Hounslow Jenna L, Fossette Sabrina, Byrnes Evan E, Whiting Scott D, Lambourne Renae N, Armstrong Nicola J, Tucker Anton D, Richardson Anthony R, Gleiss Adrian C
Centre for Sustainable Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Western Australia, Australia.
Environmental and Conservation Science, Murdoch University, Western Australia, Australia.
R Soc Open Sci. 2022 Aug 10;9(8):211860. doi: 10.1098/rsos.211860. eCollection 2022 Aug.
Diving behaviour of 'surfacers' such as sea snakes, cetaceans and turtles is complex and multi-dimensional, thus may be better captured by multi-sensor biologging data. However, analysing these large multi-faceted datasets remains challenging, though a high priority. We used high-resolution multi-sensor biologging data to provide the first detailed description of the environmental influences on flatback turtle () diving behaviour, during its foraging life-history stage. We developed an analytical method to investigate seasonal, diel and tidal effects on diving behaviour for 24 adult flatback turtles tagged with biologgers. We extracted 16 dive variables associated with three-dimensional and kinematic characteristics for 4128 dives. -means and hierarchical cluster analyses failed to identify distinct dive types. Instead, principal component analysis objectively condensed the dive variables, removing collinearity and highlighting the main features of diving behaviour. Generalized additive mixed models of the main principal components identified significant seasonal, diel and tidal effects on flatback turtle diving behaviour. Flatback turtles altered their diving behaviour in response to extreme tidal and water temperature ranges, displaying thermoregulation and predator avoidance strategies while likely optimizing foraging in this challenging environment. This study demonstrates an alternative statistical technique for objectively interpreting diving behaviour from multivariate collinear data derived from biologgers.
海蛇、鲸类和海龟等“浮出水面者”的潜水行为复杂且具有多维度性，因此多传感器生物记录数据或许能更好地捕捉到这些行为。然而，分析这些庞大且多方面的数据集仍然具有挑战性，尽管这是一个高度优先的任务。我们利用高分辨率多传感器生物记录数据，首次详细描述了在平背海龟觅食生活史阶段，环境对其潜水行为的影响。我们开发了一种分析方法，以研究季节性、昼夜性和潮汐对24只佩戴生物记录器的成年平背海龟潜水行为的影响。我们从4128次潜水中提取了与三维和运动学特征相关的16个潜水变量。均值分析和层次聚类分析未能识别出不同的潜水类型。相反，主成分分析客观地浓缩了潜水变量，消除了共线性并突出了潜水行为的主要特征。主要主成分的广义相加混合模型确定了季节性、昼夜性和潮汐对平背海龟潜水行为有显著影响。平背海龟会根据极端潮汐和水温范围改变其潜水行为，在这个具有挑战性的环境中展示出体温调节和避敌策略，同时可能优化觅食行为。这项研究展示了一种替代统计技术，用于从生物记录器获取的多变量共线数据中客观解释潜水行为。
