TY - JOUR
T1 - State-space models for bio-loggers: A methodological road map
AU - Jonsen, I.D.
AU - Basson, M.
AU - Bestley, S.
AU - Bravington, M.V.
AU - Patterson, T.A.
AU - Pedersen, Martin Wæver
AU - Thomson, R.
AU - Thygesen, Uffe Høgsbro
AU - Wotherspoon, S.J.
PY - 2012
Y1 - 2012
N2 - 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
AB - 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
U2 - 10.1016/j.dsr2.2012.07.008
DO - 10.1016/j.dsr2.2012.07.008
M3 - Journal article
SN - 0967-0645
VL - 88-89
SP - 34
EP - 46
JO - Deep-Sea Research. Part 2: Topical Studies in Oceanography
JF - Deep-Sea Research. Part 2: Topical Studies in Oceanography
ER -