Estimating roadkill risk when there is no roadkill data.

The most common way to quantify roadkill risk in different sections of infrastructures is to collect information on the location of casualties and then, model the probability using the environmental and infrastructure variables associated with the roadkill sites. This approach is not applicable in roads with low traffic intensity as they have a small number of victims (e.g. unpaved roads), where there is a high removal rate of casualties by scavengers (e.g. in natural areas), or when it has to be estimated before the infrastructure is built. We developed an indirect approach to evaluate the risk of collisions with wildlife within Doñana Natural Area (SW Spain), considering the abundance and phenology of species, the characteristics of the environment, and traffic intensity. First we characterized the road network, corresponding to 2190 km of roads (4.04 km/km2) of which only 2% were paved; and extracted environmental variables for the complete network in sections of 200m. Then, we characterized the traffic using data from automatic counting systems for main roads and for the rest we built a model of traffic intensity using data from a stratified sampling design in 62 sites using magnetometers, estimating traffic intensity to the whole network of roads. We characterized the abundance of multiple species using track censuses in 183 sites using 200 m transects; obtaining information on abundance, crossing intensity and the distance moved along the road (estimator of the time of exposure to vehicles or exposure). With this information we created a model of the number of crossing events per species in sections of 200 m using environmental predictors and applied the models to the whole network of roads. We estimated the roadkill risk using the index risk = log (no. crossings x traffic intensity x exposure), standardized between 0 and 1. We calculated the index for the whole network of roads. As an example, we show the predictions corresponding to the roadkill risk for several species, clearly identifying areas of high risk which are localized along roads with high traffic intensity and within them, specific sections with maximum risk. The predictions matched well with the observations of road-killed data recorded in the area.

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Revilla E. Barón A. D'Amico M. Rivilla J.C. Rodríguez C. y Román J. Estimating roadkill risk when there is no roadkill data. Infrastructure & Ecology Network Europe, 2021.

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Tipo de recurso Texto
Fecha de creación 02-12-2024
Fecha de última modificación 19-01-2025
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Identificador de los metadatos e2a17ff9-5beb-59d8-ac19-368ad2b41961
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Categoría temática ISO 19115 biota
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Nombre del autor Revilla, E., Barón, A., D'Amico, M., Rivilla, J.C., Rodríguez, C. y Román, J.
Nombre del editor Infrastructure & Ecology Network Europe
Identificador alternativo ISBN: 978-972-778-182-9
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