Data from: Analysis of animal accelerometer data using hidden Markov models

Use of accelerometers is now widespread within animal biologging as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data, there is a natural dependence between observations of behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, an HMM can be used for state prediction or to make inferences about drivers of behaviour. For state prediction, a supervised learning approach can be applied. That is, an HMM is trained to classify unlabelled acceleration data into a finite set of pre-specified categories. An unsupervised learning approach can be used to infer new aspects of animal behaviour when biologically meaningful response variables are used, with the caveat that the states may not map to specific behaviours. We provide the details necessary to implement and assess an HMM in both the supervised and unsupervised learning context and discuss the data requirements of each case. We outline two applications to marine and aerial systems (shark and eagle) taking the unsupervised learning approach, which is more readily applicable to animal activity measured in the field. HMMs were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour. Animal accelerometer data allow ecologists to identify important correlates and drivers of animal activity (and hence behaviour). The HMM framework is well suited to deal with the main features commonly observed in accelerometer data and can easily be extended to suit a wide range of types of animal activity data. The ability to combine direct observations of animal activity with statistical models, which account for the features of accelerometer data, offers a new way to quantify animal behaviour and energetic expenditure and to deepen our insights into individual behaviour as a constituent of populations and ecosystems.加速度计在动物生物记录（animal biologging）领域的应用现已极为广泛，因其可在无法直接观测动物的场景下，以兼具科学意义与量化特性的方式，有效衡量动物的活动状态。在序列加速度数据中，行为观测值之间存在天然的序列相关性，但这一事实在绝大多数分析中都被极大地忽略了。 针对显式建模序列相关性的加速度数据分析，现有研究大多依赖隐马尔可夫模型（Hidden Markov Models, HMMs）。根据分析目标的不同，HMM可用于状态预测，或对动物行为的驱动因素进行推断。若要开展状态预测，可采用监督学习方法：即通过训练HMM，将未标记的加速度数据划分至预先定义的有限类别集合中。若使用具备生物学意义的响应变量，则可采用无监督学习方法推断动物行为的新维度，但需注意：模型得到的状态未必能对应到具体的行为类别。 本文提供了在监督与无监督学习场景下，实现并评估HMM所需的详细方法，并讨论了两种场景各自的数据需求。我们采用更易适配野外实测动物活动数据的无监督学习方法，展示了两个分别针对海洋（鲨鱼）与空中（鹰）系统的应用案例。研究中通过HMM推断了时间、大气与潮汐因素对动物行为的影响。 动物加速度计数据能够帮助生态学家识别与动物活动（进而与行为）相关的重要关联因素与驱动因子。HMM框架非常适配加速度数据中常见的核心特征，且可轻松扩展以适配多种类型的动物活动数据。将动物活动的直接观测数据与可刻画加速度数据特征的统计模型相结合，为量化动物行为与能量消耗提供了全新途径，也有助于我们更深入地理解作为种群与生态系统组成单元的个体行为。