, David, 89, 94, 403

INDEX 497

Fish, model of the effect on plankton dynamics of, 208-10

Fisheries: collapse of, 3, 4, 6 lake fishery model, 179-81 maximum sustainable yield (MSY) and,

174-78 salmon-raising experiment, case of,

338-43, 358 traditional salmon fishing, 126-27, 129

Flannery, T., 39, 101 Flood control, 5, 6 Florida:

Everglades, see Everglades fire in Northern, 37-38

Folke, Carl, ix, 49, 96, 97-98, 121-46, 151, 158,231,243,317,324,325,333, 334,412,417,419-38

Follett, Mary Parker, 234 Foresight and intentiality, human, 99-100,

113-16,402,426-32 Fortress World Scenario, 381-82, 390 Forward-looking models, 113-15, 427-32 Foucalt, M., 227 Fox,]., 230, 232 Fragile Drnnain (Levin), 14, 89, 404 Frampton, G., 169 Francis, G., 61 Frankl, V., 108 Franklin,]. F., 26, 38, 43, 45, 76 Free-rider analysis, 220 Friesen, P., 319 Fuentes, E. R., 201 Fukuyama, F., 231

Gadgil, Madhav, 75, 97-98, 127, 132, 133, 134-35, 231

Gallopfn, Gilberto C., 361-92 Game theory, 219 Gamson, W. A., 230, 232 Garbage-can model of decision making,

358-59 Gardner, H., 232, 337 Gardner, R., 220 Gaventa, ]., 227 Gell-Mann, M., 78 General Electric, 59 General Motors, 40 Genetic algorithms, 243 Gersick, C.]. G, 318 Ghoshal, S., 231, 232 Giddens, A., 107, 109, 111, 228, 229 Gigerenzer, G., 174 Gilbert, N., 243 Gilbrat's law, 286 Gillis, M., 178 Gintis, H., 243 Giovanni, B., 243 Gleason, P.J., 38

498 INDEX

Glitzenstein,J. S., 37 Global climate change, see Climate change,

global Global Climate Coalition, 162 Global environmental change:

examples of, 3-4 historical examples of, 5

Global Scenario Group, 365, 371 Godden, D.P., 306, 307 Godet, M., 365 Goffman, E., 229 Goldstone, J., 92-94 Goodstein, E. S., 155 Gordon, H. S., 177-78 Gould, S.J., 111, 117,318 Gouldner, A. W., 117 Gradient process, 268 Grandmont,J. M., 47, 188 Granovettor, M., 228, 231 Grassy Narrows, Ontario, mercury

contamination in, 109 Gray, B., 227, 230, 231, 232 Grazing, 5, 6

property rights and, 267 . semiarid grazing systems, 42

Great Transitions scenarios, 382-86 Great War, The (Fischer), 94 Green modernism, 158 Greenwood, R., 3 34 Greer, J., 61 Grenfell, B., 423 Gricar, B., 230 Grossman, S., 427,431 Gunderson, Lance H., 3-22, 25-62, 61,

63-119, 123, 124, 131, 142, 143, 148, 158,173,189,190,206,208,226, 243, 250, 260, 271, 300, 307, 315-32, 333, 334, 395-417, 419-38

Haas, P., 227 Hammitt,J.~,253 Hanna, S., 124 Hannan, M. T., 34 Hans.ki, I., 36 Hanson, P., 48, 49, 50, 71, 99, 180, 182, 183,

184, 185, 186, 188, 189, 190, 216, 410, 424

Hardin, G., 219 Hardy, C., 227, 230 Hare, ~ F., 52 Hartvigsen, G., 421, 422 Harvey, A. C., 273 Hasselmann, ~. 253 Hauert, C., 425 Havlicek, T. D., 80 Hazardous waste management, 153, 154 Heal, G. M., 191, 213 Heath, C., 161,334, 352 Heisenberg's uncertainty principle, 364

Heizer, R. F., 125, 127 Henderson, E. W., 176 Herrera, A. 0., 366 Hierarchy(ies), 55

panarchy distinguished from, 21, 72-76 Highfield, 33 7 Hilborn, R., 143, 144, 173, 176, 177, 187,

188, 414, 423 Hindley, N., 300, 301 Hindu caste system, 96,97-98 Hinings, c. R., 334 Hodgkin,}., 89 Hodgkinson, ~ C., 300 Holistic approaches to resource

management, see Local knowledge and ecosystem dynamics

Holland,John, 14,43,46,86,202,243,422 Holland, shallow lakes of, 235-36 Holling, C. S., 3-119, 124, 125, 131, 132,

