Using Collision Data, GPS Technology and Expert Opinion to Develop Strategic Countermeasure Recommendations for Reducing Animal–Vehicle Collisions in Northern British Columbia
A report of the Wildlife Collision Working Group
for
The Insurance Corporation of British Columbia and the Road Health Task Force
by
Road Health-University Wildlife Collision Mitigation Research Team
College of Science and Management University of Northern British Columbia Prince George, British Columbia V2N 4Z9
Specific recommendations from this study and in this report are provided by the authors for sponsoring agencies to consider. These recommendations do not represent the findings, opinions or policies of sponsoring agencies or of UNBC.
December 2006
Cite this document as: Road Health-University Wildlife Collision Mitigation Research Team. 2006. Using Collision Data, GPS Technology and Expert Opinion to Develop Strategic Countermeasures Recommendations for Reducing Animal–Vehicle Collisions in Northern British Columbia. Unpublished Report. Prince George, BC. 145p.
0
EXECUTIVE SUMMARY
A study was conducted in an effort to determine the temporal and spatial dynamics of animal-vehicle collisions in northern British Columbia. The Insurance Corporation of British Columbia (ICBC) Animal strike data from January 1996 to November 2005 were analyzed in an effort to determine exactly when and where various species of animals are being struck by vehicles across northern BC. Data from the Wildlife Accident Reporting System (WARS; 1978-2004) of the Ministry of Transportation were also analysed in an effort to pinpoint where animal carcasses are being collected by highway contractors. Additionally, a unique collaboration between the University of Northern BC and a Winnipeg-based company (Persentech) led to the creation of a GPS device that was used in a pilot project with three local trucking companies to log the occurrence of moose and deer (both dead and alive) on highways leading out of Prince George, BC. These data were uploaded to a Geographical Information System and mapped and analyzed to determine where moose and deer are being observed along various sections of the highway corridor and are being killed by vehicles. These data were combined with data collected during an Expert Opinion Survey that was conducted in an effort to assess how the opinions of various local experts on wildlife-vehicle collisions compared with various datasets. Finally, a survey of logging truck drivers was conducted to record baseline data on the occurrence of animal-truck strikes on secondary roads in northern BC. The main objective of the research was to determine when and where animal-vehicle collisions are occurring in northern BC so that site- and animal-specific countermeasures could be recommended for deployment in strategic ways. Our findings suggest that collisions with different animal species peak at various times of the day and seasons of the year and that, within a species, collisions occur differently depending on what part of the province is under consideration. Collisions appear to be common throughout the north, but according to our findings from our GPS device pilot project, WARS data and Expert Opinion surveys also appear to be concentrated in certain collision prone areas. Furthermore, some animals are found along certain highways sections despite the fact that few individuals are struck by vehicles in these areas. Our review of the literature suggests that several coarse and fine filter options for countermeasure implementation exist. We recommend mitigation efforts begin now to use these data for road safety planning and be focused on implementing countermeasures in specific areas across various communities in northern BC. It is recommended that such efforts be planned out in a species-specific and temporally sensitive way.
Key Words: Animal, Car, Countermeasures, Driving Hazard, Expert Opinion, GPS, Highways, Mitigation, Motorists, Roads, Road kill, Ungulate, Wildlife Collision.
Contributing Authors in Alphabetic Order: Scott Emmons, Micheal Hurley, Nicole Klassen, Eric Rapaport and Roy Rea
Corresponding Authors: Roy Rea, Eric Rapaport and Scott Emmons
1 ACKNOWLEDGEMENTS
We would like to thank Gayle Hesse, David Dickson, Alim Karim, Dexter Hodder, Frank Fanczyk at Persentech Inc., the Prince George Wildlife Collision Working Group, Excel Transportation Inc., Lomak Bulk Carriers Corp., Grandview Transport Ltd., Vanderhoof Ambulance Service, Vanderhoof Fire Department, YellowHead Road and Bridge, Conservation Officer Service, RCMP, and the Ministry of Transportation. The Insurance Corporation of British Columbia and the RoadHealth Task Force provided funding for the research.
2 TABLE OF CONTENTS
Executive Summary………………………………………………………………………. 1 Key Words………………………………………………………………………………… 1 Acknowledgements………………………………………………………………………. 2 Table of Contents………………………………………………………………………… 3 Prologue…………………………………………………………………………………… 4 Chapter 1…………………………………………………………………………………… 5 Introduction………………………………………………………………………………. 5 Methods…………………………………………………………………………………… 6 Results……………………………………………………………………………………. 8 Discussion………………………………………………………………………………… 14 Conclusions and Recommendations……………………………………………………… 16 Summary……………………………………………………………………………………22 References………………………………………………………………………………… 23 Chapter 2……………………………………………………………………………………26 Chapter 3……………………………………………………………………………………30 Introduction…………………………………………………………………………………30 Background…………………………………………………………………………………30 Conclusions…………………………………………………………………………………33 Recommendations………………………………………………………………………… 34 Chapter 4……………………………………………………………………………………35 Introduction…………………………………………………………………………………35 Methods…………………………………………………………………………………… 35 Results………………………………………………………………………………………37 References………………………………………………………………………………… 45 Chapter 5……………………………………………………………………………………46 Introduction…………………………………………………………………………………46 Results and Discussion…………………………………………………………………… 47 Epilogue………………………………………………………………………………….. 52 Appendices………………………………………………………………………………. 53
3 PROLOGUE
Animal-vehicle collisions result in substantial personal, environmental and economic losses. In northern British Columbia, the number of material damage claims has more than doubled over the past 10 years, with corresponding increases in human suffering and damage to the animal resource (pers. comm.; Gayle Hesee; Coordinator – BC Wildlife Collision Prevention Program). In an effort to reduce such losses, a study was commissioned by the Prince George Wildlife Collision Reduction Committee and funded by the Road Health Task Force and the Insurance Corporation of British Columbia. The study was designed to collate available information on collisions across northern British Columbia in an effort to determine when and where vehicle collisions with various species of animals are occurring. The main objective of the research was to use the research findings to recommend site- and species-specific countermeasures in an effort to reduce animal-vehicle collisions. The following report outlines the objectives, methods and results of this study in chapter format1. Chapter 1 presents the findings of an analysis of 10 years of ICBC wildlife collision incident data (1996-2005). The chapter focuses on using ICBC data to elucidate spatial and temporal patterns of animal-vehicle collision throughout northern BC. How collision trends vary by regions, communities and various species of animals and how these trends compare with trends from other jurisdictions in North America is discussed. Recommendations for how to better record data to facilitate future analysis and for what forms of countermeasures should be currently considered for implementation in northern BC are underscored. Chapter 2 describes how data from WARS was uploaded into a new Flexible Internet Spatial Template (FIST) which is an Open Source, Pre-Hypertext Processor (PHP) object based application that is used to rapidly deploy internet mapping web sites. The chapter explores how this tool is used to determine collision prone sections of highways from WARS data and from a mobile GPS unit (Chapter 3) so that visits to these sites can be made, an analysis of site features conducted and recommendations for site- specific countermeasures made. Chapter 3 reports on the design and use of a GPS driving companion that is being employed by volunteer truck drivers to record location, time and date data on moose and deer movements along northern BC roads. These data allow for current patterns of animal movements and collision occurrence to be recorded and used in mitigation planning efforts. Chapter 4 discusses how this GPS technology and the opinions of local experts (RCMP, MOT, highway contractors, ambulance drivers, etc.) on wildlife-vehicle collisions is being integrated and analyzed to better pinpoint current and long-term historical trends in wildlife-vehicle collision patterns. Chapter 5 provides results from a survey given to logging truck drivers in the Prince George area. The survey was designed to assess the occurrence of encounters between logging truck drivers and moose on area roads – particularly secondary logging roads.
1 Chapters of this report will be submitted by the authors for peer review. In this respect, this report represents a draft of how these chapters will appear following a peer review process. We invite comments on this draft report and ask that you send comments to the corresponding author of each chapter.
4 Chapter 1. Elucidating Temporal and Species-Specific Distinctions in Patterns of Animal-Vehicle Collisions in Various Communities and Regions of Northern British Columbia.
Corresponding author: Roy Rea ([email protected])
INTRODUCTION
Wildlife-related vehicle collisions (WRVC’s) are an increasingly serious problem in British Columbia (pers comm. David Dickson, ICBC - Regional Loss Prevention Manager, North Central Interior of British Columbia). Furthermore, collision induced human injuries and material damages as a result of animal-related vehicular collisions are on the rise across Canada (L-P Tardiff and Associates 2003). Despite pleas from conservation groups and the motoring public to take action, collisions with animals continue to rise across the province, as do the myriad of costs associated with their aftermath. In some areas such as national parks, wildlife fencing and grade separations (under and over passes) have worked to cage out and channel animals away from the road corridor (Flaa 1989). This form of mitigation is extremely effective, but far too costly for most northern regions to implement broadly; wildlife fencing costs ~ $70,000/km to install and grade separations cost at least 4 – 6 million dollars (pers. comm. Gordon Wagner, Regional Manager – Engineering, Ministry of Transportation, Northern Region). In more rural and remotely located transportation corridors, fencing and grade separations are impractical. Finding solutions to help reduce wildlife-vehicle collisions in these areas is tougher and warrants considerable attention. Although countermeasure options other than fencing and grade separations do exist (e.g., educational signage, deer reflectors, etc.) for use on more rural roadways, such countermeasures are rarely deployed in strategic locations following analysis of hard data or the effectiveness of installed countermeasures tested. Effective deployment of countermeasures depends on several factors. First, countermeasures – whether signage, deer reflectors or other such tools, should be deployed in a species-specific fashion. Jumping “deer” signs, for example, installed in areas where collisions with moose are recurrent ill-prepares motorists for the potential of striking an animal at night that weighs 500-800 kgs and has a dark coat. Deer reflectors are ineffective at keeping deer away from the road corridor and are have not been shown to work for other species either (Waring et al. 1991). Second, hard data (and not anecdote) on what time of day and what time of year collisions are happening must be included in collision mitigation planning. For example, elk warning signage installed permanently in elk winter range falsely alerts summer drivers to hazards that are unlikely to materialize on their summer travels, desensitizing motorists to warning signage over time. Likely of greatest importance in mitigation planning, is determining exactly where collisions are recurring so that efforts can be made to address specifically the problem in a site-specific way, albeit few jurisdictions appear to record, or make fully available, such data for use by road safety planners.
5 In an effort to elucidate more clearly what wildlife species motorists in northern British Columbia are most likely to encounter, and when and where these collisions take place we analysed 77,545 collision incident reports from a 10-year period (1996-2005) that were supplied to us by the Insurance Corporation of British Columbia (ICBC). Our objective was to determine seasonal and diurnal patterns of motor vehicle collisions for the 4 most commonly struck wildlife species in northern BC (deer, moose, bear and elk) as well as caribou which are considered endangered in the province. Specifically, we sought to delineate collision patterns in ICBC’s North Central Interior Region, which approximately includes the area of northern BC from 100 Mile House to the Yukon border and from the Bella Coola/Prince Rupert coastline to the Alberta border. Furthermore, we analyzed temporal collision pattern specifics for 13 regions and 20 communities within northern BC. Our objectives were to extract collision statistics and elucidate patterns of collisions with various species that would help us to recommend specific collision countermeasures for large areas of BC, and also for distinct regions in the north where collisions were likely to vary from one part of the province to another.
METHODS
We analysed 77,545 animal (domestic and wild) collision incident reports from the Insurance Corporation of British Columbia that occurred between January 1996 and November 2005. Data from each report were separated into columns and sorted for: species of animal struck, number of animals struck, time of day that the collision occurred, month of the collision, and year of the collision. Although the raw data contained all species of animals involved in collisions, we focused our analysis on deer, moose, bear, elk and caribou. Collision data were analyzed for all of BC and for northern BC. We then sorted the data into the 190 northern communities in our database. From these 190 communities, we selected 20 to begin our regional analyses. The 20 communities selected were: 100 Mile House, Burns Lake, Chetwynd, Dawson Creek, Fort Nelson, Fort St. James, Fort St. John, Houston, Hudson’s Hope, Kitimat, Mackenzie, McBride, Prince George, Prince Rupert, Quesnel, Terrace, Tumbler Ridge, Valemount, Vanderhoof, and Williams Lake. For these communities we determined, where possible, the time of day and month of the year in which collisions with the 2 most commonly struck of our 5 species of interest were occurring. We also determined the time of day during the month in which most collisions occurred as well as a 10-year collision trend for these species in each community. Each of the 20 communities was then conjoined with smaller neighboring centers based on distance and access association to create 18 Areas. For each of these 18 areas, we sorted collision data into collisions with one of 7 categories of animal: deer, moose, bear, elk, caribou, other wild animals and domestics. We then generated pie charts for each of these areas that showed the proportion of each animal category that had been struck between 1996 and 2005 (Figure 1).
6
Figure 1. Map of British Columbia showing the proportion of animal category recorded as struck. Data is from the Insurance Corporation of British Columbia collected between 1996 and 2005.
Projection of these pie charts on a map of the province allowed us to see more clearly how animal populations were distributed across the landscape and, therefore, helped us to draw regional boundaries around certain areas for which we could generate trend data. This analysis led to the creation of 13 regions. Regional names were derived from their location in the province, the presence of lakes or city centers and already- established jurisdictional boundaries (Figure 2).
7
Figure 2. Map of British Columbia showing 13 regions of northern BC created from amalgamating areas of similar collision patterns.
For each of the 13 regions, using amalgamated data from the areas, time of day, month of year, time of day during the peak month and 10 year trends for each of the 5 species in each region were determined..
RESULTS
Our findings suggest that patterns of animal collisions in British Columbia vary by the species and the particular area of the province under consideration.
8
British Columbia
Between 1996 and 2005, 54,842 deer, 7,056 moose, 2,536 bears, 1,768 elk, 73 caribou, 3,277 other wildlife species 6,039 domestic animals and over 2,000 unknown animal species were recorded as being struck by ICBC. In British Columbia, collisions with deer peak in November and generally occur between 6:00 and 10:00 pm although collisions during the November peak tend to occur predominantly between 5:00 and 8:00 pm (Appendix 1). Collisions with moose peak between October and January and generally occur between 5:00 and 11:00 pm, although collisions during the peak tend to occur predominantly between 5:00 and 8:00 pm (Appendix 2). Collisions with bears peak in September and October and generally occur between 7:00 and 11:00 pm, both during and outside of the peak (Appendix 3). Collisions with elk peak between November and January and generally occur between 6:00 pm and 1:00 am with another peak between 6:00 and 8:00 am; collisions times during the peak tend to occur in the same pattern but with more animals being struck between 6:00 and 8:00 am than at other times of the year (Appendix 4). Collisions with caribou peak in October and November and dramatically increase between 4:00 and 5:00 pm, with a smaller peak between 5:00 and 10:00 pm (Appendix 5). Ten year collision trends vary for these 5 species (Appendices 1-5) and with the exception of the last 2 years, appear to be on the rise for all species except caribou. The number of collisions with deer has nearly doubled over the last 10 years (Appendix 1d.). Collisions with moose, bears and elk in BC more than doubled between 1996 and 2003 but have decreased somewhat in the last 2 years (Appendices 2d.-4d.). Collisions with caribou in BC have decreased from 8 in 1996 to 1 in 2005 (Appendix 5d.)
Northern British Columbia
Of the more than 77,000 incident reports analyzed, 24,713 of the animal-related vehicular collisions occurred in our study area (ICBC’s North Central Interior Region). In northern BC, collisions with deer peak in October and November and generally occur between 5:00 and 11:00 at night, although collisions during the peak tend to occur predominantly between 5:00 and 8:00 pm (Appendix 6). Collisions with moose peak between December and January and generally occur between 5:00 and 11:00 at night, although collisions during the peak tend to occur predominantly between 5:00 and 7:00 pm (Appendix 7). Collisions with bears peak in September and generally occur between 8:00 and 11:00 at night, both during and outside of the peak (Appendix 8). Collisions with elk peak between October and January and generally occur between 9:00 pm and midnight with another peak between 6:00 and 7:00 am; collisions times during the peak tend to occur in the same pattern but with more animals being struck between 6:00 and 7:00 pm than at other times of the year (Appendix 9). Collisions with caribou peak in October and generally occur between 4:00 and 10:00 pm (Appendix 10). As with the ten-year collision trend data for BC, collisions with wildlife in northern BC appear to generally show increases over the last decade. The number of deer struck by vehicles in northern BC tripled between 1996 and 2005 (Appendix 6). Collisions with moose, bear and elk in
9 northern BC increased over the 10 year period with slight decreases between 2003 and 2005 (Appendices 7-9). Collisions with caribou went from a ten year high in 1998 of 16 to no collisions in 2005 (Appendices 7-10).
Northern British Columbia - Regions
Of the ~ 25,000 animal-related vehicle collisions that occurred in northern BC between 1996 and 2005, most collisions occurred in the Peace, Cariboo and Nechako regions and the fewest occurred in Bella Coola, Northwest and Chilcotin Regions (Table 1). Because regions were artificially delineated, represent equivocal areas of the province and contain statistics that are not corrected for traffic volume, data are not directly comparable between regions. However, the data do allow us to generate numbers to calculate the proportion of various species being struck in different regions of northern BC (Table 2) and allows us to generate a working map (Figure 1) for the creation of regional trend data by species for areas that had similar distributions in animal strikes by animal type (Appendices 11-46).
