FN Archimer Export Format
PT J
TI Uncovering ecological state dynamics with hidden Markov models
BT 
AF McClintock, Brett T.
   Langrock, Roland
   Gimenez, Olivier
   Cam, Emmanuelle
   Borchers, David L.
   Glennie, Richard
   Patterson, Toby A.
   Coulson, Tim
AS 1:1;2:2;3:3;4:4;5:5;6:5;7:6;8:;
FF 1:;2:;3:;4:;5:;6:;7:;8:;
C1 NOAA National Marine Fisheries Service Seattle WA ,USA
   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, UK
   CSIRO Oceans and Atmosphere Hobart, Australia
C2 NOAA, USA
   UNIV BIELEFELD, GERMANY
   CNRS, FRANCE
   UBO, FRANCE
   UNIV ST ANDREWS, UK
   CSIRO OCEANS AND ATMOSPHERE, AUSTRALIA
UM LEMAR
IN WOS Cotutelle UMR
   copubli-france
   copubli-europe
   copubli-int-hors-europe
IF 1.88
TC 84
UR https://archimer.ifremer.fr/doc/00655/76692/77831.pdf
   https://archimer.ifremer.fr/doc/00655/76692/77832.pdf
   https://archimer.ifremer.fr/doc/00655/76692/77833.pdf
LA English
DT Article
DE ;Behavioural ecology;community ecology;ecosystem ecology;hierarchical model;movement ecology;observation error;population ecology;state-space model;time series
AB 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. 
PY 2020
PD DEC
SO Ecology Letters
SN 1461-023X
PU Wiley
VL 23
IS 12
UT 000579175800001
BP 1878
EP 1903
DI 10.1111/ele.13610
ID 76692
ER

EF