133, 142, 143, 144, 148, 149, 150, 152, 165, 180, 195, 20£r207, 208, 226, 235,246,271,272,274,275,289, 300,304,315-32,333,334,395-417, 419-38

Hohngren,Milena, 195-240 Holt, M., 210 Homer-Dixon, T. F., 227 Hommes, C. A., 48, 115, 188, 210, 211, 308,

320, 321 Homoclinic bifurcation, 209-10 Horizontal or formative social capital, 232 Horizontal social capital utilization, 233 Hubbard Brook experiment, 41 Hughes, T. P., 37, 74 Human behavior, unique aspects of, 15, 18,

98-101 communication, 100 foresight and intentiality, 99-100,

113-16,402,42Cr32 technology, see Technology, human see alsfJ Social systems

Human history, catastrophic disruptions and periods of reinvention in, 92-98

Hun dey, B. J., 293 Hurst, D.~. 20, 40, 59, 317 Hutchinson, G. E., 198, 404 Hviding, E., 137-38

mM,40 Ideological domination as source of political

power, 155 Imbricated redundancy, 84-85, 408 lmperata cylindrical (Cogon grass), 82-83 Indigenous people, see Local knowledge and

ecosystem dynamics Industrial revolution, 91 Infinite-horizon stochastic optimal-control

models, 423-24 Information cascade theory, 41fr17

Institutional and organizational theory, 318 partial truths of, 9-10

lnstitutionalle~, 123,143 Institutional memory, 96, 135, 137-41,

144-45 defined, 123

Integrated models and surprises, see Surprises, integrated models and

Integrative theories for sustainable futures, 419-38

accelerating learning through actively adaptive networking, 433-37

posing questions, 435-37 conclusions, 437-38 coping with uncertainty, 426-33

human foresight potential and, 426-32

navigating uncertainty, 432-33 ecological, economic, and social theory,

integrating, 420--21 learning our way to sustainable futures,

419,420,433-37 modeling complex systems, 421-26

infinite-horizon stochastic optimalcontrol models, 423-24

panarchies, 425-26 poised states and self-organized

criticality, 424-25 International Institute for Sustainable

Development, 12 International Institute of Applied Systems

Analysis, 12 Invasive species, 402, 403

plus human exploitation change a panarchy, 82-83

Irvine, D., 131 Ives, A. R., 423, 426

Jablonski, D., 92 Jackson, T., 378 Jager, W., 243 Janssen, Marco A., 14, 29, 70, 173, 241--60,

313, 321, 411 Jansson, B. 0., 61, 119, 325, 415 Jasanoff, S., 364 Jentoft, S., 123 Jeppesen, E., 198 Jeud, H. P. V. d., 38 Johannes, R. E., 135, 138, 141 Johnson, B., 348 Johnson,~.~.315,317,413,417 Jones, C. G., 404 Jordan, A., 247

Kahn, H., 365 Kalff, J., 198 Kalman filter, 273 Kareiva, P., 423

INDEX 499

Kassas, M., 201 Kassen, R., 407 Kauflrnnan,S.A.,69,83, 119,323 Keitt, T., 78 Kelp forests, 37, 53 Kendall, B. E., 423 Kershaw, A. P., 39 Keystone species, protection of, 133 Kiefer,~ .• 191 Kimberly,]. R., 318 Kinzig, A., 421 Kirchner, J. W., 92 Kitchell,). R, 80, 398 Knowlton,~ .• 37 Kofinas, G. P., 127 Korman,}., 176 Krogstad, D.J., 70 Krugman,P., 77,80,210,405 Krummel,]. R., 78 K-strategists, see Conservation (K) phase Kuhn, T. S., 308, 318-19, 405 Kumar, ~. 249 Kyoto climate negotiations, 361

Lake Algonquin case, 345-49, 356, 357, 358 Lake fishery model, 179-81 Lake Mendota system, 186-87 Lakes:

optimizing social utility from use of, 441-42

shallow, see Lakes, shallow Lakes, shallow:

alternative stable states of, 36, 199, 200, 398

eutrophication of, 71, 198-200,201 model of, 275-88

Holland and its, 235-36 restoration through shock therapy,

204-205 stability properties of, 203-204

Lambert, W. D., 81 Lane,. D. A., 13, 14,421 Langridge,Jenny, 313 Lao Tzu, 189 Lawton, J. H., 404 Laycock, W. A., 200, 299 Leach, M., 149, 152 Lean,J., 52 Learning:

acceleration of, through actively adaptive networking,433-37

collapse and, see Collapse, learning, and renewal

institutional, 123, 143 types of change corresponding with types

of, 404-405 on the way to sustainable futures,

419, 420, 433-37 Leavitt, P.R., 69, 106, 415, 425

500 INDEX

Lee, K., 61, 110, 119, 123, 143, 229, 325, 334, 413, 415

Lees, S. H., 138, 139 Leimar, 0., 321 Lempert, R. J., 188, 253 Levin,S.ll, 14,21, 31,47, 52,69,89, 106,

