Prioritizing road‐kill mitigation areas: A spatially explicit national‐scale model for an elusive carnivore

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Aim: Roads impact wildlife in different ways, among which road mortality has been the most studied. Budgets in conservation biology are usually small, and macroecological approaches have been employed in recent years as the first steps towards guiding management. Carnivores are particularly vulnerable to mortality on roads due to their elevated ecological needs (low population density, often low fecundity and relatively large home ranges). Our aim was to develop a ranking methodology to prioritize specific areas for road‐kill mitigation.Location: Continental Italy.Methods: We studied 271 occurrences of live polecats (Mustela putorius) and 212 polecat road‐kill sites. We used the former to generate a species distribution model and the latter to identify the variables that determined the road‐kill risk. Habitat suitability was derived from a spatial distribution model that combined the polecat occurrence data with a set of environmental variables. Prey availability was derived from the combination of suitability maps of 26 prey species. We used generalized linear modelling to identify the set of variables that best explained the occurrence of road‐kills. The variables included in the best performing model were combined to produce the road risk map and to identify the areas with the highest densities of road sections with highest risk.Results: Road‐kills were positively associated with the road sections with higher broad‐leaved forest coverage. The number of casualties was found to be higher than expected on the national and provincial roads and lower than expected on the local roads.Main conclusions: This approach allowed us to identify the 10 × 10 km cells where mitigation actions to prevent road‐kills should be prioritized. As mitigation actions (wildlife passage construction, fencing) are expensive, measures should be prioritized on the specific high‐risk road sections inside these selected cells, avoiding generalized mitigation plans.
Funding Information
  • Universidad de Alcalá
  • Comunidad de Madrid (2018T1/AMB10374)

This publication has 67 references indexed in Scilit: