Disentangling interpolation…

Species distribution models (SDMs) are increasingly used in many scientific fields, with most studies requiring the application of the SDM to predict the likelihood of occurrence and/or environmental suitability in locations and time periods outside the range of the data set used to fit the model. Uncertainty in the quality of SDM predictions caused by errors of interpolation and extrapolation has been acknowledged for a long time, but the explicit consideration of the magnitude of such errors is, as yet, uncommon. Among other issues, the spatial variation in the colinearity of the environmental predictor variables used in the development of SDMs may cause misleading predictions when applying SDMs to novel locations and time periods. In this paper, we provide a framework for the spatially explicit identification of areas prone to errors caused by changes in the inter-correlation structure (i.e. their colinearity) of environmental predictors used for SDM development. The proposed method is compatible with all SDM algorithms currently employed, and expands the available toolbox for assessing the uncertainties raising from SDM predictions. We provide an implementation of the analysis as a script for the R statistical platform in an online appendix.