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NCSSF Project A10. Decision Support Systems for Forest Biodiversity. Evaluation of Current Systems and Future Needs. K. Norman Johnson Sean Gordon Oregon State University Oct. 14, 2003. http://ncseonline.org/NCSSF/DSS/. Research Questions.
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NCSSF Project A10 Decision Support Systems for Forest Biodiversity Evaluation of Current Systems and Future Needs K. Norman Johnson Sean Gordon Oregon State University Oct. 14, 2003 http://ncseonline.org/NCSSF/DSS/
Research Questions • What problems and issues do decision support systems for forest biodiversity need to address? • What decision support systems are available for forest biodiversity analysis? • How well do they address problems and issues? • What additional tools (or capabilities) are required to meet forest biodiversity management needs?
Definitions : DSS • DSS are tools which • are policy/management-oriented (decision) • deal with complexity (support) • have a clear, reproducible protocol (system) • are computer-based • Not • HSI’s alone • research models • forest certification standards • informal expert systems (FEMAT)
Definitions: Biodiversity • …variety and variability among living organisms and the ecological complexes in which they occur…number of different items and their relative frequency (OTA 1987) • "Biodiversity is the totality of genes, species, and ecosystems in a region” (UNEP 1992) • Montreal Protocol Criterion 1 • ecosystems (1-5 coarse filter), species & genetic (6-9 fine filter) • composition, structure, and function (Franklin)
Approach ID most important biodiversity issues Inventory of available DSS Comparison: Needs vs. Tools Guidance for potential DSS users Suggestions for future DSS development
Results : Priority Issues • Nature of forest biodiversity problems • Simple, complex, wicked • Substance of forest biodiversity problems • Characterizing biodiversity • Influences on forest habitats • Analytical methods
Nature of Forest Biodiversity ProblemsPerspectives on Forestry
Nature of Forest Biodiversity ProblemsOperational Measures • Accommodate different kinds of perspectives & information (biophysical, economic, social) • Produce results for decision makers at multiple spatial scales(landowners to policymakers) • Facilitate social negotiation and learning
Substance of Forest Biodiversity ProblemsAnalytical Methods & Forest Influences Analytical Methods • Risk assessment • Trade-Offs (economic/ecological, species/species) • Landscape Analysis Influences on Forest Habitats • Silviculture & timber harvest • Biological (pests, pathogens, invasives) • Fire • Development • Climate change
Substance of Forest Biodiversity ProblemsCharacterizing Biodiversity Montreal Process Criterion 1 Indicators Aggregate forest condition measures • Area by forest type • Area by forest age / successional stage • Area by management class • Fragmentation of forest types Species measures • Diversity • Viability • Distribution • Abundance
Selection of Systems for Review • Inventory identified 114 systems total • Categorized & screened by purpose • FOR-BDV: forest biodiversity DSS • BDV: biodiversity mgmt DSS • FOR: forest management DSS • GEN: general purpose DSS applied to forest biodiversity • Other: not reviewed • 31 systems reviewed (screening additional 26)
PATCH Substance of Forest Biodiversity ProblemsCharacterizing Biodiversity
Substance of Forest Biodiversity ProblemsCharacterizing Biodiversity
RAMAS FRAGSTATS Substance of Forest Biodiversity ProblemsCharacterizing Biodiversity
Substance of Forest Biodiversity ProblemsAnalysis Methods & Forest Influences
Substance of Forest Biodiversity ProblemsAnalysis Methods & Forest Influences
Substance of Forest Biodiversity ProblemsAnalysis Methods & Forest Influences
Conclusions Given Substance of Problem • Integration of forest & biodiversity management DSS has potential to address majority of issues • Sparse on biodiversity effects of climate, biologicals, fire • Few options for small owners • Diversity of approaches, but no widely accepted DSS for forest biodiversity
Conclusions Given Nature of Problem • Emergence of ecological as well as economic worldview • DSS must accommodate fundamental assumptions of both • Decisions no longer made by an elite group • DSS must be understandable & compelling to broad audiences • No definitive solutions to wicked problems • DSS should facilitate social learning (“telling stories”)
Concluding Work • Verification of results through developer & user interviews • Continue publishing results on project web site: http://ncseonline.org/ncssf/dss/ • Add online tools to help users & potential users choose appropriate DSS(web site search capabilities / DSS selection “wizard”) • Final report