Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes

Biotic interactions are known as a major control on microbial diversity. However, biotic interactions have rarely been quantified in an adequate manner, often leaving much residual variation unexplained in microbial biogeographic studies. Herein, we propose a holistic approach to disentangle the rel...
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Gespeichert in:

Autor*in:	Guihao Li [verfasserIn] Yaping Wang [verfasserIn] Han Li [verfasserIn] Xiaoli Zhang [verfasserIn] Jun Gong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Community assembly Environmental selection Microbial biogeography Microbial interaction Stochasticity

Übergeordnetes Werk:	In: Ecological Indicators - Elsevier, 2021, 146(2023), Seite 109841-
Übergeordnetes Werk:	volume:146 ; year:2023 ; pages:109841-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.ecolind.2022.109841

Katalog-ID:	DOAJ004525051

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allfieldsSound	10.1016/j.ecolind.2022.109841 doi (DE-627)DOAJ004525051 (DE-599)DOAJd2e4338011ee4cfd9516c2f477248eed DE-627 ger DE-627 rakwb eng QH540-549.5 Guihao Li verfasserin aut Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biotic interactions are known as a major control on microbial diversity. However, biotic interactions have rarely been quantified in an adequate manner, often leaving much residual variation unexplained in microbial biogeographic studies. Herein, we propose a holistic approach to disentangle the relative importance of inter-domain interactions in shaping microbial diversity by incorporating community-level characteristics. Taking coastal bacterioplankton on a regional scale as an example, we designated a range of community characteristics of pico- and nanoeukaryotes derived from metabarcoding and flow cytometric data as inter-domain interacting proxies, which were then considered in statistical modeling. We found that the bacterial diversity indices and community structure were much more accurately explained by a number of eukaryotic characteristics than by the measured environmental variables and/or spatial variables alone, as were the richness, relative abundances, and assemblage structures of major bacterial taxa. In co-occurrence networks, the nodes of characteristics that had more edges (links) were frequently the best explanatory variables for bacterial diversity indices. Over 70% of total variation in bacterial community structure could be explained by three categories of biotic interactions: parasitism (27%), fungi-bacterial competition (32%), and trophic structure and bacterivory (13%). This study showcases a methodological framework to infer different types of inter-domain interactions at play, and stresses the importance of non-grazing interacting processes in shaping bacterial diversity and community assembly. Community assembly Environmental selection Microbial biogeography Microbial interaction Stochasticity Ecology Yaping Wang verfasserin aut Han Li verfasserin aut Xiaoli Zhang verfasserin aut Jun Gong verfasserin aut In Ecological Indicators Elsevier, 2021 146(2023), Seite 109841- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:146 year:2023 pages:109841- https://doi.org/10.1016/j.ecolind.2022.109841 kostenfrei https://doaj.org/article/d2e4338011ee4cfd9516c2f477248eed kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22013140 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 146 2023 109841-
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authorswithroles_txt_mv	Guihao Li @@aut@@ Yaping Wang @@aut@@ Han Li @@aut@@ Xiaoli Zhang @@aut@@ Jun Gong @@aut@@
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callnumber-first	Q - Science
author	Guihao Li
spellingShingle	Guihao Li misc QH540-549.5 misc Community assembly misc Environmental selection misc Microbial biogeography misc Microbial interaction misc Stochasticity misc Ecology Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes
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topic_title	QH540-549.5 Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes Community assembly Environmental selection Microbial biogeography Microbial interaction Stochasticity
topic	misc QH540-549.5 misc Community assembly misc Environmental selection misc Microbial biogeography misc Microbial interaction misc Stochasticity misc Ecology
topic_unstemmed	misc QH540-549.5 misc Community assembly misc Environmental selection misc Microbial biogeography misc Microbial interaction misc Stochasticity misc Ecology
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title	Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes
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title_full	Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes
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title_sort	quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes
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title_auth	Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes
abstract	Biotic interactions are known as a major control on microbial diversity. However, biotic interactions have rarely been quantified in an adequate manner, often leaving much residual variation unexplained in microbial biogeographic studies. Herein, we propose a holistic approach to disentangle the relative importance of inter-domain interactions in shaping microbial diversity by incorporating community-level characteristics. Taking coastal bacterioplankton on a regional scale as an example, we designated a range of community characteristics of pico- and nanoeukaryotes derived from metabarcoding and flow cytometric data as inter-domain interacting proxies, which were then considered in statistical modeling. We found that the bacterial diversity indices and community structure were much more accurately explained by a number of eukaryotic characteristics than by the measured environmental variables and/or spatial variables alone, as were the richness, relative abundances, and assemblage structures of major bacterial taxa. In co-occurrence networks, the nodes of characteristics that had more edges (links) were frequently the best explanatory variables for bacterial diversity indices. Over 70% of total variation in bacterial community structure could be explained by three categories of biotic interactions: parasitism (27%), fungi-bacterial competition (32%), and trophic structure and bacterivory (13%). This study showcases a methodological framework to infer different types of inter-domain interactions at play, and stresses the importance of non-grazing interacting processes in shaping bacterial diversity and community assembly.
