Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat

The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.全球导航卫星系统（Global Navigation Satellite Systems, GNSS）追踪系统的时空精度持续提升，使得通过追踪数据推断动物行为成为可能。本研究聚焦高频GNSS数据与动物行为的关联，旨在开发一种易于解读的分类方法，以通过位置数据推断动物行为。研究对象为佩戴高频GPS（Global Positioning System）接收器的家牛（Bos Taurus），在追踪过程中同步开展行为观测。数据采集于开阔草地与林地两种生境，并以1分钟、12秒及2秒为时间间隔计算运动指标。本次观测共识别出四类行为：觅食（Foraging）、趴卧（Lying）、站立（Standing）与行走（Walking）。随后，本研究基于定位点之间的距离与转弯角，采用分类与回归树（Classification and Regression Trees）对同步获取的GPS数据进行上述四类行为的分类。针对开阔草地生境的1分钟间隔GPS数据，样本分类准确率超过70%。12秒与2秒间隔的GPS数据则无法实现有效分类，这表明时间间隔需足够长，才能使行为通过其特征性运动指标得以界定。林地生境的GPS数据分类准确率（57%）低于开阔草地生境，这是由于GPS定位的位置误差更大，且生境类型会影响动物的行为表现模式。上述结果表明，若要实现准确的动物行为推断，需明确不同采样频率、不同生境下GNSS定位点所衍生的运动指标与行为之间的关联。当可获取空间精度较高的位置数据时，通过计算简单运动指标并采用易于解读的决策树，即可基于高频GNSS定位点推断动物行为。该方法可实现基于位置数据的动物行为与生境利用的联合研究，还有望实现个体层面的行为异常检测。