From Sensor Data to Animal Behaviour: An Oystercatcher Example

Abstract

Animal-borne sensors enable researchers to remotely track animals, their physiological state and body movements. Accelerometers, for example, have been used in several studies to measure body movement, posture, and energy expenditure, although predominantly in marine animals. In many studies, behaviour is often inferred from expert interpretation of sensor data and not validated with direct observations of the animal. The aim of this study was to derive models that could be used to classify oystercatcher (Haematopus ostralegus) behaviour based on sensor data. We measured the location, speed, and tri-axial acceleration of three oystercatchers using a flexible GPS tracking system and conducted simultaneous visual observations of the behaviour of these birds in their natural environment. We then used these data to develop three supervised classification trees of behaviour and finally applied one of the models to calculate time-activity budgets. The model based on accelerometer data developed to classify three behaviours (fly, terrestrial locomotion, and no movement) was much more accurate (cross-validation error = 0.14) than the model based on GPS-speed alone (cross-validation error = 0.35). The most parsimonious acceleration model designed to classify eight behaviours could distinguish five: fly, forage, body care, stand, and sit (cross-validation error = 0.28); other behaviours that were observed, such as aggression or handling of prey, could not be distinguished. Model limitations and potential improvements are discussed. The workflow design presented in this study can facilitate model development, be adapted to a wide range of species, and together with the appropriate measurements, can foster the study of behaviour and habitat use of free living animals throughout their annual routine.

Can accelerometry be used to distinguish between flight types in soaring birds?

“…While we did find evidence for variation in the smoothed surge signal between passive flight types, these differences were not sufficient to form discrete clusters for high accuracy classification. This is in contrast to other studies such as that by Shamoun-Baranes et al [7], which distinguished between specific behaviours from smoothed tri-axial acceleration values using supervised classification trees. As KNN has been shown to perform similarly to a number of automated classification algorithms, at least for the identification of active flapping and passive non-flapping flight [40] it would seem that, even at the high frequency of 40 and 20 Hz, passive flight types cannot be distinguished between from their acceleration values alone with such algorithms.…”

First evidence of a 200-day non-stop flight in a bird

“…Six Alpine swifts were equipped at a breeding site in Switzerland with tags logging light for information on their whereabouts 18 and acceleration for measuring their activity 19 . After their return to the breeding site in the following year, three of the six birds were recaptured.…”

Social foraging and individual consistency in following behaviour: testing the information centre hypothesis in free-ranging vultures

“…Outbound flights were longer than inbound flights, with a mean DTD of 87.5 + 61.5 km and 51.2 + 29.9 km, respectively. Overall, the foraging patterns in the two studied regions were quite similar ( figure S3), but there were no differences in DTD (t 18,7 ¼ 1.3, p ¼ 0.2) and ground speed (t 18,7 …”