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Application of Environmental DNA Metabarcoding for Predicting Anthropogenic Pollution in Rivers


Li, Feilong; Peng, Ying; Fang, Wendi; Altermatt, Florian; Xie, Yuwei; Yang, Jianghua; Zhang, Xiaowei (2018). Application of Environmental DNA Metabarcoding for Predicting Anthropogenic Pollution in Rivers. Environmental Science & Technology, 52(20):11708-11719.

Abstract

Rivers are among the most threatened freshwater ecosystems, and anthropogenic activities are affecting both river structures and water quality. While assessing the organisms can provide a comprehensive measure of a river’s ecological status, it is limited by the traditional morphotaxonomy-based biomonitoring. Recent advances in environmental DNA (eDNA) metabarcoding allow to identify prokaryotes and eukaryotes in one sequencing run, and could thus allow unprecedented resolution. Whether such eDNA-based data can be used directly to predict the pollution status of rivers as a complementation of environmental data remains unknown. Here we used eDNA metabarcoding to explore the main stressors of rivers along which community structure changes, and to identify the method’s potential for predicting pollution status based on eDNA data. We showed that a broad range of taxa in bacterial, protistan, and metazoan communities could be profiled with eDNA. Nutrients were the main driving stressor affecting communities’ structure, alpha diversity, and the ecological network. We specifically observed that the relative abundance of indicative OTUs was significantly correlated with nutrient levels. These OTUs data could be used to predict the nutrient status up to 79% accuracy on testing data sets. Thus, our study gives a novel approach to predicting the pollution status of rivers by eDNA data.

Abstract

Rivers are among the most threatened freshwater ecosystems, and anthropogenic activities are affecting both river structures and water quality. While assessing the organisms can provide a comprehensive measure of a river’s ecological status, it is limited by the traditional morphotaxonomy-based biomonitoring. Recent advances in environmental DNA (eDNA) metabarcoding allow to identify prokaryotes and eukaryotes in one sequencing run, and could thus allow unprecedented resolution. Whether such eDNA-based data can be used directly to predict the pollution status of rivers as a complementation of environmental data remains unknown. Here we used eDNA metabarcoding to explore the main stressors of rivers along which community structure changes, and to identify the method’s potential for predicting pollution status based on eDNA data. We showed that a broad range of taxa in bacterial, protistan, and metazoan communities could be profiled with eDNA. Nutrients were the main driving stressor affecting communities’ structure, alpha diversity, and the ecological network. We specifically observed that the relative abundance of indicative OTUs was significantly correlated with nutrient levels. These OTUs data could be used to predict the nutrient status up to 79% accuracy on testing data sets. Thus, our study gives a novel approach to predicting the pollution status of rivers by eDNA data.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Uncontrolled Keywords:General Chemistry, Environmental Chemistry
Language:English
Date:13 September 2018
Deposited On:11 Dec 2018 15:44
Last Modified:24 Sep 2019 23:56
Publisher:American Chemical Society (ACS)
ISSN:0013-936X
OA Status:Green
Publisher DOI:https://doi.org/10.1021/acs.est.8b03869
Project Information:
  • : FunderSNSF
  • : Grant IDPP00P3_179089
  • : Project TitleBridging biodiversity and ecosystem functioning: a meta-ecosystem perspective
  • : FunderSNSF
  • : Grant ID31003A_173074
  • : Project TitleRiverDNA: uncovering fundamental biodiversity in riverine systems using environmental DNA
  • : FunderUniversity of Zurich Research Priority Programme
  • : Grant ID
  • : Project TitleURPP “Global Change and Biodiversity”

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