Griffiths Christopher A, Patterson Toby A, Blanchard Julia L, Righton David A, Wright Serena R, Pitchford Jon W, Blackwell Paul G
School of Mathematics and Statistics University of Sheffield Sheffield UK.
Institute for Marine and Antarctic Studies University of Tasmania Hobart TAS Australia.
Ecol Evol. 2018 Jun 25;8(14):7031-7043. doi: 10.1002/ece3.4223. eCollection 2018 Jul.
Understanding how, where, and when animals move is a central problem in marine ecology and conservation. Key to improving our knowledge about what drives animal movement is the rising deployment of telemetry devices on a range of free-roaming species. An increasingly popular way of gaining meaningful inference from an animal's recorded movements is the application of hidden Markov models (HMMs), which allow for the identification of latent behavioral states in the movement paths of individuals. However, the use of HMMs to explore the population-level consequences of movement is often limited by model complexity and insufficient sample sizes. Here, we introduce an alternative approach to current practices and provide evidence of how the inclusion of prior information in model structure can simplify the application of HMMs to multiple animal movement paths with two clear benefits: (a) consistent state allocation and (b) increases in effective sample size. To demonstrate the utility of our approach, we apply HMMs and adapted HMMs to over 100 multivariate movement paths consisting of conditionally dependent daily horizontal and vertical movements in two species of demersal fish: Atlantic cod (; = 46) and European plaice (; = 61). We identify latent states corresponding to two main underlying behaviors: resident and migrating. As our analysis considers a relatively large sample size and states are allocated consistently, we use collective model output to investigate state-dependent spatiotemporal trends at the individual and population levels. In particular, we show how both species shift their movement behaviors on a seasonal basis and demonstrate population space use patterns that are consistent with previous individual-level studies. Tagging studies are increasingly being used to inform stock assessment models, spatial management strategies, and monitoring of marine fish populations. Our approach provides a promising way of adding value to tagging studies because inferences about movement behavior can be gained from a larger proportion of datasets, making tagging studies more relevant to management and more cost-effective.
了解动物如何、在何处以及何时移动是海洋生态学和保护领域的核心问题。提高我们对驱动动物移动因素认识的关键在于越来越多地在一系列自由活动的物种身上部署遥测设备。从动物记录的移动中获得有意义推断的一种越来越流行的方法是应用隐马尔可夫模型（HMMs），该模型可以识别个体移动路径中的潜在行为状态。然而，使用HMMs来探索移动在种群水平上的后果往往受到模型复杂性和样本量不足的限制。在这里，我们介绍一种不同于当前做法的替代方法，并提供证据表明在模型结构中纳入先验信息如何能够简化HMMs在多条动物移动路径上的应用，带来两个明显的好处：（a）一致的状态分配和（b）有效样本量的增加。为了证明我们方法的实用性，我们将HMMs和改进的HMMs应用于100多条多变量移动路径，这些路径由两种底栖鱼类（大西洋鳕鱼；n = 46）和欧洲鲽（n = 61）中条件依赖的每日水平和垂直移动组成。我们识别出与两种主要潜在行为相对应的潜在状态：定居和洄游。由于我们的分析考虑了相对较大的样本量且状态分配一致，我们使用集体模型输出在个体和种群水平上研究状态依赖的时空趋势。特别是，我们展示了这两个物种如何在季节性基础上改变它们的移动行为，并展示了与先前个体水平研究一致的种群空间利用模式。标记研究越来越多地被用于为种群评估模型、空间管理策略和海洋鱼类种群监测提供信息。我们的方法为标记研究增加价值提供了一种有前景的方式，因为可以从更大比例的数据集中获得关于移动行为的推断，使标记研究与管理更相关且更具成本效益。
