Temporal patterns in road crossing behaviour in roe deer (Capreolus capreolus) at sites with wildlife warning reflectors

Abstract

Every year, there are millions of documented vehicle collisions involving cervids across Europe and North America. While temporal patterns in collision occurrence are relatively well described, few studies have targeted deer behaviour as a critical component of collision prevention. In this study, we investigated weekly and daily patterns in road crossing behaviour in roe deer. Using road crossing events and movement data obtained from GPS telemetry, we employed mixed-effect models to explain frequency and timing of crossings at five road segments by a number of predictors including traffic volume, deer movement activity and the presence of wildlife warning reflectors. We analysed 13,689 road crossing events by 32 study animals. Individual variation in crossing frequency was high but daily patterns in crossing events were highly consistent among animals. Variation in the intensity of movement activity on a daily and seasonal scale was the main driver of road crossing behaviour. The seasonal variation in crossing frequency reflected differences in movement activity throughout the reproductive cycle, while daily variation in the probability to cross exhibited a clear nocturnal emphasis and reflected crepuscular activity peaks. The frequency of road crossings increased as a function of road density in the home-range, while traffic volume only exerted marginal effects. Movement activity of roe deer in our study coincided with commuter traffic mainly in the early morning and late afternoon during winter and during periods of high spatial activity such as the rut. Both timing and frequency of crossing events remained unchanged in the presence of reflectors. Our results emphasise the importance of behavioural studies for understanding roe deer vehicle-collision patterns and thus provide important information for collision prevention. We suggest that mitigation of collision risk should focus on strategic seasonal measures and animal warning systems targeting drivers.