A Comparison of Data Sets Varying in Spatial Accuracy Used to Predict the Occurrence of Wildlife-Vehicle Collisions |
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Authors: | Kari E Gunson Anthony P Clevenger Adam T Ford John A Bissonette Amanda Hardy |
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Institution: | (1) Eco-Kare International, P.O. Box 51522, Toronto, ON, M4E 1C0, Canada;(2) Western Transportation Institute, Montana State University, P.O. Box 174250, Bozeman, MT 59717, USA;(3) 138 Birch Avenue, Harvie Heights, Alberta, T1W 2W2, Canada;(4) U.S. Geological Survey, Utah Cooperative Fish and Wildlife Research Unit, Department of Wildland Resources, College of Natural Resources, Utah State University, Logan, UT 84322-5290, USA |
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Abstract: | Wildlife-vehicle collisions (WVCs) pose a significant safety and conservation concern in areas where high-traffic roads are
situated adjacent to wildlife habitat. Improving transportation safety, accurately planning highway mitigation, and identifying
key habitat linkage areas may all depend on the quality of WVC data collection. Two common approaches to describe the location
of WVCs are spatially accurate data derived from global positioning systems (GPS) or vehicle odometer measurements and less
accurate road-marker data derived from reference points (e.g., mile-markers or landmarks) along the roadside. In addition,
there are two common variable types used to predict WVC locations: (1) field-derived, site-specific measurements and (2) geographic
information system (GIS)-derived information. It is unclear whether these different approaches produce similar results when
attempting to identify and explain the location of WVCs. Our first objective was to determine and compare the spatial error
found in road-marker data (in our case the closest mile-marker) and landmark-referenced data. Our second objective was to
evaluate the performance of models explaining high- and low-probability WVC locations, using congruent, spatially accurate
(<3-m) and road-marker (<800-m) response variables in combination with field- and GIS-derived explanatory variables. Our WVC
data sets were comprised of ungulate collisions and were located along five major roads in the central Canadian Rocky Mountains.
We found that spatial error (mean ± SD) was higher for WVC data referenced to nearby landmarks (516 ± 808 m) than for data
referenced to the closest mile-marker data (401 ± 219 m). The top-performing model using the spatially accurate WVC locations
contained all explanatory variable types, whereas GIS-derived variables were only influential in the best road-marker model
and the spatially accurate reduced model. Our study showed that spatial error and sample size, using road-marker data for
ungulate species, are important to consider for model output interpretation, which will impact the appropriate scale on which
to apply modeling results. Using road-marker references <1.6 km or GPS-derived data locations may represent an optimal compromise
between data acquisition costs and analytical performance.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. |
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Keywords: | Banff National Park Data collection Mortality Road ecology Spatial accuracy Ungulate Variable selection Wildlife-vehicle collision |
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