Urban development and habitat fragmentation threaten recovery and management of the endangered Florida Key deer (Odocoileus virginianus clavium). Urban development has reduced deer dispersal from their core habitat resulting in deer “overabundance” and has increased deer-human interactions (mostly deer-vehicle collisions [DVCs]). Conversely, deer populations on outer islands have declined in recent years due to limited deer dispersal from source populations. In order to expand the Key deer’s range and reduce DVCs within their core habitat, wildlife managers determined translocations and DVC mitigation were needed. Thus, the objectives of my thesis were to determine (1) effects of translocation on the establishment of outer-island local populations, and (2) effects of United States 1 Highway (US 1) improvements (i.e., exclusion fencing, underpasses, deer guards, and extra lane creation) on DVCs and deer movements. I evaluated the efficacy of translocations by comparing annual survival and seasonal ranges between resident and translocated deer and by analyzing reproduction of translocated deer. Translocated females (yearlings and adults) had lower annual survival than resident deer. Conversely, males (yearlings and adults) demonstrated higher annual survival than resident males. Due to low sample sizes and large variation, these numbers are potentially less important than the high overall survival (only 4 of 38 died). Seasonal ranges were generally smaller for resident deer than translocated deer. I attribute differences in ranges to differences in habitat quality between the core habitat and destination islands and to use of soft releases. Presence of fawns and yearlings indicated successful reproduction of translocated deer. Overall, the project was successful in establishing populations on the destination islands. The US 1 Highway improvements reduced DVCs along the fenced section of US 1 (2003, n = 2; 2004, n = 1; 2005, n = 0); however, overall DVCs increased on Big Pine Key (1996–2000, x¯ = 79; 2003, n = 91; 2004, n = 84; 2005, n = 100). Data suggest DVCs shifted to the unfenced segment of US 1. However, monthly deer surveys also suggested an increase in deer numbers that may explain overall DVC increases observed in my study.

Roads are essential in modern societies, but as populations grow and traffic volumes rise, roads will continue to be built and expanded. As a result, the effects that roads have on wildlife will likely intensify, making it imperative that managers understand those effects so mitigation can be directed accordingly. In Utah, considerable areas of mule deer (Odocoileus hemionus) habitat have been bisected by roads. Mule deer are commonly involved in vehicle collisions and there is concern that roads and vehicle traffic are impacting populations. This project was conducted to determine the number and demographic effects of deer-vehicle collisions, to examine how movements and survival of deer were impacted by roads, and to develop a smartphone-based reporting system for wildlife-vehicle collisions. Accurate estimates of DVCs are needed to effectively mitigate the effects of roads, but great uncertainty exists with most deer-vehicle collision estimates. I estimated the number of deer-vehicle collisions using carcass surveys, while accounting for several sources of bias to improve accuracy. I estimated that 2-5 % of the statewide deer population was killed in vehicle collisions annually. The effect that vehicle collisions have on deer abundance depended not only on the number of deer killed but also on the demographic groups removed. I found that 65 % of deer killed in vehicle collisions were female and 40 % were adult females. As female deer are the primary drivers of population growth, my data suggest vehicle collisions could significantly affect population abundance. However I was unable to detect a decreasing trend in deer abundance. Deer have distinct movement patterns that affect their distribution in relationship to roads. I analyzed deer movements during two consecutive winters (2010-11 & 2011-12) to determine what effect climate had on deer movements and vehicle collision rates. I observed that as snow depth decreased, the distance that deer occurred from roads increased. As a result road crossing rates declined, as did the number of vehicle collisions. This suggests a causal mechanism by which winter conditions influence vehicle collision rates. Currently there is a need for an efficient wildlife-vehicle collision data collection. I envisioned and, working with colleagues, helped develop a smartphone-based system for reporting wildlife-vehicle collision data. The WVC Reporter system consisted of a mobile web application for data collection, a database for centralized storage of data, and a desktop application for viewing data. The system greatly improved accuracy and increased efficiency of data collection efforts, which will likely result in improved mitigation and ultimately increased safety for motorists and deer.

