Modeling Landscape Change in the Willamette Basin – A Biocomplexity Approach John Bolte Oregon State University Department of Bioengineering

Modeling Landscape Modeling Landscape Change in the Change in the

Willamette BasinWillamette Basin – A Biocomplexity – A Biocomplexity

ApproachApproach

John BolteJohn BolteOregon State UniversityOregon State University

Department of BioengineeringDepartment of Bioengineering

CollaboratorsCollaborators

Dave Hulse, Department of Landscape Architecture, Dave Hulse, Department of Landscape Architecture, Institute for a Sustainable Environment, University of Institute for a Sustainable Environment, University of OregonOregon

Court Smith, Department of Anthropology, OSU Court Smith, Department of Anthropology, OSU

Stan Gregory, Department of Fish and Wildlife, OSUStan Gregory, Department of Fish and Wildlife, OSU

Michael Guzy, Department of Bioengineering, OSUMichael Guzy, Department of Bioengineering, OSU

Frank Miller, Department of Bioengineering, OSUFrank Miller, Department of Bioengineering, OSU

And a host of others…And a host of others…

Topics Covered TodayTopics Covered Today An “biocomplexity” approach to landscape change An “biocomplexity” approach to landscape change

modeling and analysismodeling and analysis

Multi-agent simulation modelsMulti-agent simulation models

An example MAS modeling framework for landscape An example MAS modeling framework for landscape change analysis: Evolandchange analysis: Evoland

Application in the Willamette Basin, OregonApplication in the Willamette Basin, Oregon

To start - a definition of To start - a definition of biocomplexitybiocomplexity

The term “biocomplexity” is used to describe the The term “biocomplexity” is used to describe the complex structures, interactions, adaptive capabilities complex structures, interactions, adaptive capabilities and (frequently nonlinear) dynamicsand (frequently nonlinear) dynamics of a diverse set of a diverse set of biological and ecological systems, often operating of biological and ecological systems, often operating at multiple spatial and temporal scales at multiple spatial and temporal scales

Many Approaches!!!Many Approaches!!! Some focusing on capturing Some focusing on capturing richness of system dynamics, others more focused on richness of system dynamics, others more focused on complex adaptive systems approachescomplex adaptive systems approaches

Biocomplexity AnalysesBiocomplexity AnalysesTypical TraitsTypical Traits

Rich representation of interactions in the systemRich representation of interactions in the system

System response is characterized in terms of state-System response is characterized in terms of state-spaces that reflect these interactionsspaces that reflect these interactions

Focus on Focus on system propertiessystem properties like: like: VulnerabilityVulnerability ResilienceResilience ConnectednessConnectedness Capacity for adaptation and innovationCapacity for adaptation and innovation

Challenge – How to make these operational?Challenge – How to make these operational?

WRB Alternative Futures II WRB Alternative Futures II – Incorporating – Incorporating BiocomplexityBiocomplexity

Rationale:Rationale:

Large number of scenarios (100’s – 1000’s) necessary to Large number of scenarios (100’s – 1000’s) necessary to characterize range, likelihoods of landscape change characterize range, likelihoods of landscape change outcomesoutcomes

Need to incorporate explicit decision behaviors, Need to incorporate explicit decision behaviors, actions/constraints, feedback loopsactions/constraints, feedback loops

Need more flexible mechanisms for incorporating Need more flexible mechanisms for incorporating additional models, processes in a transferable, interactive additional models, processes in a transferable, interactive frameworkframework

Willamette Willamette Alternatives Alternatives

II – Study II – Study AreasAreas

Willamette Alternative Willamette Alternative Futures Revisited: Futures Revisited:

AssumptionsAssumptions Patterns of natural resources and human systems emerge Patterns of natural resources and human systems emerge

through the through the interplay of policy and patterninterplay of policy and pattern in coupled in coupled human/riverine systems as production (expressed in human/riverine systems as production (expressed in multiple forms) becomes multiple forms) becomes scarcescarce. .

We hypothesize that as We hypothesize that as resources become scarce or resources become scarce or impairedimpaired, a human/riverine system becomes , a human/riverine system becomes more tightly more tightly coupledcoupled ( (connections become more importantconnections become more important).).

The system as a whole develops The system as a whole develops policy responsespolicy responses that feed that feed back into back into emergent spatial and temporal patternsemergent spatial and temporal patterns of both of both cultural and biophysical functions. cultural and biophysical functions.

Evoland - A Biocomplexity Evoland - A Biocomplexity ModelModel

Evoland (Evoland (EvoEvolving lving LandLandscapes) is a tool for conducting alternative scapes) is a tool for conducting alternative futures analyses using:futures analyses using:

A A spatially explicitspatially explicit, GIS-based approach to landscape , GIS-based approach to landscape representationrepresentation

Actor-based (multiagent-based) approach to human Actor-based (multiagent-based) approach to human decisionmaking that decisionmaking that explicitly represents real-world decision-explicitly represents real-world decision-makers with attributes and behaviors within the modelmakers with attributes and behaviors within the model

Actor decisions are guided by Actor decisions are guided by “policies” that define, constrain “policies” that define, constrain potential behaviorspotential behaviors

Autonomous landscape process modelsAutonomous landscape process models produce non-human produce non-human induced (natural) landscape changeinduced (natural) landscape change

