AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements

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.

[1]  Jeff R M Andrews,et al.  Estimating daily walking distance of captive African elephants using an accelerometer. , 2011, Zoo biology.

[2]  Fritz Vollrath,et al.  Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana , 2012 .

[3]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[4]  Thomas Ruf,et al.  A versatile telemetry system for continuous measurement of heart rate, body temperature and locomotor activity in free‐ranging ruminants , 2010, Methods in ecology and evolution.

[5]  Wayne M Getz,et al.  Mixed strategies of griffon vultures’ (Gyps fulvus) response to food deprivation lead to a hump-shaped movement pattern , 2013, Movement Ecology.

[6]  Rory P. Wilson,et al.  Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant. , 2006, The Journal of animal ecology.

[7]  Rory P. Wilson,et al.  Prying into the intimate details of animal lives: use of a daily diary on animals , 2008 .

[8]  David A. S. Rosen,et al.  Swimming depth and ocean currents affect transit costs in Steller sea lions Eumetopias jubatus , 2010 .

[9]  S. Cooke.  Biotelemetry and biologging in endangered species research and animal conservation: relevance to regional, national, and IUCN Red List threat assessments , 2008 .

[10]  E. Revilla,et al.  A movement ecology paradigm for unifying organismal movement research , 2008, Proceedings of the National Academy of Sciences.

[11]  Willem Bouten,et al.  Optimizing acceleration-based ethograms: the use of variable-time versus fixed-time segmentation , 2014, Movement ecology.

[12]  Maressa Q Takahashi,et al.  Evaluating the utility of an accelerometer and urinary hormone analysis as indicators of estrus in a Zoo-housed koala (Phascolarctos cinereus). , 2009, Zoo biology.

[13]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[14]  Paul R Moorcroft,et al.  Stochastic modelling of animal movement , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Rory P. Wilson,et al.  Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm , 2014, PloS one.

[16]  Rory P. Wilson,et al.  Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure , 2011 .

[17]  W. Sellers,et al.  Automatic Monitoring of Primate Locomotor Behaviour Using Accelerometers , 2004, Folia Primatologica.

[18]  Ran Nathan,et al.  Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures , 2012, Journal of Experimental Biology.

[19]  Roland Kays,et al.  Observing the unwatchable through acceleration logging of animal behavior , 2013, Animal Biotelemetry.

[20]  E. Revilla,et al.  Trends and missing parts in the study of movement ecology , 2008, Proceedings of the National Academy of Sciences.

[21]  Yasuhiko Naito,et al.  A new technique for monitoring the detailed behaviour of terrestrial animals: A case study with the domestic cat , 2005 .

[22]  M. Hebblewhite,et al.  Distinguishing technology from biology: a critical review of the use of GPS telemetry data in ecology , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[23]  K. Yoda,et al.  Precise monitoring of porpoising behaviour of Adélie penguins determined using acceleration data loggers. , 1999, The Journal of experimental biology.