Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel.
J Exp Biol. 2012 Mar 15;215(Pt 6):986-96. doi: 10.1242/jeb.058602.
Integrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector machines, classification and regression trees, random forest methods and artificial neural networks and compare them with linear discriminant analysis (LDA) as a baseline for classifying behavioral modes. Using a dataset of 1035 ground-truthed ACC segments, we found that all methods can accurately classify behavior (80-90%) and, as expected, all nonlinear methods outperformed LDA. We also illustrate how ACC-identified behavioral modes provide the means to examine how vulture flight is affected by environmental factors, hence facilitating the integration of behavioral, biomechanical and ecological data. Our analysis of just over three-quarters of a million GPS and ACC measurements obtained from 43 free-ranging vultures across 9783 vulture-days suggests that their annual breeding schedule might be selected primarily in response to seasonal conditions favoring rising-air columns (thermals) and that rare long-range forays of up to 1750 km from the home range are performed despite potentially heavy energetic costs and a low rate of food intake, presumably to explore new breeding, social and long-term resource location opportunities.
整合生物力学、行为和生态学需要对产生动物运动的过程有一个机械的理解。这就需要在运动轨迹上同时获得生物力学、行为和环境数据。最近提出的统一运动生态学范式促进了将现有的生物力学、最优化、认知和随机范式整合到运动研究中。我们专注于使用三轴加速度（ACC）数据来识别 GPS 跟踪的自由放养野生动物的行为模式，并展示其在研究食腐秃鹫（Gyps fulvus，Hablizl 1783）运动中的应用。特别是，我们探索了一系列非线性和决策树方法，包括支持向量机、分类和回归树、随机森林方法和人工神经网络，并将它们与线性判别分析（LDA）进行比较，作为分类行为模式的基线。使用 1035 个经过地面验证的 ACC 段数据集，我们发现所有方法都可以准确地对行为进行分类（80-90%），并且所有非线性方法的性能都优于 LDA。我们还说明了如何使用 ACC 识别的行为模式来检查秃鹫飞行如何受到环境因素的影响，从而促进行为、生物力学和生态数据的整合。我们对 43 只自由放养的秃鹫在 9783 只秃鹫日中获得的超过 75 万 GPS 和 ACC 测量数据进行了分析，结果表明，它们的年度繁殖计划可能主要是根据有利于上升气柱（热）的季节性条件选择的，并且尽管存在潜在的高能量成本和低食物摄入量，它们仍然会进行罕见的长达 1750 公里的长途突袭，可能是为了探索新的繁殖、社会和长期资源位置机会。
