Harvey-Carroll Jessica, Menéndez-Blázquez Javier, Crespo-Picazo Jose Luis, Sagarminaga Ricardo, March David
Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.
Gothenburg Global Biodiversity Centre, Gothenburg, Sweden.
Sci Rep. 2025 Jun 6;15(1):19934. doi: 10.1038/s41598-025-05336-y.
Biologging is a rapidly advancing field providing information on previously unexplored aspects of animal ecology, including the vertical movement dimension. Understanding vertical behaviour through the use of time-depth recorders (TDRs) in marine vertebrates is critical to aid conservation and management decisions. However, using TDRs can be particularly problematic to infer animal behaviour from elusive animals, when tags are difficult to recover and collected data is satellite-relayed at lower temporal frequencies. Here, we present a novel method to process low-resolution TDR data at 5-minute intervals and infer diving behaviour from loggerhead turtles (Caretta caretta) during their elusive pelagic life stage spanning extended periods (> 250 days). Using a Hidden Markov Model (HMM) we identify four behavioural states, associated with resting, foraging, shallow exploration, and deep exploration. Three of the four behavioural states were found to have strong seasonal patterns, corroborating with known sea-turtle biology. The results presented provide a novel way of interpreting low-resolution TDR data and provide a unique insight into sea turtle ecology.
生物遥测是一个快速发展的领域，它能提供有关动物生态学中以前未被探索的方面的信息，包括垂直运动维度。通过使用时间深度记录仪（TDR）来了解海洋脊椎动物的垂直行为对于辅助保护和管理决策至关重要。然而，当标签难以回收且收集的数据以较低的时间频率通过卫星中继时，使用TDR从难以捉摸的动物身上推断其行为可能会特别成问题。在这里，我们提出了一种新颖的方法，以5分钟的间隔处理低分辨率的TDR数据，并推断蠵龟（Caretta caretta）在其长达250多天的难以捉摸的远洋生活阶段的潜水行为。使用隐马尔可夫模型（HMM），我们识别出四种行为状态，分别与休息、觅食、浅度探索和深度探索相关。发现这四种行为状态中的三种具有强烈的季节性模式，这与已知的海龟生物学相符。所呈现的结果提供了一种解释低分辨率TDR数据的新方法，并为海龟生态学提供了独特的见解。
