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Suite of simple metrics reveals common movement syndromes across vertebrate taxa

BACKGROUND: Because empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by deter...

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Autores principales: Abrahms, Briana, Seidel, Dana P., Dougherty, Eric, Hazen, Elliott L., Bograd, Steven J., Wilson, Alan M., Weldon McNutt, J., Costa, Daniel P., Blake, Stephen, Brashares, Justin S., Getz, Wayne M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5452391/
https://www.ncbi.nlm.nih.gov/pubmed/28580149
http://dx.doi.org/10.1186/s40462-017-0104-2
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author Abrahms, Briana
Seidel, Dana P.
Dougherty, Eric
Hazen, Elliott L.
Bograd, Steven J.
Wilson, Alan M.
Weldon McNutt, J.
Costa, Daniel P.
Blake, Stephen
Brashares, Justin S.
Getz, Wayne M.
author_facet Abrahms, Briana
Seidel, Dana P.
Dougherty, Eric
Hazen, Elliott L.
Bograd, Steven J.
Wilson, Alan M.
Weldon McNutt, J.
Costa, Daniel P.
Blake, Stephen
Brashares, Justin S.
Getz, Wayne M.
author_sort Abrahms, Briana
collection PubMed
description BACKGROUND: Because empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging). RESULTS: Two principal components explained 70% of the variance among the movement metrics we evaluated across the thirteen species, and were used for the cluster analysis. The resulting analysis revealed four statistically distinct clusters. All simulated individuals of each idealized movement syndrome organized into separate clusters, suggesting that the four clusters are explained by common movement syndrome. CONCLUSIONS: Our results offer early indication of widespread recurrent patterns in movement ecology that have consistent statistical signatures, regardless of taxon, body size, mode of movement, or environment. We further show that a simple set of metrics can be used to classify broad-scale movement patterns in disparate vertebrate taxa. Our comparative approach provides a general framework for quantifying and classifying animal movements, and facilitates new inquiries into relationships between movement syndromes and other ecological processes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40462-017-0104-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-54523912017-06-02 Suite of simple metrics reveals common movement syndromes across vertebrate taxa Abrahms, Briana Seidel, Dana P. Dougherty, Eric Hazen, Elliott L. Bograd, Steven J. Wilson, Alan M. Weldon McNutt, J. Costa, Daniel P. Blake, Stephen Brashares, Justin S. Getz, Wayne M. Mov Ecol Research BACKGROUND: Because empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging). RESULTS: Two principal components explained 70% of the variance among the movement metrics we evaluated across the thirteen species, and were used for the cluster analysis. The resulting analysis revealed four statistically distinct clusters. All simulated individuals of each idealized movement syndrome organized into separate clusters, suggesting that the four clusters are explained by common movement syndrome. CONCLUSIONS: Our results offer early indication of widespread recurrent patterns in movement ecology that have consistent statistical signatures, regardless of taxon, body size, mode of movement, or environment. We further show that a simple set of metrics can be used to classify broad-scale movement patterns in disparate vertebrate taxa. Our comparative approach provides a general framework for quantifying and classifying animal movements, and facilitates new inquiries into relationships between movement syndromes and other ecological processes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40462-017-0104-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-01 /pmc/articles/PMC5452391/ /pubmed/28580149 http://dx.doi.org/10.1186/s40462-017-0104-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Abrahms, Briana
Seidel, Dana P.
Dougherty, Eric
Hazen, Elliott L.
Bograd, Steven J.
Wilson, Alan M.
Weldon McNutt, J.
Costa, Daniel P.
Blake, Stephen
Brashares, Justin S.
Getz, Wayne M.
Suite of simple metrics reveals common movement syndromes across vertebrate taxa
title Suite of simple metrics reveals common movement syndromes across vertebrate taxa
title_full Suite of simple metrics reveals common movement syndromes across vertebrate taxa
title_fullStr Suite of simple metrics reveals common movement syndromes across vertebrate taxa
title_full_unstemmed Suite of simple metrics reveals common movement syndromes across vertebrate taxa
title_short Suite of simple metrics reveals common movement syndromes across vertebrate taxa
title_sort suite of simple metrics reveals common movement syndromes across vertebrate taxa
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5452391/
https://www.ncbi.nlm.nih.gov/pubmed/28580149
http://dx.doi.org/10.1186/s40462-017-0104-2
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