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

Barber, Robert A.; Ball, Stuart G.; Morris, Roger K.A.; Gilbert, Francis

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


Robert A. Barber

Stuart G. Ball

Roger K.A. Morris


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


Barber, R. A., Ball, S. G., Morris, R. K., & Gilbert, F. (2022). Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Diversity and Distributions, 28(1), 128-141.

Journal Article Type Article
Acceptance Date Oct 15, 2021
Online Publication Date Nov 19, 2021
Publication Date 2022-01
Deposit Date Feb 24, 2022
Publicly Available Date Feb 24, 2022
Journal Diversity and Distributions
Print ISSN 1366-9516
Electronic ISSN 1472-4642
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 28
Issue 1
Pages 128-141
Keywords Ecology, Evolution, Behavior and Systematics
Public URL
Publisher URL


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