Managing the timing and speed of vehicles reduces wildlife-transport collision risk. (March 2018) - Visintin, Casey
Highlights: Quantitative models help simulate collision risk between animals and transport. Temporal variation in animal activity was strongly correlated with collision risk. Reducing speeds in areas of high predicted animal occurrence reduced collision risk. Reducing speeds during periods of peak animal activity also reduced collision risk. Abstract: Understanding wildlife-vehicle collision risk is critical to mitigating its negative impacts on wildlife conservation, human health and economy. Research often focuses on collisions between wildlife and road vehicles, but collision risk factors for other types of vehicles, less examined in the literature, may also be informative. We studied spatial and temporal variation in wildlife-train collision risk in the State of Victoria, Australia. We quantified train movements in space and time, and mapped species occurrence likelihood, across the railway network. Using spatially- and temporally-resolved collision data, we fitted a model to analyse collisions between trains and kangaroos; accounting for time of day, train frequency and speed, and kangaroo occurrence. We then predicted collision rates on the passenger railway network under three management scenarios relating to train speed and occurrence of kangaroos near the railway lines. Temporal variation in animal activity was the strongest predictor of collision risk. Train speed was the second most influential variable, followed by spatial variation in likelihood of species occurrence. Reducing speeds in areas of high predicted species occurrence and during periods of peak animal activity (early morning and evening for kangaroos) was predicted to reduce collision risk the most. Our results suggest mechanisms that might improve existing wildlife-transport collision analyses. The model can help managers decide where, when and how best to mitigate collisions between animals and transport. It can also be used to predict high-risk locations or times for (a) timetable/schedule changes (b) proposals for new routes or (c) disused routes considered for re-opening.