Abstract
The influence of the sea environment on fish behaviors is an important ecological and environmental factor. In this article, we analyze the relationship between phytoplankton distribution in the sea and the behavioral process of a diadromous fish, albacore (Thunnus alalunga), by development of statistical models. It is well known that animals change their behavioral patterns, such as traveling and foraging, according to the conditions of their encountered habitat. To account for these characteristics in fish behavior, we develop a regime switching model with a hidden latent process, whose output is influenced by phytoplankton concentration. Geolocation prediction experiments suggested that the model can improve the prediction accuracies compared with the general regime switching model with no covariates, or traditional linear nonstationary time series models. Furthermore, robust geolocation predictions are yielded by the model even when the fish switches its migratory behavior.
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