@article{Li-2018-Application,
title = "Application of Environmental DNA Metabarcoding for Predicting Anthropogenic Pollution in Rivers",
author = "Li, Feilong and
Peng, Ying and
Fang, Wendi and
Altermatt, Florian and
Xie, Yuwei and
Yang, Jianghua and
Zhang, Xiaowei",
journal = "Environmental Science {\&} Technology",
year = "2018",
publisher = "American Chemical Society (ACS)",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G18-67001",
doi = "10.1021/acs.est.8b03869",
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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<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>
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%0 Journal Article
%T Application of Environmental DNA Metabarcoding for Predicting Anthropogenic Pollution in Rivers
%A Li, Feilong
%A Peng, Ying
%A Fang, Wendi
%A Altermatt, Florian
%A Xie, Yuwei
%A Yang, Jianghua
%A Zhang, Xiaowei
%J Environmental Science & Technology
%D 2018
%I American Chemical Society (ACS)
%F Li-2018-Application
%X 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.
%R 10.1021/acs.est.8b03869
%U https://gwf-uwaterloo.github.io/gwf-publications/G18-67001
%U https://doi.org/10.1021/acs.est.8b03869
Markdown (Informal)
[Application of Environmental DNA Metabarcoding for Predicting Anthropogenic Pollution in Rivers](https://gwf-uwaterloo.github.io/gwf-publications/G18-67001) (Li et al., GWF 2018)
ACL
- Feilong Li, Ying Peng, Wendi Fang, Florian Altermatt, Yuwei Xie, Jianghua Yang, and Xiaowei Zhang. 2018. Application of Environmental DNA Metabarcoding for Predicting Anthropogenic Pollution in Rivers. Environmental Science & Technology.
