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A guide to multi-objective optimization for ecological problems with an application to cackling goose management

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  • Williams, Perry J.
  • Kendall, William L.

Abstract

Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:343:y:2017:i:c:p:54-67
    DOI: 10.1016/j.ecolmodel.2016.10.010
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    References listed on IDEAS

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    1. Mouysset, L. & Doyen, L. & Jiguet, F. & Allaire, G. & Leger, F., 2011. "Bio economic modeling for a sustainable management of biodiversity in agricultural lands," Ecological Economics, Elsevier, vol. 70(4), pages 617-626, February.
    2. Williams, Byron K., 2012. "Reducing uncertainty about objective functions in adaptive management," Ecological Modelling, Elsevier, vol. 225(C), pages 61-65.
    3. Joubert, Alison R. & Leiman, Anthony & de Klerk, Helen M. & Katua, Stephen & Aggenbach, J. Coenrad, 1997. "Fynbos (fine bush) vegetation and the supply of water: a comparison of multi-criteria decision analysis and cost-benefit analysis," Ecological Economics, Elsevier, vol. 22(2), pages 123-140, August.
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    Cited by:

    1. Brias, Antoine & Munch, Stephan B., 2021. "Ecosystem based multi-species management using Empirical Dynamic Programming," Ecological Modelling, Elsevier, vol. 441(C).
    2. Korryn Bodner & Chris Brimacombe & Emily S Chenery & Ariel Greiner & Anne M McLeod & Stephanie R Penk & Juan S Vargas Soto, 2021. "Ten simple rules for tackling your first mathematical models: A guide for graduate students by graduate students," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-12, January.
    3. Karner, Katrin & Schmid, Erwin & Schneider, Uwe A. & Mitter, Hermine, 2021. "Computing stochastic Pareto frontiers between economic and environmental goals for a semi-arid agricultural production region in Austria," Ecological Economics, Elsevier, vol. 185(C).
    4. 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.

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