Large-scale spatiotemporal variation in road mortality of moose: Is it all about population density?
Journal article, Peer reviewed
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Ungulate‐vehicle collisions (UVC) constitute a widespread and increasing problem in large parts of the world. This has generated an intensive search for mitigating measures, but often based on a weak understanding of the underlying spatiotemporal factors. We examined the effects of harvest density (a proxy for moose density), traffic‐related variables and climate on the spatiotemporal variation in number of moose‐vehicle collisions (MVC) in 14 Norwegian counties based on 31 year of data. Moose density was the most important factor explaining the variation in MVC, both within and between counties. In addition, the spatiotemporal variation in MVC was positively related to traffic volume (private car mileage) and snow depth, and negatively related to winter temperature. The relationship between traffic volume and temporal variation in MVC was stronger in counties with general low traffic volume, possibly because high traffic volume can act as a barrier to moose road crossings. Likewise, the temporal effect of snow depth was mainly present in counties with on average deep snow, i.e., where it constitutes a constraint on moose movement and space use during winter. Our study highlights the different importance between areas of the factors underlying the spatiotemporal variation in MVC. A notable exception was the variation in moose density, which follows an isometric scaling to the variation in MVC in all counties. Thus, a given percentage decrease in moose density is likely to return a similar percentage decrease in MVC. A significant population reduction may therefore be an efficient mitigating measure to reduce the number of MVC in Norway. From a harvesting and conservation point of view, other possible preventive measures to reduce MVC should also be considered. However, because of the strong temporal effects of moose density and snow depth, evaluations of other mitigating actions should always seek to control for temporal variation in these variables.