State–space models of individual animal movement

Patterson, Toby A.; Thomas, Len; Wilcox, Chris; Ovaskainen, Otso; Matthiopoulos, Jason

doi:10.1016/j.tree.2007.10.009

Cited by 755 publications

(767 citation statements)

References 86 publications

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“…For any tracking technology it is important to assess location error as this impacts any subsequent movement analysis (Bradshaw et al 2007, Patterson et al 2008). There are various options to estimate location accuracy.…”

Section: Discussionmentioning

confidence: 99%

Link between vertical and horizontal movement patterns of cod in the North Sea

Hobson

Righton²,

Metcalfe³

et al. 2009

Aquat. Biol.

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We used a geolocation method based on tidal amplitude and water depth to assess the horizontal movements of 14 cod Gadus morhua equipped with time-depth recorders (TDR) in the North Sea and English Channel. Tracks ranged from 40 to 468 d and showed horizontal movements of up to 455 km and periods of continuous localised residence of up to 360 d. Cod spent time both in midwater (43% of total time) and near the seabed (57% of total time). A variety of common vertical movement patterns were seen within periods of both residence and directed horizontal movement. Hence particular patterns of vertical movement could not unequivocally define periods of migration or localised residence. After long horizontal movements, cod tended to adopt resident behaviour for several months and then return to broadly the same location where they were tagged, indicating a geospatial instinct. The results suggest that residence and homing behaviour are important features of Atlantic cod behaviour.

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Section: Discussionmentioning

confidence: 99%

Link between vertical and horizontal movement patterns of cod in the North Sea

Hobson

Righton²,

Metcalfe³

et al. 2009

Aquat. Biol.

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“…In practical terms, it would be necessary to address the population as a sum of individual agents (individual-based modelling IBM) [Grimm & Railsback 2005] to model and predict population patterns. These models could include the formulation of the dynamic energy budget theory (DEB) proposed by Kooijman [2010] to manage the physiological aspects, and the formulation of spacestate models to manage movements and behaviour of individuals [Patterson et al 2008].…”

Section: Populationmentioning

confidence: 99%

Implications for seabirds off South Africa of a long-term change in the distribution of sardine

Crawford¹,

Sabarros²,

Fairweather³

et al. 2008

African Journal of Marine Science

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“…However, behaviour may be inferred by characterizing patterns of movement based solely on the geometry or complexity of an animal's path using techniques such as tortuosity [8,9], positional entropy [10,11] or first-passage time [12]. In addition, modelling approaches such as Gaussian mixtures have been used to classify animal tracking data into discrete modes of movement [2,13], while state-space models including hidden Markov models (HMMs) [10,[14][15][16][17][18][19] have been used to identify different modes of movement and the dynamics of switching between them.…”

Section: Introductionmentioning

confidence: 99%

Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour

Dean

Freeman

Kirk

et al. 2013

J. R. Soc. Interface.

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The use of miniature data loggers is rapidly increasing our understanding of the movements and habitat preferences of pelagic seabirds. However, objectively interpreting behavioural information from the large volumes of highly detailed data collected by such devices can be challenging. We combined three biologging technologies-global positioning system (GPS), saltwater immersion and time-depth recorders-to build a detailed picture of the at-sea behaviour of the Manx shearwater (Puffinus puffinus) during the breeding season. We used a hidden Markov model to explore discrete states within the combined GPS and immersion data, and found that behaviour could be organized into three principal activities representing (i) sustained direct flight, (ii) sitting on the sea surface, and (iii) foraging, comprising tortuous flight interspersed with periods of immersion. The additional logger data verified that the foraging activity corresponded well to the occurrence of diving. Applying this approach to a large tracking dataset revealed that birds from two different colonies foraged in local waters that were exclusive, but overlapped in one key area: the Irish Sea Front (ISF). We show that the allocation of time to each activity differed between colonies, with birds breeding furthest from the ISF spending the greatest proportion of time engaged in direct flight and the smallest proportion of time engaged in foraging activity. This type of analysis has considerable potential for application in future biologging studies and in other taxa.

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State–space models of individual animal movement

Cited by 755 publications

References 86 publications

Link between vertical and horizontal movement patterns of cod in the North Sea

Link between vertical and horizontal movement patterns of cod in the North Sea

Implications for seabirds off South Africa of a long-term change in the distribution of sardine

Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour

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