9th International Deer Biology Congress

AUTHORS: Michael T. Biggerstaff, Warnell School of Forestry and Natural Resources, University of Georgia; Michael J. Cherry, College of Natural Resources and Environment, Virginia Polytechnic and State University; L. Mike Conner, Joseph W. Jones Ecological Research Center at Ichauway; Richard B. Chandler, Warnell School of Forestry and Natural Resources, University of Georgia; Charlie H. Killmaster, Georgia Department of Natural Resources, Wildlife Resources Division; James T. Johnson, Warnell School of Forestry and Natural Resources, University of Georgia; Karl V. Miller, Warnell School of Forestry and Natural Resources, University of Georgia

ABSTRACT: Temporal activity patterns of white-tailed deer (Odocoileus virginianus) have historically been evaluated using visual observations of deer or by monitoring a subsample of a population via VHF or GPS collars. However, inferring activity patterns through the use of VHF telemetry can be problematic due to infrequent sampling regimes and the inaccuracy of activity sensors. Higher location acquisition rates and greater location accuracy of GPS collars have improved identification patterns of collared animals. However, high costs often limit sample sizes and collared animals may not be representative of the population because variation in activity patterns has been reported among individual deer [1,2]. Additionally, due to behavioral differences among age groups, it is difficult to obtain a random sample of individuals that are representative of the population. High density trail camera grids may allow non-invasive monitoring of population-level activity patterns, and have been used to determine environmental effects on sika deer scientific name activity [3].
To evaluate temporal patterns of activity of white-tailed deer, and to investigate the influence of seasonal and environmental factors, we deployed cameras (1 camera/20 ha) on a 1,000 ha tract at the Joseph W. Jones Ecological Research Center at Ichauway in Georgia, USA. Between September 2014 and February 2017, we collected 13,036 observations of deer while simultaneously collecting weather data. During the study period, a record setting drought occurred in southwestern Georgia. Between 1 September 2016 and 1 December 2016, rainfall on our study site was 44% of normal. Therefore we investigated how the drought affected deer activity. We used ANOVA to determine differences in activity rates between a year with normal rainfall (2015) and a drought year (2016). Activity rates of does (p