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Decision science for effective management of populations subject to stochasticity and imperfect knowledge

  • Special Feature: Review
  • Mathematical Models for Effective Environmental Management
  • Published:
Population Ecology

Abstract

Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species. These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge. Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature. We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science’s rational approach to dealing with uncertainty.

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Acknowledgments

We are grateful to Gaku Takimoto and an anonymous reviewer for their helpful comments. This work was supported by JSPS KAKENHI Grant Number 25281057 and by the Environment Research and Technology Development Fund D-1101 (Leader: K Goka) of the Ministry of the Environment, Japan. SC and HPP are supported by the Australian Government’s National Environmental Research Program. HPP is also supported by several Australian Research Council grants and fellowships, including an ARC Centre of Excellence.

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Authors and Affiliations

  1. Center for Environmental Risk Research, National Institute for Environmental Studies, Onogawa 16-2, Tsukuba, 305-8506, Japan

    Hiroyuki Yokomizo

  2. NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science, The University of Queensland, Brisbane, QLD, 4072, Australia

    Shaun R. Coutts

  3. School of Biological Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia

    Hugh P. Possingham

  4. Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, England, UK

    Hugh P. Possingham

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  1. Hiroyuki Yokomizo
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  2. Shaun R. Coutts
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  3. Hugh P. Possingham
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Correspondence to Hiroyuki Yokomizo.

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This manuscript was submitted for the special feature based on a symposium in Chiba, Japan, held on 21 October 2012.

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Yokomizo, H., Coutts, S.R. & Possingham, H.P. Decision science for effective management of populations subject to stochasticity and imperfect knowledge. Popul Ecol 56, 41–53 (2014). https://doi.org/10.1007/s10144-013-0421-2

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