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53 animal carcass removals that were not the result of a re- datasets available from most transportation agencies. This ported WVC or DVC were likely the outcome of a property- report illustrates how spatial accuracy of the data affects the damage-only collision whose value did not require it to be process of identifying variables that contribute to wildlife reported. vehicle collisions. Based on these outcomes, the research team makes recommendations for collecting roadkill data more systematically and accurately, emphasizing the value of spatial 3.2 Limiting Effects of Roadkill accuracy in identifying and prioritizing problematic areas for Reporting Data Due to Spatial highway mitigation projects. The intent of this effort is to Inaccuracy provide an overview of considerations regarding the quality and application of wildlifevehicle collision carcass data to aid Introduction in assessing and mitigating wildlifevehicle collisions. Wildlifevehicle collisions do not occur randomly along This study was conducted in the Central Canadian Rocky roads but are spatially clustered190,124,51,134 because wildlife Mountains approximately 150 km west of Calgary, straddling movements tend to be associated with specific habitats, terrain, the Continental Divide in southwestern Alberta and south- and adjacent land-use types. Thus, landscape spatial patterns eastern British Columbia (Figure 10). The study area encom- would be expected to play an important role in determining passes 11,400 km2 of mountain landscapes in Banff, Kootenay, locations where the probability of being involved in a and Yoho national parks, and adjacent Alberta provincial wildlifevehicle collision is higher compared to other loca- lands. This region has a continental climate characterized by tions.95 Explanatory factors of wildlife roadkill locations and long winters and short summers.121 Vegetation consists of open rates vary widely among species and taxa. To properly mitigate forests dominated by lodgepole pine (Pinus contorta), Douglas road impacts for wildlife and increase motorist safety, fir (Pseudotsuga menziesii), white spruce (Picea glauca), Engle- transportation departments need to be able to identify where mann spruce (Picea englemannii), quaking aspen (Populus particular individuals, species, taxa, and vertebrate com- tremuloides), and natural grasslands. munities are susceptible to high roadkill rates along roads. Geology influences the geographic orientation of the major Quality field data documenting locations and frequencies of drainages in the region, characterized by valleys running wildlifevehicle collisions can offer empirical insights to help north to south and delineated by steep shale mountains. On address this challenging safety and ecological issue. a regional scale, east-west movements of animals across and As part of maintaining state and provincial highway sys- between these valleys are considered vital for long-term sus- tems, transportation departments often collect information tainability of healthy wildlife populations in the region. The on the location of wildlifevehicle collisions. Typically, transportation corridors associated with the major water- maintenance personnel do not conduct routine surveys of sheds influence the distribution and movement of wildlife in animal roadkilled carcasses, but instead collect this infor- the region. As the most prominent drainage, the Bow Valley mation opportunistically while carrying out their daily accommodates the Trans-Canada Highway (TCH), one of work. Occasionally the information may be referenced to the most important and, therefore, heavily traveled trans- wildlife species and specific geographical landmarks such as portation corridors in the region. 1.0-mile-markers or 0.1-mile-markers; however, oppor- Highways in the study area traverse montane and tunistically collected roadkill data are usually not spatially subalpine ecoregions through four major watersheds in the accurate. One study has shown that errors in roadkill region (Figure 10). Table 22 describes the location and gen- reporting may be 500 m or greater.53 The inherent spatial eral characteristics of the five segments of highways that were error in most agency datasets limits the types of applications included in this study. for which the data are useful in transportation planning and mitigation efforts. This report demonstrates how wildlifevehicle collision Research Approach: Methods and Data carcass data can be analyzed to guide transportation manage- Data Collection ment decision making and mitigation planning for wildlife crossings. The research team investigated the relative impor- Spatially accurate dataset. In August 1997, efforts were tance of factors associated with wildlife roadkills using two dif- initiated to maximize data collection from carcasses resulting ferent datasets: one with highly accurate (high-resolution) from WVCs and to improve the spatial accuracy (resolution) GPS location data (< _ 10 m error) representing an ideal situa- of reported locations of WVCs occurring on the highways in tion and another lower resolution dataset with high spatial the study area. The research team worked with the agencies error (<_ 0.5 mi or 800 m = low resolution), which is referred and highway maintenance contractors that were responsible to as "mile-marker" data and is more characteristic of the for collecting and reporting wildlife carcasses, primarily elk.

