Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, Maryland, USA.
Department of Biology, University of Maryland, College Park, Maryland, USA.
Conserv Biol. 2023 Oct;37(5):e14114. doi: 10.1111/cobi.14114. Epub 2023 Jul 29.
Conservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial-temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal-vertical movement model results with spatial-temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n = 28 leatherback tracks, 2004-2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle-fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) (https://www.upwell.org/sptw), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial-temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.
保护表现出广泛和多维行为的迁徙物种受到管理工作的挑战，这些工作仅利用水平运动或产生静态时空产品。对于深度潜水、极度濒危的东太平洋棱皮龟来说，迫切需要能够预测海龟面临渔业相互作用高风险的工具，以防止其种群进一步下降。我们将水平-垂直运动模型结果与时空核密度估计和威胁数据（特定渔具的渔业）相结合，开发了每月的空间风险图。具体来说，我们将多状态隐马尔可夫模型应用于生物遥测数据集（n=28 只棱皮龟轨迹，2004-2007 年）。有潜水信息的轨迹用于将海龟行为特征归类为 3 种状态之一（过境、混合潜水的居留地和深潜的居留地）。来自全球渔业观察的最新渔业努力数据与预测行为和每月空间利用估计相结合，以创建海龟与渔业相互作用的相对风险图。漂流（远洋）延绳钓渔具在研究区域的平均每月渔获努力最高，风险指数显示，这种渔具在海龟处于居留地、深潜行为状态时，与海龟发生高风险相互作用的潜力最大。所有渔具和行为的每月相对风险面都添加到南太平洋海龟观察站（SPTW）（https://www.upwell.org/sptw）中，这是该棱皮龟种群的一个动态管理工具。这些修改将完善 SPTW 的能力，为正在进行特定行为的海龟提供潜在高风险副渔获区的重要预测。我们的研究结果表明，多维运动数据、时空密度估计和威胁数据如何能够被用于创建独特的保护工具。这些方法为将行为纳入其他具有多维运动行为的水生、空中和陆地类群的类似工具提供了框架。
