Efficacy of N-mixture models for surveying and monitoring white-tailed deer populations

Automated cameras have become increasingly common for monitoring wildlife populations and estimating abundance. Most analytical methods, however, fail to account for incomplete and variable detection probabilities, which biases abundance estimates. Methods which do account for detection have not been thoroughly tested, and those that have been tested were compared to other methods of abundance estimation. The goal of this study was to evaluate the accuracy and effectiveness of the N-mixture method, which explicitly incorporates detection probability, to monitor white-tailed deer (Odocoileus virginianus) by using camera surveys and a known, marked population to collect data and estimate abundance. Motion-triggered camera surveys were conducted at Auburn University’s deer research facility in 2010. Abundance estimates were generated using N-mixture models and compared to the known number of marked deer in the population. We compared abundance estimates generated from a decreasing number of survey days used in analysis and by time periods (DAY, NIGHT, SUNRISE, SUNSET, CREPUSCULAR, ALL TIMES). Accurate abundance estimates were generated using 24 h of data and nighttime only data. Accuracy of abundance estimates increased with increasing number of survey days until day 5, and there was no improvement with additional data. This suggests that, for our system, 5-day camera surveys conducted at night were adequate for abundance estimation and population monitoring. Further, our study demonstrates that camera surveys and N-mixture models may be a highly effective method for estimation and monitoring of ungulate populations.