气候变化领域集成服务门户(CCPortal): Uncovering ecological state dynamics with hidden Markov models

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DOI	10.1111/ele.13610
	Uncovering ecological state dynamics with hidden Markov models
	McClintock B.T.; Langrock R.; Gimenez O.; Cam E.; Borchers D.L.; Glennie R.; Patterson T.A.
发表日期	2020
ISSN	1461023X
起始页码	1878
结束页码	1903
卷号	23 期号:12
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.
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
语种	英语
来源期刊	Ecology Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/204257
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
推荐引用方式 GB/T 7714	McClintock B.T.,Langrock R.,Gimenez O.,et al. Uncovering ecological state dynamics with hidden Markov models[J],2020,23(12).
APA	McClintock B.T..,Langrock R..,Gimenez O..,Cam E..,Borchers D.L..,...&Patterson T.A..(2020).Uncovering ecological state dynamics with hidden Markov models.Ecology Letters,23(12).
MLA	McClintock B.T.,et al."Uncovering ecological state dynamics with hidden Markov models".Ecology Letters 23.12(2020).