Envisioning Future Landscape Trajectories

1. Envisioning Future Landscape Trajectories An Alternative Futures Approach to Understanding Dynamics of Landscape Change John BolteBiological & Ecological Engineering DepartmentOregon State University

2. Today’s Discussion • Overview of alternative futures approach to understanding landscape dynamics • Description of one approach using Envision • Example applications • H.J. Andrews LTER • Puget Sound

3. Alternative Futures Projects • Examine multiple scenarios of trends and assumptions about future conditions, generally using one or more models of change, • Assist in incorporating stakeholder interactions to define goals, constraints, trajectories, drivers, outcomes • Allow visualization of the results in a variety of types and formats • Ultimately are intended to assist in improving land management decision-making

4. Approach: Multi-agent Modeling • Based on modeling behavior and actions of autonomous, adaptive agents (actors) • Our approach: spatially explicit, represents land management decisions of entities (actors) with authority over parcels of land • Actor decisions implemented through policies that guide & constrain potential actions • Autonomous processes (e.g. succession) simultaneously modeled

5. Envision – Conceptual Structure Actors Decision-makers managing the landscape by selecting policies responsive to their objectives Landscape Production Models Generating Landscape Metrics Reflecting Ecosystem Service Productions LandscapeFeedbacks Landscape Spatial Container in which landscape changes, ES Metrics are depicted Multiagent Decision-making Select policies and generate land management decision affecting landscape pattern Scenario Definition LandscapeFeedbacks Policies Fundamental Descriptors of constraints and actions defining land use management decisionmaking Autonomous Change Processes Models of Non-anthropogenic Landscape Change

6. ENVISION – Triad of Relationships Goals Actors Policies Values Intentions • Economic Services • Ecosystem Services • Socio-cultural Services Provide a common frame of reference for actors, policies and landscape productions Landscapes Metrics of Production

7. Policy Definition Landscape policies are decisions or plans of action for accomplishing desired outcomes. from: Lackey, R.T. 2006. Axioms of ecological policy. Fisheries. 31(6): 286-290.

8. Policies in ENVISION • Policies are a decision or plan of action for accomplishing a desired outcome; they are a fundamental unit of computation 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 • 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]

9. Models in ENVISION • Models are “plug-ins” of two types: • Autonomous Processes: Represent processes causing landscape changes independent of human decision-making – e.g. climate change, vegetative succession, forest growth, fire, flooding, ??? • Evaluative Models – Generate production statistics and report back how well the landscape is doing a producing metrics of interest – e.g. carbon sequestration, habitat production, land availability, risk, ???

10. Models in ENVISION • A well-defined, relatively simple, yet robust interface specification is defined for both Autonomous Processes and Evaluative Models. • Models can expose input and output variables • Models have full access to the underlying spatial representation, policy sets, exposed variables, actor representation, and spatial engine • Models can make changes to the underlying landscape representation • Envision automatically manages all exposed model data

11. Envision Andrews Application Data Sources Evaluative Models Parcels (IDU’s) Mean Age at Harvest Policy Set(s) Carbon Sequestration Agent Descriptors Forest Products Extraction ENVISION Autonomous Process Models Harvested Acreage Rural Residential Expansion Fish Habitat (IBI) Vegetative Succession Resource Lands Protection Climate Change

12. Envision Andrews - Scenarios Conservation - no Climate Change Development - no Climate Change Conservation - with Climate Change Development - with Climate Change

13. Envision Andrews Study Area

14. Scenario Results – Forest Carbon

15. Scenario Results – Forest Product Extraction

16. Scenario Results – Fish IBI

17. Envision Puget Sound Application Data Sources Evaluative Models IDU’s – GSU/LULC/… Impervious Surfaces Policy Set(s) Water Quality/Loading (SPARROW) Agent Descriptors Nearshore Habitat (Controlling Factors Model) ENVISION Autonomous Process Models INVEST Tier 1 Carbon Rural/Urban Development Resource Lands Protection Expansion of Nearshore Modifications Residential Land Supply Population Growth

18. Envision Puget Sound- Scenarios Status Quo – continue current trends Managed Growth – adopt a suite of additional policies aimed at conserving/restoring habitats, protecting resource lands, emphasizing denser development pattern near urban areas Unconstrained Growth – allow lower density patterns, less habitat protection, less resource land protection

19. Puget Sound

20. South Sound

21. Bainbridge Island

22. Ferry Terminal Area

25. Lessons Learned Alternative future assessments are fundamentally place-based and client-dependent: Each application is different. Commonalities do exist and should be exploited within an extensible, adaptable DSS framework Good software design is critical Engagement with stakeholders is critical to define decision processes, desired outcomes endpoints Defensible, place-specific models are critical

26. more info at:http://envision.bioe.orst.edu