Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions
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DOI：
10.1890/11-2241.1
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发表时间：
2012-11-01
期刊：
影响因子：
4.8
通讯作者：
Morales, Juan M.
中科院分区：
文献类型：
--
作者：
Langrock, Roland;King, Ruth;Morales, Juan M.
我们讨论了隐马尔可夫型模型来拟合野生动物运动数据的各种多状态随机游动。离散时间隐马尔可夫模型(HMM)通过关注在时间上规则分布且测量误差可以忽略的观测值，实现了相当大的计算收益。这些条件经常被满足，特别是对于与陆地动物相关的数据，因此基于似然的HMM方法是可行的。我们描述了用于动物运动建模的隐马尔可夫模型的一些扩展，包括更灵活的状态转移模型和个体随机效果(适合于非贝叶斯框架)。特别是，我们考虑了所谓的隐半马尔可夫模型，它可以显著提高拟合优度，并为行为状态切换动力学提供重要的见解。为了展示这些方法的方便性，我们考虑了一个分层隐藏半马尔可夫模型在多条野牛移动路径上的应用。
We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths.