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An Integer Programming Approach for Spatially and Temporally Optimizing Wildlife Populations

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This paper presents mixed integer linear programming formulations that optimize the spatial layout of management actions for providing wildlife habitat, over time. The formulations focus on wildlife growth and dispersal as a dynamic, probabilistic process. Habitat fragmentation/connectivity is thus modeled indirectly. Multiple timber age classes are accounted for as different wildlife habitat types, which define carrying capacity limitations that are tracked spatially. A variety of objective functions are specified, including ones based on piecewise-approximated nonlinear functions that relate wildlife populations to the probability of species viability. All of the formulations and objective functions are demonstrated with a case example. For. Sci. 40(1):177-191.

Keywords: Habitat fragmentation; scheduling; species richness

Document Type: Journal Article

Affiliations: Project Leader, Rocky Mountain Forest and Range Experiment Station, USDA Forest Service, Fort Collins, CO

Publication date: February 1, 1994

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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