Analysing and predicting wildlife–vehicle collision hotspots for the Swiss road network

Abstract

Context Wildlife–vehicle collisions (WVCs) are a significant threat for many species, cause financial loss and pose a serious risk to motorist safety. Objectives We used spatial data science on regional collision data from Switzerland with the objectives of identifying the key environmental collision risk factors and modelling WVC risk on a nationwide scale. Methods We used 43,000 collision records with roe deer, red deer, wild boar, and chamois from 2010 to 2015 for both midlands and mountainous landscape types. We compared a fixed-length road segmentation approach with segments based on Kernel Density Estimation, a data-driven segmentation method. The segments’ environmental properties were derived from land-cover geodata using novel neighbourhood operations. Multivariate logistic regression and random forest classifiers were used to identify and rank the relevant environmental factors and to predict collision risk in areas without collision data. Results The key factors for WVC hotspots are road sinuosity, and two composite factors for browsing/forage availability and traffic noise—a proxy for traffic flow. Our best models achieved sensitivities of 82.5% to 88.6%, with misclassifications of 20.14% and 27.03%, respectively. Our predictions were better in forested areas and revealed limitations in open landscape due to lack of up-to-date data on annual crop changes. Conclusions We illustrate the added value of using fine-grained land-cover data for WVC modelling, and show how such detailed information can be annotated to road segments using spatial neighbourhood functions. Finally, we recommend the inclusion of annual crop data for improving WVC modelling.

Crossings and collisions – Exploring how roe deer navigate the road network

“…Road-mortality through wildlife-vehicle collisions (WVC) tends to receive a lot of attention in the literature and in management since WVCs can be dangerous and costly for humans in addition to the wildlife involved (Bissonette et al 2008). A vast majority of studies in road ecology use data of WVC from, e.g., surveys of roadkill, or police and accident reports, to identify 'hot spots', i.e., locations where wildlife are particularly susceptible to WVC, the factors that may contribute to increased risk of WVC, and, thereby, locations or conditions that need to be mitigated (e.g., Laube et al 2023;Kent et al 2021;Found and Boyce 2011). However, roads and traffic can also be considered barrier to movement if they prevent individuals from successfully traversing the landscape.…”

“…However, by doing this, areas are overlooked that serve as such strong barriers to movement and are rarely being crossed in the first place, thus, preventing collisions. At these road stretches the lack of connectivity can and should be mitigated (Bíl et al 2019;Laube et al 2023).…”