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Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks

Authors

  • Laura Meller,

    Corresponding author
    1. Metapopulation Research Group, Department of Biosciences, Helsinki, Finland
    2. Laboratoire d'Ecologie Alpine, UMR-CNRS 5553, Université Joseph Fourier, Grenoble Cedex 9, France
    • Correspondence: Laura Meller, Metapopulation Research Group, Department of Biosciences, P.O. Box 65, 00014 University of Helsinki, Helsinki, Finland.

      E-mail: laura.meller@helsinki.fi

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  • Mar Cabeza,

    1. Metapopulation Research Group, Department of Biosciences, Helsinki, Finland
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  • Samuel Pironon,

    1. Laboratoire d'Ecologie Alpine, UMR-CNRS 5553, Université Joseph Fourier, Grenoble Cedex 9, France
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  • Morgane Barbet-Massin,

    1. Muséum National d'Histoire Naturelle, UMR 7204 MNHN-CNRS-UPMC, Centre de Recherches sur la Biologie des Populations d'Oiseaux, Paris, France
    2. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
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  • Luigi Maiorano,

    1. Department of Biology and Biotechnologies “Charles Darwin”, University of Rome “La Sapienza”, Rome, Italy
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  • Damien Georges,

    1. Laboratoire d'Ecologie Alpine, UMR-CNRS 5553, Université Joseph Fourier, Grenoble Cedex 9, France
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  • Wilfried Thuiller

    1. Laboratoire d'Ecologie Alpine, UMR-CNRS 5553, Université Joseph Fourier, Grenoble Cedex 9, France
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Abstract

Aim

Conservation planning exercises increasingly rely on species distributions predicted either from one particular statistical model or, more recently, from an ensemble of models (i.e. ensemble forecasting). However, it has not yet been explored how different ways of summarizing ensemble predictions affect conservation planning outcomes. We evaluate these effects and compare commonplace consensus methods, applied before the conservation prioritization phase, to a novel method that applies consensus after reserve selection.

Location

Europe.

Methods

We used an ensemble of predicted distributions of 146 Western Palaearctic bird species in alternative ways: four different consensus methods, as well as distributions discounted with variability, were used to produce inputs for spatial conservation prioritization. In addition, we developed and tested a novel method, in which we built 100 datasets by sampling the ensemble of predicted distributions, ran a conservation prioritization analysis on each of them and averaged the resulting priority ranks. We evaluated the conservation outcome against three controls: (i) a null control, based on random ranking of cells; (2) the reference solution, based on an expert-refined dataset; and (3) the independent solution, based on an independent dataset.

Results

Networks based on predicted distributions were more representative of rare species than randomly selected networks. Alternative methods to summarize ensemble predictions differed in representativeness of resulting reserve networks. Our novel method resulted in better representation of rare species than pre-selection consensus methods.

Main conclusions

Retaining information about the variation in the predicted distributions throughout the conservation prioritization seems to provide better results than summarizing the predictions before conservation prioritization. Our results highlight the need to understand and consider model-based uncertainty when using predicted distribution data in conservation prioritization.

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