Active adaptive conservation of threatened species in the face of uncertainty

Authors

  • Eve McDonald-Madden,

    1. Centre for Applied Environmental Decision Analysis, School of Biological Sciences, University of Queensland, St Lucia QLD 4069 Australia
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  • William J. M. Probert,

    1. Centre for Applied Environmental Decision Analysis, School of Biological Sciences, University of Queensland, St Lucia QLD 4069 Australia
    2. Department of Mathematics, University of Queensland, St Lucia QLD 4069 Australia
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  • Cindy E. Hauser,

    1. Australian Centre of Excellence for Risk Analysis, University of Melbourne, Melbourne 3010 Australia
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  • Michael C. Runge,

    1. United States Geological Survey, Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, MD 20708 USA
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  • Hugh P. Possingham,

    1. Centre for Applied Environmental Decision Analysis, School of Biological Sciences, University of Queensland, St Lucia QLD 4069 Australia
    2. Department of Mathematics, University of Queensland, St Lucia QLD 4069 Australia
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  • Menna E. Jones,

    1. School of Zoology, University of Tasmania, Hobart, Tasmania 7001 Australia
    2. Department of Primary Industries and Water, Hobart, Tasmania 7001 Australia
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  • Joslin L. Moore,

    1. Centre for Applied Environmental Decision Analysis, School of Botany, University of Melbourne, Parkville VIC 3010 Australia
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  • Tracy M. Rout,

    1. Centre for Applied Environmental Decision Analysis, School of Botany, University of Melbourne, Parkville VIC 3010 Australia
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  • Peter A. Vesk,

    1. Centre for Applied Environmental Decision Analysis, School of Botany, University of Melbourne, Parkville VIC 3010 Australia
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  • Brendan A. Wintle

    1. Centre for Applied Environmental Decision Analysis, School of Botany, University of Melbourne, Parkville VIC 3010 Australia
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  • Corresponding Editor: J. J. Millspaugh.

  •  Present address: CSIRO Sustainable Ecosystems, 306 Carmody Road, St Lucia, Queensland 4067 Australia. E-mail: eve.mcdonald-madden@csiro.au

Abstract

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives.

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