Drones equipped with thermal sensors have shown ability to overcome some of the limitations often associated with traditional human‐occupied aerial surveys (e.g., low detection, high operational cost, human safety risk). However, their accuracy and reliability as a valid population technique have not been adequately tested. We tested the effectiveness of using a miniaturized thermal sensor equipped to a drone (thermal drone) for surveying white‐tailed deer (Odocoileus virginianus) populations using a captive deer population with a highly constrained (hereafter, known) abundance (151–163 deer, midpoint 157 [87–94 deer/km2, midpoint 90 deer/km2]) at Auburn University's deer research facility, Alabama, USA, 16–17 March 2017. We flew 3 flights beginning 30 minutes prior to sunrise and sunset (1 morning and 2 evening) consisting of 15 nonoverlapping parallel transects (18.8 km) using a small fixed‐wing aircraft equipped with a nonradiometric thermal infrared imager. Deer were identified by 2 separate observers by their contrast against background thermal radiation and body shape. Our average thermal drone density estimate (69.8 deer/km2, 95% CI = 52.2–87.6), was 78% of the mean known value of 90.2 deer/km2, exceeding most sighting probabilities observed with thermal surveys conducted using human‐occupied aircraft. Thermal contrast between animals and background was improved during evening flights and our drone‐based density estimate (82.7 deer/km2) was 92% of the mean known value. This indicates that time of flight, in conjunction with local vegetation types, determines thermal contrast and influences ability to distinguish deer. The method provides the ability to perform accurate and reliable population surveys in a safe and cost‐effective manner compared with traditional aerial surveys and is only expected to continue to improve as sensor technology and machine learning analytics continue to advance. Furthermore, the precise replicability of autonomous flights at future dates results in methodology with superior spatial precision that increases statistical power to detect population trends across surveys. © 2020 The Wildlife Society.

Traditional methods for estimating white‐tailed deer population size and density are affected by behavioral biases, poor detection in densely forested areas, and invalid techniques for estimating effective trapping area. We evaluated a noninvasive method of capture—recapture for white‐tailed deer Odocoileus virginianus density estimation using DNA extracted from fecal pellets as an individual marker and for gender determination, coupled with a spatial detection function to estimate density (spatially explicit capture—recapture, SECR). We collected pellet groups from 11 to 22 January 2010 at randomly selected sites within a 1‐km2 area located on Arnold Air Force Base in Coffee and Franklin counties, Tennessee. We searched 703 10‐m radius plots and collected 352 pellet‐group samples from 197 plots over five two‐day sampling intervals. Using only the freshest pellets we recorded 140 captures of 33 different animals (15M:18F). Male and female densities were 1.9 (SE = 0.8) and 3.8 (SE = 1.3) deer km‐2, or a total density of 5.8 deer km‐2 (14.9 deer mile‐2). Population size was 20.8 (SE = 7.6) over a 360‐ha area, and sex ratio was 1.0 M: 2.0 F (SE = 0.71). We found DNA sampling from pellet groups improved deer abundance, density and sex ratio estimates in contiguous landscapes which could be used to track responses to harvest or other management actions.

Attitudes and motivations of white-tailed deer (Odocoileus virginianus) hunters are important for state wildlife agencies to consider when they are trying to meet harvest goals for the species. In recent years, interest in quality deer management (QDM) has grown, but little is known about hunter support for QDM. We surveyed hunters on private hunting clubs and Wildlife Management Areas where QDM was practiced, as well as statewide sportsman license holders in Tennessee, USA, following the 2004-2005 deerhunting season to identify characteristics, attitudes, and motivations of these hunter groups. Respondents in all 3 hunter groups identified QDM as a ''sensible management strategy for white-tailed deer'' and a majority (>76%) of the hunters preferred hunting areas managed under QDM guidelines. Hunter groups varied in their responses related to specific QDM guidelines and implementation. Nonetheless, all 3 hunter groups were primarily interested in herd health and buck quality, wanted a reduction in the buck bag limit, and supported harvest of antlerless deer. Motivations to hunt varied by hunter group, but respondents in all 3 groups indicated that experiencing nature was the number one reason for hunting. Our survey results suggest that though opinions may vary on how QDM might be implemented, the general deer-hunting public in Tennessee has moved away from the traditional deer-management philosophy that allowed buck harvest without age restrictions and restricted antlerless harvest. Using biological justification along with hunter opinion, we recommend that state wildlife agencies consider providing QDM opportunities where appropriate and offer annual education programs to improve hunters' understanding of deer-management strategies. This should help ensure hunter satisfaction and will help state wildlife agencies meet deermanagement objectives. ß 2012 The Wildlife Society.KEY WORDS hunter attitudes, hunter motivations, Odocoileus virginianus, quality deer management, white-tailed deer.

Population monitoring requires techniques that produce estimates with low bias and adequate precision. Distance sampling using ground-based thermal infrared imaging (ground imaging) and spotlight surveys is commonly used to estimate population densities of white-tailed deer (Odocoileus virginianus). These surveys are often conducted along roads, which may violate assumptions of distance sampling and result in density estimates that are biased high. Aerial vertical-looking infrared imaging (aerial imaging) is not restricted to roads and therefore enables random sampling and detection. We compared estimates of population density and precision, and evaluated potential sources of bias for these 3 techniques for deer on Arnold Air Force Base in Tennessee, USA, during January-February 2010. Using data from aerial imaging conducted along systematic strip transects, we found that deer were distributed close to roads and deer responded to the landscape along the road edge or to observers driving along roads. As a result of these distributional patterns, estimated deer density based on ground imaging and spotlighting from road-based surveys was 3.0-7.6 times greater than density estimated from strip transects using aerial imaging. Ground imaging did not produce better estimates than spotlighting. Observers on the ground counting all deer seen at test plots with hand-held thermal imagers saw fewer deer than were seen on aerial images, suggesting high detection of deer by aerial imaging. Despite its higher cost (US$10,000) over spotlight surveys, we recommend aerial imaging instead of road-based ground surveys for monitoring populations of deer and discourage the continued use of non-random road-based surveys as a method for estimating white-tailed deer populations. Ó 2014 The Wildlife Society.

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