Abstract
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.
Highlights
White-tailed deer (Odocoileus virginianus) (WTD) are among the largest herbivore ungulates in forested ecosystems of the eastern United States [1]
This study presents the assessment of linear model and Multi-Layer Perceptron Artificial Neural Network (MLPANN) model with three different learning algorithms (L-M, Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG)) to predict antler beam diameter and length from the WTD harvest dataset obtained from the Berry College Wildlife Management Area (BCWMA)
From the results and discussion above it can be concluded that there is a linear relationship between field-dressed weight, age of the animal, and antler characteristics. This knowledge will be very useful in making management decision. in deer herd in the WMA Based on various performance criteria (MAE, Root Mean Squared Error (RMSE), FACT2, r and Index of Agreement (IA)), Artificial Neural Network (ANN) model with BR backpropagation learning algorithm performed when compared to ANN models with L-M and SCG
Summary
White-tailed deer (Odocoileus virginianus) (WTD) are among the largest herbivore ungulates in forested ecosystems of the eastern United States [1] Numbers of this animal represents a significant economic impact. Economic damage of wildlife-vehicle collisions was reported to exceed $8 billion/year [4] Annual damage to vegetable and grain crops in the northeastern United States was estimated to exceed $168 million [5]. Information related to animal physical characteristics provide critical information about the number and overall health status of the regional deer herd. These records can assist managers in determining the appropriate management strategies to achieve defined objectives
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