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54 Banff Yoho National National Park Park Province Kootenay National Park Kananaskis Valley Figure 10. Location of Canadian study area. The agencies consisted of Parks Canada (Banff, Kootenay, province of Alberta. This collaborative effort included na- and Yoho National Parks), Alberta Sustainable Resource De- tional park wardens, provincial park rangers, and mainte- velopment (Bow Valley Wildland Park, and Kananaskis nance crews of Volker-Stevin. Country) and Volker-Stevin, maintenance contractor for the The research team provided colored pin-flags to mark Trans-Canada Highway east of Banff National Park in the the sites in the right-of-way where roadkilled wildlife were Table 22. Characteristics of the major highways in the Canadian study area. Posted Road Traffic Vehicle Highway Watershed Province Length Volume Speed (km) (ADT) (km/h) Trans-Canada Alberta, East of Banff Bow River 37 16,960a 110 Highway National Park Trans-Canada Banff National Park, Bow River 33 8,000a 90 Highway Alberta Trans-Canada Yoho National Park, Kicking Horse River 44 4,600a 90 Highway British Columbia Highway 93 Kootenay National Kootenay River 101 2,000a 90 South Park, British Columbia Highway 40 Kananaskis River Alberta 50 3,075b 90 a 2005 annual average daily traffic volume. Data from Parks Canada; Banff National Park; and Alberta Transportation, Edmonton, Alberta. b 1999 summer average daily traffic volume. Data from Alberta Transportation, Edmonton, Alberta.

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55 observed and collected. After placing a pin-flag, collaborators to the nearest whole number. Buffers of 800 m (0.5 mi) radius were asked to report to the research team via telephone, fax, were generated around each mile-marker sampling site and or email. Most wildlife carcasses were pin-flagged and re- each highway segment within the buffer was categorized as a ported within 48 hours. high-kill or low-kill zone. This categorization was determined The collaborators recorded the location of wildlife car- by comparing the total number of UVCs associated with a casses by describing the location with reference to a nearby segment to the mean number of UVCs per mile for the same landmark (e.g., 0.3 km west of Banff National Park east en- stretch of road for each of the five highways in the study area. trance gate). Each reported WVC carcass site was re-located If the summed number of UVCs associated with a single mile- and confirmed by measuring the odometer distance from the marker segment was higher than the average calculated per reported landmark to the pin-flagged site. Once the location mile for the same highway, that mile-marker segment was was confirmed, researchers recorded the actual location in considered a high-kill zone. Similarly, if the summed number Universal Transverse Mercator (UTM) grid coordinates of UVCs within a mile-marker segment was lower than the av- using a differentially correctable GPS unit (Trimble Naviga- erage for that highway, the mile-marker segment was defined tion Ltd., Sunnyvale, California, USA) with high spatial ac- as a low-kill zone. Each spatially accurate UVC location was curacy (< _ 10 m). The UTM coordinates were recorded in a classified as a high-kill or low-kill zone according to which database along with the original date of each reported road- mile-marker segment it fell within. For example, a mile- kill and information regarding the species, sex, age, and num- marker segment with greater than or equal to 2 roadkills on ber of individuals involved. Highway 40 in Kananaskis was a high-kill zone, while a For this study, the research team used only ungulate car- segment with less than 2 roadkills was a low-kill zone. cass data (UVC), because ungulate species composed 76% of the total wildlife mortalities. In addition, these species are Variables and Models often the greatest safety concern to transportation agencies given their size and relatively common occurrence in rural Site-specific variables. The research team measured site- and mountain landscapes. Ungulate species included white- specific variables at 499 sites from the GPS data and 120 sites tailed and mule deer (Odocoileus virginianus and Odocoileus from the mile-marker dataset between April 2003 and July hemionus, respectively), unidentified deer (Odocoileus sp.), 2005. Only 499 UVC locations were used; 47 UVC reports elk (Cervus elaphus), moose (Alces alces), and bighorn sheep from Kootenay Highway 93 South were excluded because (Ovis canadensis). The UVC data obtained from the methods they occurred prior to the clearing of roadside vegetation described in the previous paragraphs are hereafter referred to along a 24 km stretch of the Kootenay Highway 93 South. as the "spatially accurate," "high-resolution," or "GPS" Using a differentially correctable GPS unit to locate each sam- dataset and serve as a benchmark for the analysis. pling site, the research team measured 14 variables to be used as possible factors explaining UVC occurrence (Table 23). A Mile-marker dataset. To investigate the influence that range finder (Yardage Pro 1000, Bushnell Denver, CO) spatial accuracy and scale may have on the results and inter- measured distance to nearest vegetative cover, and the inline pretation of the data, the research team created a mile-marker and angular visibility measurements. Vegetative cover, habi- dataset using the spatially accurate dataset, but shifting each tat, topography, and slope were all estimated visually. Field UVC location to the nearest hypothetical mile-marker. To do visibility variables estimated the extent to which a motorist this, each of the five highways in the study area was divided into could see ungulates on the highway right-of-way, or con- 1.0-mile-marker segments