Should you use data integration for your distribution model?
Benjamin R Goldstein1,2, Jeffrey W Doser1, Brent S Pease3
1Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, USA.
The Journal of Animal Ecology
|January 29, 2026
Summary
Data integration in species distribution modeling can be complex. This study provides a framework to determine if integrating datasets offers benefits over single-dataset approaches, considering costs, data quality, and model performance.
Area of Science:
- Ecology
- Computational Biology
- Statistical Modeling
Background:
- Data integration is increasingly used in species distribution modeling.
- Few studies examine scenarios where data integration yields suboptimal results compared to single-dataset models.
Purpose of the Study:
- To develop a decision-making framework for assessing the utility of data integration in species distribution modeling.
- To guide researchers on when data integration provides improvements over simpler modeling approaches.
Main Methods:
- Focus on joint likelihood data integration, linking multiple datasets to a shared process model.
- Utilized a simulation study to evaluate modeling outcomes under varying data volume and bias conditions.
- Investigated a priori and a posteriori tests for data concordance.
Main Results:
- Identified three key considerations for data integration: cost, marginal benefits (data volume/coverage dependent), and dataset concordance.
- Simulation results showed consistent patterns across different joint likelihood formulations.
- Data concordance tests were found to be ineffective in predicting when joint modeling would perform poorly.
Conclusions:
- A decision-making workflow is proposed to aid analysts in choosing between integrated and single-dataset modeling.
- The framework helps evaluate the trade-offs and potential benefits of data integration for species distribution models.
- The study emphasizes careful consideration of data characteristics and modeling goals before implementing data integration.
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