The importance of 'memory' in statistical models for animal feeding behaviour.

Abstract:

:We investigate models for animal feeding behaviour, with the aim of improving understanding of how animals organise their behaviour in the short term. We consider three classes of model: hidden Markov, latent Gaussian and semi-Markov. Each can predict the typical 'clustered' feeding behaviour that is generally observed, however they differ in the extent to which 'memory' of previous behaviour is allowed to affect future behaviour. The hidden Markov model has 'lack of memory', the current behavioural state being dependent on the previous state only. The latent Gaussian model assumes feeding/non-feeding periods to occur by the thresholding of an underlying continuous variable, thereby incorporating some 'short-term memory'. The semi-Markov model, by taking into account the duration of time spent in the previous state, can be said to incorporate 'longer-term memory'. We fit each of these models to a dataset of cow feeding behaviour. We find the semi-Markov model (longer-term memory) to have the best fit to the data and the hidden Markov model (lack of memory) the worst. We argue that in view of effects of satiety on short-term feeding behaviour of animal species in general, biologically suitable models should allow 'memory' to play a role. We conclude that our findings are equally relevant for the analysis of other types of short-term behaviour that are governed by satiety-like principles.

journal_name

Behav Processes

journal_title

Behavioural processes

authors

Allcroft DJ,Tolkamp BJ,Glasbey CA,Kyriazakis I

doi

10.1016/j.beproc.2004.03.014

keywords:

subject

Has Abstract

pub_date

2004-07-30 00:00:00

pages

99-109

issue

1

eissn

0376-6357

issn

1872-8308

pii

S0376635704000634

journal_volume

67

pub_type

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