using ArcView 3.3.77 All spatially ac- versely, how far away an oncoming vehicle could be seen curate UVC data were plotted onto the road network and then from the side of the highway. Field visibility was measured via moved to the nearest mile-marker reference point. Each ob- a rangefinder as the distance that an observer, standing at one served data point was moved an average distance of 400.2 m of three positions (edge of the pavement, 5 m from pavement 218.8 m (min. 7.3, max. 793.9) to its nearest mile-marker. The edge, or 10 m from pavement edge), lost sight of a passing research team recorded the UTM coordinates of each mile- vehicle. This measurement represents the distance that an ap- marker location and summed the number of UVCs in that proaching driver might be able to see an animal from the mile-marker segment, defined as 800 m (0.5 mi) up and down road. Because in most cases it could not be determined from the road of the given mile-marker. The UVC data adjusted to what side a vehicle struck an animal, or in which direction the the closest mile-marker are hereafter referred to as the "spa- vehicle was traveling, four visibility measurements were taken tially inaccurate," "low-resolution," or "mile-marker" data. at each position (two facing each direction of traffic on both sides of the highway). These four measurements were aver- High- and low-kill locations. The mean number of road- aged to provide mean values estimating visibility at the edge kills per mile were calculated for each highway and rounded of the road, 5 m away from the edge of the road, and 10 m

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56 Table 23. Definition and description of variables used. Variable Name Definition Field variables Habitat class* Dominant habitat within a 100 m radius on both sides of the highway measured as open (O): meadows, barren ground; water (W): wetland, lake, stream; rock (R); deciduous forest (DF); coniferous forest (CF); open forest mix (OFM) Topography* Landscape scale terrain measured as flat (1), raised (2), buried-raised (3), buried (4), partially buried (5), partially raised (6) Forest cover Mean percentage (%) of continuous forest cover (trees > 1 m height) in a 100 m transect line perpendicular to the highway, taken from both sides of the road Shrub cover Mean percentage (%) of shrub cover (trees and shrubs < 1 m high) in a 100 m transect line perpendicular to the highway, taken from both sides of the road Barren ground Mean percentage (%) of area devoid of vegetation (rock, gravel, water, pavement, etc.) in a 100 m transect line perpendicular to the highway, taken from both sides of the road Vegetative cover Mean distance (m) to vegetative cover (trees and shrubs > 1 m high) taken from both sides of the road Roadside slope Mean slope ( ) of the land 05 m perpendicular to the pavement edge taken from both sides of the road Verge slope Mean slope ( ) of the land 510 m perpendicular to the pavement edge taken from both sides of the road Adjacent land slope Mean slope ( ) of the land 1030 m perpendicular to the pavement edge taken from both sides of the road Elevation GPS height (m) Road width Distance (m) from one side of the highway pavement to the other In-line visibility field* Mean distance at which an observer standing at the pavement edge could no longer see passing vehicles; taken from each direction on both sides of the highway Angular visibility 1 Mean distance at which an observer standing 5 m from the pavement edge could no longer see passing vehicles; taken from each direction on both sides of the highway Angular visibility 2 Mean distance at which an observer standing 10 m from the pavement edge could no longer see passing vehicles; taken from each direction on both sides of the highway Distance-to-landscape features Drainage Distance (m) to the nearest waterway (river, stream, or creek) that crossed the road Human use Distance (m) to the nearest human use feature along the highway Barrier-guardrail Distance (m) to the nearest Jersey barrier or guardrail GIS-generated buffer variables Road curvature Length (m) of each highway segment within each buffer Open water Area (km2) of open water within each buffer Human use Area (m2) of human use features within each buffer River length The length (m) of all rivers within each buffer Barrier length The length (m) of all Jersey barriers and guardrails in each buffer * Variable measure obtained from field measurement (1) flat (2) raised (3) buried-raised (4) buried (5) partially buried (6) partially raised from the edge of the road. These positions are defined as "in- features (Table 23) was calculated using GIS. The research line visibility," "angular visibility 1," and "angular visibility team generated 800 m (0.5 mi) radius buffers around each spa- 2," respectively, as referred to in Table 23. tially accurate and mile-marker sampling site and calculated Spatial and elevation data were collected along each high- the area or length of each landscape feature within each buffer. way approximately every 25 m, by driving at 50 km/h and The road network was used to calculate the length of each high- recording a GPS location every second. Elevation was ob- way segment within each buffer to measure curvature of the tained on site from a GPS unit for the spatially accurate data highway (Table 23). locations, whereas elevation for the mile-marker points was extracted from the GPS-created highway layer. Data Analysis GIS-generated variables. Measurements for most vari- The research team tested whether the spatially accurate ables were obtained in the field; some were obtained using Ar- UVCs were distributed randomly by comparing the spatial cView 3.3 GIS.77 Distance from each sampling site to landscape pattern of collisions with that expected by chance, in which