Remote cameras have become a popular tool for monitoring wildlife. We used remote cameras to estimate bobcat (Lynx rufus) population abundance on the Utah Test and Training Range during two sample periods between 2015 and 2017. We used two statistical methods, closed capture mark-recapture (CMR) and mark-resight Poisson log-normal (PNE), to estimate bobcat abundance within the study area. We used the maximum mean distance moved method (MMDM) to calculate the effective sample area for estimating density. Additionally, we captured bobcats and estimated home range using minimum convex polygon (MCP) and kernel density estimation (KDE) methods. Bobcat abundance on the UTTR was 35-48 in 2017 and density was 11.95 bobcats/100 km2 using CMR and 16.69 bobcats/100 km2 using PNE. The North Range of the study area experienced a decline of 36-44 percent in density between sample periods. Density declines could be explained by natural predator prey cycles, by habituation to attractants or by an increase in home range area. We recommend that bobcat abundance and density be estimated regularly to establish population trends.To improve the management of bobcats on the Utah Test and Training Range (UTTR), we investigated bobcat (Lynx rufus) habitat use. We determined habitat use points by capturing bobcats in remote camera images. Use and random points were intersected with remotely sensed data in a geographic information system. Habitat variables were evaluated at the capture point scale and home range scale. Home range size was calculated using the mean maximum distance moved method. Scales and habitat variables were compared within generalized linear mixed-effects models. Our top model (AICc weight = 1) included a measure of terrain ruggedness, mean aspect, and land cover variables related to prey availability and human avoidance.

The kit fox (Vulpes macrotis) is a species of conservation concern in western North America. Recent methods for monitoring populations of kit fox include using lures and remote cameras in an occupancy-modeling framework and habitat modeling to predict areas of occupancy. In chapter one, we tested the optimal lure and movement procedure for scent stations to maximize visits and detection of foxes, thereby improving estimates of occupancy. Between May 2015 and October 2016, we placed remote cameras at 522 random locations throughout nine study areas in the Colorado Plateau, Great Basin Desert, and Mojave Desert. Each location was randomly assigned one of three methods (Scented Predator Survey Disks, cotton swabs, or hollowed golf ball) to broadcast one of three lure types (Red and Gray Fox liquid lure, Willey liquid lure, and fatty acid lure). After seven nights, half of all stations were moved 100 meters within the same sample grid cell, while the others remained in the same location. Stations were then monitored for an additional week. We used Program MARK and AIC model selection to identify optimal lure types and broadcast methods and to estimate rates of occupancy. Detection of kit foxes differed by method of scent deployment; cotton swabs were associated with the highest rates of visitation. Detection of kit foxes did not differ by lure type. Relocating the scent station after one week did not influence detection probability. We suggest that the use of cotton swabs maximizes detection, and therefore, the precision of estimates of occupancy. For chapter two, we used resource selection functions to identify variables that best discriminated between locations where kit fox were detected and random locations. We then produced a habitat map that predicted the relative probability of kit foxes occurring across seven study areas throughout the state of Utah. We placed remote cameras at 458 randomly selected locations throughout the study areas in the Colorado Plateau, Great Basin Desert, and Mojave Desert. We detected kit foxes at 157 "use" points from these cameras between May 2015 and October 2016. We then compared the attributes of these "use" points to 14,742 available, randomly selected points located within the study areas using variables derived from a Geographic Information System (GIS). We used model selection and minimization of AIC values to determine key habitat characteristics that differentiated use and random locations. We identified slope, elevation, and soil type as significant variables (P < 0.05) in habitat selection of kit foxes. Kit foxes selected areas that were 1) less steep, 2) lower in elevation, and 3) classified as having silty soils. The identification of these specific variables from our modeling effort was generally consistent with kit fox ecology. Our study produced a habitat model that can serve as a foundation for future monitoring efforts of kit foxes in potential habitat across Utah.