Developing novel hidden Markov models from a data science perspective to inform seabird conservation

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Understanding animal movement and identifying the behaviours that influence animal movement is crucial to inform effective conservation management. Behaviours are often inferred from movement data and rarely validated because it is difficult to directly observe and record behaviours influencing animal movement in remote and hostile terrain such as the marine environment. Using data provided by the Joint Nature Conservation Committee (JNCC), the validity of behaviours inferred from movement data using hidden Markov models ( HMMs) is examined. Also, the temporal resolution of animal movement data can impact inference on animal behaviour. For instance, animal movement data obtained at a lower temporal resolution may not capture behaviours only obtainable at a finer temporal scale that comes at a cost sampling duration and GPS battery life. We investigate this problem from the optic of the Nyquist-Shannon sampling theorem (NSST) useful for selecting temporal resolutions in the animal movement trajectories such that there is no loss of information about the underlying latent behavioural process.