Prediction of wolf (Canis lupus) kill-sites using hidden Markov models

Franke, Alastair; Caelli, Terry; Kuzyk, Gerald W.; Hudson, Robert J.

doi:10.1016/j.ecolmodel.2006.02.043

Cited by 72 publications

(100 citation statements)

References 26 publications

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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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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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“…Global Positioning System (GPS) technology enables collection of high resolution spatio-temporal movement data that then can be used to locate kill sites, identify prey species, and thereby determine species-specific kill rates (Anderson and Lindzey 2003, Sand et al 2005, Franke et al 2006. However, this application of GPS technology is still not well developed and we know of published examples on only 2 species: pumas (Puma concolor: Anderson and Lindzey 2003) and wolves (Canis lupus: Sand et al 2005, Franke et al 2006, Webb et al 2008).…”

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confidence: 99%

“…However, this application of GPS technology is still not well developed and we know of published examples on only 2 species: pumas (Puma concolor: Anderson and Lindzey 2003) and wolves (Canis lupus: Sand et al 2005, Franke et al 2006, Webb et al 2008). …”

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confidence: 99%

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Methods for Locating African Lion Kills Using Global Positioning System Movement Data

et al. 2010

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Knowledge of the range, behavior, and feeding habits of large carnivores is fundamental to their successful conservation. Traditionally, the best method to obtain feeding data is through continuous observation, which is not always feasible. Reliable automated methods are needed to obtain sample sizes sufficient for statistical inference. Identification of large carnivore kill sites using Global Positioning System (GPS) data is gaining popularity. We assessed performance of generalized linear regression models (GLM) versus classification trees (CT) in a multi-predator, multi-prey African savanna ecosystem. We applied GLMs and CTs to various combinations of distance travelled data, cluster durations, and environmental factors to predict occurrence of 234 female African lion (Panthera leo) kill sites from 1,477 investigated

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“…Cluster analysis is a means of calculating kill rates of prey (Anderson and Lindzey 2003, Sand et al 2005, Franke et al 2006, Zimmerman et al 2007, Webb et al 2008, Merrill et al 2010. Although cluster analysis alone provides inaccurate estimates of kill rates, it can guide field investigations (Webb et al 2008, Merrill et al 2010.…”

Section: Wolves) Forays Were Identified Using Arcmap (Environmental mentioning

confidence: 99%

Interactions of wolves, mountain caribou and an increased moose-hunting quota --- Primary-prey management as an approach to caribou recovery.

Steenweg¹

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Prediction of wolf (Canis lupus) kill-sites using hidden Markov models

Cited by 72 publications

References 26 publications

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

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

Methods for Locating African Lion Kills Using Global Positioning System Movement Data

Interactions of wolves, mountain caribou and an increased moose-hunting quota --- Primary-prey management as an approach to caribou recovery.

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