A new method for identifying rapid decline dynamics in wild vertebrate populations

Authors

  • Martina Di Fonzo,

    Corresponding author
    1. Division of Ecology and Evolution, Imperial College London, Ascot, UK
    2. ARC Centre of Excellence for Environmental Decisions, the NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane, Queensland, Australia
    • Institute of Zoology, Zoological Society of London, London, UK
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  • Ben Collen,

    1. Institute of Zoology, Zoological Society of London, London, UK
    2. Department of Genetics, Evolution and Environment, University College London, London, UK
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  • Georgina M. Mace

    1. Division of Ecology and Evolution, Imperial College London, Ascot, UK
    2. Department of Genetics, Evolution and Environment, University College London, London, UK
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Correspondence

Martina Di Fonzo, ARC Centre of Excellence for Environmental Decisions, the NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane, Queensland 4072, Australia. Tel: +61 7 3365 2527; Fax: +61 7 3365 1655; E-mail: m.difonzo@uq.edu.au

Abstract

Tracking trends in the abundance of wildlife populations is a sensitive method for assessing biodiversity change due to the short time-lag between human pressures and corresponding shifts in population trends. This study tests for proposed associations between different types of human pressures and wildlife population abundance decline-curves and introduces a method to distinguish decline trajectories from natural fluctuations in population time-series. First, we simulated typical mammalian population time-series under different human pressure types and intensities and identified significant distinctions in population dynamics. Based on the concavity of the smoothed population trend and the algebraic function which was the closest fit to the data, we determined those differences in decline dynamics that were consistently attributable to each pressure type. We examined the robustness of the attribution of pressure type to population decline dynamics under more realistic conditions by simulating populations under different levels of environmental stochasticity and time-series data quality. Finally, we applied our newly developed method to 124 wildlife population time-series and investigated how those threat types diagnosed by our method compare to the specific threatening processes reported for those populations. We show how wildlife population decline curves can be used to discern between broad categories of pressure or threat types, but do not work for detailed threat attributions. More usefully, we find that differences in population decline curves can reliably identify populations where pressure is increasing over time, even when data quality is poor, and propose this method as a cost-effective technique for prioritizing conservation actions between populations.

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