Data from: State-space modelling of the flight behaviour of a soaring bird provides new insights to migratory strategies

1. Characterizing the spatiotemporal variation of animal behaviour can elucidate the way individuals interact with their environment and allocate energy. Increasing sophistication of tracking technologies paired with novel analytical approaches allows the characterisation of movement dynamics even when an individual is not directly observable. 2. In this study, high-resolution movement data collected via global positioning system (GPS) tracking in three dimensions were paired with topographical information and used in a Bayesian state-space model to describe the flight modes of migrating golden eagles (Aquila chrysaetos) in eastern North America. 3. Our model identified five functional behavioural states, two of which were previously undescribed variations on thermal soaring. The other states comprised gliding, perching and orographic soaring. States were discriminated by movement features in the horizontal (step length and turning angle) and vertical (change in altitude) planes, and by the association with ridgelines promoting wind deflection. Tracked eagles spent 2%, 31%, 38%, 9% and 20% of their day time in directed thermal soaring, gliding, convoluted thermal soaring, perching and orographic soaring, respectively. The analysis of the relative occurrence of these flight modes highlighted yearly, seasonal, age, individual and sex differences in flight strategy and performance. Particularly, less energy-efficient orographic soaring was more frequent in autumn, when thermals were less available. Adult birds were also better at optimising energy efficiency than sub-adults. 4. Our approach represents the first example of a state-space model for bird flight mode using altitude data in conjunction with horizontal locations, and is applicable to other flying organisms where similar data are available. The ability to describe animal movements in a three-dimensional habitat is critical to advance our understanding of the functional processes driving animals’ decisions.

1. 阐明动物行为的时空变异，可揭示个体与环境的交互方式及能量分配策略。随着追踪技术日益精进，结合新颖的分析方法，即便无法直接观测个体，也能实现其运动动态的刻画。
2. 本研究通过三维全球定位系统（GPS）追踪获取高分辨率运动数据，结合地形信息，采用贝叶斯状态空间模型（Bayesian state-space model）刻画北美东部迁徙金雕（*Aquila chrysaetos*）的飞行模式。
3. 本模型识别出5种功能性行为状态，其中2种为此前未被报道的热气流翱翔（thermal soaring）变体。其余3种状态分别为滑翔、停歇及地形气流翱翔（orographic soaring）。这些状态可通过水平（移动步长与转弯角度）和垂直（海拔变化）维度的运动特征，以及与促进气流偏转的山脊线的关联加以区分。被追踪的金雕在日间分别有2%、31%、38%、9%与20%的时间处于定向热气流翱翔、滑翔、盘旋热气流翱翔、停歇及地形气流翱翔状态。对这些飞行模式相对占比的分析，揭示了飞行策略与表现存在年度、季节、年龄、个体及性别差异。具体而言，能量效率较低的地形气流翱翔在热气流较为匮乏的秋季更为常见；成鸟也比亚成鸟更擅长优化能量利用效率。
4. 本研究方法首次实现了结合海拔数据与水平定位信息的鸟类飞行模式状态空间模型，可推广至可获取同类数据的其他飞行生物。在三维生境中刻画动物运动的能力，对于深化我们对驱动动物决策的功能性过程的理解至关重要。