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A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming
Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7501310/ https://www.ncbi.nlm.nih.gov/pubmed/32948774 http://dx.doi.org/10.1038/s41467-020-18560-z |
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author | Ning, Daliang Yuan, Mengting Wu, Linwei Zhang, Ya Guo, Xue Zhou, Xishu Yang, Yunfeng Arkin, Adam P. Firestone, Mary K. Zhou, Jizhong |
author_facet | Ning, Daliang Yuan, Mengting Wu, Linwei Zhang, Ya Guo, Xue Zhou, Xishu Yang, Yunfeng Arkin, Adam P. Firestone, Mary K. Zhou, Jizhong |
author_sort | Ning, Daliang |
collection | PubMed |
description | Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93–0.99), precision (0.80–0.94), sensitivity (0.82–0.94), and specificity (0.95–0.98) on simulated communities, which are 10–160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and ‘drift’ (59%). Interestingly, warming decreases ‘drift’ over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology. |
format | Online Article Text |
id | pubmed-7501310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75013102020-10-01 A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming Ning, Daliang Yuan, Mengting Wu, Linwei Zhang, Ya Guo, Xue Zhou, Xishu Yang, Yunfeng Arkin, Adam P. Firestone, Mary K. Zhou, Jizhong Nat Commun Article Unraveling the drivers controlling community assembly is a central issue in ecology. Although it is generally accepted that selection, dispersal, diversification and drift are major community assembly processes, defining their relative importance is very challenging. Here, we present a framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). iCAMP shows high accuracy (0.93–0.99), precision (0.80–0.94), sensitivity (0.82–0.94), and specificity (0.95–0.98) on simulated communities, which are 10–160% higher than those from the entire community-based approach. Application of iCAMP to grassland microbial communities in response to experimental warming reveals dominant roles of homogeneous selection (38%) and ‘drift’ (59%). Interestingly, warming decreases ‘drift’ over time, and enhances homogeneous selection which is primarily imposed on Bacillales. In addition, homogeneous selection has higher correlations with drought and plant productivity under warming than control. iCAMP provides an effective and robust tool to quantify microbial assembly processes, and should also be useful for plant and animal ecology. Nature Publishing Group UK 2020-09-18 /pmc/articles/PMC7501310/ /pubmed/32948774 http://dx.doi.org/10.1038/s41467-020-18560-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ning, Daliang Yuan, Mengting Wu, Linwei Zhang, Ya Guo, Xue Zhou, Xishu Yang, Yunfeng Arkin, Adam P. Firestone, Mary K. Zhou, Jizhong A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming |
title | A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming |
title_full | A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming |
title_fullStr | A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming |
title_full_unstemmed | A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming |
title_short | A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming |
title_sort | quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7501310/ https://www.ncbi.nlm.nih.gov/pubmed/32948774 http://dx.doi.org/10.1038/s41467-020-18560-z |
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