Translocating Captive Female White-tailed Deer & Training and Experience Increase Classification Accuracy in White-tailed Deer Camera Surveys

Abstract

Thousands of captive white-tailed deer (Odocoileus virginianus) facilities exist across North America for the purpose of producing trophy-quality deer (i.e., exceptionally large-antlered). Many of these deer get marketed to private landowners with the expectation that introduced deer will enhance genetics in the population, resulting in larger-antlered male deer. Previous research suggests that white-tailed deer experience highly variable survival and reproductive success post-translocation, however, little is known about the fate of translocated white-tailed deer sourced from captive-breeding operations. We translocated 24 adult female deer over a 3-year period into a private, 300-ha high-fence enclosure in east-central Alabama. We monitored survival, reproductive success, and fawn recruitment of the translocated deer using VHF radio collars and vaginal implant transmitters (VITs). We found that survival rates were greater than studies where deer were translocated from the wild, but fawn survival and recruitment was poor. We believe our findings provide a baseline of expectations for captive deer translocations. Our following research objectives focus on improving camera survey output for white-tailed deer by reducing sex-age misclassifications. Previous research suggests that misclassifications may be an important source of error in wildlife camera surveys. We developed and tested the effects of species-specific training material designed to reduce sex-age misclassification associated with white-tailed deer images. We found exposure to training material produced the greatest significant improvement on classification accuracy of deer images compared to any other respondent-based factors we investigated. We also found that other experiential factors were positively associated with classification accuracy of deer images. Our findings suggest that use of species-specific training material can reduce misclassifications, leading to more reliable data.