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Hidden Markov models (HMMs) are commonly used to model animal movement dataand infer aspects of animal behavior. An HMM assumes that each data point froma time series of observations stems from one of $N$ possible states. The statesare loosely connected to behavioral modes that manifest themselves at thetemporal resolution at which observations are made. However, due to advances intag technology, data can be collected at increasingly fine temporalresolutions. Yet, inferences at time scales cruder than those at which data arecollected, and which correspond to larger-scale behavioral processes, are notyet answered via HMMs. We include additional hierarchical structures to thebasic HMM framework in order to incorporate multiple Markov chains at varioustime scales. The hierarchically structured HMMs allow for behavioral inferencesat multiple time scales and can also serve as a means to avoid coarsening data.Our proposed framework is one of the first that models animal behaviorsimultaneously at multiple time scales, opening new possibilities in the areaof animal movement modeling. We illustrate the application of hierarchicallystructured HMMs in two real-data examples: (i) vertical movements of harborporpoises observed in the field, and (ii) garter snake movement data collectedas part of an experimental design.

Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures