A general discrete-time modeling framework for animal movement using multistate random walks
A general discrete-time modeling framework for animal movement using multistate random walks
复制标题
DOI：
10.1890/11-0326.1
复制
发表时间：
2012-08-01
影响因子：
6.1
通讯作者：
Morales, Juan M.
中科院分区：
文献类型：
--
作者：
McClintock, Brett T.;King, Ruth;Morales, Juan M.
动物追踪技术的最新发展使人们能够收集许多物种个体运动路径的详细数据。然而，这些数据的分析方法并没有以类似的速度发展，这主要是由于缺乏合适的候选模型，再加上将这些模型与数据拟合的技术困难。为了促进通用建模框架，我们建议将复杂的运动路径视为一系列运动策略，动物在受到内部和外部环境变化的影响时会在其中进行过渡。我们综合了以前存在的和新的方法，以开发一套通用的机制模型，该模型基于有偏和相关的随机游走，允许不同的行为状态用于定向（例如，迁移），探索性（例如，分散），区域受限（例如，觅食）和其他类型的运动。使用这种嵌套模型组件的“工具箱”，可以为各种各样的物种和应用定制多状态运动模型。作为一个统一的状态空间建模框架，它允许从不完全观察到的数据，包括时间分配到不同的运动行为状态，状态之间的转换，使用记忆或导航，以及吸引力（或排斥力）的强度对特定位置的动物运动的众多假设的同时调查。包含协变量信息允许进一步调查与驱动不同类型运动行为的因素相关的特定假设。使用可逆跳马尔可夫链蒙特卡罗方法，以方便贝叶斯模型的选择和多模型推理，我们应用所提出的方法，以真实的数据，通过调整它的自然历史的灰色密封（Halichoerus grypus）在北海。虽然以前的灰海豹研究往往集中在相关的运动，我们发现压倒性的证据表明，偏向拖出来或觅食的位置更好地解释海豹运动比简单或相关的随机行走。后验模型的概率也提供了证据，密封之间的过渡有指导性的，区域限制，探索运动与拖出来，觅食，和其他行为。有了这个直观的动物运动建模和解释框架，我们相信生态学家和非统计学家将更容易获得定制运动模型的开发和应用。
Recent developments in animal tracking technology have permitted the collection of detailed data on the movement paths of individuals from many species. However, analysis methods for these data have not developed at a similar pace, largely due to a lack of suitable candidate models, coupled with the technical difficulties of fitting such models to data. To facilitate a general modeling framework, we propose that complex movement paths can be conceived as a series of movement strategies among which animals transition as they are affected by changes in their internal and external environment. We synthesize previously existing and novel methodologies to develop a general suite of mechanistic models based on biased and correlated random walks that allow different behavioral states for directed (e.g., migration), exploratory (e.g., dispersal), area-restricted (e.g., foraging), and other types of movement. Using this "toolbox'' of nested model components, multistate movement models may be custom-built for a wide variety of species and applications. As a unified state-space modeling framework, it allows the simultaneous investigation of numerous hypotheses about animal movement from imperfectly observed data, including time allocations to different movement behavior states, transitions between states, the use of memory or navigation, and strengths of attraction (or repulsion) to specific locations. The inclusion of covariate information permits further investigation of specific hypotheses related to factors driving different types of movement behavior. Using reversible-jump Markov chain Monte Carlo methods to facilitate Bayesian model selection and multi-model inference, we apply the proposed methodology to real data by adapting it to the natural history of the grey seal (Halichoerus grypus) in the North Sea. Although previous grey seal studies tended to focus on correlated movements, we found overwhelming evidence that bias toward haul-out or foraging locations better explained seal movement than did simple or correlated random walks. Posterior model probabilities also provided evidence that seals transition among directed, area-restricted, and exploratory movements associated with haul-out, foraging, and other behaviors. With this intuitive framework for modeling and interpreting animal movement, we believe that the development and application of custom-made movement models will become more accessible to ecologists and non-statisticians.