The Department of Energy Oak Ridge Reservation (ORR) and the nearby adjoining City of Oak Ridge, Tennessee had experienced a rise in deer-vehicle collisions (DVCs) to the point where safety for employees and residents became a concern. I investigated the effect of hunting, land cover, road mileage, season, lunar phase, sex, and change in traffic patterns that coincide with work shifts on DVCs from 1975 - 2008. The study area was divided into grids of 1.5 km² each for administration and data recording by managing agencies. Statistical analyses were performed on the ORR (121 grids) and GIS analyses were performed on the entire study area that included ORR and the city of Oak Ridge (190 grids). The number of DVCs in 1975 was 16 and reached a high of 273 in 1985. Therefore, managers initiated a hunting program in 1985 and recorded deer harvest numbers by grid each year. Deer harvest has been occurring from 1985 until present, except when hunting was cancelled due to security concerns after the September 11 terrorist attacks in 2001. By 2008, the number of DVCs had decreased to 100 per year. When hunting first started in 1985, they harvested 926 deer. By 2008, that number was down to 481. I used GIS mapping to record DVCs, deer harvest per grid, landcover types, and mileage per grid to determine factors affecting DVCs on the smaller landscape. Following the initiation of annual hunts, both the annual deer harvest and the number of DVC’s have fallen, presumably because the overall deer population has declined from high pre-hunting levels. Deer harvest appears to be related to landcover characteristics, as a higher percentage of deer were harvested from forested areas than from other landcover types, as forested areas were most prominent. The months of October, November, and December had the highest DVC numbers. Increased traffic during starting and leaving shift times seem to increase the number of DVCs as well. Lunar phases only seem to significantly increase DVCs during the gestation and fawning seasons. Does are involved more frequently with DVCs than bucks during gestation, fawning and prerut, but not during the rutting season. I expect managers can use this data to guide intensive local management aimed at reducing DVCs by increasing the number of deer harvested and increased public education.

Deer-vehicle collisions (DVCs) along a 5.6-km segment of United States Highway 1 (US 1) on Big Pine Key (BPK), Florida responsible for approximately 26% of endangered Florida Key deer (Odocoileus virginianus clavium) annual mortalities. The Florida Department of Transportation (FDOT) constructed a 2.6-km long system of fencing, 2 underpasses, and 4 experimental deer guards to address DVCs along a portion of the US 1 roadway in 2001–2002. I evaluated the effectiveness of the project in reducing Key deer mortality by comparing (1) survival of radio-collared deer, (2) deer-vehicle collisions on US 1, and (3) determining the ability of deer to access the fenced segment. I found no significant difference in male or female survival. Key deer-vehicle collisions were reduced by 83–92% inside the fenced segment. However, overall US 1 Key deer-vehicle collisions did not change. Key deer entry into the fenced segment was minimized to 8 deer during the first-year resulting in 2 deer mortalities. I also assessed the potential impacts of the US 1 corridor project to Key deer movements by comparing (1) radio-collared Key deer annual ranges (2) radio-collared deer corridor movements, and (3) assessing Key deer underpass and corridor use. Female and male ranges and core areas did not change (P > 0.05). Deer movements within the US 1 corridor were comparable pre- (6 of 23 radio-collared deer crossed the corridor) and post-project (4 of 16). Infrared-triggered camera data indicate underpass movements increased over time. Collectively, post-project telemetry and camera data indicates US 1 highway improvements have not restricted Key deer movements. Hourly Key deer movement and US 1 traffic patterns were compared to annual US 1 DVCs. Hourly deer movements showed a positive correlation (P = 0.012, r = 0.505) to hourly DVCs for the full circadian period. Hourly US 1 traffic showed a significant positive relationship (P = 0.012, r = 0.787) with DVCs only during the night period. Evaluation of hourly deer movements and hourly traffic volume on US 1 found hourly DVCs to be the result of a combination between both variables.

