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Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures
BACKGROUND: pH is frequently reported as the main driver for prokaryotic community structure in soils. However, pH changes are also linked to “spillover effects” on other chemical parameters (e.g., availability of Al, Fe, Mn, Zn, and Cu) and plant growth, but these indirect effects on the microbial...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996553/ https://www.ncbi.nlm.nih.gov/pubmed/29891000 http://dx.doi.org/10.1186/s40168-018-0482-8 |
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author | Lammel, Daniel R. Barth, Gabriel Ovaskainen, Otso Cruz, Leonardo M. Zanatta, Josileia A. Ryo, Masahiro de Souza, Emanuel M. Pedrosa, Fábio O. |
author_facet | Lammel, Daniel R. Barth, Gabriel Ovaskainen, Otso Cruz, Leonardo M. Zanatta, Josileia A. Ryo, Masahiro de Souza, Emanuel M. Pedrosa, Fábio O. |
author_sort | Lammel, Daniel R. |
collection | PubMed |
description | BACKGROUND: pH is frequently reported as the main driver for prokaryotic community structure in soils. However, pH changes are also linked to “spillover effects” on other chemical parameters (e.g., availability of Al, Fe, Mn, Zn, and Cu) and plant growth, but these indirect effects on the microbial communities are rarely investigated. Usually, pH also co-varies with some confounding factors, such as land use, soil management (e.g., tillage and chemical inputs), plant cover, and/or edapho-climatic conditions. So, a more comprehensive analysis of the direct and indirect effects of pH brings a better understanding of the mechanisms driving prokaryotic (archaeal and bacterial) community structures. RESULTS: We evaluated an agricultural soil pH gradient (from 4 to 6, the typical range for tropical farms), in a liming gradient with confounding factors minimized, investigating relationships between prokaryotic communities (16S rRNA) and physical–chemical parameters (indirect effects). Correlations, hierarchical modeling of species communities (HMSC), and random forest (RF) modeling indicated that both direct and indirect effects of the pH gradient affected the prokaryotic communities. Some OTUs were more affected by the pH changes (e.g., some Actinobacteria), while others were more affected by the indirect pH effects (e.g., some Proteobacteria). HMSC detected a phylogenetic signal related to the effects. Both HMSC and RF indicated that the main indirect effect was the pH changes on the availability of some elements (e.g., Al, Fe, and Cu), and secondarily, effects on plant growth and nutrient cycling also affected the OTUs. Additionally, we found that some of the OTUs that responded to pH also correlated with CO(2), CH(4), and N(2)O greenhouse gas fluxes. CONCLUSIONS: Our results indicate that there are two distinct pH-related mechanisms driving prokaryotic community structures, the direct effect and “spillover effects” of pH (indirect effects). Moreover, the indirect effects are highly relevant for some OTUs and consequently for the community structure; therefore, it is a mechanism that should be further investigated in microbial ecology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-018-0482-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5996553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59965532018-07-06 Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures Lammel, Daniel R. Barth, Gabriel Ovaskainen, Otso Cruz, Leonardo M. Zanatta, Josileia A. Ryo, Masahiro de Souza, Emanuel M. Pedrosa, Fábio O. Microbiome Research BACKGROUND: pH is frequently reported as the main driver for prokaryotic community structure in soils. However, pH changes are also linked to “spillover effects” on other chemical parameters (e.g., availability of Al, Fe, Mn, Zn, and Cu) and plant growth, but these indirect effects on the microbial communities are rarely investigated. Usually, pH also co-varies with some confounding factors, such as land use, soil management (e.g., tillage and chemical inputs), plant cover, and/or edapho-climatic conditions. So, a more comprehensive analysis of the direct and indirect effects of pH brings a better understanding of the mechanisms driving prokaryotic (archaeal and bacterial) community structures. RESULTS: We evaluated an agricultural soil pH gradient (from 4 to 6, the typical range for tropical farms), in a liming gradient with confounding factors minimized, investigating relationships between prokaryotic communities (16S rRNA) and physical–chemical parameters (indirect effects). Correlations, hierarchical modeling of species communities (HMSC), and random forest (RF) modeling indicated that both direct and indirect effects of the pH gradient affected the prokaryotic communities. Some OTUs were more affected by the pH changes (e.g., some Actinobacteria), while others were more affected by the indirect pH effects (e.g., some Proteobacteria). HMSC detected a phylogenetic signal related to the effects. Both HMSC and RF indicated that the main indirect effect was the pH changes on the availability of some elements (e.g., Al, Fe, and Cu), and secondarily, effects on plant growth and nutrient cycling also affected the OTUs. Additionally, we found that some of the OTUs that responded to pH also correlated with CO(2), CH(4), and N(2)O greenhouse gas fluxes. CONCLUSIONS: Our results indicate that there are two distinct pH-related mechanisms driving prokaryotic community structures, the direct effect and “spillover effects” of pH (indirect effects). Moreover, the indirect effects are highly relevant for some OTUs and consequently for the community structure; therefore, it is a mechanism that should be further investigated in microbial ecology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-018-0482-8) contains supplementary material, which is available to authorized users. BioMed Central 2018-06-11 /pmc/articles/PMC5996553/ /pubmed/29891000 http://dx.doi.org/10.1186/s40168-018-0482-8 Text en © The Author(s). 2018 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 Lammel, Daniel R. Barth, Gabriel Ovaskainen, Otso Cruz, Leonardo M. Zanatta, Josileia A. Ryo, Masahiro de Souza, Emanuel M. Pedrosa, Fábio O. Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures |
title | Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures |
title_full | Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures |
title_fullStr | Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures |
title_full_unstemmed | Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures |
title_short | Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures |
title_sort | direct and indirect effects of a ph gradient bring insights into the mechanisms driving prokaryotic community structures |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996553/ https://www.ncbi.nlm.nih.gov/pubmed/29891000 http://dx.doi.org/10.1186/s40168-018-0482-8 |
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