social network analysis as an aid to landscape-scale conservation

SOCIAL NETWORK ANALYSIS AS AN AID TO LANDSCAPE-SCALE CONSERVATION

GNLCC Webinar: Graham McDowell

University of Oxford/Environmental Change Institute

5 December, 2012 eciEnvironmental Change Institute

Webinar Overview

¨  LCC/GNLCC background ¨  SNA applied – GNLCC case study ¨  Theoretical foundations and methods ¨  1st question/discussion period ¨  Study results ¨  SNA as and aid to landscape-scale conservation ¨  2nd question/discussion period

eciEnvironmental Change Institute

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Landscape Conservation Cooperatives

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PapahanaumokuakeaMarine National Monument

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Hawaii

Samoa

Guam

Micronesia

Wake Island

Marshall Islands

Jarvis IslandHowland Island

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Landscape Conservation Cooperatives1. Appalachian2. California3. Desert4. Eastern Tallgrass Prairie and Big Rivers5. Great Basin6. Great Northern

7. Great Plains8. Gulf Coast Prairie9. Gulf Coastal Plains and Ozarks10. North Atlantic11. North Pacific12. Peninsular Florida

13. Plains and Prairie Potholes14. South Atlantic15. Southern Rockies16. Upper Midwest and Great Lakes17. Aleutian and Bering Sea Islands18. Arctic

19. Northwestern Interior Forest20. Western Alaska21. Pacific Islands22. CaribbeanUnclassified

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2011

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U.S. Department of the InteriorLandscape Conservation Cooperatives

Albers Equal Area Conic NAD83Produced by FWS, IRTM, Denver, COMap Date: 12142011

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Puerto Rico

0 100 200 300 400 500 Miles

Geographic framework selection criteria: (1) avoid fragmentation of BCRs as much as possible (45%), (2) maintain fidelity of fundamental terrestrial and aquatic units as much as possible (40%), and (3) be mindful of existing conservation partnerships (15%)

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LCC Case Study eciEnvironmental Change Institute

The Great Northern Landscape Conservation Cooperative

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GNLCC as a Bridging Organization eciEnvironmental Change Institute

"! Coordinate collaborations among actors at multiple organizational levels; provide arenas for trust-building, vertical and horizontal collaboration, learning, identification of common interests, and conflict resolution; reduce the transaction costs of collaboration and can generate norm-based, legal, political, and/or financial support for change (Berkes, 2009)

"! Conceptually, idea provides entry

point for Social Network Analysis

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Research Question

u  How and to what extent is the GNLCC in its role as a bridging organization facilitating a transformation towards tenable landscape-scale conservation in the Great Northern region?


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Research Objectives

u  To link complementary theoretical perspectives via a novel methodological approach to advance strategies for analyzing bridging organizations and the networks they cultivate

u  To use this interdisciplinary foundation to critically examine

the GNLCC, the nature of relationships among actors in the social network it is developing, and how those relations may be improving or inhibiting the emergence of tenable landscape-scale conservation

u  To identify ways in which the GNLCC might improve its

bridging efforts


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But wait…

What is “tenable landscape-scale conservation”?

And how does one determine how and to what extent it emerges

from the actions of the GNLCC?


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Theoretical Foundations eciEnvironmental Change Institute

¨  Non-equilibrium/landscape ecology

¨  Socio-ecological resilience

¨  Political ecology

¨  Social relational theory

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SNA Terminology

¨  Nodes (e.g. organizations) ¨  Ties (i.e. relationships between nodes, can be

binary, directed, or weighted) ¨  Structural variable (e.g. influence) ¨  Social networks (comprised of nodes connected by

ties) ¨  Structural relations (i.e. structurally explicit

characteristics of tie relations and the implications thereof)

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Social Relational Theory to SNA

Introduction to social network methods: Chapter 10: Centrality and power

A moment's inspection ought to suggest that actor A has a highly favored structural position in the star network, if the network is describing a relationship such as resource exchange or resource sharing. But, exactly why is it that actor A has a "better" position than all of the others in the star network? What about the position of A in the line network? Is being at the end of the line an advantage or a disadvantage? Are all of the actors in the circle network really in exactly the same structural position?

