Interactions between human behaviour and ecological systemsbehaviour and ecological systems

E.J. Milner-GullandE.J. Milner GullandImperial College London

c. Stan Shebs

c. Noelle Kumpel

c. Stan Shebs

Predicting ecological changePredicting ecological change• The vast majority of ecological systems are human-j y g y

altered• Therefore in order to predict ecological change we

need to understand and model human decision-making

Social-ecological systemSocial ecological system

The overlooked interactionsThe overlooked interactions

CocoaDrought

How much effort to invest in farming and/or hunting?

BushmeatAlternative

Cocoa price

Can weoverhunting

Alternative livelihoods

Can we predict?

Change in Ecosystem service

Conservation intervention Animal depletion

Quantification of ecosystem servicesQuantification of ecosystem services• Much of the research interest here is spatial• First step mapping ES and their overlaps to highlight• First step – mapping ES, and their overlaps, to highlight

synergies and trade-offs in provision• The Natural Capital Project is leading the way with itsThe Natural Capital Project is leading the way with its

InVest tool:

Freshwater provision + Charcoal Pollination and water provision in Provision in the Eastern Arc

mountains, Tanzaniathe Northern tropical Andes

Economic valueEconomic value• Next, the value of these services can be quantified and

used to calculate opportunity costs of conservation action:

Naidoo & Adamowicz (2006) Cons Biol.

HoweverHowever…The feedbacks caused by market reaction to conservation

l i d i i h l b tifi dplanning decisions have very rarely been quantified.

ConservationistsConservationists themselves

cause increased demand anddemand and

therefore price

Shaded: Amount of land conserved for a

Armsworth et al PNAS

given budget

Armsworth et al. PNAS 2006;103:5403-5408

Policy changes feeding through to wellbeingPolicy changes feeding through to wellbeing

• An important question:An important question: How do policy changes feed through human gdecisions to changes in natural systems, and then through to changes in wellbeing?

• Example: changes in land use in the North Pennines AONB (Julie Black PhDAONB (Julie Black PhD, www.iccs.org.uk).

Policy ChangeScenarios of change, e.g.

consistent low income

Stakeholder response

from grouse shooting

Reported likely response, e g turn moors over top

Change in management

e.g. turn moors over to rough grazing

key

but

udy

management

Vegetation Action

Actions that increase conservation value of land, e.g. pond oo

ps a

re

in th

is s

t

BAP species presenceLandscape

construction, burning, nest guarding

Two components of value – the look of the landscape and the presenceed

back

lom

odel

led

Socio-economic values

of the landscape and the presence of species of conservation concern

Thes

e fe

eno

t m

Visitors

Residents

Government

Landowners

Non-users

Farmers This study only looked at visitor value, based on WTP for access[Black et al (2010) Env Cons ]

T

[Black et al. (2010) Env. Cons.]

Scenario: Response: 10% Landscape effect: Consistently low income from grouse

Convert to grazing10% Alt income80% Do nothing

7% decr in bog, 10% incr in grass, 3% decr in heath

Update Maxentmodel: changes in

sp distribnChange in visitor

valuationsp distribn valuation(-£201,500 p.a.)

Maxent Habitat Suitability model

Survey of WTP for landscape &

for 15 BAP species

species among visitors to AONB

Social Ecological Systems modelsSocial-Ecological Systems models

• Long and relevant tradition of modelling SES as• Long and relevant tradition of modelling SES as dynamic systemsOft h i ti i tl k t l id l f• Often heuristic in outlook, so not always ideal for guiding applied policy

• Often using agent-based modelling for additional flexibility

• Strong emphasis on uncertainties, non-linearities, thresholds,

• Example: Holdo et al, Serengeti ecosystem

Holdo et al. (2010) Ecol. Appl.

ReducedReduced rainfall

Reduced anti-poaching effort

Interventions can have counter intuitive effectsInterventions can have counter-intuitive effects

How much effort to invest in farming

Change in farming revenues

invest in farming and/or hunting?

