Doug Bruggeman
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Visiting Assistant Professor Office Location: 8 Bay St. Asheville, NC Phone: 828-505-3578 |
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Conservation programs are increasingly working at multiple spatial scales while including a greater diversity of stakeholders. Wise use of scientific principles, historical data, and information technology can make a large difference in the success or failure of such programs. My work focuses on the integration of predictive models of biodiversity and economic models describing tradeoffs between conservation and traditional economic development. Efforts in predictive modeling have focused on the development and validation of individual-based, spatially-explicit population models (SEPMs), because these mechanistic models are capable of predicting population patterns in landscapes not used to parameterize the model. Therefore, SEPMs are critical tools for predicting the consequences of land use change.
I have also integrated SEPMs with population genetic theory and natural resource economics to provide a tradable credit system for endangered species habitat -- Landscape Equivalency Analysis (LEA). LEA is the application of resource-based compensation, or the "service-to-service" approach to compensation, at a landscape scale. By incorporating population genetic theory, LEA prevents habitat trading from exacerbating the effects of habitat fragmentation and rewards trades that move the spatial allocation of habitat toward that in which the organism evolved (i.e., habitat defragmentation). LEA incorporates regional ecological effects into the local economic value of land. In other words, if loss of one habitat patch increases rates of inbreeding or local extinction for other habitat patches in the landscape, the number of LEA credits purchased must offset those changes occurring at a regional scale. The landscape context of the habitats traded, the species behavior and demography, and land values will determine which trades are viable. SEPMs are often complex, containing many parameters and thus requiring large amounts of historical data to build. I recently began collaborating with Dr. Thorsten Wiegand at the Helmholtz Centre for Environmental Research to apply Pattern Oriented Modeling (POM) as a method for managing model complexity and to estimate uncertain parameter values for SEPMs. Dr. Wiegand has devised a method to find the most parsimonious SEPM capable of reproducing a large number of patterns observed on a landscape. Together with Dr. Mike Jones, Quantitative Fisheries Center at MSU, we have extended the method as a way to incorporate Decision Analysis into habitat trading for endangered species. Decision Analysis provides a structured approach for comparing management alternatives in the face of scientific uncertainty. This is critical because we will rarely be completely certain of a species? behavior and demography at a landscape scale when decisions are made to re-allocate habitat. By integrating LEA and POM we can inform stakeholders when we know enough to trade habitat or when collecting more data on landscape patterns would improve the cost-effectiveness of decisions. In summary, my research works at the interface of basic and applied research by highlighting how an understanding of natural history improves cost-effectiveness of conservation. |
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