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.

Conventional assessment and evaluation of sediment quality are based on laboratory-based ecotoxicological and chemical measurements with lack of concern for ecological relevance. Microbiotas in sediment are responsive to pollutants and can be used as alternative ecological indicators of sediment pollutants; however, the linkage between the microbial ecology and ecotoxicological endpoints in response to sediment contamination has been poorly evaluated. Here, in situ microbiotas from the Three Gorges Reservoir (TGR) area of the Yangtze River were characterized by DNA metabarcoding approaches, and then, changes of in situ microbiotas were compared with the ecotoxicological endpoint, aryl hydrocarbon receptor (AhR) mediated activity, and level of polycyclic aromatic hydrocarbons (PAHs) in sediments. PAHs and organic pollutant mixtures mediating AhR activity had different effects on the structures of microbiotas. Specifically, Shannon indices of protistan communities were negatively correlated with the levels of AhR mediated activity and PAHs. The sediment AhR activity was positively correlated with the relative abundance of prokaryotic Acetobacteraceae, but had a negative correlation with protistan Oxytrichidae. Furthermore, a quantitative classification model was built to predict the level of AhR activity based on the relative abundances of Acetobacteraceae and Oxytrichidae. These results suggested that in situ Protista communities could provide a useful tool for monitoring and assessing ecological stressors. The observed responses of microbial community provided supplementary evidence to support that the AhR-active pollutants, such as PAHs, were the primary stressors of the aquatic community in TGR area.

Oil spills offshore can cause long-term ecological effects on coastal marine ecosystems. Despite their important ecological roles in the cycling of energy and nutrients in food webs, effects on bacteria, protists or arthropods are often neglected. Environmental DNA (eDNA) metabarcoding was applied to characterize changes in the structure of micro- and macro-biota communities of surface sediments over a 7-year period since the occurrence of Hebei Spirit oil spill on December 7, 2007. Alterations in diversities and structures of micro- and macro-biota were observed in the contaminated area where concentrations of polycyclic aromatic hydrocarbons were greater. Successions of bacterial, protists and metazoan communities revealed long-term ecological effects of residual oil. Residual oil dominated the largest cluster of the community-environment association network. Presence of bacterial families (Aerococcaceae and Carnobacteriaceae) and the protozoan family (Platyophryidae) might have conferred sensitivity of communities to oil pollution. Hydrocarbon-degrading bacterial families (Anaerolinaceae, Desulfobacteraceae, Helicobacteraceae and Piscirickettsiaceae) and algal family (Araphid pennate) were resistant to adverse effects of spilt oil. The protistan family (Subulatomonas) and arthropod families (Folsomia, Sarcophagidae Opomyzoidea, and Anomura) appeared to be positively associated with residual oil pollution. eDNA metabarcoding can provide a powerful tool for assessing effects of anthropogenic pollution, such as oil spills on sediment communities and its long-term trends in coastal marine environments.