Environmental DNA Metabarcoding Supporting Community

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Cite This: Environ. Sci. Technol. 2018, 52, 14469−14479

Environmental DNA Metabarcoding Supporting Community Assessment of Environmental Stressors in a Field-Based Sediment Microcosm Study Jianghua Yang,† Katherine Jeppe,‡ Vincent Pettigrove,‡ and Xiaowei Zhang*,†

Environ. Sci. Technol. 2018.52:14469-14479. Downloaded from pubs.acs.org by EASTERN KENTUCKY UNIV on 01/13/19. For personal use only.

†

State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China ‡ Aquatic Pollution Prevention Partnership, School of Science, Royal Melbourne Institute of Technology, RMIT University, Post Office Box 71, Bundoora 3083, Victoria, Australia S Supporting Information *

ABSTRACT: Conventional ecological risk assessment on toxic stressors in sediment is limited to a small and selected fraction of benthic communities. Ecogenomic approaches provide unprecedented capacity to monitor the changes of biodiversity and community composition in the field, but how to utilize it to assess ecological impact by contaminates remains largely unexplored. Here, an environmental DNA (eDNA) metabarcoding approach was used to assess the effect of copper on changes in biodiversity and community composition across the tree of life (including bacteria, protists, algae, fungi, and metazoa) in a field-based microcosm. Many microorganisms across a broad range of taxa groups changed their relative abundance in response to increased copper concentrations in sediments. Changes in community structure of microbiota appeared to be more sensitive to copper than survival of laboratory-bred organisms and indigenous macroinvertebrates. Copper caused a significant shift in prokaryotic community composition via substitution of dominant species. Network heterogeneity and Shannon diversity of the bacterial community decreased in the high copper treatments. eDNA metabarcoding assessed the effects of copper-contaminated sediment with less effort than manually processing samples. Our study highlighted the value of community profiling by an eDNA-based approach in prospective and retrospective risk assessment of environmental stressors.

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responses in the field, such as sensitive taxa being replaced by more tolerant organisms.12,13 It has been recognized that ecosystem function can be altered by shifts of community composition.14 Furthermore, meio- and microbiota play a critical role in many biogeochemical processes. Yet only the responses of a biased fraction of biodiversity, such as macroinvertebrates, are assessed, whereas the responses of other ecological components, such as bacteria and eukaryotic microbiota, which are difficult to identify, are often overlooked. Omics advances in ecotoxicological studies provide a wholly new paradigm for ecotoxicology by linking ecological models to mechanism-based, systems biology approaches. With the increasing development of DNA high-throughput sequencing (HTS), environmental DNA (eDNA) metabarcoding has greatly improved our ability to identify diverse taxonomic groups.15,16 eDNA metabarcoding not only has the potential to assess phylogenetic biodiversity, more reliably and rapidly than

INTRODUCTION Historically, assessing the impacts of environmental stressors on an aquatic ecosystem has relied on laboratory-based animal bioassays,1−4 which have been criticized for lacking environmental relevance and for being less sensitive than field responses.5 Sediment contaminants are widespread environmental stressors and can severely impact aquatic ecosystems and human health.6 Field-based microcosm approaches establish a semicontrolled lentic habitat for field exposure, which allows us to isolate the effects of specific contaminants and determine the responses of a range of indigenous species.7 In addition, these field-based experiments can describe the response of indigenous species that cannot be reared or tested in laboratory. Therefore, these tests provide a more environmentally relevant assessment of sediment toxicity than laboratory-based tests.8,9 A major objective of ecological risk assessment of toxic substances is to protect the local biodiversity and ecosystem function or service.10 However, it has been increasingly recognized that the traditional focus of ecotoxicology on individuals is insufficient to guide environmental management decision-making.11 A stressor may cause complex indirect © 2018 American Chemical Society

Received: Revised: Accepted: Published: 14469

September 1, 2018 November 8, 2018 November 12, 2018 November 12, 2018 DOI: 10.1021/acs.est.8b04903 Environ. Sci. Technol. 2018, 52, 14469−14479

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Environmental Science & Technology