107, 121, 122, 125, 150, 173, 271, 304, 404,421,422,423,425,433

Levinson, D. ]., 319 Levi-Strauss, C., 86, 89, 403 Lewin, K., 226 Lewis, H. T., 129, 131 Lewontin, R. C., 395 Liebrand, W. B. G., 243 Lierman, M., 176, 187 Light, S. S., 28, 59, 61, 90, 119, 315, 316,

325, 333, 334, 415 Likens, G. E., 41, 44 Limits to Growth report, 242 Lindgren, K., 321 Lindley, D. V., 113, 177, 187 Local knowledge and ecosystem dynamics,

121-46,412 adaptive responses to ecosystem change,

141-45 complementarity of Western knowledge

and, 129, 145-46 defirUtions, 122-23 in exploitation and consetvation phase of

the adaptive renewal cycle, 125-29 institutional memory, 137-41 introduction, 122-25 in the release phase of the adaptive

renewal cycle, 129-33 in the reorganization phase of the

adaptive renewal cycle, 133-37 summary and conclusions, 145-46

Loomes, G., 243 Lorenz, H., 211 Loury, G., 231 Lubchenco,]., 364 Luckman, T., 110, 228 Ludwig, Donald, 3-22, 26, 29, 42, 43, 48,

50, 54, 108, 110, 115, 118, 125, 126, 156,173-93,206,275,284-85,294, 295,297-98,300,313,398,411,414, 424,426,439

Ludwig, J., 297 Lukes, Steven, 154, 161 Lumps, 77-88,401-402,405-406,426

causes of, 79-83 decision panarchies and, 85-87 in human systems, 87-88 significance of, 84-85

Lux, T., 241

McMee, R., 219 McCauley, E., 208 McCay, B. J., 142

McClanahan, T. R., 37, 135, 398 McConnaha, W. E., 415 McCool, D., 189 McKenzie, L., 47 McKibbon, B., 104 MacMahon,J.ll,26, 38,43,45, 76 McNeill, W. H., 5, 13 Magee, S., 7, 178, 220, 222, 224, 306, 443,

445 Maier-Reimer, E., 253 Malaria and adaptive dynamics, 70, 71 Maler, K. G., 219, 261-89 Malhotra, K. C., 97 Malliaris, }l G., 281 Malthus, T., 242, 249 Mangel, M., 414, 423 Manly, B. F.J., 79 Mann, H., 400 Manne, }l S., 253 Mao Tse-tung, 298, 303 March,]. G., 11, 161, 166, 334, 352, 354, 358 Marchesi, M., 241 Marchetti, C., 318 Margalef, Ramon, 53 Margolis, R., 3 70 Market rationality, 157 Marshall, Alfred, 55 Marshall, P., 273 Martens, W. J. M., 70 Martin, P. S., 81 Marxism, 155 Maslov, S., 425 Masuch, M., 11 Matheny, }l R., 156, 157, 158, 162 Maximum sustainable yield:

conflicts with sustainability, 174-75 fisheries and, 17 4-78

Maximum utility and normative economics, see Normative economics, maximum utility and

May, R. M., 271 Meadows, D. H., 242, 249 Mearns, R., 149, 152 Meffe,G.K.,27,28, 50,131,142,207,432 Meiosis, 43, 73 Mendizabal, T., 201 Meta-population dynamics, 36 Michael, Don, 13, 108 Miles, 1., 365, 366 Miller, D., 319 Miller,]. ll, 380 Milne, B. T., 422, 423 Milpa agriculture in Mesoamerica, 86, 13 5,

136-37, 141, 144 Mintzberg, Henry, 334 Models, see Integrative theories for

sustainable futures; Surprises, integrated theories and; specific models and types of model, e.g. Backwardlooking models; Lake fishery model

Mooney, H. A., 271 More, T., 249 Morrison, P. and P., 63 Morton, S. R., 80 Mountain ecosystem sustainability,

traditional approach to, 128, 129 Munasinghe, M., 420 Munn, R. E., 419 Muratori, S., 206 Murdoch, W. W., 208 Myths and rituals, role of, 141

