title: Optimizing acceleration-based ethograms: the use of variable-time versus fixed-time segmentation.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  Useful 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.
  
  
  CONCLUSIONS
  Acceleration-based 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.
authors.affiliation: Department of Marine Ecology, Royal Netherlands Institute for Sea Research (NIOZ), 1790 AB Den Burg, P.O. Box 59, Texel, The Netherlands.
authors.forename: Roeland A
authors.initials: RA
authors.lastname: Bom
authors.affiliation: Computational Geo-Ecology, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, 1090 GE Amsterdam, The Netherlands.
authors.forename: Willem
authors.initials: W
authors.lastname: Bouten
authors.affiliation: Department of Marine Ecology, Royal Netherlands Institute for Sea Research (NIOZ), 1790 AB Den Burg, P.O. Box 59, Texel, The Netherlands ; Chair in Global Flyway Ecology, Animal Ecology Group, Centre for Ecological and Evolutionary Studies, University of Groningen, PO Box 11103, 9700 CC Groningen, The Netherlands.
authors.forename: Theunis
authors.initials: T
authors.lastname: Piersma
authors.affiliation: SOVON Dutch Centre for Field Ornithology, Coastal Ecology Team, 1790 AB Den Burg, Texel, The Netherlands.
authors.forename: Kees
authors.initials: K
authors.lastname: Oosterbeek
authors.affiliation: Department of Marine Ecology, Royal Netherlands Institute for Sea Research (NIOZ), 1790 AB Den Burg, P.O. Box 59, Texel, The Netherlands.
authors.forename: Jan A
authors.initials: JA
authors.lastname: van Gils
keywords: Behaviour classification; Change-point model; Crab plover; Dromas ardeola; Movement ethogram; Random forest; Supervised classification; Tri-axial acceleration; Video annotation