Using a behaviour discovery curve to predict optimal observation time

Behavioural observations are vital to furthering our knowledge of species' ecology. Determining a method for formalising the length of behavioural observation time (coined Behaviour Discovery Curve) is practical for both reducing disturbance to the animals observed and limiting costs to the researcher. This paper suggests a method of calculating behaviour discovery curves, which allows researchers to estimate the optimal amount of data to collect when establishing an ethogram. The curve is fitted to a logarithmic model that predicts the rate of new behaviours that will be observed in any given length of observation time. To illustrate the methods, 31 captive red pandas (Ailurus fulgens fulgens) were observed for 30 h each and a behaviour discovery curve was estimated for each animal based on the rate at which new behaviours were observed. We demonstrate how to use the curve in the evaluation of an ethogram, whilst also providing an indication of how many more behaviours would be observed in a specified longer observation period. This is an important consideration in the creation of any ethogram, since there are currently no standard methodologies for establishing ethograms, and no guidelines on how much data is 'sufficient' for determining a species' behavioural repertoire. The curve does not allow an estimate of the total size of the behavioural repertoire, but does allow a systematic analysis of the likely costs and benefits of further observation. We also suggest a method for quantifying the degree of idiosyncrasy of a population.