Naeem, S., 407 Nagler,}., 425 Nahapiet,J., 231, 232 National Park Service, 329 National Research Council, 142, 182 Nature Anarchic, 11, 12, 13, 31, 246 Nature Balanced, 11, 12-14, 31, 246 Nature Evolving, 12, 14, 25, 32, 40, 43 Nature Flat, 10-11, 12, 31, 246 Nature Resilient, 11, 12, 13-14, 15-18,

31-32,40 Nested cycles, 68-71, 195, 208-10, 357,

402-403 Nested ecosystems, 124 Neural networks, 243 New Sustainability Paradigm, 383-86,

390-92 Niamir-Fuller, M., 129, 130, 132, 133 Nicolls, G., 384 Nishimura, K., 281 Nordhaus, W. D., 253 Norgaard, R. B., 129 Normative economics, maximum utility and,

212-19 graphical theory of ecological limitations

of welfare optimization, 213-17 naive and smart ways for approaching

optimum utility, 217-19, 441-42 stakeholders and their welfare, 212-13

North, D. C., 123 Noticeability, 220, 221, 445, 446 Novelty, 10, 40, 43, 51, 61, 88-91, 118-19,

135,325,399,402-403,405 Nowak, A., 243 Noy-Meir, 1., 200, 298 Nystrom, M., 432, 433

Oceania, customary laws in, 13 8 Oldfield, M. L., 132 Olsen,}. P., 11, 166 Olson, Mancur, 286, 445 Omega [Cl] phase, see Release (omega (Cl] )

phase O'Neill, R. V., 21, 72, 304 Open access to a resource, 177-78

INDEX 501

Optimal harvesting and maximum sustainable yield, 17 4

Organizational theory, see Institutional and organizational theory

Organization for Economic Cooperation and Development (OECD), 375, 377

O'Riordan, T., 247 Ormerod, P., 243 Ornstein, R., 113 Orwell, G., 249 Ostrom, Elinor, 75, 123, 156, 157, 220, 221,

222,224,286,287,308 Owen-Smith, R. N., 39, 81

Pacala, S. W., 433 Pahl-Wostl, C., 304 Paine, R. T., 432, 433 Panarchy(ies), 32, 425-26

collapsing, 91-98 connections between levels in, 7 5 decision, 85-87 derivation of term, 5, 21, 74 ecosystem politics and, 329-32 evidence for, 77-79 hierarchy distinguished from, 21, 72-76 in human vs. ecological systems, see

Human behavior, unique aspects of lumps and, 77-88, 401-402 sustainability and, see Sustainability and

panarchies Paradoxes of sustainability:

of regional resource and ecosystem management, 6-7, 414-16

resolving, 414-17 trap of the expert, 7, 416-17

Paradox of Enhancement, 348 Pareto criterion, 263-64 Pareto optimal, 263, 264 Parson, E. A., 405 Parsons, T., 107, 228 Pauly, D., 4 Pearce, D., 270 Pearl, R., 33 Perceived effectiveness, 220-21, 445-46 Pereira, W., 132 Perrings, C. A., 261-89, 313 Pest control, 5, 6

traditional, 132 Peters, R. H., 80 Peterson, Garry D., 36-38, 42, 63-102, 107,

131,132,260,315-32,408,421 Pezzey,J. C. V., 270 Pielke, R. A., Jr., 429 PlinLn,S.L.,27,28, 150,202,203,289 Pines, D., 14 Pinker, S., 104 Pluralistic politics, 157-58, 159-61 Pokorney, J., 198

502 INDEX

Policy Reform Scenario, 376-79, 389-90, 391

Political discourse, seeking sustainability and the dynamics of, 147-69

alternate discourses: administrative rationalism, 156-64 pluralistic politics, 157-58, 159-61

alternative models and burden of proof, 153-54

common trajectories, 161-63 environmental crises as drivers of

change, 152-53 faces of power:

agenda framing, 155,161 billiard ball model, 154 ideological domination, 155

linking discourses, 163-65 the new ecology, 149-51 open questions, 165-68

how can science be made to serve citizens?, 167-68

what does a resilient politicaVecological system look like?, 165-66

where do adaptive capacity reside?, 166

why, in practice, is adaptive management not adaptive?, 166

problem of fit, 151-52 shared features of discourses:

endogeneity of preferences, 158-59 pathologies, 160-61 role of consensus, 159 role of science, 159-60 source of legitimacy, 159

