Model-based uncertainty in species range prediction

Authors

  • Richard G. Pearson,

    Corresponding author
    1. Department of Herpetology and Center for Biodiversity and Conservation, American Museum of Natural History, New York, NY, USA
      *Richard G. Pearson, Department of Herpetology and Center for Biodiversity and Conservation, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024-5192, USA.
      E-mail: pearson@amnh.org
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  • Wilfried Thuiller,

    1. Laboratoire d'Ecologie Alpine UMR CNRS 5553 BP53, Grenoble Cedex 9, France
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  • Miguel B. Araújo,

    1. Biodiversity Research Group, Oxford University Centre for the Environment, Oxford
    2. Biogeography and Conservation Laboratory, The Natural History Museum, London, UK
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    • Present address: Department of Biodiversity and Evolutionary Biology, National Museum of Natural Sciences, CSIC, C/José Gutiérrez Abascal, 2, 28006 Madrid, Spain.

  • Enrique Martinez-Meyer,

    1. Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City, Mexico
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  • Lluís Brotons,

    1. Centre Tecnolòlogic Forestal de Catalunya, Pujada del Seminari s/n, Solsona, Catalunya, Spain
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  • Colin McClean,

    1. Environment Department, University of York, Heslington, York
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  • Lera Miles,

    1. UNEP World Conservation Monitoring Centre, Cambridge, UK
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  • Pedro Segurado,

    1. Unidade de Macroecologia e Conservação, Universidade de Évora, Estrada dos Leões, Évora, Portugal
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  • Terence P. Dawson,

    1. Centre for the Study of Environmental Change and Sustainability, University of Edinburgh, Edinburgh, UK
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  • David C. Lees

    1. 11Entomology Department, The Natural History Museum, London, UK
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*Richard G. Pearson, Department of Herpetology and Center for Biodiversity and Conservation, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024-5192, USA.
E-mail: pearson@amnh.org

Abstract

Aim  Many attempts to predict the potential range of species rely on environmental niche (or ‘bioclimate envelope’) modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications.

Location  The Western Cape of South Africa.

Methods  We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis).

Results  Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges.

Main conclusions  We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy-guiding applications along with a full appreciation of uncertainty.

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