Skip to main content
Dryad

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

Mäkinen, Jussi1; Merow, Cory2; Jetz, Walter3

Published Nov 27, 2023; Updated May 15, 2026 on Dryad. https://doi.org/10.5061/dryad.k98sf7mdg

Due to licensing concerns, the original Nov 27, 2023 version of this dataset has been removed; please see instead the latest revised version

Data files

May 15, 2026 version files 559.14 MB

Click names to download individual files

Abstract

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.