Data from: Model-based data integration improves species distribution models for data deficient and narrow-ranged hummingbird species

For species with narrow ranges or low population sizes, a deficiency of
species occurrence records can limit the capacity to build accurate
species distribution models (SDMs). Model-based integration of data from
multiple sources has been offered as a solution to improve predictions of
species’ distributions at large scales, especially for data-deficient
species, but clear empirical demonstrations for this are lacking. The
study location was South and Central America. We applied a
state-of-the-art data integration technique to model the distributions of
98104 hummingbird species. We fitted SDMs using either presence-absence
(PA) data from eBird or presence-only (PO) data from eBird and the Global
Biodiversity Information Facility (GBIF) and compared them to integrated
SDMs, which utilize both PA and PO data. We fitted generalized linear
mixed-effects models and validated them with spatial block
cross-validation and expert range map adjusted validation. We also
conducted an experiment using artificially thinned datasets of 47 abundant
enough species to assess model performance under different levels of data
deficiency. Data integration improved model performance compared to PA
models for species for which PA data covered poorly the environmental
conditions in the study area. Thinning experiment showed
that even a small amount of PO data in data integration improved
the predictive accuracy in comparison to PA models which was not clear in
the cross-validation results with the full data. In comparison to PO
models, data integration improved models over all species, but especially
for data rich species with large geographical ranges. Overall, data
integration enables a more comprehensive capture of available species
information and can improve range predictions in comparison to
conventional modeling methods.