Table 1. The number of vehicle collisions in northern BC’s 13 regions between 1996 and 2005 by animal category.
Animal Category Region of Bear Caribou Deer Elk Moose Other Domestic Total Northern BC Wildlife Animals Bella Coola 8 0 117 0 0 2 9 136 Cariboo 80 0 4405 2 357 92 241 5177 Chilcotin 3 0 145 0 28 9 55 240 Lakes District 127 0 935 1 838 129 127 2157 Liard 17 34 133 17 221 39 34 495 Mackenzie 17 0 48 4 206 23 12 310 McBride 48 1 456 17 301 22 13 858 Nechako 298 5 1475 9 1811 268 337 4203 Northcoast 109 1 146 2 302 87 44 691 Northwest 30 4 18 3 73 14 17 159 Peace 71 10 5357 86 1748 218 234 7724 Queen Charlottes 14 0 342 0 0 14 5 375 Quesnel 79 1 1513 4 400 71 120 2188 Northern BC 901 56 15090 145 6285 988 1248 24713
10 Totals Table 2. The percent of vehicle collisions in northern BC’s 13 regions between 1996 and 2005 by animal category.
Animal Category Region of Bear Caribou Deer Elk Moose Other Domestic Northern BC Wildlife Animals Bella Coola 6 0 86 0 0 1 7 Cariboo 2 0 84 0 7 2 5 Chilcotin 1 0 60 0 12 4 23 Lakes District 6 0 43 0 39 6 6 Liard 3 7 27 3 45 8 7 Mackenzie 5 0 16 1 67 7 4 McBride 6 0 52 2 35 3 2 Nechako 7 0 35 0 44 6 8 Northcoast 16 0 21 0 44 13 6 Northwest 19 3 11 2 45 9 11 Peace 1 0 69 1 23 3 3 Queen Charlottes 4 0 91 0 0 4 1 Quesnel 4 0 70 0 18 3 5 Northern BC 3.6 0.2 61.1 0.6 25.4 4.0 5.1 Totals
Our analyses clearly indicate that temporal trends in wildlife-vehicle collisions vary across northern BC and that even within a species, collision patterns may vary from one region to another. For example, temporal patterns of deer-vehicle collisions vary significantly between the Bella Coola Region (Appendix 11) and the McBride Region (Appendix 26). When bears are struck in the Lakes District Region (Appendix 19) is significantly different than when bears are struck in Northwest Region (Appendix 36). The month of the year, time of the day throughout the year, time of day during the peak collision season, and the 10-year trend data for wildlife-vehicle collisions in various regions throughout BC can be found in Appendices 11- 46 (see Table 3 legend). Caribou collisions in northern BC appear to be restricted to the Liard Region of the province (Appendix 22).
11
Table 3. Appendix legend for regional collision data.
Appendix Region Species 11 Bella Coola Deer 12 Cariboo Deer 13 Cariboo Moose 14 Cariboo Bear 15 Chilcotin Deer 16 Chilcotin Moose 17 Lakes District Deer 18 Lakes District Moose 19 Lakes District Bear 20 Liard Moose 21 Liard Deer 22 Liard Caribou 23 Mackenzie Moose 24 Mackenzie Deer 25 Mackenzie Bear 26 McBride Deer 27 McBride Moose 28 McBride Bear 29 Nechako Moose 30 Nechako Deer 31 Nechako Bear 32 North Coast Moose 33 North Coast Deer 34 North Coast Bear 35 Northwest Moose 36 Northwest Bear 37 Northwest Deer 38 Peace Deer 39 Peace Moose 40 Peace Bear 41 Peace Elk 42 Queen Charlottes Deer 43 Queen Charlottes Bear 44 Quesnel Deer 45 Quesnel Moose 46 Quesnel Bear
12
Northern British Columbia – Communities
When considering the animals that are most commonly struck in various northern communities, we see that temporal trends in wildlife-vehicle collisions vary within a species from one community to another. For example, collisions with deer tend to occur predominantly in fall in the following communities: Dawson Creek (Appendix 51), Fort St. John (Appendix 55), Houston (Appendix 56), Quesnel (Appendix 63), and Williams Lake (Appendix 69) while summer and fall peaks in deer collisions occur in 100 Mile House (Appendix 47), Burns Lake (Appendix 48), Chetwynd (Appendix 50), Hudson’s Hope (Appendix 57), Tumbler Ridge (Appendix 65), Valemount (Appendix 66), and Vanderhoof (Appendix 67). Spring and summer peaks in deer collisions occur in McBride (Appendix 60) and Prince Rupert (Appendix 62). Collisions with moose tend to occur predominantly in winter in all communities of northern BC, although significant numbers of moose are struck in summer in some communities of the province (Appendices 49 – Burns Lake, 59 - Mackenzie and 61 – Prince George). Specifically, the month of the year, time of the day throughout the year, the time of day during the peak collision season, and the 10-year trend data for wildlife- vehicle collisions in various communities throughout northern BC can be found in Appendices 47 - 69 (see Table 4 legend). Again, caribou collisions in northern BC appear to be restricted to the Fort Nelson Community in the Liard Region of the province (Appendix 53).
13 Table 4. Appendix legend for community collision data. Note: Graphs were only generated for the most commonly struck animals.
Appendix Community Species 47 100 Mile House Deer 48 Burns Lake Deer 49 Burns Lake Moose 50 Chetwynd Deer 51 Dawson Creek Deer 52 Fort Nelson Moose 53 Fort Nelson Caribou 54 Fort St. James Moose 55 Fort St. John Deer 56 Houston Deer 57 Hudson’s Hope Deer 58 Kitimat Moose 59 Mackenzie Moose 60 McBride Deer 61 Prince George Moose 62 Prince Rupert Deer 63 Quesnel Deer 64 Terrace Moose 65 Tumbler Ridge` Deer 66 Valemount Deer 67 Vanderhoof Deer 68 Vanderhoof Moose 69 Williams Lake Deer
DISCUSSION
Our findings from analysis of ten years of ICBC data suggest that collisions with large animals in BC occur differentially in magnitude by species and occur at different times of the year and different times of the day depending on the species and region of the province in question. In order of occurrence, the most commonly struck large animals in BC are deer, moose, bear and elk. With the exception of bears, most wildlife-vehicle collisions in BC occur during the fall and winter months with animals being struck predominantly in the evenings, although smaller peaks in collisions also tend to occur in the early morning hours. Bear collisions peak in September and October. Because our data are not corrected for traffic volumes, these results do not necessarily reflect the odds of motorists actually encountering various species on the roads in BC, but rather reflect grossly when animal strike claims are being made. Because 86.5% of wildlife-related collisions in northern BC are with deer and moose, we focus our discussion on these two species. Our analyses suggest that
14 collisions with deer in BC occur predominantly in October and November and in the late evening and are similar to deer-vehicle collision patterns in Maine (Maine DOT 2001), South Dakota (Gleason and Jenks 1993), New York State (Decker et al. 1990), Pennsylvania (Bellis and Graves 1971) and Michigan (Reilly and Green 1974; Allen and McCullough 1976) and agree with the findings of Sielecki (2004) for BC data analyzed between 1993 and 2002. Many of these patterns appear to be attributable to when deer are most active and foraging along transportation corridors (Carbaugh et al. 1975). Moose collisions across the province occur at the same time of the day as they do in eastern North America. Collisions in eastern North America, however, tend to peak in mid-summer (Grenier 1973; Rattey and Turner 1991; Sutton 1996; Pollock and Haché 1997; Joyce and Mahoney 2001) whereas peaks in moose-collisions in our study, in Minnesota (Belant 1995) and in various parts of western North America occur in mid- winter (Thompson 1995; Sielecki 2004), albeit collisions peaks in some parks of western North America appear to be modulated by summer traffic volume (Harrison et al. 1980). Patterns of collisions in northern BC, although somewhat similar to patterns for the province as a whole, change slightly when data from southern BC are removed from the analysis. Others have noticed similar differences between various areas within larger jurisdictions (Reilly and Green 1974; Sielecki 2004). For example, our findings suggest that proportionately more moose collisions occur in May, June and July across the entire province than when moose-vehicle collisions are analyzed for northern BC alone. Likewise, although September appears to be the month in which most bear collisions are occurring across the province, proportionately more collisions occur in July and August when data are pooled for the entire province than when northern BC data for bear-vehicle collisions are analyzed separately. The peak in elk collisions across the province appears to be in December whereas the peak is in November in northern BC. These differences may (Dodd et al. 2005; Jaarsma et al. 2006) or may not necessarily (Hicks 1993; Widenmaier and Fahrig 2005) reflect variations in traffic patterns between the northern and southern parts of the province; but Oosenbrug et al. (1986) clearly illustrated that collision peaks can occur during times of the day when traffic volumes are lowest. Gleason and Jenks (1993) found no correlation between seasonal deer/vehicle mortalities and traffic volume. Among other things, differences in population densities (Joyce and Mahoney 2001), sample sizes, vehicle speeds, sex ratios and variations in overall behavioral ecologies of animals living in different parts of the jurisdiction (Hicks 1993; Sielecki 2004; Ramakrishnan and Williams 2005) are also likely to contribute to collision pattern differences across regions. Day length and the proportion of hours spent by motorists on the road in the dark, when visibility is poor, most certainly influences this intersection of animal and human ecologies. Variations in collision patterns become even more pronounced when differences in vehicle collisions with the same species (or genus) of animals across different regions and communities of the north are analyzed. Knowing that deer-vehicle collisions in the Bella Coola Region (Appendix 11) peak in April, but almost never occur in April in the Liard region of northern BC (Appendix 21) is critical in assessing how, when and what types of mitigation measures should be deployed to reduce collisions in each respective area. Understanding that collisions with bears peak in September in most areas of the province except in the Northwest Region of BC (Appendix 36), where collisions occur
15 predominantly in May, can also help road safety planners take into account more regional phenomena than what can be deduced from patterns analyzed at a coarser scale. Such differences remain apparent with or without corrections for daylight savings time in the summer for regions west of the Rocky Mountains. From a regional perspective then, knowing when collision peaks occur in northern BC or across the province is a small piece of the puzzle that alone appears insufficient for implementing site-specific countermeasures. Our findings suggest that it is equally important to understand that within regions, differences in collision patterns between communities can provide information critical in achieving successful mitigation planning. For example, although Hudson’s Hope and Fort St. John are in the same Region and have similar proportions of species being struck in each community, the deer-vehicle collision peak in Hudson’s Hope is in April and May between 6:00 and 7:00 pm (Appendix 57) but is in November in Fort St. John between 7:00 and 8:00 am (Appendix 55). Similarly, Vanderhoof and Burns Lake are merely 1.5 hours driving distance from one another, but deer in Vanderhoof are hit predominantly in October between 6:00 and 7:00 pm (Appendix 67) while deer in Burns Lake are hit predominantly in July between 8:00 and 9:00 pm (Appendix 48). Although Kitimat and Terrace are only 58 kms apart moose-related vehicular collisions in Kitimat peak in October and occur predominantly between 9:00 pm and midnight (Appendix 58) while moose-related collisions in Terrace peak in January and peak between 6:00 and 7:00 pm (Appendix 64). These variations in collision demographics require road safety planners to acknowledge such differences and seek community-specific solutions.
CONCLUSIONS/RECOMMENDATIONS
Our findings have allowed us to analyze and present differences in collision patterns between variously sized and spatially explicit regions and are useful in providing data for driver education and awareness programs, public announcements, seasonal sign placements, and other such forms of general countermeasure implementation (Table 5). Our findings are useful for establishing what season and what time of day various species are being struck in various communities and regional areas of the province. However, since ICBC records rarely contain exact location data, our analyses can not pinpoint exactly where collisions are recurrent on particular road segments. Although ICBC does not generally record data on the exact location of animal- vehicle collisions, these data are collected by the BC Ministry of Transportation (MOT) and their maintenance contractors. The MOT’s Wildlife Accident Reporting System (WARS) is designed to collect and store information on wildlife killed on numbered highways in British Columbia. The WARS database contains over 78,000 records collected since 1978 and is used by MOT primarily to identify accident-prone locations as well as accident trends (Sielecki 2004). Combining more clearly delineated location data with our current findings could allow us to apply mitigation planning to specific- accident prone areas of highways in the province by recommending some of the more site-specific options from the suite of countermeasures tabulated in Table 5.
16 Table 5. A list of countermeasures currently in use as reported by sources published between 2000 and 2005. Note: countermeasures are listed in alphabetical order and are listed regardless of efficacy. An annotated bibliography for these sources can be found in Appendix 71.
COUNTERMEASURE REFERENCE Acoustic Road markings Ujvari et al. 2004
Alternative deicing methods (ex. Lithium Knapp et al. 2003; Lo 2003 chloride) Animal frightening/flagging models Al-Ghamdi and AlGadhi 2003; Knapp et al. 2003 Deer Reflectors/mirrors Maine Department of Transportation 2001; Al-Ghamdi and AlGadhi 2003; Knapp et al. 2003; L-P Tardif & Associates Inc. 2003; Supplypost 2003; Lo 2003; Sullivan and Messner 2003; MMWR Weekly 2004; Lloyd and Casey 2005; Ramakrishnan and Williams 2005; Ramp et al. 2005
Fencing Maine Department of Transportation 2001; Al-Ghamdi and AlGadhi 2003; Chilson and Jacobson 2003; Knapp et al. 2003; L-P Tardif & Associates Inc. 2003; Lo 2003; Seiler 2003; Sullivan and Messner 2003; Supplypost 2003; MMWR Weekly 2004; Cavallaro et al. 2005; Gagnon et al. 2005; Leblanc and Martel 2005; Lloyd and Casey 2005; Ramakrishnan and Williams 2005; Ramp et al. 2005; Ruediger et al. 2005; Singleton 2006
Guardrails Chilson and Jacobson 2003
Habitat Alteration L-P Tardif & Associates. 2003; Lo 2003
Highway design modification Maine Department of Transportation 2001
Highway lighting Maine Department of Transportation 2001; Al-Ghamdi and AlGadhi 2003; Knapp et al. 2003; L-P Tardif & Associates Inc. 2003
Increased animal harvest Maine Department of Transportation 2001; Seiler 2003; MMWR Weekly 2004; Leblanc and Martel 2005
17
COUNTERMEASURE REFERENCE Infrared sensor system to warn motorists Maine Department of Transportation 2001; Lo 2003; 10Meters News Service 2002; Gearino 2004
IRD Wildlife Warning System Beaupre 2002; Canada Safety Council 2003; Fleming 2003; Lo 2003
Intelligent Transportation System L-P Tardif & Associates Inc. 2003
Intercept feeding Knapp et al. 2003; Sullivan and Messner 2003; Lloyd and Casey 2005
In vehicle technology Knapp et al. 2003
Lighted signs Ramakrishnan and Williams 2005
Lower speed limits Maine Department of Transportation 2001; Knapp et al. 2003; L-P Tardif & Associates Inc. 2003; Lo 2003; Langvelde and Jaarsma 2004; MMWR Weekly 2004
Movable warning signs Fleming 2003
NightvisionTM Canada Safety Council 2003
Optical Obstructions Maine Department of Transportation 2001
Optical warning devices Maine Department of Transportation 2001
Oversized warning signs Al-Ghamdi and AlGadhi 2003; L-P Tardif & Associates Inc. 2003; Lo 2003
Pavement Marking Maine Department of Transportation 2001
Public Awareness Maine Department of Transportation 2001; Al-Ghamdi and AlGadhi 2003; Knapp et al. 2003; L-P Tardif & Associates Inc. 2003; Lo 2003
Reduce traffic volume Langvelde and Jaarsma 2004
Removal of salt licks Leblanc and Martel 2005
18 COUNTERMEASURE REFERENCE Repellents/deterrents chemicals Maine Department of Transportation 2001; Al-Ghamdi and AlGadhi 2003; Knapp et al. 2003; Lloyd and Casey 2005; Ramp et al. 2005
Roadside vegetation management Maine Department of Transportation 2001; Al-Ghamdi and AlGadhi 2003; Knapp et al. 2003; Lo 2003; Rea 2003; Seiler 2003; MMWR Weekly 2004; Cavallaro et al. 2005; Lloyd and Casey 2005
Standard warning signs Al-Ghamdi and AlGadhi 2003; Fleming 2003; Knapp et al. 2003; L-P Tardif & Associates Inc. 2003; Lo 2003; Sullivan and Messner 2003; Supplypost 2003; Gearino 2004; MMWR Weekly 2004; Lloyd and Casey 2005; Ramakrishnan and Williams 2005
Steeper road cuts Leblanc and Martel 2005
Verge rip-rap State of Maine 2005
Whistles Knapp et al. 2003; L-P Tardif & Associates Inc. 2003; Sullivan and Messner 2003; Ramakrishnan and Williams 2005; Ramp et al. 2005
Wildlife crossing structures Jackson and Griffin 2000; Maine (overpasses, underpasses, tunnels, culverts) Department of Transportation 2001; Al- Ghamdi and AlGadhi 2003; Chilson and Jacobson 2003; Knapp et al. 2003; ; Lo 2003; L-P Tardif & Associates Inc. 2003; Seiler 2003; Stromnes and Hardy 2003; Supplypost 2003; Gearino 2004; MMWR Weekly 2004; Cavallaro et al. 2005; Clevenger and Waltho 2005; Gagnon et al. 2005; Leblanc and Martel 2005; Lloyd and Casey 2005; Ramakrishnan and Williams 2005; Ramp et al. 2005; Ruediger et al. 2005; Singleton 2006
Wildlife Protection System Canada Safety Council 2003; Kinley et al. 2003
19 The utility of pin pointing collision-prone areas of the province is unquestionably critical for road safety planning. However, whether or not areas that have been traditionally considered hotspots in northern BC will remain collision prone is somewhat speculative. Land development, climate change and other factors influence how animals move across and use the landscape from year to year. Habitat alteration from the mountain pine beetle epidemic in many parts of northern BC is likely to accelerate such changes in habitat use and animal movements. Habitats currently used by animals may be unavailable or unattractive for such use in the near future. Conversely, areas rarely used by animals in the past may become more attractive and heavily utilized in the future. How highway corridors bisect such areas influences the odds of encounter between animal and motorist. In an effort to determine better where animal movements bisect transportation corridors, our research team has been attempting to track animal activity with the use of a modified GPS device called an Otto Driving Companion (see Chapter 3) that has been specially configured with moose and deer buttons as well as a “dead” button. Ten of these units have been deployed in trucks from 3 transport companies based in Prince George (Excel Transportation Inc., Lomak Bulk Carriers Corp., and Grandview Transport Ltd.). Truck drivers heading north, south, east and west of Prince George on a daily basis watch for, and with the Otto device, record where moose and deer are observed. Data recorded include species, location, time of day and whether or not the animal is alive. Data are emailed by the trucking companies to our research team at UNBC and uploaded into GIS and then displayed on maps. Analysis of these maps allows us to pinpoint exactly where animals are being seen and killed. Collection of these data on a long-term basis will allow us collate more current data with our findings described here to predict more accurately by species, season and time of day where motorists are presently likely to encounter animals. As part of a pilot study, GPS data from Highway 16 between Prince George and Vanderhoof have been combined with WARS data and information from local experts on where they observe wildlife-vehicle collisions to be occurring (see Chapter 4). These experts include highway patrol, fire department, highway contractors, Ministry of Transportation staff, ambulance service, Conservation Officer Service, etc. An article (Appendix 70) outlines our research approaches to delineating collision ‘hotspots’ in northern BC and includes some preliminary findings. Despite the current paucity of site-specific collision data for most of northern BC (we continue to analyze WARS data), collision mitigation planning must begin now. We have regional and community specific data with which we can begin to educate northern motorists about the issues surrounding wildlife-vehicle collisions in their driving areas. Broadcasting by local media regarding what times of the year drivers should watch for different species near roads helps drivers construct a “search pattern” for animals most likely to be encountered. This allows motorists to scan for more appropriate indicators (animal coat colour, stature, morphology, etc.) on their travels. Alerting motorists to the fact that most collisions appear to occur in the evening and night and in the early morning hours can allow us to recommend broadly that motorists be alert at these times of the day, keep their headlights in good working order and their windshields clean, obey the posted recommended night-time driving speeds and actively and aggressively watch for signs of animal activity along the road corridor.
