Data from: Bunching up the background betters bias in species distribution models

Sets of presence records used to model species’ distributions typically
consist of observations collected opportunistically rather than
systematically. As a result, sampling probability is geographically
uneven, which may confound the model’s characterization of the species’
distribution. Modelers frequently address sampling bias by manipulating
training data: either subsampling presence data or creating a similar
spatial bias in non-presence background data. We tested a new method,
which we call “background thickening,” in the latter category. Background
thickening entails concentrating background locations around presence
locations in proportion to presence location density. We compared
background thickening to two established sampling bias correction methods
— target group background selection and presence thinning — using
simulated data and data from a case study. In the case study, background
thickening and presence thinning performed similarly well, both producing
better model discrimination than target group background selection, and
better model calibration than models without correction. In the
simulation, background thickening performed better than presence thinning
when the number of simulated presence locations was low, and vice versa.
We discuss drawbacks to target group background selection, why background
thickening and presence thinning are conservative but robust sampling bias
correction methods, and why background thickening is better than presence
thinning for small sample sizes. Particularly, background thickening is
advantageous for treating sampling bias when data are scarce because it
avoids discarding presence records.