Data from: Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds

1.In order to prevent further global declines in biodiversity, identifying and understanding key habitats is crucial for successful conservation strategies. For example, globally, seabird populations are under threat and animal movement data can identify key at-sea areas and provide valuable information on the state of marine ecosystems. To date, in order to locate these areas, studies have used Global Positioning System (GPS) to record position and are sometimes combined with Time Depth Recorder (TDR) devices to identify diving activity associated with foraging, a crucial aspect of at-sea behaviour. However, the use of additional devices such as TDRs can be expensive, logistically difficult, and may adversely affect the animal. Alternatively, behaviours may be resolved from measurements derived from the movement data alone. However, this behavioural analysis frequently lacks validation data for locations predicted as foraging (or other behaviours). 2.Here, we address these issues using a combined GPS and TDR dataset from 108 individuals by training deep learning models to predict diving in European shags, common guillemots and razorbills. We validate our predictions using withheld data, producing quantitative assessment of predictive accuracy. The variables used to train these models are those recorded solely by the GPS device: variation in longitude and latitude, altitude, and coverage ratio (proportion of possible fixes acquired within a set window of time). 3.Different combinations of these variables were used to explore the qualities of different models, with the optimum models for all species predicting non-diving and diving behaviour correctly over 94% and 80% of the time, respectively. We also demonstrate the superior predictive ability of these supervised deep-learning models over other commonly used behavioural prediction methods such as hidden Markov models. 4.Mapping these predictions provides useful insights into the foraging activity of a range of seabird species, highlighting important at sea locations. These models have the potential to be used to analyse historic GPS datasets and further our understanding of how environmental changes have affected these seabirds over time.1. 为阻止全球生物多样性进一步衰退，识别并理解关键生境对于制定成功的保护策略至关重要。以全球范围内受威胁的海鸟种群为例，动物运动数据可用于识别关键远洋区域，并为海洋生态系统的健康状态提供宝贵信息。迄今为止，相关研究多借助全球定位系统（Global Positioning System, GPS）记录位置信息，有时还会结合时间深度记录仪（Time Depth Recorder, TDR）设备，以识别与觅食行为相关的潜水活动——这是远洋行为的关键组成部分。然而，使用TDR等额外设备不仅成本高昂、后勤部署难度大，还可能对动物产生不利影响。另一种思路是仅通过运动数据衍生的测量值解析动物行为，但此类行为分析往往缺乏针对被预测为觅食（或其他行为）的位置的验证数据。 2. 本研究针对上述问题，利用108只个体的GPS与TDR联合数据集，通过训练深度学习模型，对欧洲绿鸬鹚、普通海鸦和刀嘴海雀的潜水行为进行预测。我们采用预留数据集对预测结果进行验证，并对预测精度进行量化评估。用于训练模型的变量仅来自GPS设备记录的数据：经纬度变化、海拔高度，以及覆盖比（即特定时间窗口内获取的有效定位点占总可能定位点的比例）。 3. 我们通过不同变量组合探索了不同模型的性能表现，结果显示，所有物种的最优模型对非潜水行为和潜水行为的正确预测率分别超过94%和80%。此外，相较于隐马尔可夫模型（hidden Markov models）等其他常用行为预测方法，这些有监督深度学习模型展现出更优异的预测能力。 4. 对这些预测结果进行可视化制图后，我们得以深入了解多种海鸟的觅食活动，并明确了关键远洋栖息区域。本研究构建的模型有望用于分析历史GPS数据集，进一步阐明环境变化如何随时间影响这些海鸟种群。