20 Emphasizing the importance of being more cautious at night makes sense from the perspective of encountering live or dead animals on the road, but is obviously not restricted to wildlife sightings; slowing down increases the odds of motorists having enough time to react to any animate or inanimate object that may obstruct the roadway. In addition to providing recommendations for regionally- and community-defined mitigation planning, we recommend some changes to how ICBC data are collected (Table 6). Most importantly, we recommend that the ICBC database be changed by adding separate columns to the data base spread sheet for the species of animal involved in the collision and a precise collision location. Because the ecology of different species can influence animal activity patterns near transportation corridors and animal morphology and weight can influence substantially the impact an animal may have on a motor vehicle in a collision – we underscore the importance of ensuring accurate record keeping on species ID. Keeping precise records on collision locations is of obvious utility to delineating collision-prone sections of road. Once sites are identified, they can be assessed in an effort to determine what site attributes (ecological variables, traffic volumes, posted speed limits, etc.) may be related to collision occurrence. An attempt to keep more precise records, in combination with our other recommendations (Table 6), will facilitate opportunities for more expeditious collision analyses by species and site and help provide more clean and robust data for road safety planners to use. At a minimum, such data should be analyzed and released yearly; implementing these recommendations could expedite such analyses.
21 Table 6. Recommendations for questions that should be asked of claimants and data recorded separately by ICBC staff on animal-related vehicular collisions. Note: These have been prioritized from a wildlife collisions mitigation perspective and organized in the order that they should be implemented. Recommended Item • Species involved in the accident • Exact location of the collision (Lats, Longs, UTM or address, estimates of distance from nearest cross streets, resource road, other road marker, or other significant landscape feature.) • Distance to nearest community • Number of vehicle occupants injured • Road conditions at the time of the collision • Weather and daylight conditions at the time of the collision • Did the animal die at the scene • Vehicle speed at the time of the collision • What part of the vehicle was damaged • Were authorities contacted • What side of the road did the animal come from • Age and gender of the driver • What type of vehicle struck the animal • Was the animal moved out of the road • Approximate age of the animal • Sex of the animal • Was the driver aware that this animal occurred in this area
SUMMARY
Wildlife-vehicle collision data are the backbone of any road safety and wildlife- vehicle collision mitigation planning. ICBC data analyzed to produce this report were critical to assessing differences in provincial, regional and community trends for wildlife- vehicle collisions across northern BC. These findings can begin to be used to implement broad-scale collision mitigation measures for different species during various seasons and at various times of the day at the regional and community levels. They cannot be used to pinpoint, assess and make recommendations for collision-prone sections of highway. Data on exactly where wildlife-related collisions are occurring (as contained in WARS and recommended for warehousing as part of the ICBC collision incident record; see
22 Table 6) are critical and necessary for helping to narrow down collision hotspots and implement countermeasures on a site-specific basis. Recording collision site-specifics and other information (Table 6) by ICBC claims personnel would streamline analysis of wildlife collision data. Such changes to the record-keeping procedures would expedite the process of monitoring the demographics of collisions across the province and allow for regular updates of the statistics on when and where collisions with various species are occurring. Furthermore, a collaborative and standardized approach by various agencies (i.e., ICBC, MOT, RCMP, Road Contractors, Ambulance service, Conservation Officer Service) across the province to maintain a comprehensive database, where data can be collectively submitted and stored, would ensure maximal inputs from collision incidents by ICBC insured, privately insured, uninsured, and out-of-province drivers. Such a system would leave little room for speculation by road safety planners about when and where wildlife-vehicle collisions are occurring and could help facilitate the immediate commencement of site-specific mitigation planning throughout northern BC.
REFERENCES
Allen, R.E. & McCullough, D.R. 1976: Deer-car accidents in southern Michigan. Journal of Wildlife Management 40: 317-325.
Bellis, E.D. & Graves, H.B. 1971: Deer mortality on a Pennsylvania interstate highway. Journal of Wildlife Management 35: 232-237.
Carbaugh, B., Vaughan, J.P., Bellis, E.D. & Graves, H.B. 1975: Distribution and activity of white-tailed deer along an interstate highway. Journal of Wildlife Management 39: 570-581.
Decker, D.J., Loconti-Lee, K.M. & Connelly, N.A. 1990: Deer-related vehicular accidents in Tompkins county, New York: incidence, costs, and implications for deer management. Transactions, Northeast Section of the Wildlife Society 47: 21-26.
Dodd, N.L., J.W. Gagnon, S. Boe and R. E. Schweinsburg. 2005. Characteristics of elk- vehicle collisions and comparison to GPS-determined highway crossing patterns. ICOET Conference Proceedings p. 461-477.
Flaa, J. 1989. Trans Canada Highway Phase 3B wildlife study. Banff National Park. 45pp.
Gleason, J.S. & Jenks, J.A. 1993: Factors influencing deer/vehicle mortality in east central South Dakota. Prairie Naturalist 25: 281-288.
Grenier, P. 1973: Moose killed on the highway in the Laurentides Park Quebec, 1962 to 1972. Proceedings of the North American Moose Conference and Workshop 9: 155-193.
23 Harrison, G., Hooper, R. & Jacobson, P. 1980: Trans-Canada highway wildlife mitigation measures, east gate to Banff traffic circle. Banff National Park. Parks Canada, Western Region, Calgary. 88pp.
Hicks, A.C. 1993. Using road-kills as an index to moose population change. Alces. 29: 243-247.
Jaarsma, C.F., F. van Langevelde and H. Botma. 2006. Flattened fauna and mitigation: Traffic victims related to road, traffic, vehicle and species characteristics. Transportation Research Part D 11: 264-276.
Joyce, T.L. and S.P. Mahoney. 2001. Spatial and temporal distributions of moose- vehicle collisions in Newfoundland. Wildlife Society Bulletin 29: 281-291.
Oosenbrug, S.M., McNeily, R.W., Mercer, E.W., & Folinsbee, J.F. 1986: Some aspects of moose-vehicle collisions in eastern Newfoundland, 1973-1985. Alces 22: 377-393.
Pollock, B. and D. Haché. 1997. Discussion paper for mitigating wildlife/vehicle accidents in Gros Morne National Park (1992-1997).
Ramakrishnan, U. and S.C. Williams. 2005. Effects of gender and season on spatial and temporal patterns of deer-vehicle collisions. ICOET Conference Proceedings p. 478-488.
Rattey, T.E. & Turner, N.E. 1991: Vehicle-moose accidents in Newfoundland. The Journal of Bone and Joint Surgery 73: 1487-1491.
Reilly, R.E. and H.E. Green. 1974. Deer mortality on a Michigan interstate highway. Journal of Wildlife Management 38: 16-19.
Sielecki, L.E. 2004. WARS 1983 – 2002. Wildlife accident reporting and mitigation in British Columbia: special annual report. Environmental Management Section, Engineering Branch, British Columbia Ministry of Transportation. Victoria, BC. 222p.
Sullivan, T.L. and T.A. Messmer. 2003. Perceptions of deer-vehicle collision management by state wildlife agency and department of transportation administrators. Wildlife Society Bulletin 31: 163-173.
Sutton, J.E. 1996: Car vs. moose. Emergency Medical Services 25: 47-50.
Tardiff, L-P & Associates. 2003. Collisions involving motor vehicles and large animals in Canada: Final Report.
Thomas, S.E. 1995: Moose-vehicle accidents on Alaska’s rural highways. Alaska Department of Transportation and Public Facilities, Division of Design and Construction, Anchorage, AK. 58pp.
24 Waring, G.H., Griffis, J.L. & Vaughn, M.E. 1991: White-tailed deer roadside behavior, wildlife warning reflectors, and highway mortality. - Applied Animal Behavior Science 29: 215-223.
Widenmaier, K. and L. Fahrig. 2005. Inferring white-tailed deer (Odocoileus virginianus) population dynamics from wildlife collisions in the city of Ottawa. ICOET Conference Proceedings p. 589-601.
25 Chapter 2. The Use of Web Mapping in Recording Animal-Vehicle Collisions
Corresponding author: Scott Emmons ([email protected])
INTRODUCTION
Spatial by Nature
Animal movement is a topic that has been studied by a large number of researchers for a considerable time. Modelling animal behaviour can be a difficult task and many approaches have been investigated. For instance, there is a variety of animal habitat mapping methods but one aspect is consistent in building these landscape models. The data needed for these types of approaches has to have a statistical and spatial dimension. Applications built around Geographical Information System(s) (GIS) have been able to supply researchers with the tools to accomplish these type of investigations.
GIS inherently provides a mechanism by which locations of animal, ungulates for example, can be stored mapped and analyzed. This provides a way to organize tremendous amounts of data, give people the tools to spatially relate these locations and deliver resultant maps. Unfortunately, there is generally a disconnect between those that are interested in animals activities on the land and those that have the particular skills to make use of GIS software.
The components that make up a GIS are often confused with the software itself. Often the expression “I have a GIS in our lab” refers to the software that is present on a computer, but by definition GIS is a mixture of : Geography (spatial features such as point location of Caribou); Information (the attributes of these animals related to each spatial feature) and the System (the combination of hardware, software and the people using them). In practice, those that have specialized training for use in GIS often do not have the necessary ecological or biological knowledge associated with animal habitat mapping. A preferred system may be one that encourages as many interested and talented people to work together for problem solving. By using other Geospatial approaches to working with information such as animal movement, we are hoping to engage a broader audience in investigation problems such as animal and automobile collisions.
Using Web Mapping to Provide GIS Abilities to a Greater Number of People
In recent years there has been a great interest in providing maps through the World Wide Web. Software and approaches are numerous and range from the Atlas of Canada to Google Maps. The countless number of web mapping sites all have a common theme of allowing users, wherever they may, to interface with the information that is being shared within these web pages. In this study, we have chosen to use and create tools built around web mapping applications to encourage more than GIS professionals to
26 become familiar with the data being collected for animal-human interactions.
The Basics of Web Mapping
Most people that use the Internet are familiar with sites such as MapQuest or Google Maps. What they are most likely not aware of is how these sites and others operate. In many cases, as with our applications, maps are rendered directly from GIS data and presented in a method that can be presented through a web browser (such as Internet Explorer or Mozilla’s Firefox). This means that the server that a user connects to for access is also providing rendered images of spatial data. These servers that render and deliver these data are Web Map servers. They can render data independent of other Web Map servers or work in conjunction with other Web Map servers. This network of servers allows people to maintain their data locally and share it globally through protocols such as Web Map Services (WMS).
This may all seem to be nothing more than another set of acronyms based on viewing data through the web, and for the most part that is all that it is. Although it may be advisable to make use of recognized standards and protocols, it is not necessary to understand how they are used for end use applications such as those created for this project. It is beneficial to know that these types of services can be made use of in a manner that provides access to information to all whom are interested in data associated with this research endeavour.
The Structure of Web Mapping Services for the Road Health Animal Collision Project
As part of the datashare project in the GIS lab at UNBC, whereby we attempt to support community based projects through web applications using Open Source Software, the animal collision web mapping site was set up to provide users access to viewing of project data. Our applications are built upon a series of existing open source programs as well as some programming specific to the needs of the research interests and data constraints of this project.
FIST The web mapping interface used for this project is called the Flexible Internet Spatial Template (FIST) and was developed initially as a student project within the datashare initiative in the GIS Lab at UNBC. This software has emerged as a very useful product and has matured into a very stable and robust application. More information on FIST can be obtained through the datashare web site at http://datashare.gis.unbc.ca.
DAI and Other Applications In order to properly place animal deaths by automobile collision recorded in the Wildlife Accident Reporting System (WARS) data, we needed to create a route traversing program. The WARS data consists of attributes describing animals that have been killed on Northern BC highways such as; the species of animal, time of day as well
27 as the distance in kilometres from the beginning of each route. In order to place a point location of an accident involving and animal, three spatial operations must be undertaken. These are:
- A shortest path algorithm is used to create the routes by which kilometres distances are measured. We use another program created through datashare called the Distance Analyzer Interface (DAI - http://datashare.gis.unbc.ca/dai). This is a web based application that first creates a network based on a road layer for British Columbia (or any other connected line GIS layer such as rivers), and then traverses this network to find the shortest distance between starting and ending points. In the case of WARS data, we input the start and ending points of each northern highway instances (eg. Prince George to Dome Creek, Dome Creek to Tete Juane Cache and so on) to create unique routes.
- To make use of these routes, we created a program (that has not be named or released for download from datashare) that traverses these routes until the distance indicated for each unique collision site has been accumulated. The kilometre distance from each route starting point is an attribute for each record in the WARS data set, and these values were used to segment each route.
- Finally the end point of each of these segments was calculated and a GIS point location was created combining the attributes of each WARS record with these locations.
Once these collision points were loaded into the spatial database, a beta version of a web based GIS analysis tools was used to perform some simple GIS operations (i.e. buffering and intersecting of points and hydro transmission layers) upon these data.
Otto Logger upload interface A simple program was created to take the files that were created from the data loggers on the Otto Companion devices and convert them to a GIS point layer to be loaded into the web mapping page. This program compensated for the methods by which dead animal records were obtained through the Otto device whereby a dead recording of either a deer or moose were actually placed in a separate data line for the animal it was to be associated with. It was determined that one minute would be sufficient for the driver to record whether or not the animal sighted was dead. Once the data is parsed from the Otto file, it is uploaded to the spatial database used for this project.
Open Source Software Supporting Our Applications The above applications make use of several open source softwares that have come to maturity in the last several years. - FIST was developed using the PHP Hypertext Preprocessor (PHP) and JavaScript programming languages. It makes use of the University of Minnesota’s Mapserver (see mapserver.gis.umn.edu) to render web images from GIS data. - The DAI and subsequent programs were written in a combination of PHP and C++ programming languages. - All of the applications make use of the data holding and manipulation capabilities of a spatial plugin for the Relational Database Management System
28 (RDBMS) PostgreSQL. This Spatial plugin was created by Refractions Research in Victoria BC and is called PostGIS (see www.postgis.org).
Future Development of Applications Suitable for use in the Animal Collision Project We have been busy creating interfaces through FIST and other applications that would allow decision makers to better view, understand, manipulate and produce resultant layers or maps based on the wildlife-vehicle collision data at hand. We are also involved in creating methods by which different permissions can be applied to web mapping pages that would allow the public to view predefined aspects of this type of research. By implementing protocols for the public and other data collectors (such as tow truck drivers) for recording animal sightings or collision instances, these type of applications could be used to record and update these data in a more instantaneous fashion.
