Alternative learning strategies for spatio-temporal processes of complex animal behavior
The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven successful for approximating nonlinear system mechanisms for prediction and classification. These alternative learning frameworks can be linked to statistical models in a hierarchical framework to improve ecological inference and prediction in the presence of uncertainty. This dissertation provides three methodological extensions of alternative learning with statistical uncertainty quantification for modeling animal behavior dynamics at different scales. First, an efficient Bayesian Markov model is developed to provide inference on white-fronted geese behavior from individual accelerometer and location data while accounting for classification uncertainty. Second, nonlinear basis function expansions produced by a spatio-temporal echo state network are used as features in a hierarchical generalized linear model for predicting spatial patterns of mallard duck settling pattern counts. Lastly, Bayesian inverse reinforcement learning is developed to estimate the behavioral state costs for collective animal groups.
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