| Summary: | The population of northern fur seals (Callorhinus ursinus) in the Pribilof Islands, Alaska has declined dramatically during the past 35 years. Arresting the decline of the species requires an understanding of their foraging behaviour at sea and is particularly important for those adult females whose foraging success is also linked to pup survival. We propose an augmented state space methodology for studying behavioural patterns using high-resolution movement time series. We show how non-stationary time series models that describe systems for whom parameters evolve slowly over time relative to the state dynamics can be estimated at relevant time scales for behavioural inference. This framework allows us to relate the time-varying parameter estimates of an auto-regressive system model to the seal's at-sea behaviour. The at-sea behaviour states of eleven lactating female northern fur seals were then matched, spatially and temporally, to a set of environmental variables, some of which were averages that represented the oceanic conditions over a large spatial area. The mismatch of scale between seal behaviour and the spatial variables was accounted for by applying an error-in-covariate Bayesian hierarchical model. Using this approach, we were able to link together northern fur seals that went to disparate regions of the eastern Bering Sea, with widely variable information about their underlying environmental fields into a single model. This application of a hierarchical model relates changes in identifiable behavioural states of the northern fur seal to changes in the Alaska commercial groundfish industry over a diurnal foraging cycle. The methodology described in this thesis is adaptable for analyzing any type of high-resolution movement data on marine predators, and will allow for the characterization of other at-sea behaviours as well as other descriptors of pelagic habitat and foraging success.
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