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may be second- or third-order Markov models, which according to Haccou
and Meelis (1992) are rare and intractable in ethology. In general, bouts
approximate to a first-order Markov process and therefore can be distin-
guished on the basis of testing their distribution or fitting a nonlinear curve to
the logarithm of observed frequencies of gap length. Because most behavioral
studies are based on parameters such as latency, duration, and time intervals,
bout length is a very important parameter to study at the outset.
STATIONARITY
Obviously, if circumstances change over a series of bouts it is confusing or mis-
leading to lump them for analysis. Consequently, it is important to identify
stationarity, during which things do not change (formally, “a process with
transition probabilities independent of time”; Haccou and Meelis 1992:193).
Plots of transition frequencies from one act to another in several consecutive
periods can be used to judge whether the behavioral process is approximately
stationary. Haccou and Meelis (1995) emphasize that nonstationarity can
mask treatment effects and make the results of an analysis ambiguous. Quite
apart from the mathematical implications of inhomogeneity in the data, the
existence of the motivational change that causes nonstationarity may be the
very object of study, so it is important to identify it from an ethological stand-
point. The complexities of stationarity reverberate through ethological
methodology. Bekoff (1977), for example, is skeptical that the concept of sta-
tionarity can be applied in a social context and therefore concludes that Mar-
kovian analysis should be avoided.
THE ETHOGRAM
Early in any study a researcher must classify the behavior patterns to be docu-
mented. This classification is an ethogram, and constitutes a dictionary of the
researcher's language; without an ethogram, meaning is not fixed. Ethograms
have become unpopular because (like dictionaries) they take up large amounts
of space and are dull to read. However, they are vital reference material for
those wishing to assess a study's conclusions critically. As a corollary, it is not
uncommon for the brunt of reviewers' comments now to fall on statistics
rather than data acquisition, reflecting the same shift in emphasis away from
data, and onto interpretation, as the hard currency of behavioral science. By
analogy, consider the shift from real objects to paper representations in finan-
cial systems: 50 years on, would you rather find a stash of gold or war bonds?