We need to think about why structural location can be advantageous or disadvantageous to actors. Let's focus our attention on why actor A is so obviously at an advantage in the star network.

Degree: In the star network, actor A has more opportunities and alternatives than other actors. If actor D elects to not provide A with a resource, A has a number of other places to go to get it; however, if D elects to not exchange with A, then D will not be able to exchange at all. The more ties an actor has then, the more power they (may) have. In the star network, Actor A has degree six, all other actors have degree one. This logic underlies measures of centrality and power based on actor degree, which we will discuss below. Actors who have more ties have greater opportunities because they have choices. This autonomy makes them less dependent on any specific other actor, and hence more powerful.

Now, consider the circle network in terms of degree. Each actor has exactly the same number of alternative trading partners (or degree), so all positions are equally advantaged or disadvantaged.

In the line network, matters are a bit more complicated. The actors at the end of the line (A and G) are actually at a structural disadvantage, but all others are apparently equal (actually, it's not really quite that simple). Generally, though, actors that are more central to the structure, in the sense of having higher degree or more connections, tend to have favored positions, and hence more power.

Closeness: The second reason why actor A is more powerful than the other actors in the star network is that actor A is closer to more actors than any other actor. Power can be exerted by direct bargaining and exchange. But power also comes from acting as a "reference point" by which other actors judge themselves, and by being a center of attention who's views are heard by larger numbers of actors. Actors who are able to reach other actors at shorter path lengths, or who are more reachable by other actors at shorter path lengths have favored positions. This structural advantage can be translated into power. In the star network, actor A is at a geodesic distance of one from all other actors; each other actor is at a geodesic distance of two from all other actors (but A). This logic of structural advantage underlies approaches that emphasize the distribution of closeness and distance as a source of power.

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Introduction to social network methods: Chapter 10: Centrality and power

Network analysts often describe the way that an actor is embedded in a relational network as imposing constraints on the actor, and offering the actor opportunities. Actors that face fewer constraints, and have more opportunities than others are in favorable structural positions. Having a favored position means that an actor may extract better bargains in exchanges, have greater influence, and that the actor will be a focus for deference and attention from those in less favored positions.

But, what do we mean by "having a favored position" and having "more opportunities" and "fewer constraints?" There are no single correct and final answers to these difficult questions. But, network analysis has made important contributions in providing precise definitions and concrete measures of several different approaches to the notion of the power that attaches to positions in structures of social relations.

To understand the approaches that network analysis uses to study power, it is useful to first think about some very simple systems. Consider the three simple graphs of networks in figures 10.1, 10.2, and 10.3, which are called the "star," "line," and "circle."

Figure 10.1. "Star" network

Figure 10.2. "Line" network

Figure 10.3. "Circle" network

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Introduction to Social Network Methods; Chapter 5: Using Matrices

target of the tie is the column. Let's look at a simple example. The directed graph of friendship choices among Bob, Carol, Ted, and Alice is shown in figure 5.4.

Figure 5.4 Bob, Carol, Ted, and Alice

We can since the ties are measured at the nominal level (that is, the data are binary choice data), we can represent the same information in a matrix that looks like:

Figure 5.5. Asymmetric adjacency matrix of the graph shown in figure 5.4.

Bob Carol Ted Alice

Bob --- 1 1 0

Carol 0 --- 1 0

Ted 1 1 --- 1

Alice 0 0 1 ---

Remember that the rows represent the source of directed ties, and the columns the targets; Bob chooses Carol here, but Carol does not choose Bob. This is an example of an "asymmetric" matrix that represents directed ties (ties that go from a source to a receiver). That is, the element i,j does not necessarily equal the element j,i. If the ties that we were representing in our matrix were "bonded-ties" (for example, ties representing the relation "is a business partner of" or "co-occurrence or co-presence," (e.g. where ties represent a relation like: "serves on the same board of directors as") the matrix would necessarily be symmetric; that is element i,j would be equal to element j,i.

