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. 为遏制全球生物多样性的进一步衰退，识别并理解关键生境对于制定成功的保护策略至关重要。以海鸟种群为例，目前全球海鸟种群均面临生存威胁，而动物运动数据可用于识别关键远洋海域，并为海洋生态系统的健康状态提供宝贵参考。迄今为止，相关研究多采用全球定位系统（Global Positioning System，GPS）记录动物位置，有时还会结合时间深度记录仪（Time Depth Recorder，TDR）来识别与觅食相关的潜水行为——这是远洋行为的核心环节。但这类额外设备（如TDR）的使用不仅成本高昂、实操难度大，还可能对受试动物造成不良影响。另一种思路是仅通过运动数据衍生的测量值来解析动物行为，但这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，采用涵盖108只个体的GPS与TDR联合数据集，通过训练深度学习模型来预测欧洲鸬鹚、崖海鸦和刀嘴海雀的潜水行为。研究使用未参与训练的留存数据对模型预测结果进行验证，并对预测精度开展量化评估。用于训练模型的变量仅来自GPS设备的记录数据：经纬度变化量、海拔高度，以及覆盖比率（即指定时间窗口内可获取的有效定位点占总潜在定位点的比例）。
3. 本研究通过调整上述变量的不同组合，探究不同模型的性能表现。结果显示，针对所有受试物种的最优模型对非潜水行为与潜水行为的预测准确率分别超过94%与80%。同时，本研究证实，相较于隐马尔可夫模型（hidden Markov models）等常用行为预测方法，这类有监督深度学习模型具备更优异的预测性能。
4. 对上述预测结果进行可视化制图，可帮助我们深入理解多种海鸟的觅食活动规律，同时明确其关键远洋栖息海域。本研究构建的模型可用于分析历史GPS数据集，从而进一步阐明环境变化随时间推移对海鸟种群造成的影响。