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Demonstrating microbial co-occurrence pattern analyses within and between ecosystems
Co-occurrence patterns are used in ecology to explore interactions between organisms and environmental effects on coexistence within biological communities. Analysis of co-occurrence patterns among microbial communities has ranged from simple pairwise comparisons between all community members to dir...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102878/ https://www.ncbi.nlm.nih.gov/pubmed/25101065 http://dx.doi.org/10.3389/fmicb.2014.00358 |
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author | Williams, Ryan J. Howe, Adina Hofmockel, Kirsten S. |
author_facet | Williams, Ryan J. Howe, Adina Hofmockel, Kirsten S. |
author_sort | Williams, Ryan J. |
collection | PubMed |
description | Co-occurrence patterns are used in ecology to explore interactions between organisms and environmental effects on coexistence within biological communities. Analysis of co-occurrence patterns among microbial communities has ranged from simple pairwise comparisons between all community members to direct hypothesis testing between focal species. However, co-occurrence patterns are rarely studied across multiple ecosystems or multiple scales of biological organization within the same study. Here we outline an approach to produce co-occurrence analyses that are focused at three different scales: co-occurrence patterns between ecosystems at the community scale, modules of co-occurring microorganisms within communities, and co-occurring pairs within modules that are nested within microbial communities. To demonstrate our co-occurrence analysis approach, we gathered publicly available 16S rRNA amplicon datasets to compare and contrast microbial co-occurrence at different taxonomic levels across different ecosystems. We found differences in community composition and co-occurrence that reflect environmental filtering at the community scale and consistent pairwise occurrences that may be used to infer ecological traits about poorly understood microbial taxa. However, we also found that conclusions derived from applying network statistics to microbial relationships can vary depending on the taxonomic level chosen and criteria used to build co-occurrence networks. We present our statistical analysis and code for public use in analysis of co-occurrence patterns across microbial communities. |
format | Online Article Text |
id | pubmed-4102878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-41028782014-08-06 Demonstrating microbial co-occurrence pattern analyses within and between ecosystems Williams, Ryan J. Howe, Adina Hofmockel, Kirsten S. Front Microbiol Microbiology Co-occurrence patterns are used in ecology to explore interactions between organisms and environmental effects on coexistence within biological communities. Analysis of co-occurrence patterns among microbial communities has ranged from simple pairwise comparisons between all community members to direct hypothesis testing between focal species. However, co-occurrence patterns are rarely studied across multiple ecosystems or multiple scales of biological organization within the same study. Here we outline an approach to produce co-occurrence analyses that are focused at three different scales: co-occurrence patterns between ecosystems at the community scale, modules of co-occurring microorganisms within communities, and co-occurring pairs within modules that are nested within microbial communities. To demonstrate our co-occurrence analysis approach, we gathered publicly available 16S rRNA amplicon datasets to compare and contrast microbial co-occurrence at different taxonomic levels across different ecosystems. We found differences in community composition and co-occurrence that reflect environmental filtering at the community scale and consistent pairwise occurrences that may be used to infer ecological traits about poorly understood microbial taxa. However, we also found that conclusions derived from applying network statistics to microbial relationships can vary depending on the taxonomic level chosen and criteria used to build co-occurrence networks. We present our statistical analysis and code for public use in analysis of co-occurrence patterns across microbial communities. Frontiers Media S.A. 2014-07-18 /pmc/articles/PMC4102878/ /pubmed/25101065 http://dx.doi.org/10.3389/fmicb.2014.00358 Text en Copyright © 2014 Williams, Howe and Hofmockel. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Williams, Ryan J. Howe, Adina Hofmockel, Kirsten S. Demonstrating microbial co-occurrence pattern analyses within and between ecosystems |
title | Demonstrating microbial co-occurrence pattern analyses within and between ecosystems |
title_full | Demonstrating microbial co-occurrence pattern analyses within and between ecosystems |
title_fullStr | Demonstrating microbial co-occurrence pattern analyses within and between ecosystems |
title_full_unstemmed | Demonstrating microbial co-occurrence pattern analyses within and between ecosystems |
title_short | Demonstrating microbial co-occurrence pattern analyses within and between ecosystems |
title_sort | demonstrating microbial co-occurrence pattern analyses within and between ecosystems |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102878/ https://www.ncbi.nlm.nih.gov/pubmed/25101065 http://dx.doi.org/10.3389/fmicb.2014.00358 |
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