The risk of moose Alces alces collision: A predictive logistic model for moose‐train accidents

• Published in: Wildlife Biology
• Main Authors: Gundersen, Hege, Andreassen, Harry P.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 1998
• Subjects: Management, Monitoring, Policy and Law; Nature and Landscape Conservation; Ecology, Evolution, Behavior and Systematics; Alces alces
• Online Access: http://dx.doi.org/10.2981/wlb.1998.007; https://onlinelibrary.wiley.com/doi/full-xml/10.2981/wlb.1998.007; https://onlinelibrary.wiley.com/doi/pdf/10.2981/wlb.1998.007

We used logistic models to estimate the risk of moose‐train collisions for the R⊘rosbanen railway in Norway. During 1990–1997, a total of 13,506 train departures were registered along R⊘rosbanen during the months when the risk of collision was highest (December to March). The statistical model selected to predict the risk of moose‐train collisions included train route, time of day, lunar phase and average train speed, as well as two climatic covariables, i.e. snow depth and temperature. Trains running at night, in the morning or in the evening experienced a higher risk of collision with moose Alces alces than day trains. The probability of collision was also higher during nights of full moons than during nights of half or no moons. As observed previously with trains in Norway moose‐kills increased with increasing snow depth and decreasing temperatures. To test the predictability of the model, we used a logistic model based on train departures during 1990–1996 to predict the number of moose‐train accidents during winter 1996/97. Although the model had a satisfactorily high predictability, the best models would probably be those based on a combination of both temporal and spatial aspects. We discuss how logistic models may be applied to introduce remedial actions on high‐risk routes or during high‐risk periods.