Evoland – General Evoland – General StructureStructurePolicies:

Fundamental Descriptors of

constraints and actions defining

land use management

decisionmakingLandscape:

Spatial Container in which land

use changes are depicted

Landscape Evaluators:

Generate landscape

metrics reflecting scarcity

Cultural Metaprocess: Manages the behavior of

actors

Policy Metaprocess: Manages existing policies, generation of new policies

Exogenous Drives: External

“program” defining key assumptions

Autonomous Change

Processes: Models of nonhuman

change

Actors: Decisionmakers

making landscape change by

selecting policies responsive to

their objectives

Policies in EvolandPolicies in Evoland Describe actions available to actorsDescribe actions available to actors

Primary CharacteristicsPrimary Characteristics::

Applicable Site Attributes (Spatial Query)Applicable Site Attributes (Spatial Query)

Effectiveness of the Policy (determined by evaluative models)Effectiveness of the Policy (determined by evaluative models)

Outcomes (possible multiple) associated with the selection and Outcomes (possible multiple) associated with the selection and application of the Policyapplication of the Policy

Policies are a Policies are a fundamental unit of computationfundamental unit of computation in Evoland in Evoland (Note: this has important consequences for representing (Note: this has important consequences for representing adaptation!)adaptation!)

ExampleExample: [: [Purchase conservations easement to allow Purchase conservations easement to allow revegetation of degraded riparian areasrevegetation of degraded riparian areas] in [] in [areas with no built areas with no built structures and high channel migration capacitystructures and high channel migration capacity] when [] when [native native fish habitat becomes scarcefish habitat becomes scarce]]

Actor Value MappingActor Value MappingEcosystem Health EconomicsEcosystem Health Economics

ACTORW T_1< -2 .33333 (4350)-2 .33333 to -1 .66667 (4680)-1 .66667 to -1 (1741)-1 to -0 .333333 (1459)-0 .333333 to 0.333333 (1167)0 .333333 to 1 (312)1 to 1 .66667 (846)1 .66667 to 2 .33333 (311)> 2 .33333 (268)N o D ata

R ange: -3-3


R ange: -3-3


R ange: -3-3


R ange: -3-3

Evoland Agent PropertiesEvoland Agent Properties

PropertyProperty MeaningMeaning EvolandEvolandReactiveReactive Responds to environmentResponds to environment YesYes

AutonomousAutonomous Controls own actionsControls own actions YesYes

Goal-orientedGoal-oriented More than responsive to environment More than responsive to environment YesYes

Temporally continuousTemporally continuous Agent behavior continuousAgent behavior continuous Once/stepOnce/step

CommunicativeCommunicative Communicates with other agentsCommunicates with other agents NoNo

MobileMobile Can transport self to other locationsCan transport self to other locations NoNo

FlexibleFlexible Actions not scriptedActions not scripted YesYes

LearningLearning Changes based on experienceChanges based on experience NoNo

CharacterCharacter Believable personality or emotionsBelievable personality or emotions NoNo

Adapted from Benenson and Torrens (2004:156)

Evoland Framework for Evoland Framework for WRBWRB

Evo

lan

d

Fish Abundance/Distributions

Floodplain Habitat

Small-Stream Macroinvertabrates

Upslope Wildlife Habitat

Parcel Market Values

Agricultural Land Supply

Forest Land Supply

Residential Land Supply

Conservation Set-Asides

Policy Set(s)

Actor Descriptors

Vegetative Succession

Flood Event

IDU Coverage

Evaluative ModelsData Sources

Autonomous ProcessModels

AnalysisAnalysis ResilienceResilience – determined by generating a large number of runs – determined by generating a large number of runs

(Monte Carlo) and identifying characteristics of attractor (Monte Carlo) and identifying characteristics of attractor basins in state spacebasins in state space

VulnerabilityVulnerability – identify those portions of landscape likely to – identify those portions of landscape likely to experience reversible, irreversible change of ecological experience reversible, irreversible change of ecological function through frequency analysisfunction through frequency analysis

ConnectednessConnectedness – experiment with turning on/off feedback – experiment with turning on/off feedback loops associated with:loops associated with: Policy GenerationPolicy Generation Actor Association BuildingActor Association Building Time Lags in evaluative model feedbackTime Lags in evaluative model feedback

Adaptive CapacityAdaptive Capacity – Enable/Disable/Throttle policy evolution – Enable/Disable/Throttle policy evolution

Next StepsNext StepsStill in development, but most major pieces are in place…Still in development, but most major pieces are in place…

Validation of Evoland-generated landscape trajectoriesValidation of Evoland-generated landscape trajectories

Richer representation of actor networks (Associations), social Richer representation of actor networks (Associations), social processes relating to land use changeprocesses relating to land use change

More explicit understanding of outputs, pattern/policy More explicit understanding of outputs, pattern/policy relationshipsrelationships

More explicit incorporation of adaptive policy generationMore explicit incorporation of adaptive policy generation

Interactive actors and role-playing Interactive actors and role-playing

For more information on For more information on EvoLandEvoLand

http://biosys.bre.orst.edu/evoland/http://biosys.bre.orst.edu/evoland/

Support from the National Science Foundation, Program InBiocomplexity in the Environment

Documents

Modeling Landscape Change in the Willamette Basin – A Biocomplexity Approach John Bolte Oregon State University Department of Bioengineering