Cohort antler size signals environmental stress in a moderate climate

Research in northern latitudes confirms that climate teleconnections exert important influences on ungulate fitness, but studies from regions with milder climates are lacking. We explored the influence of the Pacific Decadal Oscillation (PDO), Northern Atlantic Oscillation (NAO), and El Niño-Southern Oscillation (ENSO) on male, 2.5-year-old white-tailed deer (Odocoileus virginianus) antler and body mass in Mississippi, USA, a region with mild winters and warm, humid summers. Explanatory variables were seasonal averages of each climate index extending back to 3 years prior to account for possible maternal and lag effects. Seasonal climate indices from the period of gestation and the first year of life were correlated with deer morphometrics. Reduced antler mass was largely correlated (R² = 0.52) with PDO values indicating dry conditions during parturition and neonatal development and NAO values indicating warmer than normal winters during gestation and the first year of life. Body mass was less correlated (R² = 0.16) to climate indices, responding negatively to warmer winter weather during the first winter of life. Climate may promote variable fitness among cohorts through long-term effects on male competition for dominance and breeding access. Because broad-scale climate indices simplify complex weather systems, they may benefit management at larger scales. Although this study compared climate with morphological variables, it is likely that demographic characteristics can likewise be modeled using climate indices. As climate change in this region is projected to include greater variability in summer precipitation, we may see concomitantly greater variability in fitness among cohorts of white-tailed deer.

Comparison of linear model and artificial neural network using antler beam diameter and length of white-tailed deer (Odocoileus virginianus) dataset

Evaluation of harvest data remains one of the most important sources of information in the development of strategies to manage regional populations of white-tailed deer. While descriptive statistics and simple linear models are utilized extensively, the use of artificial neural networks for this type of data analyses is unexplored. Linear model was compared to Artificial Neural Networks (ANN) models with Levenberg–Marquardt (L-M), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) learning algorithms, to evaluate the relative accuracy in predicting antler beam diameter and length using age and dressed body weight in white-tailed deer. Data utilized for this study were obtained from male animals harvested by hunters between 1977–2009 at the Berry College Wildlife Management Area. Metrics for evaluating model performance indicated that linear and ANN models resulted in close match and good agreement between predicted and observed values and thus good performance for all models. However, metrics values of Mean Absolute Error and Root Mean Squared Error for linear model and the ANN-BR model indicated smaller error and lower deviation relative to the mean values of antler beam diameter and length in comparison to other ANN models, demonstrating better agreement of the predicted and observed values of antler beam diameter and length. ANN-SCG model resulted in the highest error within the models. Overall, metrics for evaluating model performance from the ANN model with BR learning algorithm and linear model indicated better agreement of the predicted and observed values of antler beam diameter and length. Results of this study suggest the use of ANN generated results that are comparable to Linear Models of harvest data to aid in the development of strategies to manage white-tailed deer.

Estimating body mass of Florida white-tailed deer from standard age and morphometric measurements

Context Measuring a mammal’s body mass has importance in understanding nutritional condition, reproductive biology and ecology. It can be impractical for a researcher to measure the body mass when equipment needed to weigh individuals is inadequate or unavailable. Aims The purpose of this study was to develop a model to accurately estimate the body mass of hunter-harvested Florida white-tailed deer (Odocoileus virginianus osceola, Odocoileus virginianus seminolus) based on the relationship between scale mass, sex and standard age and morphometric measurement predictor variables easily obtainable in the field. Methods An information-theoretic approach was used to evaluate simple and multiple linear regression models with 67% of the data, and the best model in the set was validated using the remaining 33%. Key results Chest girth was the best single predictor of body mass. A global model including sex, age, age2 and body length variables was better supported than chest girth alone, and subspecies information did not contribute significantly to the body-mass–predictor-variable relationship. The best model explained 98.5% of the variation in body mass as follows: body mass (kg) = –18.41 + 6.53 × sex (0 = female, 1 = male) + 5.04 × age (year) – 0.49 × age2 (year2) + 4.76 × 10−3 × chest girth2 (cm2) + 0.12 × body length (cm). The 95% confidence interval on the bias of the estimated body mass of the best model was –0.50 to 0.59 kg. The difference between estimated and scale body mass was –0.04 kg ± 0.28 (s.e.). Conclusions Individuals maintained a similar proportion of body mass to predictor variables, and differences between the observed and estimated body mass of model applied to the validation dataset were not significant. Implications The validated body-mass-estimation model presented will enable accurate estimates of the body mass of white-tailed deer in cases where standard age and morphometric measurements are available, but the individuals were not weighed. These results provide a basis to formulate and parameterise body-mass-estimation models for other white-tailed deer subspecies and populations. Without the need for specialised equipment, the body-mass-estimation model can be used by personnel involved in white-tailed deer research, management and sport hunting to assess trends in individual and population health in support of this species’ conservation. Photograph by Carlton Ward Jr.