Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model
Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model
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DOI：
10.3354/meps337255
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发表时间：
2007-01-01
影响因子：
2.5
通讯作者：
James, Michael C.
中科院分区：
文献类型：
--
作者：
Jonsen, Ian D.;Myers, Ransom A.;James, Michael C.
确定海洋捕食者的觅食栖息地对于了解这些物种的生态以及对它们的管理和保护至关重要。许多海洋捕食者的觅食栖息地是动态的，这对理解海洋特征如何塑造这些动物的生态构成了严峻的挑战。为了帮助解决这个问题，我们提出了一个切换状态空间模型(SSSM)来识别隐藏在容易出错的卫星遥测数据中的不同运动行为。除了对运动动力学进行建模外，SSSM还估计了动物处于特定离散行为模式的概率，例如过境或觅食。使用Argos卫星遥测棱皮海龟，我们表明SSSM很容易从噪声数据中识别出不同类别的运动行为。此外，同时收集的潜水数据中的模式与行为模式估计很好地匹配，而该模型对这些数据是盲目的。通过将模型中的行为模式估计与潜水数据相结合，我们表明，棱皮龟在过境时会进行更长时间、更深的潜水；在觅食时，它们会遇到温度在13至22摄氏度之间的较冷水域。这些差异在被研究的海龟中以及同一只海龟在不同年份的情况下是一致的。这种建模方法可以通过将行为信息纳入估计过程来增强用于识别栖息地使用的标准核密度估计器。最终，我们可以通过将环境数据和潜水行为直接纳入SSSM框架来建立栖息地使用的预测模型。
Identifying the foraging habitat of marine predators is vital to understanding the ecology of these species and for their management and conservation. Foraging habitat for many marine predators is dynamic, and this poses a serious challenge for understanding how oceanographic features may shape the ecology of these animals. To help resolve this issue, we present a switching state-space model (SSSM) for discerning different movement behaviours hidden within error-prone satellite telemetry data. Along with modelling the movement dynamics, the SSSM estimates the probability that an animal is in a particular discrete behavioural mode, such as transiting or foraging. Using Argos satellite telemetry for leatherback sea turtles, we show that the SSSM readily identifies distinct classes of movement behaviour from the noisy data. Moreover, patterns in simultaneously collected diving data, to which the model is blind, match well with behavioural mode estimates. By combining behavioural mode estimates from the model with the diving data, we show that while transiting, leatherbacks make longer, deeper dives; and while foraging, they encounter cooler waters that range from 13 to 22 degrees C. These differences are consistent among the turtles studied and within the same turtle in different years. This modelling approach can enhance standard kernel density estimators for identifying habitat use by incorporating behavioural information into the estimation procedure. Ultimately, we can build predictive models of habitat use by incorporating environmental data and diving behaviour directly into the SSSM framework.