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Uncertainty analysis for regional-scale reserve selection.

Atte Moilanen1, Brendan A Wintle, Jane Elith

  • 1Metapopulation Research Group, Department of Biological and Environmental Sciences, P.O. Box 65, FI-00014, University of Helsinki, Finland. atte.moilanen@helsinki.fi

Conservation Biology : the Journal of the Society for Conservation Biology
|December 22, 2006
PubMed
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Conservation planning often overlooks data uncertainty. This study introduces "distribution discounting" to create robust reserve selection strategies that account for prediction errors, improving conservation outcomes.

Area of Science:

  • Conservation Biology
  • Decision Theory
  • Spatial Planning

Background:

  • Reserve selection and conservation planning commonly assume input data (e.g., species presence-absence, habitat model predictions) are error-free.
  • Ignoring uncertainty in data can lead to suboptimal or ineffective conservation strategies.
  • Information-gap decision theory provides a framework for addressing uncertainty in decision-making.

Purpose of the Study:

  • To develop and evaluate a novel uncertainty analysis method for reserve selection.
  • To enhance the robustness of conservation planning by accounting for data uncertainty.
  • To integrate uncertainty analysis into existing reserve selection algorithms.

Main Methods:

  • Applied information-gap decision theory to reserve selection.

Related Experiment Videos

  • Developed a novel method called "distribution discounting" to penalize conservation value based on data error.
  • Implemented distribution discounting within the zonation reserve-selection algorithm.
  • Tested the method on reserve selection for seven priority fauna in New South Wales, Australia.

    • Main Results:

    • Distribution discounting maximizes robustness in reserve selection by accounting for uncertainty in input data.
    • The method is computationally efficient, suitable for high-dimensional problems like regional conservation assessments.
    • Successfully applied to a real-world case study, demonstrating practical utility.
    • The approach can be adapted for various data types and landscape descriptions.

    Conclusions:

    • Distribution discounting offers a practical and efficient solution for incorporating uncertainty into reserve selection.
    • The method enhances the reliability of conservation plans by ensuring robustness against data errors.
    • The approach is flexible and can be integrated into different reserve selection algorithms and software.