Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Robert G. Haight; Laurel E. Travis
    Date: 1997
    Source: Forest Science. Vol. 43 no. 1.:p. 129-139. (1997)
    Publication Series: Scientific Journal (JRNL)
    Station: North Central Research Station
    PDF: View PDF  (2.1 MB)

    Description

    Formulations for determining conservation plans for sensitive wildlife species must account for economic costs of habitat protection and uncertainties about how wildlife populations will respond. This paper describes such a formulation and addresses the computational challenge of solving it. The problem is to determine the cost-efficient level of habitat protection that satisfies a viability constraint for a sensitive wildlife population. The viability constraint requires a high probability of attaining a population size target. Because of the complexity of wildlife prediction models, population survival probabilities under alternative protection plans must be estimated using Monte Carlo simulation. The computational challenge arises from the conflicting effects of sample size: fewer replications used to estimate survival probability increases the speed of the search algorithm but reduces the precision of the estimator of the optimal protection plan. Importance sampling is demonstrated as a simulation technique for reducing estimator variance for a given sample size, particularly when the tail of the population distribution is of critical importance. The method is demonstrated on a hypothetical problem involving gray wolf management in the Great Lakes region of the United States. In comparison to random sampling, importance sampling produces a 21-fold reduction in the variance of the estimator of the minimum-cost protection plan. Results from the optimization model demonstrate the extreme sensitivity of the minimum-cost protection plan to the structure of the growth model and the magnitude of environmental variation. This sensitivity is not widely recognized in the literature on wildlife habitat planning and is a strong reason for using optimization methods that can handle stochastic population models with a wide range of structures.

    Publication Notes

    • Check the Northern Research Station web site to request a printed copy of this publication.
    • Our on-line publications are scanned and captured using Adobe Acrobat.
    • During the capture process some typographical errors may occur.
    • Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Haight, Robert G.; Travis, Laurel E. 1997. Wildlife Conservation Planning Using Stochastic Optimization and Importance Sampling. Forest Science. Vol. 43 no. 1.:p. 129-139. (1997)

    Keywords

    Importance sampling, metapopulation, Monte Carlo simulation, population modeling, retrospective optimization, wildlife management.

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/12042