We would also like to implement some basic GIS functionality into the web interface to allow decision makers to better represent the connections between spatial features such as roads and the locations where ungulates and other animals have been recorded. In the meantime, we continue to upload and analyse WARS data into the FIST software and develop interfaces that can be used by those collecting such data to record live animal and carcass sighting into a GIS. Collation of such data will elucidate more clearly where collisions with wildlife are occurring in northern BC and allow ease of access to those data required by road safety planners for mitigation planning.
29 Chapter 3: Use of Global Position Satellite and Community Participation for reporting on roadside wildlife
Corresponding author: Eric Rapaport ([email protected])
INTRODUCTION
There are a number of methods currently being applied to collect information on wildlife movement near road sides. These include standard field observation by wildlife biologists, the use of Global Position Satellite Systems (GPS) and video surveillance. In this research, a GPS device was used to capture information though voluntary community effort. The device was deployed along highways in north central British Columbia. The results of the research show a rapid and effective method for the collection of road side wildlife information. We conclude by making future speculation and recommendations of how this system can be improved and deployed.
BACKGROUND
The GPS Device
A simple commercial GPS device, known as OTTO, was modified in order to capture information on road side wildlife. The device as sold has only three standard buttons. Two buttons are dedicated to two species of wildlife, moose and deer. The third button was dedicated to indicating if the species was dead or alive. Other information captured by the device is the latitude and longitude of a location; the time and the date (see Table 1) Table 1. An example of data downloaded from the GPS device. DATE TIME LAT LONG SPECIES 10/19/2006 23:11:57 53.92864 -122.7723 DEER 10/23/2006 10:49:58 54.18153 -122.6116 MOOSE 10/24/2006 17:28:33 54.43874 -122.637 DEER 10/26/2006 5:10:44 54.11141 -122.6474 DEAD DEER 10/31/2006 10:41:28 53.92904 -122.772 DEAD DEER 10/31/2006 10:44:35 53.92908 -122.772 DEAD DEER 10/31/2006 10:59:49 53.92888 -122.7723 MOOSE 10/31/2006 10:59:53 53.92889 -122.7722 DEER
The Community Volunteers
A unique aspect to this research was the deployment of the GPS. Excel, Grandview and Lomak were three companies who volunteered to install the GPS device (Figure 1). The device was mounted to the dash boards of semi trail trucks. The height of
30 the driver seat provides a relatively superior view along a facing road corridor and roadside landscape as compared to other vehicles.
Figure 1. One of the volunteer truck drivers holding the GPS device.
Each company was given 3 GPS units assigned to different route destinations (see Table 2). The origin for all the routes was the City of Prince George, British Columbia. The trucks all ran two 10 hour shifts and thus would spend a maximum of 20 hours a day traveling along the same route. Not presented in this report, is the actual number of times a driver drove back and forth between the origin and designation.
Table 2. The trucks all originated from the City of Prince George and travel along 4 highways going north-south and east-west. In some cases the trucks cover the same route. Company Origin Designation city Designation city Designation city (Route) (Route) (Route) Excel Prince George McBride Fraser Lake Bear Lake Transportation ( East- Highway 16) (West- Highway 16) (North Highway 97) Inc.
Grandview Prince George Mackenzie Vanderhoof Dunkley Transport Ltd (North-Highway 97) West-Highway 16) (South-Highway 97) Lomak Bulk Prince George Oosta Lake Quesnel Vanderhoof Carriers Corp. (West-Highway 16) (South-Highway 97) ( West-Highway 16)
Data Assembling
At the end of each month the GPS device was remove and connected to a company computer. The data collect is automatically downloaded as a comma delimited ASCII file and transferred via emailed to the University of Northern British Columbia
31 (UNBC). UNBC researchers loaded the raw data up into commercially available geographical information system for spatial representation and spreadsheet programs for non spatial analysis of the data sets (Figure 2 & Appendices 71-75).
The non spatial data can be analyzed from a number of aspects, including, if there is different cycles or peaks to wildlife sightings by season or time of day and if wildlife are sightings prevalent at specific locations. Table 7 shows the amount of data collected over a four month period. As data continues is collect annually, it will be possible to begin determine if there any patterns to where and or when spatial road side wildlife sightings and accidents are occurring.
Figure 2. A detailed section along highway 16 used to demonstrate the symbols developed as part of the research.
32 Table 3. Summary of sighting data collection over a 4 month period. It is obvious that dead wildlife along a roadside might not identifiable. Animal North South East West Total Dead unknown 3 1 3 2 9 Deer Dead 31 10 14 18 73 Deer Alive 168 79 532 236 1015 Moose Dead 19 4 15 9 47 Moose Alive 179 9 194 68 450
CONCLUSIONS
The experiment with the GPS device provides an opportunity to explore new ways to capture roadside wildlife information. There are many different conclusions that can be drawn from the work thus far:
1) The GPS system does not impact the ability of the driver to operate the vehicle. The research team did not receive any complaints about the use of the GPS from the drivers involved.
2) The GPS system is easy to install and download data from and import raw information into secondary spread sheet and mapping programs. There were no set backs nor a need to create special programs in order to complete any of the tasks involved in data aggregation.
3) The use of volunteers provides a rapid mean to collect information that can be used for a number of agendas: wildlife roadside research, increases knowledge on the location of both alive and dead wildlife, provides data to help select locations for wildlife-vehicle mitigation programs, and can potentially be used to improve the current British Columbia wildlife accident reporting system.
4) There are some issues that should be addressed. The first is that multiple GPS along the same route could result in drivers marking off the same animal. This would be especially true in the case of road side carcasses, which are not removed by highway maintenance companies. This can be over come by careful examination of dates and times but will not be perfected. Another issue is that some wildlife along roadsides cannot be identified as moose or deer. Some wildlife in a collision will end up hidden away from the drivers view.
5) The use of volunteer trucks is a cost-effective means for data collection, however, it is limited to when and how often the trucks are on the road. There maybe
33 certain periods of the day or week, such as weekend, when there is not data collected. This would limit the interpretation of the results.
RECOMMENDATIONS
1) Explore and develop GPS devices that can collect a variety of wildlife data. In this study only two species were selected. However, there are also other animals which should included both endanger and common wildlife. This includes bears, caribou, coyotes, and cougars, elk and other large wildlife species.
2) Explore the ability to include the GPS device in contracted road maintenance vehicles. The current wildlife reporting system of British Columbia, known as WARS, uses a kilometer marker system. These kilometer marks are often missing. The GPS unit will provide a more accurate reflection of where animals have been killed.
3) Provide funds and support for a 3-5 year data collection and exploration period using the GPS devices. A 3 to 5 year program should be implemented. This includes exploring alternative devices to include more wildlife species in the data capture and the purchasing of more devices. The collecting of data should be done over a 3 to 5 year period, in order to better determine how the device might provide new insights into wildlife roadside occurrences and trends and explore the ability of the device to isolate wildlife road side hotspots for collisions.
34 Chapter 4: Wildlife Vehicle Collision Hotspots: Using Experts and British Columbia Wildlife Accident Reporting System
Corresponding author: Eric Rapaport ([email protected])
INTRODUCTION
In the past, the identification of Wildlife-Vehicle Collision (WVC) “hotspots” and the associated mitigation options have typically been based on stretches of highway which have exceeded an arbitrary threshold of WVC. The WVC data these thresholds are based on could potentially have originated from incomplete databases containing unknown spatial inaccuracies. Alternative hotspot identification procedures including predictive modeling (Malo et al. 2004, Clevenger et al. 2002) and expert opinion (Clevenger et al. 2002, Ruediger and Lloyd 2003) have, however, proven effective.
To pinpoint when and where collisions with various species of wildlife are occurring on northern BC roads, we are analyzing WARS (Wildlife Accident Reporting System; provided by BC Ministry of Transportation) data and information that we have collected from local experts (road maintenance contractors, highway patrol, conservation officer service, etc) on where collisions are perceived to be recurrent. In addition to these methods, we have partnered with three local trucking companies (Excel Transportation Inc., Lomak Bulk Carriers Corp. and Grandview Transport Ltd.) based in Prince George, BC to record data on roadside animal occurrences using GPS technology (Rea et al. 2006).
In this study, we investigated the use of an expert-based approach for two reasons. First, the WARS database of WVC contained an unknown extent of spatially-related inaccuracies leading to an unreliable data source on its own. In addition, the availability of expert knowledge in the study area was, in effect, an untapped resource to explore. The knowledge of experts in the field of WVC was a resource that had the potential for gathering information and modeling the effects of roads on wildlife (Clevenger et al. 2002). Expert-based modeling can be an attractive highway planning tool where the full data sets necessary for empirical models lack spatial accuracy and thus dependency (Clevenger et al. 2002). The use of expert opinion offered the potential to collect knowledge into the biological processes associated with each hotspot and thus resulted in the consideration of more reliable and promising mitigation measures that took site- specific and seasonal considerations into account.
METHODS
Highway 16 connecting Vanderhoof and Prince George, British Columbia was chosen as the study area primarily as a starting point in a pilot study to assess the effectiveness of the GPS technology and expert-based procedure in Northern British Columbia. The stretch of highway falls under the maintenance responsibility of YRB
35 (Yellowhead Road and Bridge). The highway is split into two maintenance segments at the community of Bednesti, approximately halfway between Prince George and Vanderhoof. YRB based in Prince George is accountable from Prince George to Bednesti, while the YRB Vanderhoof office is responsible from Bednesti to Vanderhoof. YRB is contracted through the Ministry of Transportation to remove and record the position of dead wildlife carcasses.
Expert-Based Procedure
To determine the hotspots, experts were selected from both Prince George or Vanderhoof. The experts selected for participation included those with career-related knowledge of where wildlife had either frequently collided with vehicles or crossed the highway. Experts selected included YRB highway maintenance crews, conservation officers, MOT staff, ambulance drivers, fire department, and RCMP.
Meetings were conducted through expert consultation using both GIS and paper maps to indicate location of hotspot segments, lengths, primary species involved, reasons for identification, and the hotspot priority level relative to the entire highway segment. Each map provided the experts with information on towns, topography, land cover type, water bodies, highways, secondary roads, communication lines, and general development. No collision data were presented to the experts unless requested and was then used only as a means of providing further evidence and stimulating discussion after an initial hotspot had been nominated.
The relative level of risk associated with each hotspot was based on the combined opinions of the experts present at a meeting. Hotspots were classified as high risk based on expert recollection of where highway segments contained the most WVC, and/or visual sightings of wildlife on or alongside the highway. Low and medium risk hotspots were based on expert opinion identifying lower rates of WVC or visual sightings of wildlife.
When an expert identified a hotspot, the location was drawn on the paper map and later transferred to a GIS-based format using ESRI ArcMap. A new hotspot was assigned a number based on the meeting number and the hotspot number (i.e. 5th meeting and 7th hotspot of the meeting would be assigned 5.7).
BC WARS Mapping Procedure
The British Columbia Wildlife Accident Reporting System (WARS) data was integrated into a Geographical Information System. The highway kilometer marker was used as the bases at determine an x and y coordinate for each point and a dynamic segmentation module developed in the FIT program. This resulted in map showing where moose or deer vehicle collision occurred (See Figure 3 and 4).
36 In discussion with the Ministry of Transportation it was determined that two collisions occurring with 0.5 km between one another should be consider a hotspot location for the WARS data.
RESULTS
Expert-Based Hotspot Identification
The stretch of highway 16 connecting Prince George to Vanderhoof is 97 km long. Along this highway stretch, experts identified 34 hotspots totaling 82.8 km (Table 1). This total length only covers 53.85km due to overlapping of certain hotspots. The minimum hotspot length was 0.3 km while the longest identified hotspot measured 9.8 km. The mean hotspot length measured 2.43 km. The mean hotspot length is biased upwards by the identification of two long hotspots.
37 Table 1. Hotspots as identified by expert opinion between Prince George and Vanderhoof, BC.
HOTSPOT FOCAL SPECIES JUSTIFICATION RISK PRIORITY LENGTH ID 1.9 MOOSE ROADKILL AND MEDIUM 0.59 VISUAL 1.8 BEAR VISUAL LOW 0.90 1.0 MOOSE AND ROADKILL AND MEDIUM 1.50 DEER VISUAL 1.2 MOOSE AND ROADKILL HIGH 1.00 DEER 4.1 MOOSE ROADKILL LOW 1.80 2.0 MOOSE ROADKILL AND MEDIUM 0.40 VISUAL 4.2 DEER ROADKILL LOW 0.30 4.3 DEER VISUAL LOW 0.35 1.1 MOOSE AND ROADKILL AND HIGH 0.85 DEER VISUAL 4.0 MOOSE AND VISUAL MEDIUM 8.25 DEER 4.4 MOOSE ROADKILL AND MEDIUM 1.90 VISUAL 1.3 MOOSE ROADKILL HIGH 0.90 1.5 MOOSE ROADILL AND MEDIUM 0.90 VISUAL 1.6 MOOSE ROADKILL AND MEDIUM 0.80 VISUAL 1.7 MOOSE ROADKILL AND MEDIUM 0.75 VISUAL 2.1 MOOSE ROADKILL AND LOW 2.00 VISUAL 2.5 MOOSE ROADKILL AND MEDIUM 1.50 VISUAL 3.0 MOOSE AND ROADKILL MEDIUM 3.25 DEER 2.6 DEER ROADKILL AND MEDIUM 3.50 VISUAL 2.9 DEER ROADKILL AND HIGH 1.80 VISUAL 2.8 MOOSE ROADKILL MEDIUM 1.20 2.2 MOOSE ROADKILL AND HIGH 1.10 VISUAL 2.7 DEER ROADKILL AND HIGH 1.70 VISUAL
38 HOTSPOT FOCAL SPECIES JUSTIFICATION RISK PRIORITY LENGTH ID 2.4 MOOSE ROADKILL AND HIGH 1.60 VISUAL 2.3 MOOSE AND ROADKILL AND MEDIUM 9.80 DEER VISUAL 3.1 BEAR VISUAL MEDIUM 4.60 4.5 DEER ROADKILL AND HIGH 3.40 VISUAL ROADKILL AND 5.0 DEER VISUAL MEDIUM 4.40 ROADKILL AND 5.1 MOOSE VISUAL HIGH 6.90 ROADKILL AND 5.2 MOOSE VISUAL HIGH 3.40 MOOSE AND ROADKILL AND 5.3 DEER VISUAL HIGH 1.80 ROADKILL AND 5.4 MOOSE VISUAL HIGH 3.90 ROADKILL AND 5.5 MOOSE VISUAL MEDIUM 3.70
Moose were the predominant species recognized by experts as the motive for hotspot identification, being responsible for 18 of the 34 hotspots (Figures 1 and 2). Moose were also identified as being a risk within 7 additional hotspots; however, deer shared the focus in these hotspots. Deer, as an individual species, were accountable for 7 hotspots. The remaining 2 hotspots were acknowledged due to the recollection of past visual sightings of bear.
Thirteen hotspots were classified as high risk. Seven of the 13 segments noted moose as the primary species, 3 stated deer, while 3 pointed out both moose and deer. The majority of the hotspots were rated as medium risk priority with 16 of the total 34 falling under this class. The 5 remaining hotspots were classified as low risk priority. Of these 5 low priority hotspots, 2 were based on moose, 2 due to deer, and one as a result of bear.
The majority of the hotspots were based on the recollection of both past WVC and visual sightings with 24 of the 34 having this distinction. Six hotspots were strictly WVC-based while 4 were nominated on the basis of visual sightings only.
Notice should be placed on the partial overlapping of hotspots by two or more different experts. Hotspots 5.2, 5.3, 2.9 and 2.8 were all contained by the longer hotspots 2.3 and 4.5. Hotspot number 4.5 also overlapped with 2.6. In addition, 2.4 and 2.7 also overlapped one another. Hotspot numbers 1.1, 3.1 and 5.5 were all within hotspot 4.0.
39 Hotspot 5.6 covered both 1.5 and 1.6. Number 5.4 overlapped all of 1.2 and 1.0. Hotspots 2.5 and 3.0 were completely within hotspot 5.1. The combination of the WARS data and Expert Opinion does show a clear distinguished pattern to define specific hotspots for wildlife vehicle collisions. There specific stretches of highway demonstration places as hotspot WARS WVC, which the experts did not point out. Thus one should not reply sole upon expert opinion.
Figure 1. Map showing sections of Highway 16 from Prince George west to Bednesti determined by experts to be “hotspots” for collisions with wildlife.
40
Figure 2. Map showing sections of Highway 16 from Bednesti west to Vanderhoof determined by experts to be “hotspots” for collisions with wildlife.
41
Figure 3. Map showing sections of Highway 16 from Prince George west to Bednesti with the associated WARS collisions with wildlife.
42
Figure 4. Map showing sections of Highway 16 from Bednesti west to Vanderhoof with the associated WARS collisions with wildlife.
43
Figure 5. Map showing the WARS collision data with 500 meter buffers in black circles. The Expert Opinion mapping are the black solid lines following the roads. There is clearly is difference in how a hotspot would be determined using these two forms of assessment.
44
REFERENCES
Burson III, S.L., J.L. Belant, K.A. Fortier, and W.C. Tomkiewicz III. 2000. The effect of vehicle traffic on wildlife in Denali National Park. Arctic 53:146.
Clevenger, A.P., J. Wierzchowski, B. Chruszcz, and K. Gunson. 2002. GIS-generated, expertbased models for identifying wildlife habitat linkages and planning mitigation passages. Conservation Biology 16:503-514.
