Environmental filtering improves ecological niche models across multiple scales

Abstract A clear challenge for ecological niche modelling is determining how to best mitigate the effects of sampling bias from commonly collected biodiversity data. Recent approaches have focused on filtering occurrences in overrepresented regions based on geographic or environmental proximity. We tested the efficacy of filtering in geographic and environmental space using occurrence data from four species. Our evaluation strategies examined 14 distance measures in geographic and environmental spaces and eight combinations of environmental variables and their ordinations. This resulted in 78 datasets for each species, which we evaluated using area under the curve ( AUC ), the difference between training and testing AUC , omission rate, the true skill statistic, and Schoener's D to examine the effects of different filtering schemes. The degree of change produced by filtering on predicted suitability and evaluation statistics increased with increasing range size. Environmental filtering resulted in higher model fit at larger extents and retained more occurrences than geographic filtering. Our results indicate that models should be evaluated using multiple evaluation statistics at multiple thresholds. The use of bin sizes when filtering in environmental space allows for simple comparison between species and filter types and makes for an easily reportable and repeatable distance metric. We specifically recommend that ecological niche models using natural history collection data filter in environmental space with variables derived from permutation importance or the first few axes of a principal components ordination.