We present a process-based approach to estimate residency and behavior from uncertain and temporally correlated movement data collected with electronic tags. The estimation problem is formulated as a hidden Markov model (HMM) on a spatial grid in continuous time, which allows straightforward implementation of barriers to movement. Using the grid to explicitly resolve space, location estimation can be supplemented by or based entirely on environmental data (e.g. temperature, daylight). The HMM method can therefore analyze any type of electronic tag data. The HMM computes the joint posterior probability distribution of location and behavior at each point in time. With this, the behavioral state of the animal can be associated to regions in space, thus revealing migration corridors and residence areas. We demonstrate the inferential potential of the method by analyzing satellite-linked archival tag data from a southern bluefin tuna Thunnus maccoyii where longitudinal coordinates inferred from daylight are supplemented by latitudinal information in recorded sea surface temperatures.

Item Type:	Refereed Article
Keywords:	behavioral ecology, data set, day length, estimation method, longitudinal gradient
Research Division:	Environmental Sciences
Research Group:	Environmental management
Research Field:	Conservation and biodiversity
Objective Division:	Environmental Management
Objective Group:	Other environmental management
Objective Field:	Other environmental management not elsewhere classified
UTAS Author:	Patterson, TA (Dr Toby Patterson)
ID Code:	119760
Year Published:	2011
Web of Science® Times Cited:	86
Deposited By:	Ecology and Biodiversity
Deposited On:	2017-08-04
Last Modified:	2017-09-29
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