Satiety Splits Feeding Behaviour into Bouts

Animal behaviour is frequently displayed in bouts. Bout analysis aims at finding a bout criterion, i.e. that time between events that separates intervals within, from intervals between, bouts. Methods used for quantitative bout analysis are log-supervivorship and log-frequency analysis. Both models assume that the probability of the start of an event (or a bout) is independent of the time since the last event (or bout) and that, therefore, events as well as bouts occur according to Poisson processes, i.e. purely at random. The frequencies of intervals within, as well as between, bouts are then distributed as negative exponentials. These models are also widely applied in feeding behaviour analysis, where bouts can be meals. However, the satiety concept predicts that after terminating a meal, the animalʹs feeding motivation will be low. The probability of the animal initiating the next meal is expected to increase with time since the last meal and, therefore, meals will not likely be randomly distributed. A negative exponential is then not the most appropriate model to describe the frequency distribution of intervals between meals. Results of an experiment in which feeding behaviour of 16 cows was recorded continuously for 30 days were used to test the suitability of existing bout analysis techniques. It is concluded that these techniques are inadequate for the description of the observed interval distributions. A new model is proposed that takes account of the observed “shortage” of short intervals between meals. In contrast to existing models, that describe log-transformed frequency distributions of interval lengths, the proposed model describes frequency distributions of log-transformed interval lengths. Compared with existing models, this log-normal model is in better agreement with the biological phenomenon of satiety, it gave a better fit to the observed interval distribution and led to a more meaningful meal criterion.