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

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.