Target‐group backgrounds prove effective at correcting sampling bias in Maxent models

Aim Accounting for sampling bias is the greatest challenge facing presence-only and presence-background species distribution models; no matter what type of model chosen, using biased data will mask true relationship between occurrences environmental predictors. To address this issue, we review four established correction techniques, empirical with known effort, virtual distributions. Innovation Occurrence come from a national recording scheme hoverflies (Syrphidae) in Great Britain, spanning 1983–2002. Target-group backgrounds, distance-restricted travel time to cities human population density were used account 58 hoverfly. Distributions generated by techniques compared geographical space produced accounting Schoener's distance, centroid shifts range size changes. validate our results, performed same comparisons 50 randomly species. We effort hoverfly structure regime, emulating complex real-life bias. Main conclusions Models made without any typically distributions that mapped rather than underlying habitat suitability. backgrounds best at unbiased occurrences, but also showed signs overcompensation places. Other methods better no-correction, often differences difficult visually detect. In line previous studies, when unknown, target-group provide useful tool reducing effect should be inspected biological realism identify areas potential overcompensation. Given disparity corrected un-corrected models, constitutes major source error modelling, more research needed confidently issue.