globalchange  > 气候变化与战略
DOI: 10.1111/ele.13610
论文题名:
Uncovering ecological state dynamics with hidden Markov models
作者: McClintock B.T.; Langrock R.; Gimenez O.; Cam E.; Borchers D.L.; Glennie R.; Patterson T.A.
刊名: Ecology Letters
ISSN: 1461023X
出版年: 2020
卷: 23, 期:12
起始页码: 1878
结束页码: 1903
语种: 英语
中文关键词: Behavioural ecology ; community ecology ; ecosystem ecology ; hierarchical model ; movement ecology ; observation error ; population ecology ; state-space model ; time series
英文关键词: ecological approach ; empirical analysis ; numerical model ; terminology ; ecology ; ecosystem ; human ; Markov chain ; Ecology ; Ecosystem ; Humans ; Markov Chains
英文摘要: Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or ‘hidden’. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists. Published 2020. This article is a U.S. Government work and is in the public domain in the USA. Ecology Letters published by John Wiley & Sons Ltd.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/166669
Appears in Collections:气候变化与战略

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作者单位: NOAA National Marine Fisheries Service, Seattle, WA, United States; Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany; CNRS Centre d'Ecologie Fonctionnelle et Evolutive, Montpellier, France; Laboratoire des Sciences de l'Environnement Marin, Institut Universitaire Européen de la Mer, Univ. Brest, CNRS, IRD, Ifremer, France; School of Mathematics and Statistics, University of St Andrews, St Andrews, United Kingdom; CSIRO Oceans and Atmosphere, Hobart, Australia

Recommended Citation:
McClintock B.T.,Langrock R.,Gimenez O.,et al. Uncovering ecological state dynamics with hidden Markov models[J]. Ecology Letters,2020-01-01,23(12)
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