Lloyd, J. and A. Casey. 2005. Wildlife Hot Spots along Highways in Northwestern Oregon. Oregon Department of Transportation. Prepared for: Mason, Bruce & Girard, Inc. Portland, OR.
Rea, R.V. R.K. Rapaport, D.P. Hodder, M.V. Hurley, N.A. Klassen. 2006. Using wildlife vehicle collision data, expert opinions and GPS technology to more accurately predict and mitigate vehicular collisions with wildlife in Northern British Columbia. Wildlife Afield. 3:1.
Ruediger, W., and J. D. Lloyd. 2003. A rapid assessment process for determining potential wildlife, fish, and plant habitat linkages for highways. Pages 205-225. Proceedings of the International Conference on Ecology and Transportation. Center for Transportation and the Environment, North Carolina State University, Raleigh, North Carolina.
Huijser, M.P., D.E. Galarus, and A. Hardy. 2005. Software for pocket PC to collect road- kill data. Proceedings from the International Conference on Ecology and Transportation 2005, San Diego, CA.
Hulbert, I.A.R., and J. French. 2001. The accuracy of GPS for wildlife telemetry and habitat mapping. Journal of Applied Ecology 38:869-878.
Stedman, R., D.R. Diefenbach, C.B. Swope, J.C. Finley, A.E. Luloff, H.C. Zinn, G.J. San Julian, and G.A. Wang. 2004. Integrating wildlife and human-dimensions research methods to study hunters. Journal of Wildlife Management 68:762-773.
45 Chapter 5: Wildlife Collision with Logging Truckers Survey in Prince George Area
Corresponding author: Eric Rapaport ([email protected])
INTRODUCTION
Through the University of Northern British Columbia, the wildlife-vehicle collision (WVC) research team conducted a voluntary survey to gain insight into the number of collisions between logging trucks and wildlife, with an emphasis on moose. The research objective is to collect information with the potential to improve trucker safety and overall highway safety. This survey aimed to provide information about unreported accidents and help improve Ministry of Transportation assessment of WVC.
METHODS
The survey (Figure 1) was divided into four sections: collision recollection, location information, description of temporal factors, and a description of past collision reporting. Participants were not required to answer all four of these sections. The participant names were not required as the survey process was to be completely anonymous. Ethic approval was granted through the UNBC Research Ethics Board (REB).
The first section aimed to provide data on the number, species, and road type of past WVC with logging trucks. Participants were asked to recall the total number of animals they had a work-related collision with in the past 6 years. For each collision, participants were asked whether the collision was reported to any authority or not. The location of each WVC was distinguished between Forest Service Roads (FSR) or public paved roads, such as highways or other secondary rural roads.
The second section focused specifically on the location of past moose-vehicle collisions (MVC). The question asked for an indication of highway sections where a participant had collided with a moose in the past 6 years or had visually sighted moose more than once. Descriptions were based on both the highway section and/or the closest landmarks.
The third section of the survey aimed to gain insight into temporal realm of MVC. Responses provided information on the time of day and months when moose are most likely observed on the highway.
The final section focused on the reporting of WVC to the appropriate authority. As WVC are not required to be reported by law, the thought was that many could potentially be going unreported due to various reasons with a lack of vehicle damage
46 being the suspected primary factor. Participants were asked to identify any past WVC they experienced in which they did not report and then were asked to explain why.
Surveys were distributed in person to the Prince George Canfor weigh scales of Blackwater, Polar, Rustad, and Prince George. The Fort St. John area was included using electronic copies to the regional woodland manager. Prince George Woodlands Logging Contractors were provided with the survey in electronic format. A total of 8 contractors were contacted. In addition, surveys could be accessed via the UNBC wildlife-vehicle collision research website at http://wvc.unbc.ca/.
RESULTS AND DISCUSSION
There were 15 surveys were returned, which we interpret is highly under sampled and not representative of the population.
Table 1. Breakdown of the number of collisions from all respondents Reported Unreported Collision Collision Species Public Road Forest Service Road Public Road Forest Service Road Moose 4 0 1 1 Deer 3 1 7 0 Bear 0 0 2 1 Wolf 0 0 0 1
High Risk MVC Highway Segments
High risk MVC highway sections identified ranged from broad statements to specific highway locations. Two respondents stated that all highways east, west, north, and south of Prince George were high risk for MVC. Highway 97 was identified by several respondents. Highway 97 North of Prince George was the highway most frequently mentioned with four respondents. In particular, the highway section between Salmon Valley and Summit Lake was twice stated as being high risk for MVC. Another area of significance along Highway 97 was north of the Pine Pass and south of Chetwynd. Blackwater Road also received four independent identifications as a high risk MVC road. The most specific road section identified along Blackwater Road is from the Highway 16 turnoff south to the Radar Base. Highway 16 was stated by two respondents, both identifying the segment around Mud River.
Temporal Information
The shift times for drivers ranged throughout the day and night due to drivers frequently rotating shift times. The time of day drivers most often saw moose on the highways was almost completely identical with dusk and dawn being singled out in
47 almost all cases. Not one driver identified the daytime in between 8am and 4pm. The night was singled out as a high risk driving time by a few drivers, however, this was coupled with the dawn as well.
Reasons for not reporting a WVC
The majority of respondents either stated that they did not have any collisions to report or left this section blank. If a driver experienced a collision, the most common reason for not reporting it was due to the lack of damage caused to the truck. Only one respondent stated that all WVC in the past were reported. One respondent stated that reporting was not necessary due to the deer having run into the forest after the collision.
The Future
Since the survey did not capture a large enough population in order to make any conclusion at this point. The UNBC research team will continue to make efforts to collect data in other parts of British Columbia. Specifically this includes continuing to try to get companies involved in the logging industry to encourage their employees to taker time to fill out the survey.
48
Figure 1. Sample Survey
You are being asked to participate in a University of Northern British Columbia voluntary survey. The objective of the research is to gain insight into the number of collisions between logging trucks and wildlife. The research objective is to collect information with the potential to improve trucker safety and overall highway safety. This survey will provide information about unreported accidents and help improve Min. of Transportation assessment of wildlife vehicle collisions. The survey is not being used to assess driving ability as a wildlife vehicle collision is often a random incident. There are 4 questions and you are not required to answer all of them. Your results will be aggregated with other participants and will be used in a published report. Your name and other personal information is not being collected and you can withdraw from the survey at any time. ______Survey (There are questions on the front and backside of this paper) Q1) In the past 6 years, please try to recall which animals you have had a work vehicle- wildlife collision with. We would like you to distinguish between those collisions that were unreported and those reported to any authority. We would like for you to distinguish if the collision was on a Forest Service Road (FSR) or a public paved road, such as a highway or other secondary rural roads. Please fill in the boxes with the total number of collision with each animal.
Reported accidents involving work vehicle Year Moose Deer Bear Caribou Elk Big Horn Sheep Mountain Goat Public Forest Public Forest Public Forest Public Forest Public Forest Public Forest Public Forest Road Service Road Service Road Service Road Service Road Service Road Service Road Service Road Road Road Road Road Road Road 2006 2005 2004 2003 2002 2001
49
Unreported accidents involving work vehicle Year Moose Deer Bear Caribou Elk Big Horn Sheep Mountain Goat Public Forest Public Forest Public Forest Public Forest Public Forest Public Forest Public Forest Road Service Road Service Road Service Road Service Road Service Road Service Road Service Road Road Road Road Road Road Road 2006 2005 2004 2003 2002 2001 Q2) Highways and Moose Collisions In order to help us identify which highways are unsafe, we would like to know if you could indicate sections of highways you have had a collision with a moose that went unreported between 2001 and 2006 or have seen moose more than once. Please identify the highway section and closest landmarks. For example you can explain as, Highway 16 East of Prince George between Bowron River Bridge and Bowron Logging Road Turn off. Please list as many sections of road as possible. ______
Q3) Moose, and Season and Time of Day
Between what hours do you regularly operate your truck? ______(am) ______(pm)
What time of day do you most often see moose on the highways? ______(am) ______(pm)
What months do you regularly operate your truck (please circle the season and if possible which months)? Winter (Jan, Feb, Mar) Spring (Apr, May, Jun) Summer (Jul, Aug, Sept) Fall (Oct, Nov, Dec)
50
Which month(s) do you most often see moose on the highways (please circle the season and if possible which months)? Winter (Jan, Feb, Mar,) Spring (Apr, May, Jun) Summer (Jul, Aug, Sept) Fall (Oct, Nov, Dec)
Q4) Unreported collisions- You are not required by law to report a collision between your vehicle and wildlife. If you did not report any wildlife collision to ICBC, the Ministry of Transportation or other authorities, please explain why in your own words. ______
51
EPILOGUE
This document reports on several avenues of research that were undertaken during 2006 to better understand the extent of the problem of wildlife-vehicle collisions in northern British Columbia. As a result of analyzing 10 years of ICBC data, we now know that most animal-vehicle collisions in northern BC are a result of motorists striking deer and moose (86%). Our analyses suggest that these collisions, and their associated costs, are on the rise. In northern BC, collisions with deer and moose tend to occur predominantly in the winter and at night, but these patterns vary between different regions and communities within northern BC. Understanding the differences in when various species are being struck across difference parts of the province allows road safety planners to use these findings to tailor countermeasure implementation in a region- and species-specific fashion. Furthermore, the use of WARS data allows for site-specific mitigation planning based on recorded locations of where animals have been killed by vehicles. The mobile GPS unit (currently being used in our pilot) provides the most up-to- date and easily accessible location data for where and when deer and moose are found along roadside habitat and where these animals are being struck. Analysis of these data can provide clues about what times of the day, as well as seasons of the year that animals are most active along roadsides and can allow for distinctions to be made between where animals are observed and where they are most often struck. These data can then be added to the opinions of experts (and WARS data) about where collisions have historically occurred so that the combined data can be used to create a more robust set of tools for countermeasure planning. The trucker survey is a unique tool that allows for the collection of secondary road data that are not reported in the WARS system and gives clues about what is happening between truckers and moose in the off-highway environment. This type of survey also provides an opportunity for researchers to collect data on where unreported collisions are occurring and gather impressions from truckers about what times of the year and sections of road are most dangerous relative to wildlife-vehicle collisions. Together, our research findings combined with our review and collation of available countermeasure options, allow road safety planners access to a variety of information sources (both historical and current) and tools that can be helpful in implementing coarse and fine filter approaches to mitigating the effects of wildlife- vehicle collisions in northern BC. It is our recommendation that these findings be immediately implemented into road safety planning in northern BC. Subsequent implementation and continued monitoring will determine whether or not such measures were successful in reducing wildlife-vehicle collisions.
52
APPENDICES
53
CHAPTER 1 APPENDICES
54
5000 A. B. 4500 4000
8000 3500
7000 3000
6000 2500
5000 2000 4000 1500 (British Columbia) (British Columbia) 3000 1000 2000 500
1000 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 1800 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 1600 D. 1400 10000
1200 8000 1000
800 6000
600 4000 400 (British Columbia)
200 2000 (British Columbia: October and November) Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 1. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the province of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data were not available at time of press).
55
700
A. B. 600
900 500
800 400 700
600 300 500
400 200 (British Columbia) (British Columbia) 300 100 200
100 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 500 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 400 1400
1200 300 1000
800 200 600 (British Columbia) 100 400 (British Columbia: October - January) 200 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 2. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the province of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
56
350
A. B. 300
700 250
600 200
500 150 400 100 300 (British Columbia) (British Columbia) 200 50
100 Number of Bear-Vehicle Collisions
Number of Bear-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 200 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 180 D. 160 450
140 400
120 350
100 300
80 250 60 200
40 (British Columbia) 150 20 100 (British Columbia: September and October) Number of Bear-Vehicle Collisions 50 0 Number of Bear-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 3. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the province of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
57
180 A. B. 160 140 280 260 120 240 220 100 200 180 80 160 140 60
120 (British Columbia) (British Columbia) 100 40 80 60 20
40 Number of Elk-Vehicle Collisions Number of Elk-Vehicle Collisions 20 0 0
0 100 200 300 400 500 600 700 800 900 80 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 70 D.
60 300
50 250
40 200 30 150 20 (British Columbia) 100 10 (British Columbia: November - January)
Number of Elk-Vehicle Collisions 50 0 Number of Elk-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 4. Patterns of automobile collisions with elk between January 1st, 1996 and November 30th, 2005 for the province of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
58
12 A. B. 10
16 8 14
12 6
10 Vehicle Collisions
8 4 (British Columbia)
(British Columbia) 6 2 4
2 Number of Caribou- 0 0 Number of Caribou-Vehicle Collisions 100 0 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 18 16
14
12
10
8
(British Columbia) 6
4
2 Number of Caribou-Vehicle Collisions 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 5. Patterns of automobile collisions with caribou between January 1st, 1996 and November 30th, 2005 for the province of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to insufficient data).
59
1400
A. B. 1200
2400 1000 2200 2000 800 1800 1600 600 1400 1200 400 1000
800 (Northern British Columbia) (Northern British Columbia) 600 200 400 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 200 0 0
0 100 200 300 400 500 600 700 800 900 600 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 500 2600 2400 400 2200 2000 300 1800 1600 1400 200 1200 1000 800
100 (Northern British Columbia) 600 (Northern BC: October and November) 400 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 200 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 6. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for Northern British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
60
700
A. B. 600
1200 500
1000 400
800 300
600 200
400 (Northern British Columbia) (Northern British Columbia) 100
200 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 260 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 240 C. 220 D. 1200 200
180 1000 160
140 800 120 100 600 80 60 400
40 (Northern British Columbia)
(Northern BC: December and January) 20 200
Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 7. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for Northern British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
61
120 A. B. 100
260 240 80 220 200 60 180 160 140 40 120 100
80 (Northern British Columbia)
(Northern British Columbia) 20 60 40 Number of Bear-Vehicle Collisions
Number of Bear-Vehicle Collisions 20 0 0
0 100 200 300 400 500 600 700 800 900 50 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 45 D. 40 160 35 140 30 120 25 100 20 80 15 60 10 (Northern British Columbia) 40
(Northern British Columbia: September) 5
Number of Bear-Vehicle Collisions 20 0 Number of Bear-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 8. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for Northern British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
62
16
A. B. 14
12 22 20 10 18 16 8 14 12 6 10 4 8 (Northern British Columbia) (Northern British Columbia) 6 2 4 Number of Elk-Vehicle Collisions Number of Elk-Vehicle Collisions 2 0 0
0 100 200 300 400 500 600 700 800 900 12 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 10 30
8 25
6 20
15 4
10
2 (Northern British Columbia) (Northern BC: October - January) 5 Number of Elk-Vehicle Collisions 0 Number of Elk-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 9. Patterns of automobile collisions with elk between January 1st, 1996 and November 30th, 2005 for Northern British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
63
9 A. B. 8 7 14 6 12 5 10
Vehicle Collisions 4 8 3 6
(Northern British Columbia) British (Northern 2
(Northern British Columbia) 4 1
2 Number of Caribou- 0 0 Number of Caribou-Vehicle Collisions 100 0 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 18 16
14
12
10
8
6
(Northern British Columbia) 4
2 Number of Caribou-Vehicle Collisions 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 10. Patterns of automobile collisions with caribou between January 1st, 1996 and November 30th, 2005 for Northern British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
64
12
A. B. 10
22 20 8 18 16 6 14 12 4 10
8 (Bella Coola Region) (Bella Coola Region) 6 2 4 Number of Deer-Vehicle Collisions 2 0 0 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions
0 100 200 300 400 500 600 700 800 900 5 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 4 20 18
16 3 14
12 2 10
8
1 (Bella Coola Region) 6 (Bella Coola Region: October) 4
Number of Deer-Vehicle Collisions 0 2 0 Number of Deer-Vehicle Collisions
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 11. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Bella Coola Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
65
400
A. B. 350
300 900
800 250
700 200 600
500 150
400 (Cariboo Region) 100 (Cariboo Region) 300
200 50
100 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 120 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 100 800
700 80 600
60 500
400 40 300 (Cariboo Region) (Cariboo Region: October) 20 200
Number of Deer-Vehicle Collisions 100
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 12. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Cariboo Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
66
45
A. B. 40 35 60 30
50 25
40 20
30 15 14 (Cariboo Region)
(Cariboo Region) 10 20 12 5 10
10 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 8 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 14 C. 6 D. 70 12 4 60 ehicle Collisions 10 2 50
(Cariboo Region: June and July) Region: (Cariboo 8
Number of Moose-V 0 40 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 6 TIME OF DAY 30 (Cariboo Region) 4 20 ehicle Collisions 2 10 (Cariboo Region: January) Region: (Cariboo Number of Moose-Vehicle Collisions 0 Number of Moose-V 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 13. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Cariboo Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the 2 peak times of year (summer on top; winter on bottom) in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
67
12
A. B. 10
22 20 8 18 16 6 14 12 4 10 (Cariboo Region)
(Cariboo Region) 8 6 2 4 Number of Bear-Vehicle Collisions
Number of Bear-Vehicle Collisions 2 0 0
0 100 200 300 400 500 600 700 800 900 10 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 9 D. 8 14 7 12 6 5 10 4 8
3 6 2 (Cariboo Region) 4 (Cariboo Region:July - September) 1
Number of Bear-Vehicle Collisions 2
0 Number of Bear-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 14. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Cariboo Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
68
16
A. B. 14
12 35
30 10
25 8
20 6 6 15 (Chilcotin Region) 4 (Chilcotin Region)
10 5 2
5 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 4 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 3 C. 6 D. 30 2 5 25 1 4 20 (Chilcotin Region: April and May) April Region: (Chilcotin
Number of Deer-Vehicle Collisions 0 0
3 100 200 300 400 500 600 700 800 900
1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 15 TIME OF DAY
2 (Chilcotin Region) 10
1 5 Number of Deer-Vehicle Collisions (Chilcotin Region: October) Region: (Chilcotin 0 Number of Deer-Vehicle Collisions 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 15. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Chilcotin Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
69
6
A. B. 5
9 4 8
7 3 6
5 2 4 (Chilcotin Region) (Chilcotin Region) 3 1 2
1 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 3 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 8
7 2 6
5
4 1 3 (Chilcotin Region) (Chilcotin Region: January) 2
1 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 16. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Chilcotin Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
70
90 A. B. 80 70 180 60 160
140 50 120 40 100 30 80
(Lakes District Region) 20
(Lakes District Region) 60
40 10
20 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 45 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 40 D. 160 35 140 30 120 25 100 20 80 15 60 10 (Lakes District Region) 40 5 Number of Deer-Vehicle Collisions
(Lakes District Region: October and November) 20
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 17. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Lakes District Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
71
80
A. B. 70
60 160
140 50
120 40 100 30 80 16 20 60 (Lakes District Region) (Lakes District Region) 14 40 10
2012 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
010 100 200 300 400 500 600 700 800 900 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 8 C. 45 D. 406 140 354
ehicle Collisions 120 302
(Lakes District Region: July) Region: District (Lakes 100 25 Number of Moose-V 0 0 80 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 20 TIME OF DAY 60 15 (Lakes District Area) 40
ehicle Collisions 10 20 5 Number of Moose-Vehicle Collisions 0 (Lakes District Region: January and December) January Region: District (Lakes Number of Moose-V 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 18. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Lakes District Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
72
18 A. B. 16 14 40 12 35 10 30
25 8
20 6
15 (Lakes District Region) 4 (Lakes District Region) 10 2
5 Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 14 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 12 D. 24 10 22 20 8 18 16 6 14 12 4 10
(Lakes District Region) 8 (Lakes District Region) 2 6 4 Number of Bear-Vehicle Collisions
0 Number of Bear-Vehicle Collisions 2 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 19. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Lakes District Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
73
22 20 A. B. 18
50 16 45 14 40 12 35 10 30 8 25 (Liard Region)
(Liard Region) 6 20
15 4
10 2
5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 12 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 10 50
8 40
6 30
4 20 (Liard Region)
2
(Liard Region: December and January) 10
Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 20. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Liard Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
74
16
A. B. 14
12 22 20 10 18 16 8 14 12 6
105 (Liard Region) (Liard Region) 4 8 6 2 44 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 2 0 0
0 100 200 300 400 500 600 700 800 900 3 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 9 C. D. 2 8 24 22 7 1 20 6 18 (Liard Region: May) Region: (Liard 16
Number of Deer-Vehicle Collisions 5 0 14 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 12 4 TIME OF DAY 10 (Liard Region) 3 8 2 6 4
1 Number of Deer-Vehicle Collisions 2 (Liard Region: October - December) - October Region: (Liard 0 Number of Deer-Vehicle Collisions 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 21. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Liard Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
75
8
A. B. 7
6 12 5 10 4 8 Vehicle Collisions 3 6 (Liard Region) (Liard
(Liard Region) 2 4 1 2 Number of Caribou- 0 0 Number of Caribou-Vehicle Collisions 100 0 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C.
