Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models

Authors

  • Sara Varela,

    1. Dept of Ecology, Faculty of Science, Charles Univ., CZ-128 44 Praha 2, Czech Republic
    2. Inst. of Environmental Sciences, Univ. of Castilla-La Mancha, ES-45071 Toledo, Spain
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  • Robert P. Anderson,

    1. Dept of Biology, City College of the City Univ. of New York, New York, USA
    2. Graduate Center of the City Univ. of New York, New York, USA
    3. Division of Vertebrate Zoology (Mammalogy), American Museum of Natural History, New York, USA
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  • Raúl García-Valdés,

    1. Inst. of Environmental Sciences, Univ. of Castilla-La Mancha, ES-45071 Toledo, Spain
    2. Dept of Biodiversity and Evolutionary Biology, National Museum of Natural Sciences, CSIC, Calle José Gutiérrez Abascal, 2, Spain
    3. Ecology and Forest Restoration Group, Life Sciences Dept, Sciences Building, Univ. of Alcalá de Henares, Carretera Madrid-Barcelona, Km. 33,600, Alcalá de Henares, Spain
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  • Federico Fernández-González

    1. Inst. of Environmental Sciences, Univ. of Castilla-La Mancha, ES-45071 Toledo, Spain
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Abstract

Ecological niche models represent key tools in biogeography but the effects of biased sampling hinder their use. Here, we address the utility of two forms of filtering the calibration data set (geographic and environmental) to reduce the effects of sampling bias. To do so we created a virtual species, projected its niche to the Iberian Peninsula and took samples from its binary geographic distribution using several biases. We then built models for various sample sizes after applying each of the filtering approaches. While geographic filtering did not improve discriminatory ability (and sometimes worsened it), environmental filtering consistently led to better models. Models made with few but climatically filtered points performed better than those made with many unfiltered (biased) points. Future research should address additional factors such as the complexity of the species’ niche, strength of filtering, and ability to predict suitability (rather than focus purely on discrimination).

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