Predicting spatiotemporal patterns of road mortality for medium-large mammals.

We modelled the spatiotemporal patterns of road mortality for seven medium-large mammals, using a roadkill dataset from Mato Grosso do Sul, Brazil (800 km of roads surveyed every two weeks, for two years). We related roadkill presence-absence along the road sections (1000 m) and across the survey dates with a collection of environmental variables, including land cover, forest cover, distance to rivers, temperature, precipitation and vegetation productivity. We further included two variables aiming to reflect the intrinsic spatial and temporal roadkill risk. Environmental variables were obtained through remote sensing and weather stations, allowing the estimate of the roadkill risk for the entire surveyed roads and survey periods. Overall, the models could explain a small fraction of the spatiotemporal patterns of roadkills (<0.23), probably due to species being habitat generalists, but still had reasonable discrimination power (AUC averaging 0.70 ± 0.07). The intrinsic spatial and temporal roadkill risk were the most important variables, followed by land cover, climate and NDVI. We show that identifying spatiotemporal roadkill patterns may provide valuable information to define specific management actions focused on road sections and time periods, in complement to permanent road mitigation measures. Our approach thus offers a new insight into the understanding of road effects and how to plan and strategize monitoring and mitigation.

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Ascensão F. Yogui D. Alves M. Medici E.P. y Desbiez A. Predicting spatiotemporal patterns of road mortality for medium-large mammals. Elsevier, 2019. https://doi.org/10.1016/j.jenvman.2019.109320

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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 fcadbff5-6813-5a1b-8eb0-4ce902305c8f
Metadata language Spanish
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ISO 19115 topic category biota
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Name of the dataset creator Ascensão, F., Yogui, D., Alves, M., Medici, E.P. y Desbiez, A.
Name of the dataset editor Elsevier
Other identifier DOI: 10.1016/j.jenvman.2019.109320
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