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A pathway for multivariate analysis of ecological communities using copulas

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity‐based methods, to integrate information across species and provide a holistic v...

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Detalles Bibliográficos
Autores principales: Anderson, Marti J., de Valpine, Perry, Punnett, Andrew, Miller, Arden E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434552/
https://www.ncbi.nlm.nih.gov/pubmed/30962892
http://dx.doi.org/10.1002/ece3.4948
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author Anderson, Marti J.
de Valpine, Perry
Punnett, Andrew
Miller, Arden E.
author_facet Anderson, Marti J.
de Valpine, Perry
Punnett, Andrew
Miller, Arden E.
author_sort Anderson, Marti J.
collection PubMed
description We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity‐based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over‐dispersion, and/or zero‐inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation‐based filter with exact family‐wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity‐based settings. Our proposed analysis pathway provides new opportunities to combine model‐based approaches with dissimilarity‐based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.
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spelling pubmed-64345522019-04-08 A pathway for multivariate analysis of ecological communities using copulas Anderson, Marti J. de Valpine, Perry Punnett, Andrew Miller, Arden E. Ecol Evol Original Research We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity‐based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over‐dispersion, and/or zero‐inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation‐based filter with exact family‐wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity‐based settings. Our proposed analysis pathway provides new opportunities to combine model‐based approaches with dissimilarity‐based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve. John Wiley and Sons Inc. 2019-03-05 /pmc/articles/PMC6434552/ /pubmed/30962892 http://dx.doi.org/10.1002/ece3.4948 Text en © 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Anderson, Marti J.
de Valpine, Perry
Punnett, Andrew
Miller, Arden E.
A pathway for multivariate analysis of ecological communities using copulas
title A pathway for multivariate analysis of ecological communities using copulas
title_full A pathway for multivariate analysis of ecological communities using copulas
title_fullStr A pathway for multivariate analysis of ecological communities using copulas
title_full_unstemmed A pathway for multivariate analysis of ecological communities using copulas
title_short A pathway for multivariate analysis of ecological communities using copulas
title_sort pathway for multivariate analysis of ecological communities using copulas
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434552/
https://www.ncbi.nlm.nih.gov/pubmed/30962892
http://dx.doi.org/10.1002/ece3.4948
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