Collaborators

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

2. Collaborators • 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…

3. 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

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

5. 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?

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

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 • 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

10. 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

11. 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]

12. Actor Value Mapping Ecosystem Health Economics

13. Evoland Agent Properties Adapted from Benenson and Torrens (2004:156)

14. 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 Evoland Parcel Market Values Autonomous Process Models Agricultural Land Supply Forest Land Supply Vegetative Succession Residential Land Supply Flood Event Conservation Set-Asides

15. Analysis • 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

16. 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

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