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Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox
The correlation of multivariate data is a common task in investigations of soil biology and in ecology in general. Procrustes analysis and the Mantel test are two approaches that often meet this objective and are considered analogous in many situations especially when used as a statistical test to a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074130/ https://www.ncbi.nlm.nih.gov/pubmed/24971901 http://dx.doi.org/10.1371/journal.pone.0101238 |
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author | Lisboa, Francy Junio Gonçalves Peres-Neto, Pedro R. Chaer, Guilherme Montandon Jesus, Ederson da Conceição Mitchell, Ruth Joy Chapman, Stephen James Berbara, Ricardo Luis Louro |
author_facet | Lisboa, Francy Junio Gonçalves Peres-Neto, Pedro R. Chaer, Guilherme Montandon Jesus, Ederson da Conceição Mitchell, Ruth Joy Chapman, Stephen James Berbara, Ricardo Luis Louro |
author_sort | Lisboa, Francy Junio Gonçalves |
collection | PubMed |
description | The correlation of multivariate data is a common task in investigations of soil biology and in ecology in general. Procrustes analysis and the Mantel test are two approaches that often meet this objective and are considered analogous in many situations especially when used as a statistical test to assess the statistical significance between multivariate data tables. Here we call the attention of ecologists to the advantages of a less familiar application of the Procrustean framework, namely the Procrustean association metric (a vector of Procrustean residuals). These residuals represent differences in fit between multivariate data tables regarding homologous observations (e.g., sampling sites) that can be used to estimate local levels of association (e.g., some groups of sites are more similar in their association between biotic and environmental features than other groups of sites). Given that in the Mantel framework, multivariate information is translated into a pairwise distance matrix, we lose the ability to contrast homologous data points across dimensions and data matrices after their fit. In this paper, we attempt to familiarize ecologists with the benefits of using these Procrustean residual differences to further gain insights about the processes underlying the association among multivariate data tables using real and hypothetical examples. |
format | Online Article Text |
id | pubmed-4074130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-40741302014-07-02 Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox Lisboa, Francy Junio Gonçalves Peres-Neto, Pedro R. Chaer, Guilherme Montandon Jesus, Ederson da Conceição Mitchell, Ruth Joy Chapman, Stephen James Berbara, Ricardo Luis Louro PLoS One Research Article The correlation of multivariate data is a common task in investigations of soil biology and in ecology in general. Procrustes analysis and the Mantel test are two approaches that often meet this objective and are considered analogous in many situations especially when used as a statistical test to assess the statistical significance between multivariate data tables. Here we call the attention of ecologists to the advantages of a less familiar application of the Procrustean framework, namely the Procrustean association metric (a vector of Procrustean residuals). These residuals represent differences in fit between multivariate data tables regarding homologous observations (e.g., sampling sites) that can be used to estimate local levels of association (e.g., some groups of sites are more similar in their association between biotic and environmental features than other groups of sites). Given that in the Mantel framework, multivariate information is translated into a pairwise distance matrix, we lose the ability to contrast homologous data points across dimensions and data matrices after their fit. In this paper, we attempt to familiarize ecologists with the benefits of using these Procrustean residual differences to further gain insights about the processes underlying the association among multivariate data tables using real and hypothetical examples. Public Library of Science 2014-06-27 /pmc/articles/PMC4074130/ /pubmed/24971901 http://dx.doi.org/10.1371/journal.pone.0101238 Text en © 2014 Lisboa et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Lisboa, Francy Junio Gonçalves Peres-Neto, Pedro R. Chaer, Guilherme Montandon Jesus, Ederson da Conceição Mitchell, Ruth Joy Chapman, Stephen James Berbara, Ricardo Luis Louro Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox |
title | Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox |
title_full | Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox |
title_fullStr | Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox |
title_full_unstemmed | Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox |
title_short | Much beyond Mantel: Bringing Procrustes Association Metric to the Plant and Soil Ecologist’s Toolbox |
title_sort | much beyond mantel: bringing procrustes association metric to the plant and soil ecologist’s toolbox |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074130/ https://www.ncbi.nlm.nih.gov/pubmed/24971901 http://dx.doi.org/10.1371/journal.pone.0101238 |
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