New measures for assessing model equilibrium and prediction mismatch in species distribution models

Authors

  • A. Márcia Barbosa,

    Corresponding author
    1. Division of Biology, Imperial College London, Ascot (Berkshire), UK
    • ‘Rui Nabeiro – Delta Cafés’ Biodiversity Chair, Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO), University of Évora, Évora, Portugal
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  • Raimundo Real,

    1. Biogeography, Diversity and Conservation Lab, Department of Animal Biology, Faculty of Sciences, University of Málaga, Málaga, Spain
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  • A.-Román Muñoz,

    1. Fundación MIGRES, Algeciras, Spain
    2. Área de Didáctica de las Ciencias Experimentales, Departamento de Didáctica de la Matemática, de las Ciencias Sociales y de las Ciencias Experimentales, Faculty of Education Sciences, University of Malaga, Malaga, Spain
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  • Jennifer A. Brown

    1. Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
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Correspondence: A. Márcia Barbosa, Cátedra ‘Rui Nabeiro – Delta Cafés’ Biodiversidade, Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO), Universidade de Évora, 7004-516 Évora, Portugal.

E-mail: barbosa@uevora.pt

Abstract

Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accuracy is then evaluated through a number of measures that have been the subject of recent reviews. We propose four additional measures that analyse observation-prediction mismatch from a different angle – namely, from the perspective of the predicted rather than the observed area – and add to the existing toolset of model evaluation methods. We explain how these measures can complete the view provided by the existing measures, allowing further insights into distribution model predictions. We also describe how they can be particularly useful when using models to forecast the spread of diseases or of invasive species and to predict modifications in species’ distributions under climate and land-use change.

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