"wicked problems," 155 Politics, 398

collective action problems and their effecton,220-23,445-47

dynamic interaction of societies and ecosystems and, 211-25

sustainability and the dynamics of political discourse, see Political discourse, seeking sustainability and the dynamics of

Pontryagin's maximum principle, 280 Population growth, 367 Porter, M., 228 Posey, D. A, 131 Positive economics, 219-25 Postel, S. L., 182 Potential and connectedness, adaptive

renewal cycle and, 33-40, 49-50, 51-52, 398-99

Poverty trap, 95-96, 329, 401 Powell, T. M., 425 Powell, W W, 106,233 Powers ofTen (Morrison and Morrison),

63-66 Predator-prey cycle, 206

Prigogine, I., 384 Principles of &onomics (Marshall), 55 Prins, H. H. T., 38 Prisoner's dilemma, 321 Pritchard, Lowell, Jr., 98, 147-69, 178,412 Problem domains, 227-28, 351, 355-57

defined,227 dynamic nature of, 229-31 factors governing domain structure,

228-29 repeated patterns of domain and social

capital dynamics, 233-35 Profeta, T. H., 150 Proops,]. L. R., 249 Property rights, 267-69 Public choice theory, 306 Puidefabregas, J., 201 Putnam, R. D., 231

Quah, D., 88 Quinn, D., 110

Radner, R., 287 Raffaelli, F., 79, 81 Rangelands, resilient, 293-313

the adaptive cycle applied to, 296-97 alternative stable states of, 398 aspects of resilience, 294-96 common change to Old World

rangelands, 293 as complex adaptive systems, 304-309 description of rangeland ecology, 293 determinants of ecological resilience,

309-12 overview, 293-94 pulses, 297-98 scales, changes at different, 299-304

local-scale changes, 2 99-3 01 overview of regional changes,

303-304 regional-scale changes, 301-303

summary and conclusions, 312-13 Raskin, P., 363, 370, 371 Rayner, S., 247 Rebertus, A]., 3 7 Redman, Charles, 116, 173 Redundancy, 132-33,408

imbricated, 84-85, 408 traditional practices to survive

disturbance, 13 2 Reed, WJ., 271 Reed-Anderson, T., 187 Reefs, 53, 13 3

alternative stable states of, 36-37, 398 Rees, M., 423 Reference Scenario, 374-76, 388-89 Referent organizations, 226-27 Reflexivity, human capacity of, 110-13

Regier, H., 61 Regional sustainability, integrative theories

for, see Integrative theories for sustainable futures

Regulatory mechanisms: emissions permits, 286 taxation,218-19,243-44

Release (omega 0) phase, 34, 35, 38, 52, 56-59,90,320,323,326,399

collapse, learning, and renewal, see Collapse, learning, and renewal

institutional dynamics and, 250 local knowledge and, 129-33 shift from conservation phase to, 45 shift to reorganization phase from, 45-47

Reny, R., 219 Reorganization (a) phase, 34-35, 38, 41, 43,

52,56-60,90,317-18,320,322,323, 326, 399

institutional dynamics and, 250 local knowledge and, 133-37 shift from release phase to, 45-47 see also Collapse, learning, and renewal

Repetto, R., 178 Resilience, concept of, 15-18, 202-204,

270-75 Resilience and the adaptive cycle, 25-62,

399 the adaptive renewal cycle, see Adaptive

renewal cycle in complex systems, rangeland example

of, see Rangelands, resilient definitions of resilience, 27-30, 180,

202-203,271,294,323,399 effect on the economics of natural

systems, see Economic analysis of nonlinear dynamic systems

extreme examples, 53-61 aquatic systems, 53-54 large organizations, 5 5-60 semiarid savanna ecosystems, 54-55

myths of nature, 31-32 overview, 2 5 policies applying fixed rules for achieving

constant yields and loss of, 27 the raft as metaphor of stability and,

16-17 in social systems, 3 2 3-26 stability, two views of, 27-30 summary and conclusions, 62 testing the limits of the adaptive cycle

metaphor, 49-52 Resilience Network, ix, xxi, 180, 294 Resilience Project, 59 Resource management, 131

adaptive cycle applied to, 326-29, 399-401

adaptive management, see Adaptive management

as centralized and bureaucratized, 121

INDEX 503

Resource management (continued) "command and control," 117-18,

307-308 failures, 90-91

examples of, 5-6 paradox of, 6-7

institutional memory of management agencies, 143

local knowledge and, see Local knowledge and ecosystem dynamics

paradox of the expert, 7, 416-17 possible responses to crises, 141-45

Restrepo, C., 79 Revolution, 92-94, 318-19 Revolution and Rebellion in the Early Modern