14
12
10
8
6 (Liard Region)
4
2 Number of Caribou-Vehicle Collisions 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 22. Patterns of automobile collisions with caribou between January 1st, 1996 and November 30th, 2005 for the Liard Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
76
22 20 A. B. 18
45 16
40 14
35 12
30 10 25 8 20 12 6 (Mackenzie Region) (Mackenzie Region) 15 4 10 10 2
5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 8 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 6 C. 9 D. 50 8 4 7
ehicle Collisions 40 2 6
(Mackenzie Region: June and July) Region: (Mackenzie 5 30 Number of Moose-V 0 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 4 TIME OF DAY 20 3 (Mackenzie Region)
ehicle Collisions 2 10 1 Number of Moose-Vehicle Collisions
(Mackenzie Region: December and January) December Region: (Mackenzie 0 Number of Moose-V 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 23. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Mackenzie Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
77
6 A. B. 5
12 4 10 3 8
6 2 (Mackenzie Region) (Mackenzie Region) 4 1
2 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 12
10
8
6
(Mackenzie Region) 4
2 Number of Deer-Vehicle Collisions
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 24. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Mackenzie Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
78
A. B. 5
6 4 5
4 3
3 2 (Mackenzie Region) (Mackenzie Region) 2 1 1 Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions
0 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 MONTH YEAR Appendix 25. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Mackenzie Region of British Columbia. A. Number of collisions by month. B. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and both the time of day and the time of day during the collision peak was not produced due to lack of data).
79
45 A. B. 40 35 60 30
50 25
40 20
30 15 (McBride Region)
(McBride Region) 10 20 5 10 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 8 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 7 D.
6 80 70 5 60 4 50 3 40
2 (McBride Region) 30 (McBride Region: August) 1 20
Number of Deer-Vehicle Collisions 10 0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 26. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the McBride Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
80
35
A. B. 30
70 25
60 20 50 15 40 12 10 30 (McBride Region) (McBride Region)
20 10 5 10 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 8 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 6 C. 16 D. 60 144 50
ehicle Collisions 12 2 10 40 (McBride Region: June and July) Region: (McBride
Number of Moose-V 0 0
8 100 200 300 400 500 600 700 800 900
1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 30 TIME OF DAY 6
(McBride Region) 20 4 ehicle Collisions 10 2 Number of Moose-Vehicle Collisions
(McBride Region: December and January) December Region: (McBride 0 Number of Moose-V 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 27. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the McBride Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
81
7
A. B. 6
16 5
14 4 12
10 3
8 2 (McBride Region)
(McBride Region) 6
4 1
2 Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 5 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 4 10
8 3
6 2
4 1 (McBride Region) (McBride Region: September) 2 Number of Bear-Vehicle Collisions
0 Number of Bear-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 28. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the McBride Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
82
240 220 A. B. 200 180 280 260 160 240 140 220 200 120 180 100 160 140 80 100 120 (Nechako Region) 60 (Nechako Region) 100 90 80 40 60 80 20 40 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 20 70 0
0 100 200 300 400 500 600 700 800 900 60 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 50 C. 80 D. 40 70 30 300
ehicle Collisions 60 20 250 50 10 (Nechako Region: June and July) Region: (Nechako 200 Number of Moose-V 0 0
40 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY 150 30 (Nechako Region) 100 20 ehicle Collisions 50 10 Number of Moose-Vehicle Collisions
(Nechako Region: December and January) December Region: (Nechako 0 Number of Moose-V 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 29. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Nechako Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
83
140
A. B. 120
200 100 180
160 80
140 60 120
100 50 40 80 (Nechako Region) (Nechako Region) 60 20 4040 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 20 0 0
0 100 200 300 400 500 600 700 800 900 30 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 50 D. 20 280 260 40 240 10 220 200
(Nechako Region: May - July) May - Region: (Nechako 30 180
Number of Deer-Vehicle Collisions 0 160 0 100 200 300 400 500 600 700 800 900
1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 140 TIME OF DAY 20 120 100 (Nechako Region) 80 10 60 40
Number of Deer-Vehicle Collisions 20
(Nechako Region: October and November) October Region: (Nechako 0 Number of Deer-Vehicle Collisions 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 30. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Nechako Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
84
40
A. B. 35
30 90
80 25
70 20 60
50 15
40 (Nechako Region) 10 (Nechako Region) 30
20 5
10 Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 20 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 18 D. 16 60 14 50 12 10 40 8 30 6
4 (Nechako Region) 20 (Nechako Region: September)
2 10 Number of Bear-Vehicle Collisions
0 Number of Bear-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 31. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Nechako Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
85
40
A. B. 35
30 60 25 50 20 40 15 30
(Northcoast Region) 10 (Northcoast Region) 20 5 10
Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 9 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 8 D. 7 60 6 50 5 40 4
3 30
2 (Northcoast Region) 20 (Northcoast Region: January) 1 10
Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 32. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Northcoast Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
86
16
A. B. 14
12 22 20 10 18 16 8 14 12 6 10
(Northcoast Region) 4
(Northcoast Region) 8 6 2 4 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 2 0 0
0 100 200 300 400 500 600 700 800 900 4 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 26 3 24 22 20 18 2 16 14 12 1 10 (Northcoast Region) 8 (Northcoast Region: August) 6 4 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 2 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 33. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Northcoast Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
87
14
A. B. 12
30 10 28 26 24 8 22 20 6 18 16 14 4
12 (Northcoast Region) (Northcoast Region) 10 8 2 6
4 Number of Bear-Vehicle Collisions
Number of Bear-Vehicle Collisions 0
2 0
0 100 200 300 400 500 600 700 800 900 6 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 5 20
18 4 16
14 3 12
10 2 8
(Northcoast Region) 6
(Northcoast Region: September) 1 4 Number of Bear-Vehicle Collisions
0 Number of Bear-Vehicle Collisions 2 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 34. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Northcoast Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
88
10 A. B. 9 8 18 7 16 6 14 5 12 4 10
8 3 (Northwest Region) (Northwest Region) 6 2
4 1
2 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 6 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 5 14
4 12
3 10
8 2 6 (Northwest Region) 1 4
(Northwest Region: December and January) 2 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 35. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Northwest Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
89
7
A. B. 6
8 5
7 4 6
5 3
4 2 (Northwest Region)
(Northwest Region) 3
2 1
1 Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 12
10
8
6
(Northwest Region) 4
2 Number of Bear-Vehicle Collisions
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 36. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Northwest Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
90
A. B. 5
5 4
4
3 3
2 2 (Northwest Region) (Northwest Region)
1 1 Number of Deer-Vehicle Collsions Number of Deer-Vehicle Collisions
0 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 MONTH YEAR Appendix 37. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Northwest Region of British Columbia. A. Number of collisions by month. B. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph and time of day for the collision peak graph were not produced due to lack of data).
91
600 A. B. 500
1000 400 900
800 300 700
600
500 200
80 (Peace Region)
(Peace Region) 400 30070 100 200
60 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 100 0 0
500 100 200 300 400 500 600 700 800 900 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 40 C. 160 D. 30 900 140 800 20 120 700
(Peace Region: June) Region: (Peace 10 100 600
Number of Deer-Vehicle Collisions 0 500 0
80 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY 400 60 (Peace Region) 300
40 200
20 100 Number of Deer-Vehicle Collisions (Peace Region: November) Region: (Peace 0 Number of Deer-Vehicle Collisions 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 38. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Peace Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
92
180 A. B. 160 140 300 120
250 100
200 80
150 60 (Peace Region) (Peace Region) 40 100 20 50
Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 140 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 120 D. 300
100 250 80 200 60 150 40 (Peace Region) 100 20 (Peace Region: November - January) 50
Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 39. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Peace Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
93
14
A. B. 12
28 10 26 24 8 22 20 18 6 16 14
12 (Peace Region) 4 (Peace Region) 10 8 2 6
4 Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions 2 0 0
0 100 200 300 400 500 600 700 800 900 6 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 5 14
4 12
10 3 8
2 6 (Peace Region)
(Peace Region: September) 1 4
2 Number of Bear-Vehicle Collisions
0 Number of Bear-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 40. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Peace Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
94
10 A. B. 9 8 14 7
12 6
10 5
8 4 3 6 (Peace Region) (Peace Region) 2 4 1
2 Number of Elk-Vehicle Collisions Number of Elk-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 7 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 6 D. 18
5 16
14 4 12
3 10
8 2 (Peace Region) 6
1 4 (Peace Region: November - January) Number of Elk-Vehicle Collisions 0 Number of Elk-Vehicle Collisions 2 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 41. Patterns of automobile collisions with elk between January 1st, 1996 and November 30th, 2005 for the Peace Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
95
45 A. B. 40 35 60 30
50 25
40 20
30 15 10 20 (Queen Charlotte Islands) (Queen Charlotte Islands) 5 10 Number of Deer-Vehicle Collisions 0 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 30 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 28 MONTH TIME OF DAY C. 26 D. 24 60 22 20 50 18 16 40 14 12 30 10 8 6 20 4 (Queen Charlotte Islands) (Queen Charlotte Islands: May - July) 2 10 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 42. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Queen Charlotte Islands of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
96
A. B. 5
6 4 5
4 3
3 2
2 (Queen Charlotte Islands) (Queen Charlotte Islands) 1 1 Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions Number of Bear-Vehicle Collisions 0 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 MONTH YEAR Appendix 43. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Queen Charlotte Islands of British Columbia. A. Number of collisions by month. B. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph and time of day for the collision peak graph were not produced due to lack of data).
97
140
A. B. 120
280 100 260 240 80 220 200 180 60 160 140 40 120 (Quesnel Region) (Quesnel Region) 100 80 20 60
40 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 20 0 0
0 100 200 300 400 500 600 700 800 900 70 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 60 D. 300
50 250 40 200 30 150 20
(Quesnel Region) 100 10
(Quesnel Region: October and November) 50 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 44. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Quesnel Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
98
50 A. B. 45 40 80 35 70 30 60 25 50 20 40 10 15 (Quesnel Region)
(Quesnel Region) 30 9 10 20 8 5 10 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 7 0
0 100 200 300 400 500 600 700 800 900 6 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 5 C. 22 D. 204 80 183 70 ehicle Collisions 162 60 (Quesnel Region: July) Region: (Quesnel 141 50 Number of Moose-V 120 0 100 200 300 400 500 600 700 800 900 10 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY 40 8
(Quesnel Region) 30 6 20 ehicle Collisions 4 10
2 Number of Moose-Vehicle Collisions
(Quesnel Region: December and January) December Region: (Quesnel 0 Number of Moose-V 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 TIME OF DAY YEAR Appendix 45. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Quesnel Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
99
10 A. B. 9 8 24 7 22 20 6 18 5 16 14 4 12 3 10 (Quesnel Region) (Quesnel Region) 8 2 6 1 4 Number of Bear-Vehicle Collisions
Number of Bear-Vehicle Collisions 2 0 0
0 100 200 300 400 500 600 700 800 900 4 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 20
3 18
16
14 2 12
10
8 1 (Quesnel Region) 6 (Quesnel Region: September) 4 Number of Bear-Vehicle Collisions
0 Number of Bear-Vehicle Collisions 2 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 46. Patterns of automobile collisions with bear between January 1st, 1996 and November 30th, 2005 for the Quesnel Region of British Columbia. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
100
60 A. B. 50
90 40 80
70 30 60
50 20
40 (100 Mile House) (100 Mile House) 30 10 20
10 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 14 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 12 D.
10 100
8 80
6 60
4 40 (100 Mile House) (100 Mile House: October) 2 20 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 47. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the 100 Mile House Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
101
18 A. B. 16 14
40 12
35 10 30 8 25 6 20 (Burns Lake) (Burns Lake) 15 4
10 2
5 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JULY AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 50
40
30
(Burns Lake) 20
10 Number of Deer-Vehicle Collisions
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 48. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Burns Lake Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
102
22 20 A. B. 18 16 45 14 40 12 35 10 30
25 8 (Burns Lake)
(Burns Lake) 20 6
15 4
10 2
5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 8 JAN FEB MAR APR MAY JUN JULY AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 7 D. 45 6 40
5 35
4 30 25 3 20 (Burns Lake) 2 15 (Burns Lake: December)
1 10
5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 49. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Burns Lake Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
103
80
A. B. 70
60 120 50 100 40 80 30
60 (Chetwynd) (Chetwynd) 20 40 10 20 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 22 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 20 C. D. 18 140 16 14 120
12 100 10 80 8
6 (Chetwynd) 60 (Chetwynd: November) 4 40 2
Number of Deer-Vehicle Collisions 20
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 50. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Chetwynd Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
104
140
A. B. 120
220 100 200 180 80 160 140 60 120
100 (Dawson Creek) 40 (Dawson Creek) 80 60 20 40 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 20 0 0
0 100 200 300 400 500 600 700 800 900 40 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 35 D. 220 30 200 25 180 160 20 140 120 15 100
10 (Dawson Creek) 80
(Dawson Creek: November) 60 5 40 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 20 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 51. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Dawson Creek Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
105
20 A. B. 18 16 40 14 35 12 30 10 25 8 20 (Fort Nelson) 6 (Fort Nelson) 15 4 10 2 5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 10 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 9 D. 8 40 7 35
6 30
5 25 4 20 3 (Fort Nelson) 15 2 10
(Fort Nelson: December and January) 1 5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 52. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Fort Nelson Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
106
A. B. 10
7 8 6
5 6
4
4 (Fort Nelson) (Fort Nelson) 3
2 2 1 Number of Caribou-Vehicle Collisions Number of Caribou-Vehicle Collisions 0 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 MONTH YEAR Appendix 53. Patterns of automobile collisions with caribou between January 1st, 1996 and November 30th, 2005 for the Fort Nelson Community. A. Number of collisions by month. B. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph and the time of day graph for the collision peak were not produced due to lack of data).
