Literature DB >> 29679777

Integrating microbial biomass, composition and function to discern the level of anthropogenic activity in a river ecosystem.

Kailingli Liao1, Yaohui Bai2, Yang Huo1, Zhiyu Jian2, Wanchao Hu1, Chen Zhao2, Jiuhui Qu3.   

Abstract

Anthropogenic activities (e.g., wastewater discharge and pesticide and fertilizer use) have considerable impact on the biotic properties of natural aquatic ecosystems, especially the microbial community and function. Microbes can respond to anthropogenic activities and are thus potential indicators of activity levels. Several reports have documented the impacts of anthropogenic activities on the variations in the microbial community, but the direct use of microbial community indices to discern anthropogenic activity levels remains limited. Here, we integrated flow cytometry, 16S rRNA sequencing, and natural organic matter metabolism determination to investigate microbial biomass, composition, and function in three areas along a gradient of anthropogenic disturbance (less-disturbed mountainous area, wastewater-discharge urban area, and pesticide and fertilizer used agricultural area) in a river ecosystem. Multiple statistical methods were used to explore the causal relationships between changes in environmental factors and microbial variation. Results showed that anthropogenic activities (e.g., wastewater discharge, pesticide and fertilizer use) facilitated bacterial production, affected dominant species distribution, and accelerated natural organic matter (NOM) metabolic rate by microbes. After screening the possible factors influencing the microbial community, we determined that cyanobacterial concentration could be a diagnostic indicator of nutrient levels. We also developed a NOM metabolic index to quantitatively reflect the holistic influence of nutrients and xenobiotics.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  Anthropogenic activity; Bioindicator; Biomass; Composition; Function; Microbial community

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Year:  2018        PMID: 29679777     DOI: 10.1016/j.envint.2018.04.003

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  2 in total

Review 1.  Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges.

Authors:  James M W R McElhinney; Mary Krystelle Catacutan; Aurelie Mawart; Ayesha Hasan; Jorge Dias
Journal:  Front Microbiol       Date:  2022-04-25       Impact factor: 6.064

2.  Impact of long-term industrial contamination on the bacterial communities in urban river sediments.

Authors:  Lei Zhang; Demei Tu; Xingchen Li; Wenxuan Lu; Jing Li
Journal:  BMC Microbiol       Date:  2020-08-14       Impact factor: 3.605

  2 in total

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