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62 Landscape vs. road-related variables. Wildlife tends to of a U.S.Canadian standard for recording WVCs and be associated with specific habitats, terrain, and adjacent land suggest that further research into new technologies that will use types. Thus, landscape spatial patterns would be expected enable transportation agencies to collect WVC data of to play an important role in determining roadkill locations appropriate spatial resolution is needed. and rates.95 Explanatory factors of wildlife roadkills vary widely between species, often explained by habitat prefer- Conclusions and Suggested Research ences and species abundance patterns.52,192 Increasingly, stud- ies are beginning to look at the types of variables that explain The primary result of this analysis was that a UVC model wildlifevehicle collisions, whether they are associated with developed with high-resolution location data had high landscape and habitat characteristics, or physical parameters predictive power in identifying factors that contribute to related to the road environment.208,206 In this study, 22 vari- collisions. The location of where high-kill versus low-kill ables were evaluated, 11 associated with landscape or habitat UVC zones are likely to occur is highly dependent on the attributes and 9 associated with the road environment. In the resolution of the models used. univariate analysis, 10 variables were significant in explaining Plotting animal-vehicle collisions on maps using grid coor- UVCs; 8 were related to landscape, while only 2 were associ- dinates may not improve spatial accuracy. In this study, the ated with the road environment. In the logistic regression average distance reporting error associated with roadkill analysis, three explanatory variables were significant; two records using UTM grid coordinate references on occurrence were landscape based and one was from the road environ- reports and mortality cards from the mountain national parks ment. These results demonstrate the importance of ecologi- was 969 m ± 1322 m.53 The research team found that model- cal attributes in the analysis and suggest that analyses that fail ing collision-related parameters with low-resolution location to adequately consider ecological variables in UVC analyses data did not produce models with high predictability. As a along with road-related variables may be appropriate for consequence, the models could not be expected to produce safety considerations but are likely to provide unreliable re- properly directed or applied mitigation of WVCs. sults when wildlife population viability is a concern. These results have important implications for transporta- tion agencies that may be analyzing data that has been refer- Summary. This study is the first of which the research enced to a mile-marker system or that is, unknown to them, team is aware that tested the value of low-resolution spatial spatially inaccurate. These implications are equally important data accurate to the mile-marker with a high-resolution GPS for state-wide analyses or even the smaller districts. Low- dataset accurate to within a few meters. High-resolution data resolution data or data that is spatially accurate to the mile- were found to make a significant difference in the ability of marker may be used for coarse-scale analysis to identify UVC models to provide biologically meaningful predictions of the hotspots. However, for finer scale needs (project or district variables responsible for UVCs. The analyses used the largest level), higher resolution spatial data appear essential for a rig- database of its kind with spatially accurate information on the orous analysis and development of sound mitigation recom- occurrence and specific carcass location of UVCs. The data- mendations. A U.S.Canadian standard for recording WVCs base covered the period from August 1997 to November not only would stimulate transportation departments and 2003. Most noteworthy was the significant difference in pre- other organizations to collect more spatially accurate roadkill dictive ability between the models. The high-resolution UVC data, but also would allow for a better integration and analyses model had higher predictive power in identifying factors that of the data. These two initiatives, spatially accurate (higher res- contributed to collisions when compared to a lower resolu- olution) data and standardized data collection, can help agen- tion dataset based on mile-marker references. Additionally, cies to collect data that will eventually lead to more informed the high-resolution models were more robust than models analyses for transportation decision making. from the low-resolution mile-marker dataset. UVCs were clustered on all highways in the study area. The 3.3 Hotspots Modeling high-resolution model had substantially more significant Introduction variables explaining the factors associated with UVCs than the mile-marker model. Adjacent habitat type was the most Wildlifevehicle collisions are a significant problem in important variable in explaining UVCs in the high-resolution North America, particularly in rural or suburban areas where model. Distance to nearest drainage also was significant and people rank them as a major safety concern. A recent survey negatively correlated with the occurrence of UVCs. There was of motorists in Montana, Idaho, and Wyoming ranked ani- a greater tendency for traffic collisions close to drainages sys- mals on the roadway as one of the top three safety issues.82 tems and close to barriers such as Jersey barriers and A survey of northern California and rural Oregon stakehold- guardrails. These findings lend support for the development ers reported similar concerns. In much of the western United