Wavelet analysis to detect regime shifts in animal movement

Animals most often move in a non-homogeneous way as a long movement path through a heterogeneous landscape that corresponds to a sequence of various behavioural states. Hence, a large majority of movement analyses make the assumption that long movements combine typical behaviours like intensive sear...

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Main Author: C. Gaucherel
Format: Article
Language:English
Published: International Academy of Ecology and Environmental Sciences 2011-06-01
Series:Computational Ecology and Software
Subjects:
Online Access:http://www.iaees.org/publications/journals/ces/articles/2011-1(2)/Wavelet-analysis-to-detect-regime-shifts.pdf
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spelling doaj-b2351eb266c24ee4a1ac05608d2380002020-11-24T20:42:26ZengInternational Academy of Ecology and Environmental SciencesComputational Ecology and Software2220-721X2011-06-01126985Wavelet analysis to detect regime shifts in animal movementC. GaucherelAnimals most often move in a non-homogeneous way as a long movement path through a heterogeneous landscape that corresponds to a sequence of various behavioural states. Hence, a large majority of movement analyses make the assumption that long movements combine typical behaviours like intensive search or resting which are separated by sharp transitions. This study aimed at providing an alternative method for identifying intensive search areas using sharp as well as more continuous (smooth) transitions. I proposed analyzing movement data over temporal and spatial scales by the use of the wavelet analysis and drew inferences about the behaviours that shape movements. I computed a synthetic index built with wavelet time-spectra of turning angle and speed parameters, this method offered a robust and automatic way to characterize movement transitions. The first step was to work on simulated movements to define the confidence levels of detection. The second was to illustrate the use of wavelet analysis on the movements of wandering albatrosses. As a result, this study outlined two fundamental areas of interest in animal movement analysis: i) it is relevant to select behavioural modes with continuous transitions between them along the animal's movement, as it is done with usual segmentation methods; ii) to suppose that every behaviour and every transition between them is intrinsically multiscale (i.e. with a scaling property) appeared to be an interesting approach to identify and characterize them. The mathematical robustness and predictive ability of wavelet analysis make it a promising road towards multiscale movement ecology that fuses insights from the study of animal behaviour and environmental properties.http://www.iaees.org/publications/journals/ces/articles/2011-1(2)/Wavelet-analysis-to-detect-regime-shifts.pdfmarine predatorscalefrequency analysistrajectorypath analysis
collection DOAJ
language English
format Article
sources DOAJ
author C. Gaucherel
spellingShingle C. Gaucherel
Wavelet analysis to detect regime shifts in animal movement
Computational Ecology and Software
marine predator
scale
frequency analysis
trajectory
path analysis
author_facet C. Gaucherel
author_sort C. Gaucherel
title Wavelet analysis to detect regime shifts in animal movement
title_short Wavelet analysis to detect regime shifts in animal movement
title_full Wavelet analysis to detect regime shifts in animal movement
title_fullStr Wavelet analysis to detect regime shifts in animal movement
title_full_unstemmed Wavelet analysis to detect regime shifts in animal movement
title_sort wavelet analysis to detect regime shifts in animal movement
publisher International Academy of Ecology and Environmental Sciences
series Computational Ecology and Software
issn 2220-721X
publishDate 2011-06-01
description Animals most often move in a non-homogeneous way as a long movement path through a heterogeneous landscape that corresponds to a sequence of various behavioural states. Hence, a large majority of movement analyses make the assumption that long movements combine typical behaviours like intensive search or resting which are separated by sharp transitions. This study aimed at providing an alternative method for identifying intensive search areas using sharp as well as more continuous (smooth) transitions. I proposed analyzing movement data over temporal and spatial scales by the use of the wavelet analysis and drew inferences about the behaviours that shape movements. I computed a synthetic index built with wavelet time-spectra of turning angle and speed parameters, this method offered a robust and automatic way to characterize movement transitions. The first step was to work on simulated movements to define the confidence levels of detection. The second was to illustrate the use of wavelet analysis on the movements of wandering albatrosses. As a result, this study outlined two fundamental areas of interest in animal movement analysis: i) it is relevant to select behavioural modes with continuous transitions between them along the animal's movement, as it is done with usual segmentation methods; ii) to suppose that every behaviour and every transition between them is intrinsically multiscale (i.e. with a scaling property) appeared to be an interesting approach to identify and characterize them. The mathematical robustness and predictive ability of wavelet analysis make it a promising road towards multiscale movement ecology that fuses insights from the study of animal behaviour and environmental properties.
topic marine predator
scale
frequency analysis
trajectory
path analysis
url http://www.iaees.org/publications/journals/ces/articles/2011-1(2)/Wavelet-analysis-to-detect-regime-shifts.pdf
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