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The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities
The large taxonomic variability of microbial community composition is a consequence of the combination of environmental variability, mediated through ecological interactions, and stochasticity. Most of the analysis aiming to infer the biological factors determining this difference in community struc...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007381/ https://www.ncbi.nlm.nih.gov/pubmed/35363772 http://dx.doi.org/10.1371/journal.pcbi.1010043 |
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author | Zaoli, Silvia Grilli, Jacopo |
author_facet | Zaoli, Silvia Grilli, Jacopo |
author_sort | Zaoli, Silvia |
collection | PubMed |
description | The large taxonomic variability of microbial community composition is a consequence of the combination of environmental variability, mediated through ecological interactions, and stochasticity. Most of the analysis aiming to infer the biological factors determining this difference in community structure start by quantifying how much communities are similar in their composition, trough beta-diversity metrics. The central role that these metrics play in microbial ecology does not parallel with a quantitative understanding of their relationships and statistical properties. In particular, we lack a framework that reproduces the empirical statistical properties of beta-diversity metrics. Here we take a macroecological approach and introduce a model to reproduce the statistical properties of community similarity. The model is based on the statistical properties of individual communities and on a single tunable parameter, the correlation of species’ carrying capacities across communities, which sets the difference of two communities. The model reproduces quantitatively the empirical values of several commonly-used beta-diversity metrics, as well as the relationships between them. In particular, this modeling framework naturally reproduces the negative correlation between overlap and dissimilarity, which has been observed in both empirical and experimental communities and previously related to the existence of universal features of community dynamics. In this framework, such correlation naturally emerges due to the effect of random sampling. |
format | Online Article Text |
id | pubmed-9007381 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90073812022-04-14 The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities Zaoli, Silvia Grilli, Jacopo PLoS Comput Biol Research Article The large taxonomic variability of microbial community composition is a consequence of the combination of environmental variability, mediated through ecological interactions, and stochasticity. Most of the analysis aiming to infer the biological factors determining this difference in community structure start by quantifying how much communities are similar in their composition, trough beta-diversity metrics. The central role that these metrics play in microbial ecology does not parallel with a quantitative understanding of their relationships and statistical properties. In particular, we lack a framework that reproduces the empirical statistical properties of beta-diversity metrics. Here we take a macroecological approach and introduce a model to reproduce the statistical properties of community similarity. The model is based on the statistical properties of individual communities and on a single tunable parameter, the correlation of species’ carrying capacities across communities, which sets the difference of two communities. The model reproduces quantitatively the empirical values of several commonly-used beta-diversity metrics, as well as the relationships between them. In particular, this modeling framework naturally reproduces the negative correlation between overlap and dissimilarity, which has been observed in both empirical and experimental communities and previously related to the existence of universal features of community dynamics. In this framework, such correlation naturally emerges due to the effect of random sampling. Public Library of Science 2022-04-01 /pmc/articles/PMC9007381/ /pubmed/35363772 http://dx.doi.org/10.1371/journal.pcbi.1010043 Text en © 2022 Zaoli, Grilli https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zaoli, Silvia Grilli, Jacopo The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities |
title | The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities |
title_full | The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities |
title_fullStr | The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities |
title_full_unstemmed | The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities |
title_short | The stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities |
title_sort | stochastic logistic model with correlated carrying capacities reproduces beta-diversity metrics of microbial communities |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007381/ https://www.ncbi.nlm.nih.gov/pubmed/35363772 http://dx.doi.org/10.1371/journal.pcbi.1010043 |
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