Minimizing the cost of environmental management decisions by optimizing statistical thresholds

Authors

  • Scott A. Field,

    Corresponding author
    1. The Ecology Centre, University of Queensland, St Lucia 4072, Queensland, Australia
      * Correspondence and Present address: Dr Scott A. Field, Department of Environmental Biology, Benham Building, University of Adelaide, North Terrace 5005, SA, Australia
      E-mail: scott.field@adelaide.edu.au
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  • Andrew J. Tyre,

    1. The Ecology Centre, University of Queensland, St Lucia 4072, Queensland, Australia
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  • Niclas Jonzén,

    1. The Ecology Centre, University of Queensland, St Lucia 4072, Queensland, Australia
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  • Jonathan R. Rhodes,

    1. The Ecology Centre, University of Queensland, St Lucia 4072, Queensland, Australia
    2. School of Geography, University of Queensland, Planning and Architecture, St Lucia 4072, Queensland, Australia
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  • Hugh P. Possingham

    1. The Ecology Centre, University of Queensland, St Lucia 4072, Queensland, Australia
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  • Present address: Andrew J. Tyre, School of Natural Resource Sciences, University of Nebraska-Lincoln, Lincoln, NE 68583-0819, USA

  • Present address: Niclas Jonzén, Department of Theoretical Ecology, Ecology Building, Lund University, SE-223 62 Lund, Sweden

* Correspondence and Present address: Dr Scott A. Field, Department of Environmental Biology, Benham Building, University of Adelaide, North Terrace 5005, SA, Australia
E-mail: scott.field@adelaide.edu.au

Abstract

Environmental management decisions are prone to expensive mistakes if they are triggered by hypothesis tests using the conventional Type I error rate (α) of 0.05. We derive optimal α-levels for decision-making by minimizing a cost function that specifies the overall cost of monitoring and management. When managing an economically valuable koala population, it shows that a decision based on α = 0.05 carries an expected cost over $5 million greater than the optimal decision. For a species of such value, there is never any benefit in guarding against the spurious detection of declines and therefore management should proceed directly to recovery action. This result holds in most circumstances where the species’ value substantially exceeds its recovery costs. For species of lower economic value, we show that the conventional α-level of 0.05 rarely approximates the optimal decision-making threshold. This analysis supports calls for reversing the statistical ‘burden of proof’ in environmental decision-making when the cost of Type II errors is relatively high.

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