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.

Funding provided by: E.O. Wilson Biodiversity FoundationCrossref Funder Registry ID: https://ror.org/03a01dc42Award Number: Funding provided by: National Aeronautics and Space AdministrationCrossref Funder Registry ID: https://ror.org/027ka1x80Award Number: 80NSSC22K0883 Funding provided by: National Aeronautics and Space AdministrationCrossref Funder Registry ID: https://ror.org/027ka1x80Award Number: 80NSSC17K0282 Funding provided by: National Aeronautics and Space AdministrationCrossref Funder Registry ID: https://ror.org/027ka1x80Award Number: 80NSSC18K0435 Funding provided by: National Science FoundationCrossref Funder Registry ID: https://ror.org/021nxhr62Award Number: 2225078

A detailed methodology associated with the environmental variables and species data can be found from the references used in the original publication.