Deer-vehicle collisions (DVCs) along a 5.6-km segment of United States Highway 1 (US 1) on Big Pine Key (BPK), Florida responsible for approximately 26% of endangered Florida Key deer (Odocoileus virginianus clavium) annual mortalities. The Florida Department of Transportation (FDOT) constructed a 2.6-km long system of fencing, 2 underpasses, and 4 experimental deer guards to address DVCs along a portion of the US 1 roadway in 2001–2002. I evaluated the effectiveness of the project in reducing Key deer mortality by comparing (1) survival of radio-collared deer, (2) deer-vehicle collisions on US 1, and (3) determining the ability of deer to access the fenced segment. I found no significant difference in male or female survival. Key deer-vehicle collisions were reduced by 83–92% inside the fenced segment. However, overall US 1 Key deer-vehicle collisions did not change. Key deer entry into the fenced segment was minimized to 8 deer during the first-year resulting in 2 deer mortalities. I also assessed the potential impacts of the US 1 corridor project to Key deer movements by comparing (1) radio-collared Key deer annual ranges (2) radio-collared deer corridor movements, and (3) assessing Key deer underpass and corridor use. Female and male ranges and core areas did not change (P > 0.05). Deer movements within the US 1 corridor were comparable pre- (6 of 23 radio-collared deer crossed the corridor) and post-project (4 of 16). Infrared-triggered camera data indicate underpass movements increased over time. Collectively, post-project telemetry and camera data indicates US 1 highway improvements have not restricted Key deer movements. Hourly Key deer movement and US 1 traffic patterns were compared to annual US 1 DVCs. Hourly deer movements showed a positive correlation (P = 0.012, r = 0.505) to hourly DVCs for the full circadian period. Hourly US 1 traffic showed a significant positive relationship (P = 0.012, r = 0.787) with DVCs only during the night period. Evaluation of hourly deer movements and hourly traffic volume on US 1 found hourly DVCs to be the result of a combination between both variables.

The Department of Energy Oak Ridge Reservation (ORR) and the nearby adjoining City of Oak Ridge, Tennessee had experienced a rise in deer-vehicle collisions (DVCs) to the point where safety for employees and residents became a concern. I investigated the effect of hunting, land cover, road mileage, season, lunar phase, sex, and change in traffic patterns that coincide with work shifts on DVCs from 1975 - 2008. The study area was divided into grids of 1.5 km² each for administration and data recording by managing agencies. Statistical analyses were performed on the ORR (121 grids) and GIS analyses were performed on the entire study area that included ORR and the city of Oak Ridge (190 grids). The number of DVCs in 1975 was 16 and reached a high of 273 in 1985. Therefore, managers initiated a hunting program in 1985 and recorded deer harvest numbers by grid each year. Deer harvest has been occurring from 1985 until present, except when hunting was cancelled due to security concerns after the September 11 terrorist attacks in 2001. By 2008, the number of DVCs had decreased to 100 per year. When hunting first started in 1985, they harvested 926 deer. By 2008, that number was down to 481. I used GIS mapping to record DVCs, deer harvest per grid, landcover types, and mileage per grid to determine factors affecting DVCs on the smaller landscape. Following the initiation of annual hunts, both the annual deer harvest and the number of DVC’s have fallen, presumably because the overall deer population has declined from high pre-hunting levels. Deer harvest appears to be related to landcover characteristics, as a higher percentage of deer were harvested from forested areas than from other landcover types, as forested areas were most prominent. The months of October, November, and December had the highest DVC numbers. Increased traffic during starting and leaving shift times seem to increase the number of DVCs as well. Lunar phases only seem to significantly increase DVCs during the gestation and fawning seasons. Does are involved more frequently with DVCs than bucks during gestation, fawning and prerut, but not during the rutting season. I expect managers can use this data to guide intensive local management aimed at reducing DVCs by increasing the number of deer harvested and increased public education.

No description available.

Deer Vehicle Collisions (DVCs) are a global problem that is not only resulting in seriousinjuries to humans but also results in loss of human and deer lives. Deer are more active and less attentive during the mating and hunting seasons. Roadside deer activity such as feeding and strolling along the roadside has a significant correlation with DVCs. To mitigate DVCs, several strategies were used that include vegetation management, fences, underpasses and overpasses, population reduction, warning signs and animal detection systems (ADS). These strategies vary in their efficacy. These strategies may help to reduce DVCs. However, they are not always easily feasible due to false alarms, high cost, unsuitable terrain, land ownership, and other factors. Thus, DVCs are increasing due to the increase in number of vehicles and the absence of intelligent highway safety and alert systems. Detecting deer in real-time on our roads is a challenging problem. Thus, this research work proposed a deer detection and movement DDM technique to automate DVCs mitigation system. The DDM combines computer vision, artificial intelligent methods with deep learning techniques. DDM includes two main deep learning algorithms 1)onestage deep learning algorithm based on Yolov5 that generates a detection model(DM) to detect deer and 2) deep learning algorithm developed by python toolkit DeepLabCut to generate movement model(MM) for detecting the movement of the deer. The proposed method can detect deer with 99.7% precision and succeeds to ascertain if the deer is moving or static with an inference speed of 0.29s. The proposed method can detect deer with 99.7% precision and using DeepLabCut toolkit on the detected deer we can ascertain if the deer is moving or static with an inference speed of 0.29s.