Publication Detail
UCD-ITS-RP-18-107 Journal Article |
Suggested Citation:
Ha, Hoehun and Fraser M. Shilling (2018) Modelling Potential Wildlife-Vehicle Collisions (Wvc) Locations Using Environmental Factors and Human Population Density: A Case-Study From 3 State Highways in Central California. Institute of Transportation Studies, University of California, Davis, Journal Article UCD-ITS-RP-18-107
Roads can exert direct and indirect impacts on ecosystems and organisms. In particular, wildlife-vehicle collisions (WVC) may be a considerable threat for populations of certain wildlife species. Despite such threat, there is still incomplete understanding of the factors responsible for high road mortality. Only a few empirical studies have tested the idea that spatial variation of roadkill is affected by environmental characteristics and socio-demographic factors. This study examines the relationships between WVC involving different taxonomic groups (i.e. ungulate, avian, medium mammal, small mammal) and physical and human population characteristics of landscapes by adapting the ecological model, Maxent, to distribution modelling of carcasses resulting from WVC. We used observations from the California Roadkill Observation System of four taxonomic groups recorded along highways in northern California. Our results indicated that current carcass-observation locations were explained primarily by total forest area (cells) within 500 m buffer and road density within a 500 m neighborhood. Our results found that current carcass-observation locations are modelled well using environmental variables and human population density together. Moreover, a comparison of projected potential roadkill locations based on environmental factors and human population density among different taxonomic groups revealed substantially different distributions. These results indicate potential areas where wildlife populations are at increased risk of coming into contact with traffic and the potential utility of this methodology for modelling current and future distributions of wildlife across landscapes using the Maxent approach.
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