Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes

Species distribution models are increasingly being used by conservation practitioners to provide information on the distribution of organisms. Such models require the accurate co-location of occurrence data with environmental variables. A common assumption is that species occurrence data are accurately georeferenced, which is unverified. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models in the marine environment. Results suggest that seemingly high-performing fine-scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrates the need to consider predictions across geographic space rather than performance alone.

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