  Open-source biodiversity databases contain a large amount of species
occurrence records, but these are often spatially biased, which affects the
reliability of species distribution models based on these records. Sample bias
correction techniques include data filtering at the cost of record numbers or
require considerable additional sampling effort. However, independent data are
rarely available and assessment of the correction technique must rely on
performance metrics computed with subsets of the only available (biased) data,
which may be misleading. Here we assess the extent to which an acknowledged
sample bias correction technique is likely to improve models' ability to
predict species distributions in the absence of independent data. We assessed
the variation in model predictions induced by the correction and model
stochasticity. We present an index of the effect of correction relative to
model stochasticity, the Relative Overlap Index (ROI). We tested whether the
ROI better represented the effect of correction than classic performance
metrics and absolute overlap metrics using 70 vertebrate species and 18 virtual
species with a generated sample bias. When based on absolute overlaps and
cross-validation performance metrics, we found no effect of correction, except
for cAUC. When considering its effect relative to model stochasticity, the
effect of correction depended on the site and the species. Virtual species
enabled us to verify that the correction actually improved distribution
predictions and the biological relevance of the selected variables at the sites
with a clear gradient of sample bias, and when species distribution predictors
are not correlated with sample bias patterns.
