Understanding spatio-temporal patterns of deer-vehicle collisions to improve roadkill mitigation.

Vehicles collide with hundreds of thousands of deer on European roads each year. This leads to animal deaths and suffering, economic damage and risks for human safety, making the reduction of road mortality a major field in conservation biology. In order to successfully reduce roadkill, we need improved knowledge regarding spatio-temporal patterns of deer-vehicle collisions (DVCs) on a landscape scale. Here, we analyzed >85,000 DVCs collected over 17 years in Denmark to investigate changes in the number of DVCs over time and to find spatio-temporal patterns of DVC occurrence. We used a use-availability design - originally developed for habitat selection analyses - to compare DVCs involving roe deer (Capreolus capreolus), red deer (Cervus elaphus) and fallow deer (Dama dama) with random road locations on a landscape scale. This approach enabled us to combine temporal (seasonal and diel variation), spatial (land cover, road density and type) and other relevant variables (deer population density, traffic, and deer activity) within the same analysis. We found that factors related to infrastructure and land cover were most important in explaining patterns of DVCs, but seasonal and diel changes, deer activity, and population density were also important in predicting the occurrence of DVCs. Importantly, patterns of DVCs were largely similar between the three deer species, with more DVCs occurring at intermediate traffic density, increasing forest cover, during dusk and dawn, and with increasing deer activity and population density. The strong and consistent patterns found here will allow the development of flexible mitigation measures. We propose that our findings could be used to develop a spatio-temporally flexible warning system for smartphones and navigation systems that is based on existing map providers, making it a widely available and cheap mitigation measure.