Extending the Functionality of Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula minor)
Extending the Functionality of Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula minor)
复制标题
使用 k 均值聚类扩展行为变化点分析的功能：小企鹅 (Eudyptulaminor) 的案例研究
作者：
Jingjing Zhang;K. O’Reilly;G. Perry;G. Taylor;T. Dennis
我们提出了一个简单的框架，用于在动物的地理空间生命线内对相互排斥的行为状态进行分类。该方法涉及使用三个顺序应用的统计程序：(1)行为变化点分析，根据运动参数的时空自相关结构的突变，将运动轨迹划分为离散的相同状态行为；(2)分层多变量聚类分析，确定不同行为状态的数量；以及(3)k-均值聚类，将推断的相同状态位置观察结果分类为行为模式。我们通过分析由不同相关的随机游动组成的已知“人工行为”的合成轨迹，以及通过全球定位系统遥测获得的小企鹅(Eudyptula Minor)的真实觅食轨迹，展示了该方法的应用。我们的结果表明，建模过程正确地对合成轨迹中所有个体位置观测的92.5%进行了分类，显示了成功区分行为模式的合理能力。大多数个体小企鹅被发现表现出三种独特的行为状态(休息、通勤/积极寻找、区域限制觅食)，观察的时间和地点的变化显然与环境光、水深以及靠近海岸线和河口有关。K-均值聚类的加入扩展了行为变化点分析的用途，提供了一种简单的方法，通过该方法可以客观地对包括个人运动轨迹的位置观测所推断的行为进行分类。
We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known ‘artificial behaviours’ comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.