Hounslow_GAMMS_5.R from Multivariate analysis of biologging data reveals the environmental determinants of diving behaviour in a marine reptile

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 (Natator depressus) 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. K-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.

浮出水面型动物（如海蛇、鲸类与海龟）的潜水行为兼具复杂性与多维性，因此多传感器生物记录（biologging）数据或能更精准地捕捉其行为特征。然而，尽管此类多维度大规模数据集的分析是学界的重点研究方向，但仍存在诸多挑战。本研究采用高分辨率多传感器生物记录数据，首次详细阐述了觅食生活史阶段内，环境因素对平背龟（Natator depressus）潜水行为的影响。研究人员为24只佩戴生物记录仪的成年平背龟开发了一套分析方法，用以探究季节、昼夜与潮汐对其潜水行为的影响。本次研究共提取了4128次潜水的16项与三维运动学特征相关的潜水变量。K均值（K-means）聚类与层次聚类分析未能区分出明确的潜水类型。取而代之的是，主成分分析（Principal Component Analysis）客观地对潜水变量进行了降维整合，消除了变量间的共线性问题，并凸显了潜水行为的核心特征。基于核心主成分构建的广义相加混合模型（Generalized Additive Mixed Models）分析结果显示，季节、昼夜与潮汐对平背龟的潜水行为存在显著影响。平背龟会根据极端潮汐与水温范围调整其潜水行为，展现出体温调节与躲避天敌的策略，同时或在该复杂环境中优化觅食效率。本研究提出了一种可替代的统计方法，可用于客观解读由生物记录仪获取的多变量共线性数据中的潜水行为信息。