Is geographic sampling bias representative of environmental space?

Abstract

Species occurrence data from public repositories are widely used in biogeography, and conservation research. However, these data are prone to several sampling biases limiting their usefulness in biodiversity studies, and particularly in species distribution models (SDMs). Specifically, a geographic sampling bias can lead to overfitted SDMs while an environmental sampling bias can affect the estimate of the true environmental space occupied by a species. Several methods can be used to correct for sampling biases, but most of these are focused on the geographic component only. We aim to assess if sampling bias correction based on geographical factors can represent a viable solution also for environmental bias. We focused our analyses on plants and invertebrates in Africa. To model sampling bias in the spatial dimension we estimated sampling rates as a function of distance from a set of potential bias factors (roads, cities, waterbodies, and coastline) in a Bayesian framework. To model sampling bias in the environmental dimension we calculated the environmental multivariate distance between the average climate in the species' occurrences and the rest of the study area by using a set of bioclimatic variables. We quantified the spatial relationship between geographic and environmental bias calculating a Local Indicator of Multivariate Spatial Association (LISA), highlighting local clusters of spatial correlation. For both plants and invertebrates, almost 50% of Africa showed a non-significant relationship between the geographical and the environmental bias components, indicating the absence of a direct correspondence between the two types of biases. Furthermore, from 20.4% to 24.4% of the study area showed an opposite pattern between the two biases, clearly indicating that a correction in the spatial component would be detrimental in the environmental component. Our findings showed that geographical factors cannot be considered as good proxies for environmental bias and, consequently, we suggest considering both geographic and environmental corrections when sampling bias is a problem.