The importance of ‘memory’ in statistical models for animal feeding behaviour

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: latent Gaussian, hidden Markov and semi-Markov. Each can predict the typical `clustered ' feeding behaviour that is generally observed, however they dier in the extent to which `memory ' of previous behaviour is allowed to aect 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, 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 t each of these models to a dataset of cow feeding behaviour. We nd the semi-Markov model (longer-term memory) to have the best t to the data and the hidden Markov (lack of memory) model the worst. We argue that in view of eects 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 ndings are equally relevant for the analysis of other types of short-term behaviour that are governed by satiety-like principles