abstractGer	Biotic interactions are known as a major control on microbial diversity. However, biotic interactions have rarely been quantified in an adequate manner, often leaving much residual variation unexplained in microbial biogeographic studies. Herein, we propose a holistic approach to disentangle the relative importance of inter-domain interactions in shaping microbial diversity by incorporating community-level characteristics. Taking coastal bacterioplankton on a regional scale as an example, we designated a range of community characteristics of pico- and nanoeukaryotes derived from metabarcoding and flow cytometric data as inter-domain interacting proxies, which were then considered in statistical modeling. We found that the bacterial diversity indices and community structure were much more accurately explained by a number of eukaryotic characteristics than by the measured environmental variables and/or spatial variables alone, as were the richness, relative abundances, and assemblage structures of major bacterial taxa. In co-occurrence networks, the nodes of characteristics that had more edges (links) were frequently the best explanatory variables for bacterial diversity indices. Over 70% of total variation in bacterial community structure could be explained by three categories of biotic interactions: parasitism (27%), fungi-bacterial competition (32%), and trophic structure and bacterivory (13%). This study showcases a methodological framework to infer different types of inter-domain interactions at play, and stresses the importance of non-grazing interacting processes in shaping bacterial diversity and community assembly.
abstract_unstemmed	Biotic interactions are known as a major control on microbial diversity. However, biotic interactions have rarely been quantified in an adequate manner, often leaving much residual variation unexplained in microbial biogeographic studies. Herein, we propose a holistic approach to disentangle the relative importance of inter-domain interactions in shaping microbial diversity by incorporating community-level characteristics. Taking coastal bacterioplankton on a regional scale as an example, we designated a range of community characteristics of pico- and nanoeukaryotes derived from metabarcoding and flow cytometric data as inter-domain interacting proxies, which were then considered in statistical modeling. We found that the bacterial diversity indices and community structure were much more accurately explained by a number of eukaryotic characteristics than by the measured environmental variables and/or spatial variables alone, as were the richness, relative abundances, and assemblage structures of major bacterial taxa. In co-occurrence networks, the nodes of characteristics that had more edges (links) were frequently the best explanatory variables for bacterial diversity indices. Over 70% of total variation in bacterial community structure could be explained by three categories of biotic interactions: parasitism (27%), fungi-bacterial competition (32%), and trophic structure and bacterivory (13%). This study showcases a methodological framework to infer different types of inter-domain interactions at play, and stresses the importance of non-grazing interacting processes in shaping bacterial diversity and community assembly.
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title_short	Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes
url	https://doi.org/10.1016/j.ecolind.2022.109841 https://doaj.org/article/d2e4338011ee4cfd9516c2f477248eed http://www.sciencedirect.com/science/article/pii/S1470160X22013140 https://doaj.org/toc/1470-160X
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However, biotic interactions have rarely been quantified in an adequate manner, often leaving much residual variation unexplained in microbial biogeographic studies. Herein, we propose a holistic approach to disentangle the relative importance of inter-domain interactions in shaping microbial diversity by incorporating community-level characteristics. Taking coastal bacterioplankton on a regional scale as an example, we designated a range of community characteristics of pico- and nanoeukaryotes derived from metabarcoding and flow cytometric data as inter-domain interacting proxies, which were then considered in statistical modeling. We found that the bacterial diversity indices and community structure were much more accurately explained by a number of eukaryotic characteristics than by the measured environmental variables and/or spatial variables alone, as were the richness, relative abundances, and assemblage structures of major bacterial taxa. In co-occurrence networks, the nodes of characteristics that had more edges (links) were frequently the best explanatory variables for bacterial diversity indices. Over 70% of total variation in bacterial community structure could be explained by three categories of biotic interactions: parasitism (27%), fungi-bacterial competition (32%), and trophic structure and bacterivory (13%). This study showcases a methodological framework to infer different types of inter-domain interactions at play, and stresses the importance of non-grazing interacting processes in shaping bacterial diversity and community assembly.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Community assembly</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Environmental selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microbial biogeography</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microbial interaction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochasticity</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ecology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yaping Wang</subfield><subfield code="e">verfasserin</subfield><subfield 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Quantifying relative contributions of biotic interactions to bacterial diversity and community assembly by using community characteristics of microbial eukaryotes

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