Adaptively Managing Wildlife for Climate Change: A Fuzzy Logic Approach |
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Authors: | Tony Prato |
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Institution: | (1) CARES, University of Missouri, Columbia, Agricultural Economics, 212 Mumford Hall University of Missouri, Columbia, MO 65211, USA |
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Abstract: | Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse
impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can
evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate
change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules
requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships
between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated
and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases
that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple
ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple
time periods. |
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