107
16
A. B. 14
12 35
30 10
25 8
20 6
15 James) St. (Fort (Fort St. James) St. (Fort 4
10 2
5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 9 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 8 D. 30 7
6 25
5 20 4 15 3
2 James) St. (Fort 10
1 (Fort St. James: and January) December 5
Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 54. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Fort St. James Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
108
200 A. B. 180 160 350 140
300 120
250 100
200 80
(Fort St. John) 60
(Fort St. John) 150 40 100 20
50 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 50 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY 45 C. D. 300 40 35 250 30 200 25 20 150
15 John) St. (Fort 100
(Fort St. John: November) 10 50 5 Number of Deer-Vehicle Collisions Deer-Vehicle of Number Number of Deer-Vehicle Collisions 0 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 YEAR TIME OF DAY Appendix 55. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Fort St. John Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
109
22 20 A. B. 18
45 16
40 14
35 12
30 10 25 8 (Houston) (Houston) 20 6 15 4 10 2
5 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 9 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 8 D. 40 7 35 6 30 5 25 4 20 3 (Houston)
(Houston: October) 15 2 10 1
Number of Deer-Vehicle Collisions 5
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 56. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Houston Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
110
35
A. B. 30
35 25
30 20 25 15 20
15 (Hudson's Hope) 10 (Hudson's Hope)
10 5
5 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 8 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 7 D.
6 50
5 40 4 30 3
20 2 (Hudson's Hope)
(Hudson's Hope: and May) April 1 10 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 57. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Hudson’s Hope Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
111
14
A. B. 12
18 10
16 8 14
12 6 10 (Kitimat) (Kitimat) 8 4
6 2 4
2 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 20 18
16
14
12
10
(Kitimat) 8
6
4
2 Number of Moose-Vehicle Collisions 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 58. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Kitimat Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
112
16
A. B. 14
12 45
40 10
35 8 30
25 6 (Mackenzie) (Mackenzie) 20 4 15
10 2
5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 6 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 5 35
4 30
25 3 20
2 15 (Mackenzie) (Mackenzie: December) 1 10
5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 59. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Mackenzie Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
113
24 22 A. B. 20 18 40 16 35 14 30 12 25 10
20 (McBride) 8 (McBride) 6 15 4 10 2
5 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C.
50
40
30 (McBride) 20
10 Number of Deer-Vehicle Collisions
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 60. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the McBride Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
114
160
A. B. 140
120 200 180 100 160
140 80
120 60 100 (Prince George)
(Prince George) 80 40
60 20 40
20 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 45 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 40 D. 35 240 220 30 200 25 180 160 20 140 15 120 100 (Prince George) 10 80 60 5 (Prince George: December and January) 40
Number of Moose-Vehicle Collisions 0 20 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 61. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Prince George Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
115
12 A. B. 10
14 8 12
10 6
8 4
6 (Prince Rupert) (Prince Rupert)
4 2
2 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C.
16
14
12
10
8
(Prince Rupert) 6
4
2 Number of Deer-Vehicle Collisions
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 62. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Prince Rupert Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
116
120 A. B. 100
260 240 80 220 200 60 180 160 140
(Quesnel) 40
(Quesnel) 120 100 80 20 60 40 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 20 0 0
0 100 200 300 400 500 600 700 800 900 60 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 50 240 220 40 200 180 30 160 140 20 120 (Quesnel) 100 80 10
(Quesnel: October and November) 60 40 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 20 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 63. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Quesnel Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
117
20 A. B. 18 16 35 14
30 12
25 10
20 8 (Terrace)
(Terrace) 6 15 4 10 2 5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900 16 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 14 D. 40 12 35 10 30
8 25
6 20 (Terrace) 4 15
(Terrace: December - February) 2 10 5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 64. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Terrace Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
118
20 A. B. 18 16 35 14
30 12
25 10
20 8 6 15 (Tumbler Ridge) (Tumbler Ridge) 4 10 2
5 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 40 35
30
25
20
15 (Tumbler Ridge)
10
5 Number of Deer-Vehicle Collisions
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 65. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Tumbler Ridge Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
119
18 A. B. 16 14 22 20 12 18 10 16 14 8 12 6 10 (Valemount) (Valemount) 8 4 6 2 4 Number of Deer-Vehicle Collisions
Number of Deer-Vehicle Collisions 2 0 0
0 100 200 300 400 500 600 700 800 900 7 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 6 D. 40
5 35
4 30 25 3 20
2 (Valemount) 15
1 10 (Valemount: October and November)
Number of Deer-Vehicle Collisions 5
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 66. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Valemount Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
120
35
A. B. 30
40 25
35 20 30
25 15
20 (Vanderhoof) 10 (Vanderhoof) 15
10 5
5 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 60
50
40
30 (Vanderhoof) 20
10 Number of Deer-Vehicle Collisions
0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 67. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Vanderhoof Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
121
22 20 A. B. 18
35 16 14 30 12 25 10 20 8 (Vanderhoof)
(Vanderhoof) 15 6 4 10 2 5 Number of Moose-Vehicle Collisions 0 Number of Moose-Vehicle Collisions 0
0 100 200 300 400 500 600 700 800 900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. 26 24 22 20 18 16 14
(Vanderhoof) 12 10 8 6 Number of Moose-Vehicle Collisions 4 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR Appendix 68. Patterns of automobile collisions with moose between January 1st, 1996 and November 30th, 2005 for the Vanderhoof Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press and the time of day graph for the collision peak was not produced due to lack of data).
122
160
A. B. 140
120 450 400 100 350 80 300
250 60
200 Lake) (Williams
(Williams Lake) (Williams 40 150
100 20
50 Number of Deer-Vehicle Collisions Number of Deer-Vehicle Collisions 0 0
0 100 200 300 400 500 600 700 800 900 50 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 MONTH TIME OF DAY C. D. 40 300
250 30 200
20 150
(Williams Lake) (Williams 100 (Williams Lake: October) (Williams 10
50 Number of Deer-Vehicle Collisions
0 Number of Deer-Vehicle Collisions 0
100 200 300 400 500 600 700 800 900 0 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 TIME OF DAY YEAR Appendix 69. Patterns of automobile collisions with deer between January 1st, 1996 and November 30th, 2005 for the Williams Lake Community. A. Number of collisions by month. B. Number of collisions by time of day. C. Number of collisions by time of day during the time of year in which most collisions occur. D. Number of collisions per year between 1996 and 2005 (Note: December 2005 collision data was not available at time of press).
123
Appendix 70. Article published by authors in Wildlife Afield (3):1 Supplement on research combining collision data, GPS technology and expert opinion.
124
125
126
127
Appendix 71. Annotated bibliography of sources reporting countermeasures used to reduce collisions with animals and listed in Table 5.
Al-Ghamdi A. & AlGadhi S. 2003. Warning Signs as countermeasures to camel-vehicle collisions in Saudi Arabia. Accident Analysis and Prevention.
The effectiveness of warning signs was the main mitigation topic of this study, however other means of mitigation were also researched: driver education, roadway lighting, animal crossing structures such as overpasses/underpasses, roadside vegetation clearing, roadway iron fencing, optical fencing (mirrors and reflectors), animal frightening models, and deterrent chemicals. It was concluded that the implementation of driver education only slightly improves a driver’s behavior regarding wildlife whereas road lighting had no effect on a driver’s speed. The clearing of roadside vegetation, the use of wildlife crossings such as overpasses and underpasses and iron fencing are all very effective methods of mitigation. Optical fencing (reflectors), ultrasound whistles, animal frightening models and the deterrent chemical Wolfin have been seen to have no observable effect in many of the studies. However, the chemical deterrent Dutazun is claiming to be effective at reducing wildlife collisions. With most warning signs, as soon as the drivers become accustomed to them, they loose their effect in reducing speeds. A new sign that was tested in Canada had a moose silhouette in which the eyes were shining yellow and these were found to reduce the number of moose-vehicle collisions by 50%. From this study, the recommended warning sign is an oversized one that is twice the size of a standard and is made out of diamond reflective material. The conclusion, however, is that no single countermeasure is able to address the wildlife-vehicle collision problem and that a combination should be tested and used.
Anonymous. Providing Safe Passage
The Ministry of Transportation currently uses fencing, highway lighting, warning signs, reflectors, wildlife over and under passes, various noisemakers and driver education as means of mitigation. A new mitigation measure is the Wildlife Protection System that uses technology that allows the driver to be alerted in real time when wildlife is present. Once wildlife is detected, flashing lights are triggered which warns the driver to slow down. The NASA cameras are high-resolution and can not only see through dark, smoke, snow, rain and fog, but can also detect a heat difference of one-hundredth of a degree centigrade over several kilometers.
Beaupre, V. 2002. Pilot Project to Deter Wildlife-Vehicle Collisions. News Release- Saskatchewan, Highways and Transportation Regina.
The IRD Wildlife Warning System warns animals of oncoming traffic so that they will not enter the roadway. Sensors and warning devices are in a small cabinet and placed every 300 meters. The units are powered by solar panel energy and batteries. Once a vehicle triggers the sensor, sounds and lights are alternated to warn animals without allowing the animals to become habituated to them. If no vehicles are present, the animals are free to cross the roadway which
128
means that there is no interference to their migration patterns. The pilot project will be held for 2 years over 5 kilometers on Highway 7 south of Harris.
Canada Safety Council. 2003. Tis’ the Season to be Wary. Anonymous
There are a couple of new mitigation technologies on the market: the Wildlife Warning System, the Wildlife Protection System, and NightvisionTM. The Wildlife Warning System using technologies that senses vehicles and warns the animals. The oncoming traffic triggers a sensor that activates a deterrent. The Wildlife Protection System uses infrared cameras to detect the presence of wildlife on or near the highway for use to warn drivers. NightvisionTM is available in some GM vehicles and it increases the driver’s ability to detect wildlife on the roadway also through infrared technology. Other mitigation measures include reflectors and fencing in conjunction with overpasses and underpasses.
Cavallaro, L., Sanden, K., Schellhase, J., Tanaka, M. 2005. Designing Road Crossings for Safe Wildlife Passage: Ventura County Guidelines. University of California. Advisor Frank Davis.
Wildlife crossing structures are a type of mitigation measure and their success is often dependent upon a few different factors. Although the placement of the crossing structure near natural mitigation corridors is a key factor, the crossing also needs to have proper habitat, minimal human disturbance, funneling or fencing, wildlife accessibility, appropriate road design and structure design and ongoing maintenance. Although fencing is the best method for encouraging the animals to use the crossing structures (collision rate can drop by 90 percent when fencing is used in conjunction with the structures), the vegetation can also help control the animals. The immediate roadside should have minimal vegetation whereas the crossing structure should have dense, suitable vegetation which not only attracts the animals as a food source, but decreases traffic noise. There should be no street lights at the crossing as this is unnatural to the wildlife. There are four main types of crossing structures; pipe culverts, box culverts, bridge underpasses and overpasses. For large mammals, crossing structures must be at least 6 ft high, have an openness ratio of at least 0.75, but preferably 9, be easily accessible, have associated fencing that is at least 8ft tall with one-way gates and a field of view so that the animal can see suitable habitat on the other side of the crossing.
Chilson, P., Jacobson, S. 2003. Right of Way. Audubon: Cutting Edge.
In Europe, some of the mitigation measures that are used to prevent wildlife-vehicle collisions are guardrails, fencing and wildlife crossing structures. The guardrails are designed so that the natural slopes of the landscape reach their tops. This creates a large drop off that discourages big horn sheep and ungulates to climb up to the roadway. Wildlife crossings have been very effective and fencing is used to encourage the animals to use the crossing structures. Since no one is sure how the wildlife will react to the crossing structures, predictions were made. In some studies, it was shown that it can take years for animals to get used to crossing structures; 4 years
129
or longer. Therefore there must be a certain amount of trial and error when planning wildlife crossing structures.
Clevenger, A., Waltho, N. 2005. Performance indices to identify attributes of highway crossing structures facilitating movement of large mammals. 121(3): 453-464.
Wildlife crossing structures were built around Banff with the attempt to reduce wildlife-vehicle collisions. This study evaluated 13 of the crossing structures as there is very little know about the effectiveness due to the lack of monitoring pre and post construction. There has been monitoring on a short-term basis but this is insufficient as it may take several years for the animals to habitualize to the structures. The factors that determine whether or not the animals are using the structures seems to be the structural attributes, vegetation at entrance, the amount of human activity and the noise level. Since there are differences at how each species responds to the passages, designing one for all species is challenging and often a crossing structure of mixed size classes is used.
Fleming, D. 2003. Caution: Moose Crossing. Blethen Maine Newspapers Inc, Portland Press Herald Writer.
To try to prevent moose collisions, infrared, moose-activated signs are to be installed along roadways in Maine. The infrared sensor system is composed of an emitter and a receiver that activates two signs placed at each end of the site when a large animal crosses the beam when entering the road. It is the hope of state officials that these signs will slow motorists down. Traditional wildlife signs tend to be ignored by motorists and movable signs that are only placed out during seasons with a high frequency of collisions are being experimented with. Wildlife warning signs with car-animal collision diagrams seem to be more effective than the traditional signs.
Gagnon, J., Schweinsburg, R., Dodd, N., & Manzo A. 2005. Use of Video Surveillance to Assess Wildlife Behaviour and Use of Wildlife Underpasses in Arizona. ICOET Proceedings: Chapter 10 On the Road to Stewardship, Wildlife Vehicle Collisions: pgs 534-544.
The success of underpasses is determined by a few different features; design and placement as well as knowledge of local species all play a role in the success. Different species will react differently to structure aspects such as ledges, visual openings, tunnel effect, openness ratios and human activity. To increase the success of underpasses, fencing should be used to channel the animals to the underpasses as elk and deer preferred to cross the highway without the fencing in place. It is important to monitor the usage of underpasses in the future as their usage may increase as wildlife learn underpass locations.
130
Gearino, J. 2004. Wildlife-vehicle collisions take toll in state. Southwest Wyoming Bureau, Jackson Hole Star Tribune.
Wildlife crossing signs are posted in problem areas and drivers should slow down when they come across one of these signs as there may be wildlife in the area. An underpass with associated fencing was placed in an area where deer populate and a sensor system to turn on sign warnings for motorists is another potential mitigation measure.
Jackson, S.D. and C.R. Griffin. 2000. A Strategy for Mitigating Highway Impacts on Wildlife. Pp. 143-159 In Messmer, T.A. and B. West, (eds) Wildlife and Highways: Seeking Solutions to an Ecological and Socio-economic Dilemma. The Wildlife Society.
There are many different types of wildlife crossing structures that can be used for mitigation. Tunnels can be used to help wildlife cross the road and their design and placement often dictates their effectiveness. Factors such as size, noise levels, substrate, vegetative cover, moisture, temperature, light, human disturbance, approaches and travel distances all affect the usage of tunnels. Overpasses are one of the more recent forms of mitigation and they help to facilitate the passage of a wider range of species. In order for the wildlife to use the overpasses fencing appears to be necessary to help guide the animals to the crossings so that they will actually use them. Underpasses are also a type of crossing structure and if fencing is not used in conjunction with them, ungulates will avoid them. Since overpasses and underpasses are expensive, it is important to place them in locations that have been identified as travel corridors. Some of the important things to consider when mitigating is to avoid highway fencing or jersey barriers when they are not associated with passage structures, place crossing structures at connectivity zones, monitor and maintain the mitigation system and to ensure that future transportation systems are designed with ecological infrastructure in mind.
Kinley, T., Page, H., Newhouse, N. 2003. Use of Infrared Camera Video Footage from a Wildlife Protection System to Assess Collision-Risk Behaviour by Deer in Kootenay National Park, British Columbia. Prepared for Graham Gilfillan. Sylvan Consulting Ltd.
InTransTech, which is owned by Rainbow Group of Companies, has been developing the Wildlife Protection System with the cooperation of ICBC (Insurance Corporation of British Columbia). Wildlife is detected with infrared cameras with Quantum Well Infrared Photodetector focal plane arrays. Motorists are warned of approaching animals by flashing lights on road signs when an animal is detected. This system has many benefits including the facts that it is portable, it doesn’t affect wildlife movement patterns, there are no issues of animal habituation and motorists are likely to respond as it is only activated when an animal is actually present. Since this system is able to collect video footage, animal behavior can be studied and other varieties of effective mitigation measures can be developed.
131
Knapp, K., Yi, X., Oakasa, T. 2003. Deer-Vehicle Crash Countermeasures Effectiveness Research Review. Mid-Continent Transportation Research Symposium, Iowa State University.
Two years were spent reviewing the mitigation measures used to reduce deer-vehicle collisions. Studies are often inconclusive in their determination of the effectiveness of different countermeasures. The effectiveness of reflectors/mirrors and highway lighting is inconclusive whereas deer whistles and deer flagging models have been concluded to be ineffective. Intercept feeding is a mitigation technique that is not justified due to the insufficient reduction in collisions verses the amount of work involved. Deer crossing signs were found to be the most effective when lights were flashing and deer decoy was present however a speed limit reduction is generally ignored by the driving public and sometimes increases the risk of a collision with an other vehicle (due to the impatience to pass slow vehicles). The large number of studies on repellents all had varying approaches but it was found that Big Game RepellentTM and predator odors were typically the most effective. Whether this mitigation measure should be employed depends on the time intervals involved, cost and how widespread it should be. Research is still being done on alternative deicing methods/salts and in-vehicle technologies. The problem with the latter is current high cost. Reviews have yet to be done on fencing (locations, heights), grade separations, one-way gates, vegetation/roadside management, highway planning and public awareness sessions. It is important to understand that each situation may require a different mitigation measure or a combination of measures.
Langevelde, F. & Jaarsma C. 2004. Using traffic flow theory to model traffic mortality in mammals. Landscape Ecology 00: 1-13.