World (Goldstone), 93 Richards, P., 152 Rietkerk, M., 200, 201 Rigidity trap, 95, 96-98, 401 Riker, W., 224 Rinaldi, S., 206, 211 Ritchie,]. C., 52 Rituals and myths, role of, 141 Roe, E., 104, 152 Rotmans,J., 250, 366 Roughgarden, J., 80

r-strategists, see Exploitation (r) phase Rubinstein, D., 110 Ruddle, K., 13 5

Sacred groves, 132, 133-35 Sagoff, M., 159, 191 Salmon-raising experiment, case of, 338-43,

358 Sanderson, Steven E., 98, 147-69, 150, 178,

304, 333, 412 Sandpile models, 51-52, 62, 421, 424-25 Sarewitz, D., 429 Sargent, T., 14, 50, 114, 425 Sauer, J. D., 82 Savannas, 3 8

collapse of productive, 96 semiarid savanna ecosystems, 54-55

Scale(s), 26, 63-68 abrupt breaks in patterns of, 66-67 cross-scale interactions, 21, 61 human impact and medium affected, 67 political decisions about ecological issues

and, 151-52 rangeland changes at different, 299-304 space and time aspect of, 66-67, 68, 402

Scenarios, 365-92 adaptive cycle and, 387-92 Barbarization, 379-82, 390 Conventional Worlds, 373-79 critical trends expected to effect the

global future, 367-71 decentralization of authority, 368-69 economic growth, 367-68

504 INDEX

Scenarios (continued) equity trends, 369 other trends, 370-71 pollution and global environmental

change, 370 population growth, 367 resource depletion, 369-70 technological change, 3 68

defined, 365 developing alternative futures, 371-72 driving forces of, 3 72 Great Transitions, 382-86 purpose of, 365 steps in building, 365-66 transitions among alternative futures,

386-87 Scheffe~~arten,29, 36,48, 53, 71,173,

178, 188, 195-240, 275, 398 Scheinkman,J., 421 Schon, D. A., 404 Schreiter, T., 198 Schulze, E. D., 271 Schumacher, E. F., 13 Schumpeter,J. A., 34, 45, 319, 320,400, 403 Schuster, H., 425 Schwartz, P., 246, 365, 366, 379 Schwinghammer, P., 79 Science, xxi Scoones, I., 149 Scott, F. R., 123, 165 Sea otters, 3 7 Search for a theory of adaptive change, 3-22

generality and, 19-20 nature, alternative myths of, 10-14 paradoxes:

of regional resource and ecosystem management, 6-7, 414-16

resolving, 414-17 trap of the expert, 7, 416-17

partial theories and partial explanation, 18-20

partial truths and bad decisions, 195 in ecology, 9 in economics, 8-9 in evolutionary theory, 10 in institutional and organizational

theory, 9-10 reasons the world hasn't collapsed, 15-18 requirements of a theory, 32 resource management failures and, 5-6 seeking simplicity and, 21-22, 409-11

Sea urchins, 3 7 Self-organized criticality, poised states and,

424-25 Self-organizing processes, 404 Sent, E. ~., 224 Serengeti-~ara, Tanzania, 201 Shadow prices, 270, 280 Shallow lakes, see Lakes, shallow Shapiro, C., 44

Shearer, W., 420 Shifting cultivation, 136-37, 141, 144 Shils, E., 228 Siemann, E., 79 Sigmund, Karl, 321 Simon, Herbert A., 72, 224, 243, 426 Sinclair, A. R. E., 29 Skiba, A. K, 281 Skubinna,J. P., 198 Slade,~. E., 273 Slow-fast cycles, 206-207, 426 Smith, L., 416,417 Sober, E., 111, 427 Social capital, 231-35

common culture as a catalyzer, 232-33 defined, 2 31 repeated patterns of domain and social

capital dynamics, 233-35 structural holes in social networks,

231-32 Social change, theories of, 318-26

evolving systems producing stages of growth and transformation, 319-23

life-cycle representations, 318 revolutionary change, 318-19

Social systems: defined, 107 dimensions of, 107-119 distinctions from ecosystems, see Social

systems and ecosystems, similarities and differences between

dynamic interaction of ecosystems and, see Societies and ecosystems, dynamic interaction of

"structures of domination," 107, 229 "structures oflegitimation," 107, 229 "structures of signification," 107, 108-19,

