Understanding spatial effects in species distribution models
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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.
Paradinas , I , Illian , J & Smout , S 2023 , ' Understanding spatial effects in species distribution models ' , PLoS ONE , vol. 18 , no. 5 , e0285463 . https://doi.org/10.1371/journal.pone.0285463
Copyright: © 2023 Paradinas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DescriptionFunding: IP was funded by a Marie Skłodowska-Curie Research Fellowship (GAP-847014).
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