Animal behaviour is frequently displayed in bouts. Bout analysis aims
at finding a bout criterion, i.e. that time between events that separa
tes intervals within, from intervals between, bouts. Methods used for
quantitative bout analysis are log-survivorship and log-frequency anal
ysis. 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 Poiss
on processes, i.e. purely at random. The frequencies of intervals with
in, as well as between, bouts are then distributed as negative exponen
tials. These models are also widely applied in feeding behaviour analy
sis, where bouts can be meals. However, the satiety concept predicts t
hat after terminating a meal, the animal's feeding motivation will be
low. The probability of the animal initiating the next meal is expecte
d to increase with time since the last meal and, therefore, meals will
not likely be randomly distributed. A negative exponential is then no
t the most appropriate model to describe the frequency distribution of
intervals between meals. Results of an experiment in which feeding be
haviour of 16 cows was recorded continuously for 30 days were used to
test the suitability of existing bout analysis techniques. It is concl
uded that these techniques are inadequate for the description of the o
bserved interval distributions. A new model is proposed that takes acc
ount 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 freque
ncy distributions of log-transformed interval lengths. Compared with e
xisting 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. (C
) 1998 Academic Press.