Data from: Flexible methods for species distribution modeling with small samples

Species distribution models (SDMs) predict where species live or could
potentially live and are a key resource for ecological research and
conservation decision-making. However, current SDM methods often perform
poorly for rare or inadequately sampled species, which include most
species on earth, as well as most of those of the greatest conservation
concern. Here, we evaluated the performance of three modeling approaches
designed for data-deficient situations: plug-and-play modeling,
density-ratio modeling, and environmental-range modeling. We compared the
performance of algorithms within these approaches with the maximum entropy
(MaxEnt) model, a widely used density-ratio algorithm, both for data-poor
species and more generally. We also tested to what extent model
cross-validation performance on training data predicts model performance
on independent, presence-absence data. We found that no algorithm
performed best in all situations. Across all species, MaxEnt performed
best on average but was outperformed by one or more of the plug-and-play,
density-ratio, or environmental-range algorithms in 72 % of cases. Six of
the other algorithms had the area under the receiver operating
characteristic curve (AUC) distributions not significantly different from
MaxEnt’s, and for data-poor species (those with 20 or fewer occurrences),
24 of the algorithms considered had AUC distributions not significantly
different from MaxEnt’s. However, we found that the algorithm outputs
(when thresholded to predict presence vs absence) spanned a wide
sensitivity-specificity gradient. Specificity and prediction accuracy
assessed on training data were strongly correlated with specificity and
prediction accuracy assessed on independent presence-absence data.
However, AUC and sensitivity were weakly correlated in training vs testing
sets, with only 22 % of species having the same model perform best when
evaluated on training and independent, presence absence data. Finally, we
show how ensembles of algorithms that span the sensitivity-specificity
gradient can represent model disagreement in poorly sampled species and
improve model predictions.