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    Collinearity in ecological niche modeling: Confusions and challenges

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    Name:
    Feng_et_al-2019-Ecology_and_Ev ...
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    Description:
    Final Published Version
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    Author
    Feng, Xiao
    Park, Daniel S.
    Liang, Ye
    Pandey, Ranjit
    Papeş, Monica
    Affiliation
    Univ Arizona, Inst Environm
    Univ Arizona, Sch Nat Resources & Environm
    Issue Date
    2019-08-20
    Keywords
    bioclim
    collinearity shift
    ecological niche
    mammal
    model transfer
    predictor selection
    species distribution model
    
    Metadata
    Show full item record
    Publisher
    WILEY
    Citation
    Feng X, Park DS, Liang Y, Pandey R, Papeş M. Collinearity in ecological niche modeling: Confusions and challenges. Ecol Evol. 2019;00:1–12. https://doi.org/10.1002/ece3.5555
    Journal
    ECOLOGY AND EVOLUTION
    Rights
    Copyright © 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of consensus, quantitative examinations of the effects of collinearity on Maxent modeling, especially in model transfer scenarios, are lacking. To address this knowledge gap, here we quantify the effects of collinearity under different scenarios of Maxent model training and projection. We separately examine the effects of predictor collinearity, collinearity shifts between training and testing data, and environmental novelty on model performance. We demonstrate that excluding highly correlated predictor variables does not significantly influence model performance. However, we find that collinearity shift and environmental novelty have significant negative effects on the performance of model transfer. We thus conclude that (a) Maxent is robust to predictor collinearity in model training; (b) the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables; and (c) collinearity shift and environmental novelty can negatively affect Maxent model transferability. We therefore recommend to quantify and report collinearity shift and environmental novelty to better infer model accuracy when models are spatially and/or temporally transferred.
    Note
    Open access journal
    ISSN
    2045-7758
    DOI
    10.1002/ece3.5555
    Version
    Final published version
    Sponsors
    University of Arizona Office of Research, Discovery, and Innovation; Oklahoma State University [NSF-OCI 1126330]; University of Tennessee
    ae974a485f413a2113503eed53cd6c53
    10.1002/ece3.5555
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