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Understanding spatial effects in species distribution models

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  • Iosu Paradinas
  • Janine Illian
  • Sophie Smout

Abstract

Species Distribution Models often include spatial effects which may improve prediction at unsampled locations and reduce Type I errors when identifying environmental drivers. In some cases ecologists try to ecologically interpret the spatial patterns displayed by the spatial effect. However, spatial autocorrelation may be driven by many different unaccounted drivers, which complicates the ecological interpretation of fitted spatial effects. This study aims to provide a practical demonstration that spatial effects are able to smooth the effect of multiple unaccounted drivers. To do so we use a simulation study that fit model-based spatial models using both geostatistics and 2D smoothing splines. Results show that fitted spatial effects resemble the sum of the unaccounted covariate surface(s) in each model.

Suggested Citation

  • Iosu Paradinas & Janine Illian & Sophie Smout, 2023. "Understanding spatial effects in species distribution models," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-6, May.
    • Handle: RePEc:plo:pone00:0285463
    DOI: 10.1371/journal.pone.0285463
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    References listed on IDEAS

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    1. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    2. David W Redding & Tim C D Lucas & Tim M Blackburn & Kate E Jones, 2017. "Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-13, November.
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