Optimising Occurrence Data in Species Distribution Models: Sample Size, Positional Uncertainty, and Sampling Bias Matter

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional error, and sampling bias. In addition, it is widely recognized that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations are dependent on species ecology. While numerous studies have experimentally evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species-environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional error, sampling bias, and species ecology on SDMs outputs. We integrate their findings into a step-by-step guide for critical assessment of spatial data intended for use in SDMs.