Binary choice data are usually represented with zeros and ones, indicating the presence or

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Network structures matters…

…and can be defined in quantitative terms.

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Methods

u  Rapid institutional ethnography (qualitative method) u Content analysis u Key informant interviews u Participant observation

u  Social Network Analysis (quantitative method) u Questionnaire

u  Data analysis


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Any questions at this point?

http://www.rockymountainconsulting.com/ 13

Results

Theme 1: Inter-organizational interaction on issues related to landscape-scale stressors HOW:

¤  GNLCC node level betweeness score = 22.26

¤  Network density = 66.7% ¤  Connectivity = 4 ¤  Cut points = 0 ¤  Maximum link distance of 2 (1 = 66.7%, 2 = 33.3%)


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Betweeness (network) (node based) Centralization index (%) Mean Std. Dev. 5.61 6 5.97 Betweeness (node level) (node based) Top 3 orgs. and score GNLCC (22.26) NPS (20.3) USGS (11.36) Bottom 3 orgs. and score WNPC (0.64) NOAA (0.56) WGF (0.43) Distance (geodesic) Link distance Frequency (%) Average

distance 1 228 66.7 1.3 2 114 33.3 Reciprocity (hybrid) Reciprocated ties (%) 55.1 Clustering Network clustering (%) 71% Hierarchy (Krackhardt GTD Measures)

Connectedness Hierarchy Efficiency LUB

1 0 0.16 1 Factions Faction 1 CWSEC, WNPC Faction 2 APD, AESRD, CBFC, CTUIR, HRI, GNLCC, IWJV,

MFWP, NOAA, NPS, NRCS, USFWSP Goodness of fit 0.63 Cut points 0

Figure 7: Visualization of inter-organizational interaction on issues related to landscape-scale stressors. Interpretation: Black ties = less than five interactions per year; blue ties = five or more interactions. Organizations with shorter geodesic distance (i.e. greater similarity in their interaction characteristics) are located closer in space.

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Results

Theme 2: Inter-organizational information seeking on issues related to landscape-scale stressors HOW:

¤  GNLCC node level betweeness = 92.23

¤  Network density = 36.8% ¤  GNLCC node level centrality in degree = 13 ¤  Average link distance 1.8; 90% of connections no more

than 2 links away


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Betweeness (node level) (node based)

Top 3 orgs. and score GNLCC (92.23) USGS (18.98) IWJV (14.74) Bottom 3 orgs. and score CBFC (0) WCS (0) CTUIR (0) Distance (geodesic) Link distance Frequency (%) Average

distance 1 100 37

1.8 2 143 53 3 13 5 4 16 6 Reciprocity (hybrid) Reciprocated ties (%) 42.9 Clustering Network clustering (%) 54.3 Hierarchy (Krackhardt GTD Measures)


1 0.12 0.55 1 Factions Faction 1 HRI, GNLCC, IWJV, MFWP, NPS, NRCS, USFWSP,

USGS, WDFW, WCS, WGF Faction 2 APD, AESRD, CWSEC, CBFC, CTUIR, WNPC Goodness of fit 0.65 Cut points 0

Figure 8: Visualization of inter-organizational information seeking on issues related to landscape-scale stressors. Interpretation: Organizations with shorter geodesic distance (i.e. greater similarity in their information seeking characteristics) are located closer in space. Arrows indicate the direction of information seeking.