Increase inIncrease in focus on gun hunting

Reduction in primate

populationsDamania et al. (2005) Proc Roy Soc. B

populations

Many topical questionsMany topical questions…

H d fi h t t th• How do fishers react to the imposition of no-take zones? Do they redistribute effort elsewherethey redistribute effort elsewhere, fish illegally in the zone, or leave fishing? c. Stan Shebs

• How might forest protection under g pREDD affect deforestation elsewhere through leakage? (a

i il ti )very similar question)

How can we get the data we need?How can we get the data we need?• In order to model and predict human responses toIn order to model and predict human responses to

interventions, we need an understanding of decision-making

• Theoretical basis is already present in social science literature (e.g. Ostrom on common property

Aj ’ Th f Pl d B h iresources, Ajzen’s Theory of Planned Behaviour, literature on behavioural science)

• Data can be correlational cross sectional• Data can be correlational, cross-sectional, transferred from the literature, or gathered through responses to hypothetical scenarios (cf Black’s p yp (study)

• Another nice approach: Experimental games

Extraction of fish from a common poolExtraction of fish from a common pool

• How many fish would you take y yfrom a hypothetical common pool resource, under different institutional conditions?institutional conditions?

• 100 fish in the pool at the start, 10 people, max 10 fish can be taken per person per ro ndtaken per person per round.

• Every fish you harvest gets you 80 Cambodian Riel. Every fish yleft in the pool at the end of a round is worth 12 Riel per person.person.

• Play for real money (expected earnings 2-3x daily wage) Travers et al. (in revision) Ecol Econ

How do different interventions affect people’s behaviour?

treatment penalty threshold payment policy intervention expected cooperation

control na na na none weak

peer pressure na na na social (dis)approval weak

low enforcement 20% na na external enforcement weak

high enforcement 80% na na external enforcement strongg g

external individual payments na na 200 KHR individual payments strong

internal individual payments  na na 200 KHR individual payments strong

weak communal payment na 50 fish 200 KHR communal payment weak

low communal payment na 20 fish 200 KHR communal payment strong

high communal payment na 20 fish 400 KHR communal payment strong

What reduces number of fish taken?What reduces number of fish taken?

Variablechange in

fishp value Communal payments are

highly effective

high enforcement -0.656 0.010

external individual payments -0.550 0.047

Communal and internally decided treatments

internal individual payments -0.728 0.036

low communal payment -0.896 < 0.001

reduce take in next round, regardless of what

that next treatment is

high communal payment -0.840 0.001

Previous: internal individual payments -0.936 0.001

Highly significant effect on individual’s take of

whether the groupPrevious: high communal payment -1.460 0.002

Group decision recorded -0.671 << 0.001

whether the group reached a decision

Incentive based conservationIncentive-based conservationIndividual Nest ProtectorsProtectors

WCS (Contractor)Contracts individuals to protect nests at a rate of $2/day, half of which is paid on successful fledging of the nest.

Individual Nest Protectors

Individual Nest Protectors

Individual Nest Protectors

WCS: Monitoring of contracts and bird nestsWCS: Monitoring of contracts and bird nests

(SMP) Cooperative middleman

MarketProduce sold in

Village‐level farmer groups

Verified by Committee middleman

Buys produce at a guaranteed price. Farmers must keep to land‐use agreements.

Produce sold in local markets and;In hotels and restaurants as ‘wildlife‐friendly’.

Village‐level farmer groups

Village‐level farmer groups

Verified by Committee

Verified by Committee

SMP: Monitoring of conservation agreements and trade prices

Clements et al. (2010) Ecol Econ

Prediction to what end?Prediction to what end?

