Performance tradeoffs in target-group bias correction for species distribution models

The present study aims at evaluating the impacts of sampling bias and bias correction on SDM performance. To this end, we designed a realistic system of sampling bias and virtual species based on 92 terrestrial mammal species occurring in the Mediterranean basin. We manipulated presence and background data selection to calibrate four SDM types. Unbiased (unbiased presence data) and biased (biased presence data) SDMs were calibrated using randomly distributed background data. We used real and TG-estimated sampling efforts in background selection to correct for sampling bias in presence data.

Species distribution models (SDMs) are often calibrated using presence-only datasets plagued with environmental sampling bias, which leads to a decrease of model accuracy. In order to compensate for this bias, it has been suggested that background data (or pseudoabsences) should represent the area that has been sampled. However, spatially-explicit knowledge of sampling effort is rarely available. In multi-species studies, sampling effort has been inferred following the target-group (TG) approach, where aggregated occurrence of TG species informs the selection of background data. However, little is known about the species-specific response to this type of bias correction.