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Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis

Marine microbes provide the backbone for pelagic ecosystems by cycling and fixing nutrients and establishing the base of food webs. Microbial communities are often assumed to be highly connected and genetically mixed, with localized environmental filters driving minor changes in structure. Our study...

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Autores principales: Gill, John Geoffrey, Hill-Spanik, Kristina M., Whittaker, Kerry A., Jones, Martin L., Plante, Craig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8893026/
https://www.ncbi.nlm.nih.gov/pubmed/35251777
http://dx.doi.org/10.7717/peerj.12835
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author Gill, John Geoffrey
Hill-Spanik, Kristina M.
Whittaker, Kerry A.
Jones, Martin L.
Plante, Craig
author_facet Gill, John Geoffrey
Hill-Spanik, Kristina M.
Whittaker, Kerry A.
Jones, Martin L.
Plante, Craig
author_sort Gill, John Geoffrey
collection PubMed
description Marine microbes provide the backbone for pelagic ecosystems by cycling and fixing nutrients and establishing the base of food webs. Microbial communities are often assumed to be highly connected and genetically mixed, with localized environmental filters driving minor changes in structure. Our study applied high-throughput Illumina 16S ribosomal RNA gene amplicon sequencing on whole-community bacterial samples to characterize geographic, environmental, and stochastic drivers of community diversity. DNA was extracted from seawater collected from the surface (N = 18) and at depth just below the deep chlorophyll-a maximum (DCM mean depth = 115.4 m; N = 22) in the Sargasso Sea and adjacent oceanographic regions. Discrete bacterioplankton assemblages were observed at varying depths in the North Sargasso Sea, with a signal for distance-decay of bacterioplankton community similarity found only in surface waters. Bacterial communities from different oceanic regions could be distinguished statistically but exhibited a low magnitude of divergence. Redundancy analysis identified temperature as the key environmental variable correlated with community structuring. The effect of dispersal limitation was weak, while variation partitioning and neutral community modeling demonstrated stochastic processes influencing the communities. This study advances understanding of microbial biogeography in the pelagic ocean and highlights the use of high-throughput sequencing methods in studying microbial community structure.
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spelling pubmed-88930262022-03-04 Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis Gill, John Geoffrey Hill-Spanik, Kristina M. Whittaker, Kerry A. Jones, Martin L. Plante, Craig PeerJ Biogeography Marine microbes provide the backbone for pelagic ecosystems by cycling and fixing nutrients and establishing the base of food webs. Microbial communities are often assumed to be highly connected and genetically mixed, with localized environmental filters driving minor changes in structure. Our study applied high-throughput Illumina 16S ribosomal RNA gene amplicon sequencing on whole-community bacterial samples to characterize geographic, environmental, and stochastic drivers of community diversity. DNA was extracted from seawater collected from the surface (N = 18) and at depth just below the deep chlorophyll-a maximum (DCM mean depth = 115.4 m; N = 22) in the Sargasso Sea and adjacent oceanographic regions. Discrete bacterioplankton assemblages were observed at varying depths in the North Sargasso Sea, with a signal for distance-decay of bacterioplankton community similarity found only in surface waters. Bacterial communities from different oceanic regions could be distinguished statistically but exhibited a low magnitude of divergence. Redundancy analysis identified temperature as the key environmental variable correlated with community structuring. The effect of dispersal limitation was weak, while variation partitioning and neutral community modeling demonstrated stochastic processes influencing the communities. This study advances understanding of microbial biogeography in the pelagic ocean and highlights the use of high-throughput sequencing methods in studying microbial community structure. PeerJ Inc. 2022-02-28 /pmc/articles/PMC8893026/ /pubmed/35251777 http://dx.doi.org/10.7717/peerj.12835 Text en © 2022 Gill et al. 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Biogeography
Gill, John Geoffrey
Hill-Spanik, Kristina M.
Whittaker, Kerry A.
Jones, Martin L.
Plante, Craig
Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis
title Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis
title_full Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis
title_fullStr Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis
title_full_unstemmed Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis
title_short Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis
title_sort sargasso sea bacterioplankton community structure and drivers of variance as revealed by dna metabarcoding analysis
topic Biogeography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8893026/
https://www.ncbi.nlm.nih.gov/pubmed/35251777
http://dx.doi.org/10.7717/peerj.12835
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