229 see also Human behavior, unique aspects

of Social systems and ecosystems, similarities

and differences between, 103-19, 315-32

abstraction, 108-10 adaptive cycle and:

dynamics of resource management institutions, 326-29

Everglades water management, 315-18

theories of social change, 319-23 externalization and technologic

development, 113-19 modeling backward- and forward

looking behaviors, 113-16 panarchies and ecosystem politics,

329-32 reflexivity, 110-13. resilience in social systems, 323-26 summary and conclusions, 119, 3 3 2 theories of social change, 318-26

Social systems and ecosystems, similarities and differences between (continued)

water management in the Everglades, example of, 315-18, 323-26, 329-32

Social theory, integrating ecological, economic,and,420-21

Social welfare function, 269 Societies and ecosystems, dynamic

interaction of: catastrophic change:

resilience and, 202-203 restoration through shock therapy,

204-205 stability landscapes, 203-204

cycles and complex dynamics, 205-11 the adaptive cycle and other

oscillations, 206-207 the link between cycles and

catastrophe folds, 207-208 nested cycles and complex dynamics,

208-10 parallels between dynamics of

ecosystems and human societies, 210-11

underlying principles, 205-206 economics and politics, driving forces of,

211-25 normative economics, 212-19 positive economics, 219-25

ecosystem response to human use, 196-205

catastrophic versus smooth responses, 196-98

eutrophication of shallow lakes, 198-200

vegetation degradation in arid ecosystems, 200-202

examples of society-nature systems, 235-37

Chile and its evergreen shrublands, 236-37

Holland and its shallow lakes, 235-36 introduction, 195-96 social structure, key role of, 22 5-3 5

dynamic nature of problem domains, 229-31

factors governing domain structure, 228-29

problem domains, 227-28 social capital, role of, 231-35

summary and conclusions, 238-39 Solow, R., 55, 99, 114, 430 Sorenson, P., 416, 417 Sousa, W. P., 29 South Florida Water Management District,

317, 324, 327, 329 Soviet Union, dictatorship of, 31-32, 98 Special interest groups, 164 Spender,]. C., 228

INDEX 505

Spruce-budworm cycles in boreal forests, 30, 206

Spruce Lake case, 343-45 Stability, 206-207

alternative stable states, see Alternative stable states

engineering resilience, 27, 28-30, 202, 203

Staggs, M., 348 Stanley, H. E., 78 Stanley, S. M., 78, 112 Starr, R. V., 72 Starrett, D., 281 Steele, J. H., 106, 144, 176, 425 Stem, D. 1., 273 Stewarr, Lorenz, 322 Stiglitz,]., 9 Stommel plot, 106 Strauss, A., 2 3 3 Strobel, M., 249 Subsidization:

of agriculture, 118 of overexploitation of forests, 178

Surprises, integrated models and, 241-60 global climate change, example of,

251-60 institutional change and, 250-51 modeling human behavior, 242-49

general framework for, 243-45 overview, 242-43 perspectives on reality, 246-49 summary and conclusions, 260

Sustainability, 270-73 defined, 76 discoveries for, see Discoveries for

sustainable futures integrated models to explore future, see

Surprises, integrated models and integrative theories for, see Integrative

theories for sustainable futures panarchies and, see Sustainability and

panarchies politics and, see Political discourse,

seeking sustainability and the dynamics of

Sustainability and panarchies, 63-102 hierarchies and panarchies, 72-76 in human vs. ecological systems, 98-101 lumps and panarchies, 77-88, 401-402 nested cycles, 68-71, 402-403 summary and conclusions, 101-102 transformation of panarchies:

collapsing panarchies, 91-98 by novelty, 88-91

Sustainable development, 61, 158, 241-42 definitions of, 76, 269-70, 361, 403 obstacles to, 362 projecting the future of, 363-64 scenario approach to, see Scenarios

Swezey, S. L., 126, 127

506 INDEX

Swidden system (shifting cultivation), 136-37

Symposium on Contingent Valuation, 213 Szasz, A., 152, 15 5

Taboos, 133-35, 141 Tainter, J. A., 298, 307 Tainton, N. M., 299 Taxation as regulatory mechanism, 218.:.19,

443-44 Technology, human, 101, 116-19, 368

as double-edged sword, 117 Telephone system in the United States,

history of, 56-58, 59 Tenner, E., 13 Tennessee Valley Authority (IVA), 189 Terry, P. ]., 82 Thaler, R. H., 241, 243 Thapar, R., 97 Thompson, M., 13, 246, 247, 250, 256, 320,

