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Selecting ecological models using multi-objective optimization

Author

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  • Williams, Perry J.
  • Kendall, William L.
  • Hooten, Mevin B.

Abstract

Choices in ecological research and natural resource management require balancing multiple, often competing objectives. Examples include maximizing species persistence in a wildlife conservation context, while minimizing cost, or balancing opposing stakeholder objectives when managing wildlife populations. Multiple-objective optimization (MOO) provides a unifying framework for solving multiple objective problems. Model selection is a critical component of ecological inference and prediction and requires balancing the competing objectives of model fit and model complexity. The tradeoff between model fit and model complexity provides a basis for describing the model-selection problem within the MOO framework. We discuss MOO and two strategies for solving the MOO problem; modeling preferences pre-optimization and post-optimization. Most conventional model selection methods can be formulated as solutions of MOO problems via specification of pre-optimization preferences. We reconcile model selection within the MOO framework. We also consider model selection using post-optimization specification of preferences. That is, by first identifying Pareto optimal solutions, and then selecting among them. We demonstrate concepts with an ecological application of model selection using avian species richness data in the continental United States.

Suggested Citation

  • Williams, Perry J. & Kendall, William L. & Hooten, Mevin B., 2019. "Selecting ecological models using multi-objective optimization," Ecological Modelling, Elsevier, vol. 404(C), pages 21-26.
    • Handle: RePEc:eee:ecomod:v:404:y:2019:i:c:p:21-26
    DOI: 10.1016/j.ecolmodel.2019.04.012
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    References listed on IDEAS

    as
    1. Williams, Perry J. & Kendall, William L., 2017. "A guide to multi-objective optimization for ecological problems with an application to cackling goose management," Ecological Modelling, Elsevier, vol. 343(C), pages 54-67.
    2. Hutton Barron & Charles P. Schmidt, 1988. "Sensitivity Analysis of Additive Multiattribute Value Models," Operations Research, INFORMS, vol. 36(1), pages 122-127, February.
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    Cited by:

    1. Peng Liang & Cheng-Zhi Qin & A-Xing Zhu, 2024. "Using Automated Machine Learning for Spatial Prediction—The Heshan Soil Subgroups Case Study," Land, MDPI, vol. 13(4), pages 1-12, April.

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