these macroinvertebrates and laboratory-deployed species was reported elsewhere.24 Environmental DNA Isolation. One week before the experiment concluded, three microcosms of each treatment were randomly selected to collect about 10 g of sediment by use of a polypropylene pipet. Each treatment had three replicates. Sediments were freeze-dried at −40 °C and homogenized before DNA isolation. About 0.3 g of homogenized sediment was used to extract the eDNA with the MoBio power soil DNA kit (MoBio Laboratories Inc.) following the manual. Genomic DNA was checked for purity in a Take3 microplate in the Synergy H4 hybrid multimode microplate reader (BioTek) and quantified by Qubit dsDNA HS assay kits (Invitrogen). Polymerase Chain Reaction Amplification and Sequencing. Bacterial 16S rRNA genes and eukaryotic 18S rRNA genes were amplified by use of V3 primers (modified primers 341F and 518R)26 and V9 primers (1380F and 1510R),27 respectively. Triplicate PCR reactions were performed for each sample to minimize potential PCR bias. The PCR amplicons were sequenced by the Ion Torrent Proton (Life Technologies) according to the manufacturer’s protocols. Bioinformatics. QIIME v1.8.0 (quantitative insights into microbial ecology) pipeline was used to process the raw sequences.28 Low-quality sequences were trimmed via the “split_libraries.py” script with “−w 50 −q 20”. PCR chimera filtering was performed via “parallel_identify_chimeric_seqs.py” script with the default parameters. Operational taxonomic units (OTUs) were selected with a sequence similarity cutoff of 97% following the UPARSE pipeline in both 16S and 18S sequencing.29 The homopolymer errors were removed before OTU clustering in QIIME by the split_libraries.py function with the −H 6 parameter. For each OTU, a representative sequence was chosen and taxonomy was assigned by use of the RDP classifier30 against the Greengenes database31 and SILVA database32 for prokaryote and eukaryote communities, respectively. Statistical Analysis. All statistical analysis was conducted in R (version 3.3.0, released on May 3, 2016). Samples were rarefied at the lowest sequencing depth to reduce biases resulting from differences in sequencing depth (8134 and 10 410 for eukaryote and prokaryote communities, respectively). Beta diversity was characterized by weighted UniFrac distances calculated between samples.33 OTU tables underwent cumulative sum scaling (CSS) normalization. The response of relative abundance of microbiota taxa to copper was modeled with a three-parameter log−logistic model and the 50% effect concentration (EC50) was calculated. A correlation network of bacteria community was generated by SparCC with 100 bootstraps to assign two-sided pseudo p values.34 Samples were arbitrarily divided into three subgroups (low copper, control and 62.5 mg/kg; medium copper, 125 and 250 mg/kg; and high copper, 500 and 750 mg/kg) to increase the reliability of the correlation analysis. The network was filtered by correlation magnitudes >0.6 and 10 was considered as present. The singletons were removed before OTU clustering. (C) Changes in biointeraction measured by the Mantel test. Width of lines shows ρ in Mantel test between two communities. Low copper, 0 and 62.5 mg/kg; medium copper, 125 and 250 mg/kg; high copper, 500 and 750 mg/kg.

0.001). Protists (low 109−120, medium 99−112, high 119− 132) had fewer taxa upon medium copper treatments (p < 0.05) and more taxa upon high copper treatments (p < 0.01), compared to the control. The community structure of prokaryotes and eukaryotes was also significantly different between treatments, being separated along the two principal components of bacterial, metazoan, protist, algal and fungal communities (Figure 5). About 49.3% of the variance in the bacteria community was explained by PC1, which shows a strong gradient change with copper concentration. About 90% of metazoan variance was explained by the first two principal components, and all copper treatments were separated from the control by the PC1 (81.6% of variance explained). Protist and algal communities displayed significant differences in PC1 after low and high copper treatments (71.9% and 66.1% of variance explained in protist and algal communities, respectively). Finally, the biointeractions between communities substantially differed between copper treatments. The interactions upon low copper treatments were dominated by metazoan− algae, algae−fungi, and fungi−protist (Figure 4C). Copper strengthened the interactions of metazoan−protist and metazoan−fungi. The interactions of protist−bacteria and protist−algae were strengthened by the medium copper treatments. Biointeraction network had lower heterogeneity and nodes at higher copper concentrations (Figure S13 and

occupied 16.9% of Ascomycota reads had less relative abundance at higher copper concentrations (Table S4). Algae represented a smaller portion of sequence reads compared to other eukaryotic communities (Table S2). Chlorophyta (4.6%) was the dominant algal phylum and displayed varied responses to copper (Figure 2 and Table S4). About 38% of Chlorophyta were positively correlated and 10.3% were negatively correlated with increased copper concentrations (Figure 2 and Figure S11). Responses of Sedimentary Biota at the Community Level to Copper Exposure. There were significant differences in sedimentary communities between copper treatments, including population density (relative abundance), biodiversity (unique taxon number), community structure, and biointeractions. Metazoans had a lower relative abundance at higher copper concentrations (Figure 4A). Algae had a lower relative abundance (mean 7.4%) at medium copper concentrations (125 and 250 mg/kg) and a higher relative abundance (mean 15.5%) at high copper concentrations (500 and 750 mg/kg). Biodiversity, as indicated by number of unique taxons, was significantly different between treatments (Figure 4B and Figure S12). Metazoans (low 23−29, high 18−23) and bacteria (low 297−307, high 279−291) had less diversity upon higher copper treatments (p < 0.001). Conversely, fungi (low 17−19, high 22−24) and algae (low 33−38, high 42−43) had greater diversity upon higher copper treatments (p < 14473