400 Tikopia, response to hurricane damage by,

138-39 Tilman, D., 27, 28, 272, 407, 423 Toledo, V. M., 129, 133, 135, 141, 144, 152 Tongway, David, 54, 294, 300, 301, 313 Town meeting, 157 Traditional resource management, see Local

knowledge and ecosystem dynamics Trigger-transfer-reserve-pulse (TTRP)

framework, 297-98, 300 Trisoglio, A., 249 Trist, E., 226, 227, 228 Trompenaars, F., 231 Tsur, Y., 271 Tuan, Yi-Fu, 118 Tuchman, Barbara, 13 Turner, N.J., 132, 144, 187 Turnovsky, S., 431

United Nations 2000, 367 United Nations Development Programme

(UNDP), 369, 371 United Nations Environment Programme

(UNEP), 182, 371 U.S. Department of Agriculture (USDA), 160 University of Wisconsin-Madison, 180 Uphoff, N., 230 Utility function, 262-63 Utopias, 249

global climate change models and, 254-55

Utterback, J., 319 Uzzi, B., 231

Vallacher, R. R., 243 Van de Koppel, J., 200, 201

Van den Bosch, F., 200, 201 Vant, W. N., 198 Varian H., 44 Velner, H., 61, 119, 325, 415 Vertical or bridging social capital, 232 Vespignani, A., 421, 425 Visible Hand (Chandler), 321-22 Vitousek, P., 21, 117 Volkman, J. M., 415 Vredenburg, H., 117, 228, 229, 230

Waide, J. B., 28 Waldrop, M. M., 243,384 Walker, Brian H., 29, 42, 43, 48, 79, 84, 130,

144,149,200,275,293-313,325, 398,405,407,415,419-38,426

Walker, J., 220 Walters, C. J., 27, 29, 91, 143, 173, 176, 177,

188, 191, 334, 413, 414 Warglien, M., 11 Warren, D. M., 123 Warrilow, D. A, 52 Waterfleas (Daphnia), cycling of populations

of, 208-10 Water management in the Everglades,

315-18, 323-26 Water quality and agriculture model,

181-84, 185 Watson, R. T., 182 WCED,419 Webb, T. I., 46 Weber, M., 94 Webster, J. R., 28 Weick, K, 108, 233, 334, 337, 353, 354 Wei!, A., 92 Weitzman, M., 191 Welch, Jack, 59 Welfare economics, theorems of, 263-64

continuity, 266-67 convexity, 264-66 pareto criterion, 263-64 pareto optimal, 263, 264

West, G. B., 78 Westley, Frances, 49, 72, 75, 91, 103-19,

108,148,189,195-240,333-60,400, 414

Westoby, M., 200, 272, 293, 298, 301, 313 Wheatley, M., 337, 342 Whitaker,]. K, 75 Whitten, A. ]., 82 Whittington, R., 110 Wiener, A., 365 Wiener,]. B., 150 Wildavsky, A., 246 Williams, B. A., 156, 157, 158, 162 Williamson, 0., 224 Wilson, A. D., 294 Wilson, D., Ill, 427 Wilson, J. Q., 189

Wilson,} .Q., 178 Wilson, R., 218 Woodford, M., 421 Woodland, semiarid, 201 Woodman, R., 110 World Bank, 270,371 World Commission on Environment and

Development, 269, 361 World Health Organization, 70 World Resource Institute, 12, 294, 370 Worster, D., 152

Xepapadeas, A., 289

Yorque, Ralf, 419-38 Young, L., 7

Zapperi, S., 421, 425 Zemel, A., 271 Zhang, Y., 425 Zimmerman, B.]., 40 Zimov, S. A., 81

INDEX 507

ISLAND PREss BoARD OF DIRECTORS

CHAIR

Henry Reath President, Collector's Reprints, Inc.

VICE-CHAIR

Victor M. Sher Environmental Lawyer

SECRETARY

Dane Nichols Chairman, The Natural Step, u.s.

TREAsURER

Drummond Pike President, The Tides Foundation

Robert E. Baensch Professor of Publishing New York University

Mabel H. Cabot

David C. Cole Sunnyside Farms, LLC

Catherine M. Conover

Gene E. Likens Director, The Institute of Ecosystem Studies

Carolyn Peachey Campbell Peachey & Associates

Wit/Rogers Trust for Public Lands

Charles C. Savitt President, Center for Resource Economics/Island Press

Susan E. Sechler Director of Global Programs The Rockefeller Foundation

Peter R. Stein Managing Partner, The Lyme Timber Company

Richard Trudell Executive Director, American Indian Resources Institute

Wren Wirth President, The "Winslow Foundation

panarchy: understanding transformations in human and natural systems

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