Evidence map›Paper›PMID 29794413›Full record

ArticleMicrobes and environments2018

Co-occurrence Network Reveals the Higher Fragmentation of the Bacterial Community in Kaidu River Than Its Tributaries in Northwestern China.

Yang Hu, Chengrong Bai, Jian Cai, Jiangyu Dai, Keqiang Shao, Xiangming Tang, Guang Gao

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Article in Microbes and environments, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yang HuState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences.
Chengrong BaiState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences.
Jian CaiState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences.
Jiangyu DaiState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute.
Keqiang ShaoState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences.
Xiangming TangState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences.
Guang GaoState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rivers and their tributaries sculpt the earth's surface, and play an important role in substance circulation and energy flow. Bacteria are involved in most biogeochemical processes in the fluvial ecosystem; however, their pattern distribution in a river and its tributaries has not yet been investigated in detail. In the present study, high-throughput sequencing was employed to examine bacterial communities and their co-occurrence networks between Kaidu River and its nine tributaries in northwestern China. The results obtained demonstrated that both bacterial communities shared a similar dominant sub-community, mainly consisting of Actinobacteria, Bacteroidetes, and Proteobacteria, with Limnohabitans and Variovorax as the dominant genera. In spite of these commonalities, bacterial community structures still significantly differed between these two habitats, which may be related to the distance-related dispersal limitation. Their co-occurrence networks were generally both positively structured. The structural analysis showed that OTUs from the same phyla were more likely to co-occur. Although the keystone genera were taxonomically different between Kaidu River and its tributaries, they both shared common trophic properties in exploiting niches under oligotrophic conditions. We noted that their relative abundances were less than 1%, indicating the over-proportional roles of rare genera in the bacterial community. In addition, the inferred networks showed less nodes and edges, but higher modularity in Kaidu River than its tributaries, suggesting the higher fragmentation of the bacterial community in the mainstream.

Indexed as

BiodiversityMicrobial InteractionsWater MicrobiologyBacteriaChinaComputational BiologyDNA, BacterialEcosystemMetagenomicsRiversRNA, Ribosomal, 16SDNA, BacterialRNA, Ribosomal, 16Sbacterial communityco-occurrence networkKaidu Rivertributaries

Identifiers

PMID29794413
PMCPMC6031398

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.