Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions

Aim Species distribution models (SDMs) that integrate presence-only and
presence-absence data offer a promising avenue to improve information on
species’ geographic distributions. The use of such ‘integrated SDMs’ on a
species range-wide extent has been constrained by the often-limited
presence-absence data and by the heterogeneous sampling of the
presence-only data. Here, we evaluate integrated SDMs for studying species
ranges with a novel expert range map-based evaluation. We build a new
understanding about how integrated SDMs address issues of estimation
accuracy and data deficiency and thereby offer advantages over traditional
SDMs. Location South and Central America. Time period 1979-2017. Major
taxa studied Hummingbirds. Methods We build integrated SDMs by linking two
observation models – one for each data type – to the same underlying
spatial process. We validate SDMs with two schemes: i) cross-validation
with presence-absence data and ii) comparison with respect to the species’
whole range as defined with IUCN range maps. We also compare models
relative to the estimated response curves and compute the association
between the benefit of the data integration and the number of presence
records in each data set. Results The integrated SDM accounting for the
spatially varying sampling intensity of the presence-only data was one of
the top-performing models in both model validation schemes. Presence-only
data alleviated overly large niche estimates, and data integration was
beneficial compared to modelling solely presence-only data for species
that had few presence points when predicting the species’ whole range. On
the community level, integrated models improved the species richness
prediction. Main conclusions Integrated SDMs combining presence-only and
presence-absence data are successfully able to borrow strengths from both
data types and offer improved predictions of species’ ranges. Integrated
SDMs can potentially alleviate the impacts of taxonomically and
geographically uneven sampling and to leverage the detailed sampling
information in presence-absence data.