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

2. Collaborators Dave Hulse, Department of Landscape Architecture, Institute for a Sustainable Environment, University of Oregon Dave Hulse, Department of Landscape Architecture, Institute for a Sustainable Environment, University of Oregon Court Smith, Department of Anthropology, OSU Court Smith, Department of Anthropology, OSU Stan Gregory, Department of Fish and Wildlife, OSU Stan Gregory, Department of Fish and Wildlife, OSU Michael Guzy, Department of Bioengineering, OSU Michael Guzy, Department of Bioengineering, OSU Frank Miller, Department of Bioengineering, OSU Frank Miller, Department of Bioengineering, OSU And a host of others… And a host of others…

3. Topics Covered Today An “biocomplexity” approach to landscape change modeling and analysis An “biocomplexity” approach to landscape change modeling and analysis Multi-agent simulation models Multi-agent simulation models An example MAS modeling framework for landscape change analysis: Evoland An example MAS modeling framework for landscape change analysis: Evoland Application in the Willamette Basin, Oregon Application in the Willamette Basin, Oregon

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

5. Biocomplexity Analyses Typical Traits Rich representation of interactions in the system Rich representation of interactions in the system System response is characterized in terms of state- spaces that reflect these interactions System response is characterized in terms of state- spaces that reflect these interactions Focus on system properties like: Focus on system properties like: Vulnerability Vulnerability Resilience Resilience Connectedness Connectedness Capacity for adaptation and innovation Capacity for adaptation and innovation Challenge – How to make these operational? Challenge – How to make these operational?

6. WRB Alternative Futures II – Incorporating Biocomplexity Rationale: Large number of scenarios (100’s – 1000’s) necessary to characterize range, likelihoods of landscape change outcomes 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 to incorporate explicit decision behaviors, actions/constraints, feedback loops Need more flexible mechanisms for incorporating additional models, processes in a transferable, interactive framework Need more flexible mechanisms for incorporating additional models, processes in a transferable, interactive framework

7. Willamette Alternatives II – Study Areas

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

9. 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 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-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 Actor decisions are guided by “policies” that define, constrain potential behaviors Autonomous landscape process models produce non-human induced (natural) landscape change Autonomous landscape process models produce non-human induced (natural) landscape change

10. Evoland – General Structure Policies: Fundamental Descriptors of constraints and actions defining land use management decisionmaking Landscape: 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

11. Policies in Evoland Describe actions available to actors Describe actions available to actors Primary Characteristics: Primary 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 application of the Policy 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!) 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] 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]

12. Actor Value Mapping Ecosystem Health Economics

13. Evoland Agent Properties PropertyMeaningEvoland Reactive Responds to environment Yes Autonomous Controls own actions Yes Goal-oriented More than responsive to environment Yes Temporally continuous Agent behavior continuous Once/step Communicative Communicates with other agents No Mobile Can transport self to other locations No Flexible Actions not scripted Yes Learning Changes based on experience No Character Believable personality or emotions No Adapted from Benenson and Torrens (2004:156)

14. Evoland Framework for WRB Evoland 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 Models Data Sources Autonomous Process Models

15. Analysis Resilience – determined by generating a large number of runs (Monte Carlo) and identifying characteristics of attractor basins in state space 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 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: Connectedness – experiment with turning on/off feedback loops associated with: Policy Generation Policy Generation Actor Association Building Actor Association Building Time Lags in evaluative model feedback Time Lags in evaluative model feedback Adaptive Capacity – Enable/Disable/Throttle policy evolution Adaptive Capacity – Enable/Disable/Throttle policy evolution

16. Next Steps Still in development, but most major pieces are in place… Validation of Evoland-generated landscape trajectories Validation of Evoland-generated landscape trajectories Richer representation of actor networks (Associations), social processes relating to land use change Richer representation of actor networks (Associations), social processes relating to land use change More explicit understanding of outputs, pattern/policy relationships More explicit understanding of outputs, pattern/policy relationships More explicit incorporation of adaptive policy generation More explicit incorporation of adaptive policy generation Interactive actors and role-playing Interactive actors and role-playing

17. For more information on EvoLand http://biosys.bre.orst.edu/evoland/ Support from the National Science Foundation, Program In Biocomplexity in the Environment