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，识别并厘清关键栖息地对于制定有效的保护策略至关重要。以全球海鸟种群为例，其正面临生存威胁，而动物移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珍贵参考信息。迄今为止，研究人员多借助全球定位系统（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）设备，以识别与觅食行为相关的潜水活动——这是海鸟远洋行为的核心环节。但这类额外设备的部署成本高昂、后勤协调难度大，且可能对受试动物造成不良影响。另一种思路是仅通过移动数据衍生的测量参数解析动物行为，但此类行为分析往往缺乏针对被预测为觅食（或其他行为）的位点的验证数据。 2. 本研究依托108只个体的GPS与TDR联合数据集，训练深度学习模型以预测欧洲鸬鹚、普通海鸠及刀嘴海雀的潜水行为，以此解决上述问题。我们通过预留验证数据集对预测结果进行校验，并对预测精度开展定量评估。用于训练模型的变量仅由GPS设备采集：经纬度变化量、海拔高度，以及覆盖比率（即指定时间窗口内获取的有效定位点占总潜在定位点的比例）。 3. 我们通过变量的不同组合方案，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正确识别率分别超过94%与80%。此外，本研究还证实，这类有监督深度学习模型的预测性能优于其他常用行为预测方法，例如隐马尔可夫模型（hidden Markov models）。 4. 将上述预测结果进行空间制图，可为多种海鸟物种的觅食活动提供关键洞察，精准划定重要的远洋活动区域。此类模型有望用于分析历史GPS数据集，进一步深化我们对环境变化如何长期影响这些海鸟种群的认知。