Abstract
BackgroundThe study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data.DescriptionHere we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models.ConclusionsAcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-014-0027-0) contains supplementary material, which is available to authorized users.
Highlights
The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement
Promoting movement ecology research and the desirable unification across species and movement phenomena requires developing additional sensors and Resheff et al Movement Ecology (2014) 2:27 tools providing simultaneous information about the movement, energy expenditure and behavior of the focal organisms, and the environmental conditions they encounter en route [5]
Recall and precision of the Artificial Neural Network (ANN) model were slightly better compared to other models (Table 2 & Additional file 4: Table S4), but in general all models preformed reasonably well (Table 2)
Summary
The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data. Resheff et al Movement Ecology (2014) 2:27 tools providing simultaneous information about the movement, energy expenditure and behavior of the focal organisms, and the environmental conditions they encounter en route [5] To help bridge this gap, accelerometers were introduced as a means of identifying moment-to-moment behavioral modes [6] and estimating energy expenditure [7] of tagged animals. These measures have facilitated movement-related research for a wide range of topics in ecology and animal behavior [5,9,10,11] as well as other fields of research such as animal conservation and welfare [10,12] and biomechanics [13,14]
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