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Results

Theme 1: Inter-organizational interaction on issues related to landscape-scale stressors TO WHAT EXTENT:

¤  US Federal agency group weighted density = 62

¤  State agency group weighted density = 10 ¤  Network clustering = 71% ¤  Hierarchy = 1, 0, 0.16, 1


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Betweeness (network) (node based) Centralization index (%) Mean Std. Dev. 5.61 6 5.97 Betweeness (node level) (node based) Top 3 orgs. and score GNLCC (22.26) NPS (20.3) USGS (11.36) Bottom 3 orgs. and score WNPC (0.64) NOAA (0.56) WGF (0.43) Distance (geodesic) Link distance Frequency (%) Average

distance 1 228 66.7 1.3 2 114 33.3 Reciprocity (hybrid) Reciprocated ties (%) 55.1 Clustering Network clustering (%) 71% Hierarchy (Krackhardt GTD Measures)


1 0 0.16 1 Factions Faction 1 CWSEC, WNPC Faction 2 APD, AESRD, CBFC, CTUIR, HRI, GNLCC, IWJV,

MFWP, NOAA, NPS, NRCS, USFWSP Goodness of fit 0.63 Cut points 0

Figure 7: Visualization of inter-organizational interaction on issues related to landscape-scale stressors. Interpretation: Black ties = less than five interactions per year; blue ties = five or more interactions. Organizations with shorter geodesic distance (i.e. greater similarity in their interaction characteristics) are located closer in space.

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Results

Theme 2: Inter-organizational information seeking on issues related to landscape-scale stressors TO WHAT EXTENT: ¤  US Federal group density = 2.6 ¤  State group density = 2

¤  Hierarchy = 1, 0.12, 0.55, 1 ¤  Link distances up to 4


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Betweeness (node level) (node based)

Top 3 orgs. and score GNLCC (92.23) USGS (18.98) IWJV (14.74) Bottom 3 orgs. and score CBFC (0) WCS (0) CTUIR (0) Distance (geodesic) Link distance Frequency (%) Average

distance 1 100 37

1.8 2 143 53 3 13 5 4 16 6 Reciprocity (hybrid) Reciprocated ties (%) 42.9 Clustering Network clustering (%) 54.3 Hierarchy (Krackhardt GTD Measures)


1 0.12 0.55 1 Factions Faction 1 HRI, GNLCC, IWJV, MFWP, NPS, NRCS, USFWSP,

USGS, WDFW, WCS, WGF Faction 2 APD, AESRD, CWSEC, CBFC, CTUIR, WNPC Goodness of fit 0.65 Cut points 0

Figure 8: Visualization of inter-organizational information seeking on issues related to landscape-scale stressors. Interpretation: Organizations with shorter geodesic distance (i.e. greater similarity in their information seeking characteristics) are located closer in space. Arrows indicate the direction of information seeking.

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SNA Derived Recommendations

¨  Actively encourage more inter-affiliation type interaction among conservation partners

¨  Commit time and resources to engaging relevant but currently underrepresented organizations as well as consequentially unrepresented sectors (e.g. including energy and transportation)

¨  Work to identify how diverse epistemological perspectives and expertise can be brought together in practice to obtain the range of knowledge needed to understand and address landscape-scale stressors

¨  Consider utilizing SNA as a performance metric for quantifying

progress on facilitating collaborative responses to landscape-scale stressors


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Case Study – Concluding Thoughts

¨  So…is the GNLCC in its role as a bridging organization facilitating a transformation towards tenable landscape-scale conservation in the Great Northern region?

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SNA as an aid to Landscape-Scale Conservation

¨  Understanding collaborative landscape-scale conservation

¨  Improving collaborative landscape-scale conservation

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Key SNA References

¨  Bodin, O. & C. Prell. 2011. Social Networks and Natural Resource Management: Uncovering the Social Fabric of Environmental Governance. Cambridge: Cambridge University Press. ¤  Examples of SNA in the context of natural resource management

¨  Knoke, D. & S. Yang. 2008. Social Network Analysis. Thousand Oaks:

Sage Publications, Inc. ¤  Excellent and accessible SNA methods reference

¨  Wasserman, S. & K. Faust. 1994. Social Network Analysis: Methods And Applications. Cambridge: Cambridge University Press. ¤  The authoritative text on SNA theory and methods


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Thank You Any Questions?