Management Strategy EvaluationManagement Strategy Evaluation

• Explore the effects of potential policies in a• Explore the effects of potential policies in a virtual worldUse a decision analysis framework:• Use a decision analysis framework:– Clear goals– Performance measures for these goals– Test strategies in a range of scenariosg g– Show strategy performance against measures

in each scenarioin each scenario– User decides – no “best” solution offered

MSE frameworkMSE framework

Manager

Observation modelAssessment model

Ob M k l

Observation model

Observes Makes rules

Implementation model

Resource stockOperating modelPerformance

measures

An example SESSF AustraliaAn example – SESSF, Australia

C l lti iComplex multi-species fishery, worth Aus$96 million in 2006/7 (most valuable fishery in Australia)

Managed using MSE since 2006

Main success - stakeholderMain success stakeholder buy-in, so lower quotas set, and agreement reached much quicker (2 days not severalquicker (2 days not several weeks)

HoweverHowever…

• MSE as practiced in fisheries is not perfect– Very focussed on the biological side: y g

implementation models very sketchy, little economic or social insight includedg

– Complicated modelling procedures, highly technicaltechnical

– Narrow focus on individual target species (though this is improving)(though this is improving)

Bunnefeld et al. (inBunnefeld et al. (in press) TREE

Fulfilling MSE’s potentialFulfilling MSE s potential• Can we take the framework and apply it to otherCan we take the framework and apply it to other

systems?• Can we improve it so that the human side is fully p y

included?

c. Noelle Kumpel

ManagerManagerObservation

model

Assessment model

ObservesMakes rules

Observes

Implementation model

Resource stock Resource usersResource stock

Resource Operating model

Resource users

User Operating modelResource Operating model

Performance

User Operating model

Milner-Gulland (2011)measures

Milner Gulland (2011) PNAS

Real case studiesReal case studies• Lions in Tanzania (with Panthera)Lions in Tanzania (with Panthera)• Bushmeat hunting in the Serengeti (Ana Nuno)• Lynx control in Norway (with E Nilsen Univ Oslo)Lynx control in Norway (with E. Nilsen, Univ. Oslo)• Bear hunting in N. Dinarics (Nils Bunnefeld)• Mountain nyala trophy hunting in Ethiopia (Nils Bunnefeld)• Mountain nyala trophy hunting in Ethiopia (Nils Bunnefeld)

c. K. Skogren

c. A. Atickem

Bushmeat hunting in the SerengetiBushmeat hunting in the SerengetiHunting by residents is legal, with a licence, but these are virtually g y g , , ynever obtainedA range of land management types from National Parks to village landslandsComplex multi-species system, with migratory and resident speciesPoor local people hunt (illegally) for food and incomePotentially an issue for sustainabilityPotentially an issue for sustainabilityA number of different types of conservation intervention in use

c. N. Bunnefeld

Questions the MSE can help withQuestions the MSE can help with• What is the long-term sustainability of the system, under current and

potential scenarios for human wellbeing and animal populations?potential scenarios, for human wellbeing and animal populations?• What would the effect on our management be of improving the

uncertainty in the observation process?• How can managers best affect hunting behaviour

– Through law enforcement, improving returns to labour in alternative livelihoods or direct payments?alternative livelihoods, or direct payments?

c. epcpp p

To summariseTo summarise…• There is a real and urgent need for predictive research

that includes human behaviour into ecological systems modelsI ti l h i t th f db k f h• In particular, research into the feedback of human responses to interventions is under-represented These indirect effects could be substantial especially in• These indirect effects could be substantial, especially in poor areas where people have multiple livelihoods

• In these circumstances our predictions could be very• In these circumstances, our predictions could be very misleading if they are based on static or uni-directional models.

The futureThe future• There is already a solid foundation on which to buildThere is already a solid foundation on which to build,

within social science, within economics, anthropology, social psychology and behavioural science

• Prediction is not enough – there needs to be active experimentation and testing, in an adaptive or MSE framework

• This requires a shake-up in the relationship between h d i l tresearchers and implementers

SoSo…• It is crucial to understand

how our interventions alterhow our interventions alter the incentives and behaviour of those causing biodiversity loss (at any scale)

• Unless this research• Unless this research agenda is addressed, robust predictions of the dynamics of human-ecological systems under environmental social andenvironmental, social and policy change will remain elusive.

Thank you for listening and to my fundersThank you for listening, and to my funders…

htt //f 7h t t/http://fp7hunt.net/

For more information about our work: www.iccs.org.uk