Computational Geo-Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands.
PLoS One. 2012;7(5):e37997. doi: 10.1371/journal.pone.0037997. Epub 2012 May 31.
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.
动物携带的传感器使研究人员能够远程跟踪动物及其生理状态和身体运动。例如，加速度计已被用于多项研究中，以测量身体运动、姿势和能量消耗，尽管主要是在海洋动物中。在许多研究中，行为通常是根据传感器数据的专家解释推断出来的，而不是通过对动物的直接观察来验证。本研究的目的是基于传感器数据开发可以用于分类蛎鹬（Haematopus ostralegus）行为的模型。我们使用灵活的 GPS 跟踪系统测量了三只蛎鹬的位置、速度和三轴加速度，并对这些鸟类在自然环境中的行为进行了同步的视觉观察。然后，我们使用这些数据开发了三种行为的监督分类树，最后应用其中一种模型来计算时间活动预算。基于加速度计数据开发的用于分类三种行为（飞行、陆地运动和无运动）的模型准确性更高（交叉验证误差=0.14），而仅基于 GPS 速度的模型准确性更低（交叉验证误差=0.35）。设计用于分类八种行为的最简约加速度模型可以区分五种行为：飞行、觅食、身体护理、站立和坐下（交叉验证误差=0.28）；观察到的其他行为，如攻击或处理猎物，无法区分。讨论了模型的局限性和潜在改进。本研究中提出的工作流程设计可以促进模型的开发，可以适应广泛的物种，并且结合适当的测量方法，可以促进对自由生活动物的行为和栖息地利用的研究，贯穿其全年的日常生活。
