Predicting the Risk of Deer-vehicle Collisions by Inferring Rules Learnt from Deer Experience and Movement Patterns in the Vicinity of Roads

摘要

Estimates of annual deer-vehicle collisions exceed one million incidences in Europe. Consequently, we were analyzing whether an animal’s experience and movement pattern close to roads can provide crucial information for accident prevention and mitigation measures. We applied an innovative approach using machine learning and step selection analyses to find rules and patterns in deer movement data for a better understanding of the spatio-temporal dynamics in wildlife-vehicle collisions. The rule tree indicated highest collision probabilities when the mean distance to a road of a roe deer tracking path was shorter than 192 meters and the roe deer crossed in more unfamiliar areas of its home range. The step selection function analysis revealed no obvious road avoidance and more road crossings in areas with less understory vegetation. Our results demonstrate the power of learned threshold values and step selection functions modelling results for a better understanding of the factors driving deer behavior in the vicinity of roads.