Traffic speed and volume affect the frequency of wildlife collisions in that by reducing both, the frequency of collisions also decreases. The amount of time it takes an animal to cross the road is related to the angle of crossing and the width of the road and a decreased time is correlated to decreased collisions. To determine where mitigating measures should be applied, models based on road and traffic characteristics and the distribution and size of local populations of species could be useful.
Lo, A. 2003. Wildlife-Vehicle Collision Countermeasures. TSB Newsletter (Technical Standards Branch). 2(1): 6-8.
Warning signs are a method of mitigation but typical wildlife warning signs and speed reductions are static signs that cannot inform the motorists of when and where animals are going to appear along the roadway. They also tend to loose their novelty over time; even the oversized warning signs are ignored eventually if the motorist never witnesses an animal in the area. Other mitigation measures that have been implemented involved clearing trees and bush from the right- of-ways in order to improve sight lines and initiating public awareness campaigns. Wildlife reflectors are still in extensive research as to their effectiveness of reducing collisions. They are a nighttime countermeasure only, as they work with the driver’s headlights. Road salt and attractive foliage has a tendency to attract wildlife to the roadways and the use of lithium chloride and non-palatable plants appears to deter animals. The best way, at the time of this
132
study, to prevent animals from being on the roadway is a combination of fencing and overpasses/underpasses. This method, however, is also very expensive. New technologies such as infrared vision to give drivers advanced warnings of animals on the roadway. There are also technologies such as the IRD Wildlife Warning System that alerts the animal of oncoming vehicles that is just reaching the testing stage.
Leblanc, Y., Martel, D. 2005. Upgrading a 144km Section of Highway in Prime Moose Habitat: Where, Why and How To Reduce Moose-Vehicle Collisions. ICOET 2005 Proceedings, Chapter 10, On the Road to Stewardship, Wildlife-Vehicle Collisions: pgs 524-533.
Moose are a danger to motorists and have caused numerous injuries and fatalities. Mitigation measures often need to be directed at each species separately and those that may reduce moose- vehicle collisions are the presence of steeper road cuts and the removal of salt licks in the area at least 1 kilometer away. Moose are not inclined to move on steep slopes unless forage is available and they are strongly attracted to salt pools. Both of these mitigation techniques have not been successfully tested for efficiency. Fencing and underpasses are strongly recommended as a mitigation measure for moose and trails with salt pools are suggested at each end of the underpass to help attract moose through the underpass. Finally, since moose-vehicle collisions increase with increasing moose density, it is suggested that harvest quotas should be increased.
Lloyd, J., Casey, A. 2005. Wildlife Hot Spots along Highways in Northwestern Oregon. Oregon Department of Transportation.
Structural mitigation measures include fencing and overpasses or underpasses as well as modifications to existing bridge structures by adding a bench for wildlife passage. Other mitigation measures include management of roadside vegetation, intercept feeding, reflectors, repellents and warning signs, however the latter three have little effect in reducing animal- vehicle collisions.
L-P Tardif & Associates Inc. 2003. Collisions Involving Motor Vehicles and Large Animals in Canada.
Mitigation measures can help to reduce wildlife collisions in a couple of different ways: by changing the behavior of the motorists or by preventing wildlife from spending time near or on the roadway. The former involves warning signs, lower speed limits, public awareness programs and highway lighting which all help to caution motorists and allow motorists additional time to react. Warning signs are a standard part of highway design and are required on dangerous sections of the roadway. They would be more effective if drivers actually reduced their speed, and due to the abundance of highway signs, they often get ignored unless one has had a previous encounter with wildlife in the area. The use of over-sized signs is another approach but this was tested in Newfoundland and they don’t appear to be effective. Warning signs can be accompanied by lower speed limits or the speed limit may be decreased in areas where there is reduced visibility and a high frequency of wildlife collisions. Also in areas with a high
133
frequency of wildlife collisions, highway lighting has been used to assist in the dark, as most collisions occur between sunset and sunrise. Public awareness educates drivers on how to take precautions in wildlife collision zones and information on animal behavior. In order to alter the behavior of the wildlife, reflectors/mirrors, ultrasonic warning whistles, habitat alteration, exclusion fencing, and under/overpasses have been deployed. The most effective is the combination between exclusion fencing and under/overpasses. Ultrasonic warning whistles have been found to be ineffective and whether or not reflectors are effective varies among experiments. To keep wildlife away from the roadway, habitat can be altered to make roadways unattractive to animals and/or attractive habitat can be created a distance from the roadway. Finally, intelligent transportation systems are being offered in some vehicles that would have the ability to detect ungulate presence on or approaching the roadway and to detect the driver’s response to warning signs. These intelligent transportation systems can consist of microwave radar, passive and active infrared images, fiber-optic grating, seismic sensors and thermal imaging technologies. Overall, the relative effectiveness and cost of various mitigation measures is poorly understood.
Maine Department of Transportation. 2001. Collisions Between Large Wildlife Species and Motor Vehicles in Maine. Interim Report Maine Department of Transportation, Maine Department of Inland Fisheries and Wildlife, Office of the Secretary of State, Maine Department of Public Safety, Maine Turnpike Authority April 2001
Mitigation measures that were researched included fencing, lighting, overpasses and underpasses, pavement marking, reflective devices, vegetation management, repellents, optical obstructions, optical warning devices, increased harvest, speed limit alteration, driver education, highway design modification, audible warning devices, thermal sensors and electric field differential sensors. These measures either control the animals, control the crash site or control the driver/vehicle. Warning sign placement should be evaluated and re-evaluated and the location should be changed when necessary.
Malo, J., Suarez, F., Diez, A. 2004. Can we mitigate animal-vehicle accidents using predictive models? Journal of Applied Ecology. 41: 701-710.
The recommendation of this study was to use mitigation measures such as herd-size control, installation of fences, specific fauna crossings and warning signs for motorists in combination. Although there are many other mitigation measures such as mirrors and whistles, no single method has been proven to be totally effective. One of the important aspects of mitigation is the location. Physical structures such as overpasses or underpasses are very expensive and can only be placed in specific locations whereas warning signs, that have little cost associated with them, become ineffective over long stretches of roadway. Therefore it is important to predict the location of sites with the highest collision probability to allow mitigation measures to be used to their full advantage.
134
McMurtray, J. Seeking Solutions to Wildlife/Highway Conflicts Using an Advocacy-Based Approach.
One of the keys to reducing wildlife-vehicle collisions is to participate in transportation planning that considers wildlife considerations and the effort should be made to incorporate these considerations into the planning or project prioritization stage of road construction. The use of natural resource mapping and knowing what areas contain endangered animals or rare habitat is important and should be in mind when planning a road. The idea is to avoid rare areas if you can, if not, then to minimize the disturbance, and finally, mitigate the road accordingly. The mitigation planning should be based off existing wildlife-vehicle conflict on roads examples with the hopes of predicting what might occur on the planned road.
MMWR Weekly. 2004. Nonfatal Motor-Vehicle Animal Crash-Related Injuries—United States, 2001-2002. 53(30): 675-678.
Mitigation measures such as warning signs, speed restrictions, roadside clearing, roadside mirrors and reflectors and reduction of deer populations by harvesting have the disadvantage of inconsistent results in terms of cost or effectiveness. Fencing combined with overpasses or underpasses is a mitigation method that has some supportive evidence of its effectiveness however this is also the most expensive mitigation measure to build and maintain.
Ramakrishnan, U., Williams, S. 2005. Effects of Gender and Season on Spatial and Temporal Patterns of Deer-Vehicle Collisions. ICOET 2005 Proceedings, Chapter 10 Wildlife Vehicle Collisions: pgs 478-488.
There has been mixed results on numerous mitigation methods studied. Deer whistles are popular but there is no research to show that deer are startled by the sound and one study has shown that deer whistles did not alter the behavior of deer. Light reflectors also had mixed results and are only helpful when a vehicle’s headlights shine on them during the night. Underpasses are found to be successful and the success rate increases when used in conjunction with fencing. The problem with underpasses is that they are difficult to construct under already existing roads and the cost is high to install. Fencing can also stand alone as a successful mitigation method as long as it is a minimum of 2.4m high and inspected regularly. It is cost effect for short stretches of highway and should be utilized in areas of high frequency of wildlife- collisions. Static wildlife signs are often ignored as they are plentiful along the roadways and few motorists have seen or collided with wildlife at these sites. Light and animated deer crossing signs have been experimented with, but there was no change in the number of collisions when the lights were on or off. To slow drivers down, it is suggested that carcasses should be placed on the road as they tend to have a substantial impact on speed or at least targeted impact-based warning signs should be used. This research suggests that it is important to move mitigation signs to hotspots based on vegetation or seasonal conditions.
135
Ramp, D., Caldwell, J., Edwards, K., Warton, D., Croft, D. 2005. Modeling of wildlife fatality hotspots along the Snowy Mountain Highway in New South Wales, Australia. Biological Conservation, 126 (4): 474-490.
Although the types of animals in Canada and the US are different from that in Australia, the use of fencing and underpasses as a mitigation measure there is showing success. Reflectors and whistles (Shu Roo) have been viewed as ineffective in recent studies. A new approach to the prevention of wildlife collisions is to exploit the innate fear of predators through scent.
Rea, R.V. 2003. Modifying roadside vegetation management practices to reduce vehicular collisions with moose Alces alces. Wildlife Biology 9(2): 81-91.
This review determined that the timing of roadside brush cutting practices are likely to influence plant response and the quality and attractiveness of regenerating shoots for browsers such as moose. How roadside cutting of brush can be more precisely timed to reduce the attractiveness of browse and the use of such browse in the transportation corridor is advocated as a means by which encounters between moose and motorists can be reduced.
Ruediger, W., Wall, K., Wall, R. 2005. Effects of Highways on Elk (Cervus Elaphus) Habitat in the Western United States and Proposed Mitigation Approaches. ICOET 2005 Proceedings, On the Road to Stewardship, Chapter 8, Wildlife Impacts and Conservation Solutions: pgs 269- 278.
Wildlife crossings are a good mitigation method for highway safety and the prevention of elk habitat fragmentation, however, often the high costs of such structures are seen as a waste of public funds. This is untrue as the cost of implementing this mitigation measure is offset in a few years due to the reduction in the collisions it causes. There are several crossing designs such as bridge extensions, wildlife overpasses or ecoducts, open-span underpasses, box culverts and large elliptical culverts; all which are effective. The most effective for elk are overpasses that are large and wide; 50 meters wide is optimal! The problem with wildlife overpasses is that there is a lack of appropriate locations. Open-span crossings are large bridge-like structures that are wide at the top and narrower at the bottom and these are almost as effective as wildlife overpasses and are less costly. It is recommended that fencing is used to channel animals towards the wildlife crossings as this commonly increases use by 80 percent or more. Researchers have found that time also causes an increase in the use of the wildlife crossings as the animals become accustomed to them and mothers bring their young to them. Fencing for elk usually consists of 8-foot page wire and sometimes contains jump-out shoots or texas gates to allow animals trapped on the roadway to retreat to safety. If the fencing runs into side roads then gates or cattle guards are used, however in the winter, cattle guards can pack with snow and allow the animals access to the roadway. Fencing is expensive and sometimes will exceed the costs of the actual wildlife crossings. With fencing, maintenance is expensive and required, as any damage to the fence will allow wildlife access to the road. A combination of fencing and wildlife crossings is recommended as other measures of mitigation such as wildlife warning signs have limited or no success in reducing elk mortality or vehicle accidents.
136
Seiler, A. 2003. The toll of the automobile: Wildlife and roads in Sweden. Swedish University of Agricultural Science. Pgs. 33-38.
Although there have been various mitigation measures used and tested, only a few have been proven effective. Exclusion fencing and roadside clearing have been proven to work efficiently in Sweden since the 1970s. Together with traffic speed and volume, fencing appeared as a dominant factor in determining the frequency of collisions. Fencing off roads has certain issues as extended fencing causes isolation among species populations whereas fencing only a short distance often just shifts the problem to the end of the fencing. To counteract these problems it is suggested that fencing is used in conjunction with fauna passages. Sometimes fauna passages can be created from existing bridges and tunnels. Finally, reducing animal density is a mitigation method that is not discussed often because it would not be supported by both the politicians and the public. However, there is doubt that a reduction in wildlife density would decrease the frequency of animal collisions as it is the animals’ activity and mobility rather than the density that locally influences collision risk.
Singleton, P. 2006. Highways and Habitat: Managing Habitat Connectivity and Landscape Permeability for Wildlife. Science Findings, Pacific Northwest Research Station, Issue 79.
Creating wildlife crossings for different species becomes a challenge for road ecologists as they must find the right structure for each species. Oversized culverts are often used by rodents and amphibians whereas tunnels and grass-covered bridges are needed for larger species. Along the Trans-Canada Highway in Banff National Park in Alberta, fencing is used to channel animals to large wildlife crossings; some of which are more than 150 feet wide.
State of Maine. 2005. Summary of 2004 Moose collision deterrents. State of Maine Multiagency Animal Crash Task Force. 5p.
A report that provides an annual reference source for State of Maine Moose collision deterrent activities throughout Maine. The report references a novel technique in which a 4-8 feet wide strip of angular rip rap is installed at the toe of the ditch slope. It is suggested that the “speed bumps” would slow moose down on their approach to the roadbed, thus allow drivers more time to react to the approach.
Stromnes, J., Hardy, A. 2003. Biologist finding what wildlife crosses the road. Missoulian News.
Since the mitigation measure used to counteract wildlife-vehicle collisions is often species specific, one must determine what species are occupying the site. In this study, a 327-foot (100- meter) strip of landscape fabric about 3 feet wide was placed on the shoulder of the roadway of interest. It was then covered with several inches of construction sand and raked smooth. The site was visited every few weeks and the marking that have accumulated along the strip are
137
carefully inspected. There were several strips laid out randomly along sections of the highway. Once the species have been identified, then specific wildlife crossing structures can be designed. Sullivan, T., Messmer, T. 2003. Perceptions of deer-vehicle collision management by state wildlife agency and department of transportation administrators. Wildlife Society Bulletin, 31(1): 163-173.
Researching the effectiveness of varying mitigation measures is important and a role that is believed to be rested with the Federal Highway Administration according to survey respondents. If there was a standardized integrated database regarding the effectiveness of mitigation techniques then it would save time and money that is being spent on repeated research and ineffective mitigation measures. One of the most common mitigation techniques is the use of deer-crossing signs but the survey said that they were not effective and motorists tend to habituate to them. The signs are believed to be used to reduce liability over the Department of Transportation and even signs that have been modified with lights and animated figures have not resulted in reduced deer vehicle collisions. The most effective use of signs is to place them at high-risk areas only during high-risk times. For a long term reduction in deer vehicle collisions, fencing is the most cost-effective. However, the initial cost to install fencing and the added expense of maintaining it has discouraged it from being frequently employed. It also fragments wildlife populations by creating a barrier. The administrators that took the survey believed that deer whistles and mirrors had no effect on reducing collisions and the Swareflex reflectors had mixed results. Intercept feeding was seen as a cost effective mitigation method if only used for short periods of time and combined with other techniques.
SupplyPost Editorial. 2003. Avoid Taking a Ride On The Wild Side
Mitigation measures used by Alberta Transportation include wildlife over and under passes, fencing, warning signs and roadside reflectors. This transportation department also ensures that proper clearing of bushes, grasses and trees takes place on either side of the highway to give drivers increased visibility.
Ten Meters News Service. 2002. Infrared Eye Keeps Tabs on Bambi and Bullwinkle.
QWIP Technologies and InTransTech are working together to produce an infrared camera system that will warn motorists if an animal is wandering on the road ahead. QWIP Technologies is contributing the infrared sensor system originally created for missile seeking and detection for NASA and InTransTech is contributing 4 by 8 foot digital roadside warning signs. When an animal is detected, the signs will issue a warning for drivers as well as identifying what animal is on the roadway. The infrared sensor system scans for heat signatures and can see through darkness, smoke, snow, fog and rain and is sensitive enough to distinguish between different types of animals. The system is not cheap; it costs $50,000 per camera. However, it can scan for several miles of straight highway so its cost is less than several miles of fencing.
138
Ujvári, M., Baagøe, H.J. & Madsen, A.B. 2004: Effectiveness of acoustic road markings in reducing deer-vehicle collisions: a behavioral study. - Wildl. Biol. 10: 155-159.
Fallow deer were exposed to repeatedly occurring acoustic road markings over 13 nights to determine their behavior towards them. Over time, the deer became increasing indifferent to the acoustic road markings; hence they were becoming habituated to them. This is therefore not a reliable method for reducing deer-vehicle collisions on a long term basis.
Wells, P., Woods, J., Bridgewater, G., Morrison, H. 1999. Wildlife Mortalities on Railways: Monitoring Methods and Mitigation Strategies.
To prevent wildlife from being attracted to railways, carcasses should be removed to reduce scavenger kill, grain spillage should cleaned up as soon as possible and the forage should be re- vegetated with plants that hold no interest to bears or other ungulates.
139
CHAPTER 3 APPENDICES
140
Appendix 71. An Overview map of alive and dead moose data collected along highways 16 and 97 surrounding Prince George, British Columbia
141
Appendix 72. Data collect from Highway 16 traveling from Prince George east.
142
Appendix 73. Data collect from Highway 16 traveling from Prince George west.
143
Appendix 74. Data collect from Highway 97 traveling from Prince George north.
144
Appendix 75. Data collect from Highway 97 traveling from Prince George south
145