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Modeling Landscape Change in the Willamette Basin – A Biocomplexity Approach John Bolte Oregon State University Department of Bioengineering. Collaborators. Dave Hulse, Department of Landscape Architecture, Institute for a Sustainable Environment, University of Oregon

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Modeling Landscape Change in the Willamette Basin – A Biocomplexity ApproachJohn BolteOregon State UniversityDepartment of Bioengineering

  • Dave Hulse, Department of Landscape Architecture, Institute for a Sustainable Environment, University of Oregon
  • Court Smith, Department of Anthropology, OSU
  • Stan Gregory, Department of Fish and Wildlife, OSU
  • Michael Guzy, Department of Bioengineering, OSU
  • Frank Miller, Department of Bioengineering, OSU
  • And a host of others…
topics covered today
Topics Covered Today
  • An “biocomplexity” approach to landscape change modeling and analysis
  • Multi-agent simulation models
  • An example MAS modeling framework for landscape change analysis: Evoland
  • Application in the Willamette Basin, Oregon
to start a definition of biocomplexity
To start - a definition of biocomplexity
  • The term “biocomplexity” is used to describe the complex structures, interactions, adaptive capabilities and (frequently nonlinear) dynamics of a diverse set of biological and ecological systems, often operating at multiple spatial and temporal scales
  • Many Approaches!!! Some focusing on capturing richness of system dynamics, others more focused on complex adaptive systems approaches
biocomplexity analyses typical traits
Biocomplexity AnalysesTypical Traits
  • Rich representation of interactions in the system
  • System response is characterized in terms of state-spaces that reflect these interactions
  • Focus on system properties like:
    • Vulnerability
    • Resilience
    • Connectedness
    • Capacity for adaptation and innovation
  • Challenge – How to make these operational?
wrb alternative futures ii incorporating biocomplexity
WRB Alternative Futures II – Incorporating Biocomplexity


  • Large number of scenarios (100’s – 1000’s) necessary to characterize range, likelihoods of landscape change outcomes
  • Need to incorporate explicit decision behaviors, actions/constraints, feedback loops
  • Need more flexible mechanisms for incorporating additional models, processes in a transferable, interactive framework
willamette alternative futures revisited assumptions
Willamette Alternative Futures Revisited: Assumptions
  • Patterns of natural resources and human systems emerge through the interplay of policy and pattern in coupled human/riverine systems as production (expressed in multiple forms) becomes scarce.
  • We hypothesize that as resources become scarce or impaired, a human/riverine system becomes more tightly coupled (connections become more important).
  • The system as a whole develops policy responses that feed back into emergent spatial and temporal patterns of both cultural and biophysical functions.
evoland a biocomplexity model
Evoland - A Biocomplexity Model

Evoland (Evolving Landscapes) is a tool for conducting alternative futures analyses using:

  • A spatially explicit, GIS-based approach to landscape representation
  • Actor-based (multiagent-based) approach to human decisionmaking that explicitly represents real-world decision-makers with attributes and behaviors within the model
  • Actor decisions are guided by “policies” that define, constrain potential behaviors
  • Autonomous landscape process models produce non-human induced (natural) landscape change
evoland general structure
Evoland – General Structure

Policies: Fundamental Descriptors of constraints and actions defining land use management decisionmaking

Policy Metaprocess: Manages existing policies, generation of new policies

Landscape: Spatial Container in which land use changes are depicted

Landscape Evaluators: Generate landscape metrics reflecting scarcity

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

Cultural Metaprocess: Manages the behavior of actors

policies in evoland
Policies in Evoland
  • Describe actions available to actors
  • Primary Characteristics:
    • Applicable Site Attributes (Spatial Query)
    • Effectiveness of the Policy (determined by evaluative models)
    • Outcomes (possible multiple) associated with the selection and application of the Policy
  • Policies are a fundamental unit of computation in Evoland (Note: this has important consequences for representing adaptation!)
  • Example: [Purchase conservations easement to allow revegetation of degraded riparian areas] in [areas with no built structures and high channel migration capacity] when [native fish habitat becomes scarce]
actor value mapping
Actor Value Mapping

Ecosystem Health Economics

evoland agent properties
Evoland Agent Properties

Adapted from Benenson and Torrens (2004:156)

evoland framework for wrb
Evoland Framework for WRB

Evaluative Models

Data Sources

Fish Abundance/Distributions

IDU Coverage

Floodplain Habitat

Policy Set(s)

Small-Stream Macroinvertabrates

Actor Descriptors

Upslope Wildlife Habitat


Parcel Market Values

Autonomous Process


Agricultural Land Supply

Forest Land Supply

Vegetative Succession

Residential Land Supply

Flood Event

Conservation Set-Asides

  • Resilience – determined by generating a large number of runs (Monte Carlo) and identifying characteristics of attractor basins in state space
  • Vulnerability – identify those portions of landscape likely to experience reversible, irreversible change of ecological function through frequency analysis
  • Connectedness – experiment with turning on/off feedback loops associated with:
    • Policy Generation
    • Actor Association Building
    • Time Lags in evaluative model feedback
  • Adaptive Capacity – Enable/Disable/Throttle policy evolution
next steps
Next Steps

Still in development, but most major pieces are in place…

  • Validation of Evoland-generated landscape trajectories
  • Richer representation of actor networks (Associations), social processes relating to land use change
  • More explicit understanding of outputs, pattern/policy relationships
  • More explicit incorporation of adaptive policy generation
  • Interactive actors and role-playing

For more information on EvoLand

Support from the National Science Foundation, Program In

Biocomplexity in the Environment