Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

The performance of species distribution models is known to be affected by the analysis grain and the positional error of species occurrences. Coarsening of the spatial analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in predictive models developed for conservation and climate change studies it is increasingly important to test this assumption. Species distribution models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate species occurrences might easier end up in unsuitable environment, which can result in inappropriate conservation actions.
Here, we examine the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated virtual species using tree canopy height, topography wetness index, and altitude deriv...