Identification of behaviours from accelerometer data in a wild social primate

Abstract

Background: The use of accelerometers in bio-logging devices has proved to be a powerful tool for the quantification of animal behaviour. While bio-logging techniques are being used on wide range of species, to date they have only been seldom used with non-human primates. This is likely due to three main factors: the long tradition of direct field observations, a difficulty of attaching bio-logging devices to wild primates and the challenge of deciphering acceleration signals in species' with remarkable locomotor and behavioural diversity. Here, we overcome these aforementioned obstacles and provide methodology for identification of behaviours from accelerometer data of wild chacma baboons (Papio ursinus) in Cape Town, South Africa. Results:We apply machine learning techniques to process complex accelerometer data, collected by bespoke tracking collars to quantify a range of behaviours (focusing on locomotion and foraging behaviour). We successfully identify six broad state behaviours that represent 93.3% of the time budget of the baboons. Resting, walking, running and foraging were all identified with high recall and precision representing the first classification of multiple behavioural states from accelerometer data for a wild primate. Conclusion:Our 'end to end' process-from collar design and build to the collection and quantification of acceleration data-provides advantages over gathering data by traditional observation, not least because it affords data collection without the presence of an observer which may affect an animal's behaviour. Furthermore, our methodology and findings open new possibilities for the fine-scale study of movement and foraging ecology in wild primates, and in particular our baboon study population which is in conflict with people.

Machine learning goes wild: Using data from captive individuals to infer wildlife behaviour

“…The ANN with the moving window approach, however, was able to infer caching and walking behaviour much better than the other two. Both RF and SVM generally performed well in inferring behaviour during validation (Table 1) and showed comparable results to other studies (Nathan et al, 2012;Fehlmann et al, 2017;Kröschel et al, 2017). When applied to the wild foxes, however, they both failed to discriminate the different behaviours ( Table 2, Table S5).…”

GPS‐identified, low‐level nocturnal activity of vervets (Chlorocebus pygerythrus) and olive baboons (Papio anubis) in Laikipia, Kenya

“…To discriminate behaviors based on accelerometer data requires substantial time‐matched data on behavioral states from direct observations. A recent study has used accelerometer data to delineate a more complete behavioral ethogram from remotely sensed data (Fehlmann et al, ). Time‐matched data on behavioral states from direct observations are difficult to collect at night, however, because our vision is adapted for sunlight.…”

Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis)

“…These classifiers can then automatically categorise large datasets according to the chosen behaviours. Automatic behaviour classification has already been applied to a range of animals using various statistical classification and machine learning techniques, including artificial neural networks [9], decision trees [8][9][10][11][12][13], discriminant function analysis [14], hidden Markov models [15], k-nearest neighbours [8,10,16], linear discriminant analysis [9], moving averages with thresholds [17], quadratic discriminant analysis [18], random forests [9,19] and support vector machines [9,15,20]. These systems have accomplished automatic behaviour classification with high accuracy, as listed in Table 1.…”