Predicting bat roosts in bridges using Bayesian Additive Regression Trees
Global Ecology and Conservation
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- More information: Publisher Index Page (via DOI)
- Data Release: USGS data release - North American Bat Monitoring Program (NABat) OneHealth (ver. 2.0, June 2025)
- Open Access Version: Publisher Index Page
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Abstract
Human-built structures can provide important habitat for wildlife, but predicting which structures are most likely to be used remains challenging. To evaluate the predictive capabilities of data-driven ensemble modeling approaches, we conducted surveys for bats and signs of bat use, such as urine and guano staining, at bridges across the southwestern United States. We developed a bat roost discovery tool using Bayesian Additive Regression Trees (BART) and evaluated the predictive ability of this model against other commonly used approaches. We found that the lack of nearby water resources was associated with a lower predicted probability of bat presence or signs of bat use at bridges. While the presence of nearby water resources was associated with higher average predicted probability of bat presence or signs of bat use, high uncertainty surrounding these estimates indicates that other factors also play a role in determining which bridge roosts bats are more likely to use. As such, our model could be particularly useful for predicting which bridges can be excluded from survey efforts due to low probability of bat presence or signs of bat use. We extrapolated our model to unsurveyed bridges across the study region and provide an interactive dashboard application interface for the exploration of these results. Overall, this study demonstrates the application of BART as a predictive tool for prioritizing future bridge surveys for bats roosting in transportation structures.
Suggested Citation
Oram, J., Wray, A.K., Davis, H.T., de Wit, L., Frick, W.F., Hoegh, A.B., Irvine, K.M., Pollock, P., Schuhmann, A.N., Tousley, F.C., and Reichert, B., 2025, Predicting bat roosts in bridges using Bayesian Additive Regression Trees: Global Ecology and Conservation, v. 60, e03551, 12 p., https://doi.org/10.1016/j.gecco.2025.e03551.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Predicting bat roosts in bridges using Bayesian Additive Regression Trees |
| Series title | Global Ecology and Conservation |
| DOI | 10.1016/j.gecco.2025.e03551 |
| Volume | 60 |
| Publication Date | March 22, 2025 |
| Year Published | 2025 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Fort Collins Science Center |
| Description | e03551, 12 p. |
| Country | United States |
| State | Arizona, California, Nevada, New Mexico, Texas |
| Other Geospatial | southwestern United States |