Abstract

Human-wildlife conflicts like wildlife-vehicle collisions pose major challenges for the management and conservation of mobile wildlife in human-dominated landscapes, particularly when large species are involved. Mitigation measures to reduce risk of collisions may be based on information given by wildlife movement and collision data. To test whether movement and collision data indicate different spatiotemporal risk zones, we predicted year-around probabilities of road-crossings of GPS-marked female moose (Alces alces) (n = 102), and compared them with spatiotemporal patterns of police recorded moose-vehicle collisions (n = 1158). Probability of moose road-crossings peaked in May, June, and between mid November and the beginning of January, i.e. during moose migration. Moose-vehicle collisions were more likely during autumn and winter. Comparing environmental attributes of crossing and collision sites showed significant differences. The likelihood of collisions increased with the abundance of human-modified areas and higher allowed speed, and was lower on forest roads. We found that animal movement data alone are insufficient to predict collision risk zones, while analyses of collision data alone overestimate the collision risk in certain habitats. Our findings suggest that higher collision risk is largely due to low light and poor road surface conditions rather than to more animal road-crossings. This suggests that efforts to reduce wildlife collisions should focus on driver attitudes and road conditions rather than animal movement, and any efforts to model the collision risk will require actual collision data, and not just movement data. (c) 2011 Elsevier Ltd. All rights reserved.

Keywords

Alces alces; Ungulate; Movement data; Collision data; Generalized additive model; Generalized linear mixed model

Published in

Biological Conservation
2012, Volume: 145, number: 1, pages: 70-78
Publisher: ELSEVIER SCI LTD

SLU Authors

• Neumann Sivertsson, Wiebke

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    

• Ericsson, Göran

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    

• Dettki, Holger

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    

• Bunnefeld, Nils

  • Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

SDG9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation


UKÄ Subject classification

Landscape Architecture
Environmental Sciences related to Agriculture and Land-use


Publication identifier

DOI: https://doi.org/10.1016/j.biocon.2011.10.011

Permanent link to this page (URI)

https://res.slu.se/id/publ/56302