Ecologists have an unprecedented array of bio-logging technologies available to conduct in situ studies of horizontal and vertical movement patterns of marine animals. These tracking data provide key information about foraging, migratory, and other behaviours that can be linked with bio-physical datasets to understand physiological and ecological influences on habitat selection. In most cases, however, the behavioural context is not directly observable and therefore, must be inferred. Animal movement data are complex in structure, entailing a need for stochastic analysis methods. The recent development of state-space modelling approaches for animal movement data provides statistical rigor for inferring hidden behavioural states, relating these states to bio-physical data, and ultimately for predicting the potential impacts of climate change. Despite the widespread utility, and current popularity, of state-space models for analysis of animal tracking data, these tools are not simple and require considerable care in their use. Here we develop a methodological ‘‘road map’’ for ecologists by reviewing currently available state-space implementations. We discuss appropriate use of state-space methods for location and/or behavioural state estimation from different tracking data types. Finally, we outline key areas where the methodology is advancing, and where it needs further development.

Item Type:	Refereed Article
Research Division:	Biological Sciences
Research Group:	Ecology
Research Field:	Marine and estuarine ecology (incl. marine ichthyology)
Objective Division:	Environmental Management
Objective Group:	Coastal and estuarine systems and management
Objective Field:	Assessment and management of coastal and estuarine ecosystems
UTAS Author:	Bestley, S (Dr Sophie Bestley)
UTAS Author:	Wotherspoon, SJ (Dr Simon Wotherspoon)
ID Code:	89568
Year Published:	2013
Web of Science® Times Cited:	156
Deposited By:	IMAS Research and Education Centre
Deposited On:	2014-03-07
Last Modified:	2014-06-06
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