Continuous-time markov chains as models for animal behaviour

Metz, H; Dienske, Herman; Jonge, Gerrit De; Putters, F.A.

doi:10.1016/s0092-8240(83)80017-2

Cited by 12 publications

(12 citation statements)

References 15 publications

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“…MCs have been used frequently in animal behaviour studies to identify social patterns (e.g. Cane 1978;Metz et al 1983;Harcourt et al 2010). However, the main aim of this study was to bridge the gap between pattern and dynamics of social networks by developing a MC model of short-term social interactions that can be used to (a) investigate the social organisation of fish (i.e.…”

Section: Introductionmentioning

confidence: 99%

Dynamic social networks in guppies (Poecilia reticulata)

Wilson

Krause

James

et al. 2014

Behav Ecol Sociobiol

104

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One of the main challenges in the study of social networks in vertebrates is to close the gap between group patterns and dynamics. Usually scan samples or transect data are recorded to provide information about social patterns of animals, but these techniques themselves do not shed much light on the underlying dynamics of such groups. Here we show an approach which captures the fission-fusion dynamics of a fish population in the wild and demonstrates how the gap between pattern and dynamics may be closed. Our analysis revealed that guppies have complex association patterns that are characterised by close strong connections between individuals of similar behavioural type. Intriguingly, the preference for particular social partners is not expressed in the length of associations but in their frequency. Finally, we show that the observed association preferences could have important consequences for transmission processes in animal social networks, thus moving the emphasis of network research from descriptive mechanistic studies to functional and predictive ones.

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Section: Introductionmentioning

confidence: 99%

Dynamic social networks in guppies (Poecilia reticulata)

Wilson

Krause

James

et al. 2014

Behav Ecol Sociobiol

104

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“…Discrete time Markov (DTM) theory assumes that transitions between states occur at the end of predetermined time intervals. Each transition is associated with a one-step transition probability, p (Crane, 1978;Garcia-Perez, Mazzoni, Zoccolan, Robinson, & Torre, 2005;Metz, Dienske, De Jonge, & Putters, 1983;Olson et al, 1985). A continuous time Markov process differs from a discrete time Markov process because the holding times in each state are not constant (as with DTM) but rather are exponentially distributed.…”

Section: Markov Process Theorymentioning

confidence: 99%

Application of continuous time markov theory in modeling courtship behavior and copulation rate of the calanoid copepod Eurytemora affinis

Steele

Scarfe

2011

Adaptive Behavior

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We present a simple model, derived using a continuous time Markov (CTM) technique of model formulation, for predicting the probability of a courtship resulting in copulation for a laboratory population of Eurytemora affinis. This model represents a novel use, in behavioral ecology, of CTM as a method for sequential analysis. The model results in an acceptable agreement (validated by 2 goodness-of-fit analysis) between the predicted and observed copulation rates of the laboratory population of E. affinis. The copulation rate predicted by the model, with estimated weightings to the transition rates, is 16.7%; the observed rate was 13.0% (16 copulations out of 123 courtship bouts observed), for a discrepancy of 3.7%. Continuous time Markov theory appears to adequately describe mating behavior in this experimental population, implying ''forgetfulness'' in the courtship sequence.

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“…This experiment uses a novel method to give us a descriptive insight into the conditions necessary for producing leaders in groups. While MCMC models have been used previously to investigate behavioral phenomena, 8,9 they enable us here to determine, for the first time, the specific mechanisms responsible for the emergence of leadership. Bold fish were most commonly found to become leaders, but this was due to their limited responsiveness Figure 1.…”

Section: The Ability Of Such Followers To Inspire Bolder Fish Suggestmentioning

confidence: 99%

Leadership, personality and social feedback

Harcourt

Ang

Sweetman

et al. 2009

Communicative & Integrative Biology

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In a recent paper, we showed that leadership arises from individual behavioral differences in pairs of foraging stickleback (Gasterosteus aculeatus). Foraging data from randomly combined pairs of fish were analyzed using Markov Chain models to infer the individual movement rules underlying joint behavior. While both fish responded to partner movement, bolder individuals were the least responsive and showed greater individual initiative. Shy partners were more faithful followers and were also found to bring about greater leadership tendencies in their bold partners. The ability of such followers to inspire bolder fish suggests that leadership may be dependent on individual temperament differences, reinforced by social feedback.

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Continuous-time markov chains as models for animal behaviour

Cited by 12 publications

References 15 publications

Dynamic social networks in guppies (Poecilia reticulata)

Dynamic social networks in guppies (Poecilia reticulata)

Application of continuous time markov theory in modeling courtship behavior and copulation rate of the calanoid copepod Eurytemora affinis

Leadership, personality and social feedback

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