Animal-borne accelerometers measure body orientation and movement and can thus be used to classify animal behaviour.
To univocally and automatically analyse the large volume of data generated, we need classification models. An important step
in the process of classification is the segmentation of acceleration data, i.e. the assignment of the boundaries between different
behavioural classes in a time series. So far, analysts have worked with fixed-time segments, but this may weaken the strength
of the derived classification models because transitions of behaviour do not necessarily coincide with boundaries of the segments.
Here we develop random forest automated supervised classification models either built on variable-time segments generated
with a so-called ‘change-point model’, or on fixed-time segments, and compare for eight behavioural classes the classification
performance. The approach makes use of acceleration data measured in eight free-ranging crab plovers Dromas ardeola.
classification was achieved by both the variable-time and fixed-time approach for flying (89% vs. 91%, respectively), walking
(88% vs. 87%) and body care (68% vs. 72%). By using the variable-time segment approach, significant gains in classification
performance were obtained for inactive behaviours (95% vs. 92%) and for two major foraging activities, i.e. handling (84%
vs. 77%) and searching (78% vs. 67%). Attacking a prey and pecking were never accurately classified by either method.
behavioural classification can be optimized using a variable-time segmentation approach. After implementing variable-time
segments to our sample data, we achieved useful levels of classification performance for almost all behavioural classes. This
enables behaviour, including motion, to be set in known spatial contexts, and the measurement of behavioural time-budgets
of free-living birds with unprecedented coverage and precision. The methods developed here can be easily adopted in other
studies, but we emphasize that for each species and set of questions, the presented string of work steps should be run through.