Abstract/Summary

Aim: Wildlife-vehicle collisions are recognized as one of the major causes of mortality for many species. Empirical estimates of road mortality show that some species are more likely to be killed than others but to what extend this variation can be explained and predicted using intrinsic species characteristics remains poorly understood. This study aims to identify general macroecological patterns associated to road mortality and generate spatial and species-level predictions of risks. Location: Brazil Time period: 2001-2014 Major taxa: Birds and mammals Methods: We fitted trait-based random forest regression models (controlling for survey characteristics) to explain 783 empirical road mortality rates from Brazil, representing 170 bird and 73 mammalian species. Fitted models were then used to make spatial and species-level prediction of road mortality risk in Brazil considering 1775 birds and 623 mammals which occur within the country’s continental boundaries. Results: Survey frequency and geographic location were key predictors of observed rates, but mortality was also explained by species’ body size, reproductive speed and ecological specialization. Spatial predictions revealed high potential standardized (per km road) mortality risk in Amazonia for birds and mammals, and additionally high risk in Southern Brazil for mammals. Given the existing road network, these predictions mean more than 8 million birds and 2 million mammals could be killed per year in Brazilian roads. Furthermore, predicted rates for all Brazilian endotherm uncovered potential vulnerability to road mortality of several understudied species which are currently listed as threatened by the IUCN. Conclusion: With a fast-expanding global road network, there is an urgent need to develop improved approaches to assess and predict road-related impacts. This study illustrates the potential of trait-based models as assessment tools to better understand correlates of vulnerability to road mortality across species, and as predictive tools for difficult to sample or understudied species and areas.