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another of the same behavior presupposes that the observer can distinguish a
bout from an interval between two consecutive bouts).
The complexities of defining bouts seem particularly difficult to accom-
modate in studies of mammals, for which one action pattern often appears to
slur into another. For example, although the task of defining a bout of drink-
ing by a fox (made up of a string of lapping events) is feasible, the parallel quest
of distinguishing the gulps of a rat or primate (which drink continuously)
might be dubious. In comparison it would be straightforward, in a parallel
study of a bird, to identify consecutive pecks and the interval between them,
which can be analyzed using log-survivor plots (Machlis 1977). The awkward
truth is that the convenient hierarchy of bout, state, and event is often arbi-
trary and often concerns a continuum of lengths (clearly revealed by tech-
niques such as the Noldus videotape analysis system, which measures even the
durations of events).
To continue with the example of allogrooming wood mice (figure 10.1)
and opting for Haccou and Meelis's (1995) definition of a bout as either the
duration of a state or the interval between two states, it can be straightforward
to recognize bouts. Wood mice, for example, often switch between two states
during exploration: scanning while walking and rearing up or scanning while
immobile. In this example, the behavioral elements
scan, rear up,
and
walk
each have durations and are therefore states. For example, the duration of scan-
ning is a bout. During scanning, however, wood mice often turn their heads
back for only a tenth of a second;
turn-head-back
is an event (Stopka and Mac-
donald, 1998). In this case, one bout may include scanning interrupted by sev-
eral turn-head-back events. Sometimes, however, it is difficult to distinguish
between bouts, although methods are available to do so (Langton et al. 1995;
Sibley et al. 1990). When behavior is studied in sequences (i.e., continuous
records), the existence of bouts can be confirmed because the distribution of
their lengths follows an exponential distribution as long as successive bouts
comply with the assumptions of a first-order continuous-time Markov chain
model (Haccou and Meelis 1992). In practice, if bout durations do not follow
an exponential distribution, there are two possible explanations. First, the
observer incorrectly recognized the bouts and therefore measured the wrong
thing, perhaps because some bouts were only partially observed through insuf-
ficient time for observation (Bressers et al. 1991). Second, the first-order Mar-
kovian assumption is not upheld. In the latter case, there are again two possi-
bilities. First, bout lengths may exhibit dependency (between successive bouts
or every second or third bout). In this case it is always better to use a semi-Mar-
kovian model (Haccou and Meelis 1992). Second, and more abstruse, there
