A spatial approach for modeling amphibian road-kills: comparison of regression techniques.

Road networks are the main source of mortality for many species. Amphibians, which are in global decline, are the most road-killed fauna group, due to their activity patterns and preferred habitats. Many different methodologies have been applied in modeling the relationship between environment and road-kills events, such as logistic regression. Here, we compared the performance of five regression techniques to relate amphibians’ road-kill frequency to environmental variables. For this, we surveyed three country roads in northern Portugal in search of road-killed amphibians. To explain the presence of road-kills, we selected a set of environmental variables important for the presence of amphibians and the occurrence of road-kills. We compared the performances of five modeling techniques: (i) generalized linear models, (ii) generalized additive models, (iii) random forest, (iv) boosted regression trees, and (v) geographically weighted regression. The boosted regression trees and geographically weighted regression techniques performed the best, with a percentage of deviance explained between 61.8% and 76.6% and between 55.3% and 66.7%, respectively. Moreover, the geographically weighted regression showed a great advantage over the other techniques, as it allows mapping local parameter coefficients as well as local model performance (pseudo-R2). The results suggest that geographically weighted regression is a useful tool for road-kill modeling, as well as to better visualize and map the spatial variability of the models.

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Sousa-Guedes D. Franch M. y Sillero N. A spatial approach for modeling amphibian road-kills: comparison of regression techniques. MDPI, 2021. https://doi.org/10.3390/ijgi10050343

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Resource type Text
Date of creation 2025-05-23
Date of last revision 2025-05-23
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Metadata identifier e50d00ae-0d38-548c-b9f6-d12ed29ccb20
Metadata language Spanish
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ISO 19115 topic category biota
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Name of the dataset creator Sousa-Guedes, D., Franch, M. y Sillero, N.
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Other identifier DOI: 10.3390/ijgi10050343
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