Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time
Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time
复制标题
连续时间内多状态“步进和转弯”动物运动的贝叶斯推理
DOI：
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发表时间：
2017
影响因子：
1.4
通讯作者：
P. Blackwell
中科院分区：
文献类型：
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作者：
A. Parton;P. Blackwell
动物运动的机械建模通常在离散时间中制定，尽管存在尺度不变性的问题，例如处理不规则的定时观察。一个自然的解决方案是在连续的时间内制定，但这一点的吸收一直很慢。这种缺乏执行的情况往往以难以解释为借口。在这里，我们的目标是通过开发一个具有可解释参数的连续时间模型来支持使用，类似于使用转向角和步长的流行离散时间模型。运动由关节轴承和速度过程定义，参数取决于连续时间行为切换过程，从而创建灵活的运动模型。马尔可夫链蒙特卡罗推理的方法给出了不规则的观察，涉及增强观察到的位置与重建的基本运动过程。这是适用于公知的GPS数据从麋鹿（马鹿），这是以前在离散时间建模。我们证明了连续时间模型的可解释性，发现随着时间的推移行为的明显差异，并对在离散时间中无法获得的短期行为进行了深入了解。
Mechanistic modelling of animal movement is often formulated in discrete time despite problems with scale invariance, such as handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow. This lack of implementation is often excused by a difficulty in interpretation. Here we aim to bolster usage by developing a continuous-time model with interpretable parameters, similar to those of popular discrete-time models that use turning angles and step lengths. Movement is defined by a joint bearing and speed process, with parameters dependent on a continuous-time behavioural switching process, creating a flexible class of movement models. Methodology is presented for Markov chain Monte Carlo inference given irregular observations, involving augmenting observed locations with a reconstruction of the underlying movement process. This is applied to well-known GPS data from elk (Cervus elaphus), which have previously been modelled in discrete time. We demonstrate the interpretable nature of the continuous-time model, finding clear differences in behaviour over time and insights into short-term behaviour that could not have been obtained in discrete time.
影响因子：
2.1
作者：
Boakes, Daniel E.;Codling, Edward A.;Steinke, Michael
通讯作者：
Steinke, Michael