MEPS 337:255-264 (2007)  -  doi:10.3354/meps337255

ABSTRACT: Identifying the foraging habitat of marine predators is vital to understanding the ecology of these species and for their management and conservation. Foraging habitat for many marine predators is dynamic, and this poses a serious challenge for understanding how oceanographic features may shape the ecology of these animals. To help resolve this issue, we present a switching state-space model (SSSM) for discerning different movement behaviours hidden within error-prone satellite telemetry data. Along with modelling the movement dynamics, the SSSM estimates the probability that an animal is in a particular discrete behavioural mode, such as transiting or foraging. Using Argos satellite telemetry for leatherback sea turtles, we show that the SSSM readily identifies distinct classes of movement behaviour from the noisy data. Moreover, patterns in simultaneously collected diving data, to which the model is blind, match well with behavioural mode estimates. By combining behavioural mode estimates from the model with the diving data, we show that while transiting, leatherbacks make longer, deeper dives; and while foraging, they encounter cooler waters that range from 13 to 22°C. These differences are consistent among the turtles studied and within the same turtle in different years. This modelling approach can enhance standard kernel density estimators for identifying habitat use by incorporating behavioural information into the estimation procedure. Ultimately, we can build predictive models of habitat use by incorporating environmental data and diving behaviour directly into the SSSM framework.

KEY WORDS: Bayesian · Correlated random walk · Dermochelys coriacea · Diving behaviour · Habitat · Hidden Markov models · Meta-analysis · Uncertainty