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Keywords:

  • antler measures;
  • density;
  • density-dependence;
  • Odocoileus virginianus;
  • physical condition;
  • population ecology;
  • population management;
  • weights;
  • white-tailed deer

Abstract: Management of white-tailed deer (Odocoileus virginianus) populations is important because of the popularity of this species for sport hunting and the ecological and economic damage deer can cause. Managers traditionally have collected data from hunter-harvested deer to provide information for making these harvest management decisions. We tested for predictive relationships between these data and density using long-term (15–31 years duration, median = 26 years) harvest data for 9 populations in the southeastern United States that spanned several physiographic provinces and a wide range of densities (3–32 deer/km2) that varied by a factor of 1.67–5.50 within populations over the study period. Mean annual harvest for these populations ranged from 265–5,242 animals (median = 626). We tested 8 physical and 3 recruitment measures commonly collected on harvested deer for their relationship to reconstructed herd density estimates using correlation and linear regression models. Yearling male dressed body weights (range of average annual sample sizes: 53–260, median = 146) proved to be the parameter most consistently (7 of 9 populations) related to density (P < 0.05) among those tested. Yearling antler points (sample sizes the same as for yearling male weights) and yearling female dressed body weights (range of average annual sample sizes: 17–126, median = 72) also were related to density (P < 0.05). The most appropriate models were based on 2-year lags for density. Three-year running averages for both dependent and independent variables were used to smooth annual variability. Using data from 3 additional, independent populations validated that predicted and observed densities were highly correlated (r = 0.71–0.96; P < 0.01). The efficacy of time lags should serve to caution managers not to look for immediate responses in herds. The populations examined in this study provided long-term data that captured stochasticity resulting from density-independent factors. Despite any potential impact from these factors, we detected significant density-dependent relationships.