Evidence map›Paper›PMID 34361967›Full record

ArticleMicroorganisms2021

Spatial and Temporal Distribution of Bacterioplankton Molecular Ecological Networks in the Yuan River under Different Human Activity Intensity.

Bobo Wu, Peng Wang, Adam T Devlin, Lu Chen, Yang Xia, Hua Zhang, Minghua Nie, Mingjun Ding

Abstract read
In one paragraph

Article in Microorganisms, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 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

8 authors.

Bobo WuSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Peng WangSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Adam T DevlinSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Lu ChenSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Yang XiaSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Hua ZhangSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Minghua NieSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Mingjun DingSchool of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacterioplankton communities play a crucial role in freshwater ecosystem functioning, but it is unknown how co-occurrence networks within these communities respond to human activity disturbances. This represents an important knowledge gap because changes in microbial networks could have implications for their functionality and vulnerability to future disturbances. Here, we compare the spatiotemporal and biogeographical patterns of bacterioplankton molecular ecological networks using high-throughput sequencing of Illumina HiSeq and multivariate statistical analyses from a subtropical river during wet and dry seasons. Results demonstrated that the lower reaches (high human activity intensity) network had less of an average degree (10.568/18.363), especially during the dry season, when compared with the upper reaches (low human activity intensity) network (10.685/37.552) during the wet and dry seasons, respectively. The latter formed more complexity networks with more modularity (0.622/0.556) than the lower reaches (high human activity intensity) network (0.505/0.41) during the wet and dry seasons, respectively. Bacterioplankton molecular ecological network under high human activity intensity became significantly less robust, which is mainly caused by altering of the environmental conditions and keystone species. Human activity altered the composition of modules but preserved their ecological roles in the network and environmental factors (dissolved organic carbon, temperature, arsenic, oxidation-reduction potential and Chao1 index) were the best parameters for explaining the variations in bacterioplankton molecular ecological network structure and modules.

Indexed as

bacterioplanktonfreshwater ecosystem functioninghuman activity intensitymolecular ecological networks

Identifiers

PMID34361967
PMCPMC8306320

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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.