Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1783240409539936256 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5452391 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT abrahmsbriana suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT seideldanap suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT doughertyeric suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT hazenelliottl suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT bogradstevenj suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT wilsonalanm suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT weldonmcnuttj suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT costadanielp suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT blakestephen suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT brasharesjustins suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa AT getzwaynem suiteofsimplemetricsrevealscommonmovementsyndromesacrossvertebratetaxa |