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.

BackgroundAnimal-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.ResultsUseful 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.ConclusionAcceleration-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.

Extreme altitude changes between night and day during marathon flights of great snipes Highlights d Great snipes follow a diel altitude cycle, flying much higher at day than at night d Most birds reached above 6,000 m and one bird reached a record height of 8,700 m d Daytime ascents may relate to orientation, predator avoidance, or need for cooling d Repeated flight altitude changes may be a common phenomenon among migrating birds Authors Å ke Lindströ m, Thomas Alerstam,

Long-distance migratory species often include multiple breeding populations, with distinct migration routes, wintering areas and annual-cycle timing. Detailed knowledge on population structure and migratory connectivity provides the basis for studies on the evolution of migration strategies and for species conservation. Currently, five subspecies of Bar-tailed Godwits Limosa lapponica have been described. However, with two apparently separate breeding and wintering areas, the taxonomic status of the subspecies L. l. taymyrensis remains unclear. Here we compare taymyrensis Bar-tailed Godwits wintering in the Middle East and West Africa, respectively, with respect to migration behaviour, breeding area, morphology and population genetic differentation in mitochondrial DNA. By tracking 52 individuals from wintering and staging areas over multiple years, we show that Bar-tailed Godwits wintering in the Middle East bred on the northern West-Siberian Plain (n = 19), while birds from West Africa bred further east, mostly on the Taimyr Peninsula (n = 12). The two groups differed significantly in body size and shape, and also in the timing of both northward and southward migrations. However, they were not genetically differentiated, indicating that the phenotypic (i.e. geographical, morphological and phenological) differences arose either very recently or without current reproductive isolation. We conclude that the taymyrensis taxon consists of two distinct