Adaptively Managing Wildlife for Climate Change: A Fuzzy Logic Approach

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...

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Bibliographic Details
Published in:Environmental management (New York) 2011-07, Vol.48 (1), p.142-149
Main Author: Prato, Tony
Format: Article
Language:English
Subjects:
Adaptation, Psychological
Animal populations
Animals
Animals, Wild
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Climate Change
Conservation of Natural Resources
Earth and Environmental Science
Ecological conditions
Ecology
Ecosystem
Effectiveness
Environment
Environmental Management
Environmental policy
Forestry Management
Fuzzy
Fuzzy Logic
Fuzzy set theory
Fuzzy sets
Global warming
Greenhouse effect
Management
Mathematical functions
Nature Conservation
Phenology
Population
Population number
Probability
Probability distribution
Waste Water Technology
Water Management
Water Pollution Control
Wildlife management
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Summary: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.
ISSN:0364-152X
1432-1009
DOI:10.1007/s00267-011-9648-x