DOI: 10.1021/acs.est.8b04903 Environ. Sci. Technol. 2018, 52, 14469−14479

Article

Environmental Science & Technology

Figure 5. Responses of eukaryote and prokaryote community structure in copper-contaminated sediment. Principal component analysis was used to differentiate the changes in community structure. Box-and-whiskers plots (at the bottom of each panel) show the differences of PC1 among different copper treatments according to t-test. (*) p < 0.05; (**) p < 0.01; (***) p < 0.001.

shared between laboratory and field-based tests (Figure 6A). Proteobacteria and Opisthokonta were the most affected organisms in both experiments (Figure 6B). Cyanobacteria showed only a monotonic negative response to copper in the laboratory-based test, but both positive and negative responses were found in the field-based microcosms and the total abundance of Cyanobacteria was stable across treatments. Archaeplastida showed distinct responses to copper in the laboratory and field-based experiments, with the abundance decreasing in the laboratory and increasing in the field experiments with increased copper concentrations (Figure 6B). Species Sensitivity Distribution between Morphology and Metabarcoding. The invertebrate species sensitivity distribution (SSD) curve of copper identified by morphology was very close to the sensitive distribution of microbiota calculated from eDNA metabarcoding (Figure 6C). However, eDNA identified some microbiota having greater sensitivity to copper than morphology-based macroinvertebrate indices. There were 18 OTUs identified with eDNA (mostly Cyanobacteria and Proteobacteria) affected by copper with EC50 < 100 mg/kg (Figure S16), compared with morphologically identified macroinvertebrates, which had the most sensitive EC50 of 89 ± 19 mg/kg (Paratanytarsus abundance) . The HC5 of copper in sediment derived from

Table 1). Shannon−Wiener diversity of the bacteria community also decreased (p < 0.01) upon high copper treatments (500 and 750 mg/kg) (Figure S14). Comparison between Morphology and Metabarcoding on the Response of Mollusca. Five Mollusca taxa were identified by eDNA metabarcoding and their population density, as measured by relative abundance, decreased with increased copper concentrations (Figure S15). Caenogastropoda (9.28% abundance), Cecina manchurica (0.13%), and Blanfordia japonica (0.04%) have similar dose-dependent responses as determined by morphological examination of P. antipodarum (numbers of juveniles and embryos). Heterobranchia taxa (4.27%) detected by DNA metabarcoding displayed a highly sensitive response (EC50 was 82 ± 85 mg/kg) to copper compared with two laboratory-bred snails ( P. antipodarum and P. acuta). Comparison between Laboratory and Field-Based Microcosm Tests. The response of microbiota to copper in field-based microcosms were compared to our previous laboratory-based experiments.19 Overall, 61 microorganism OTUs exhibited significant responses to copper (|ρ| > 0.6) in the laboratory-based sediment toxicity test, and 138 OTUs responded to copper in a positive or negative manner in the field-based microcosms. Of these, 27 affected OTUs were 14474

DOI: 10.1021/acs.est.8b04903 Environ. Sci. Technol. 2018, 52, 14469−14479

Article

Environmental Science & Technology

dominant species with an overall stable relative abundance at phylum level demonstrates that prokaryotic communities have a strong ability to resist copper toxicity.38 Yet the change in dominant species also provides potential bioindicators for copper pollution. It must be noted that analysis of community changes was highly based on relative abundance variation that does not fully represent the variation of absolute abundance. Some increases in relative abundance of some taxa can be the result of disappearance of another one, while their absolute abundance remain the same, and vice versa. A Large Number of Operational Taxonomic Units across a Broad Range of Groups Were Responsive to Copper. Although the ecological functions of most microbiota remain unclear, the exploding amount of information provided by eDNA metabarcoding can strengthen our understanding of how sedimentary communities respond to pollution. For example, a substantial proportion of copper-tolerant bacteria were Bacteroidetes. Bacteroidetes comprises a high proportion of the total bacterial community in sediments.39 The distribution of Bacteroidetes has been correlated with algal blooms, plus many Bacteroidetes clones and isolates have been found in hydrocarbon-contaminated environments.40,41 Here Bacteroidetes also have higher abundance at higher copper concentrations. This suggests that they are likely to be advantaged in polluted sediments compared to other groups and they may be a good indicator of polluted sediments. Nematodes have often been considered potential indicators of sediment and soil quality.42−44 However, two nematode OTUs that represented only 3.3% of total nematodes reads were affected by copper, and therefore, nematodes may not be suitable indicators of copper pollution.45 Protists are an important component of ecosystems and inhabit almost every habitat.46 They exhibit a broad range of sensitivities to metal toxicity, with some species being sensitive to and others tolerant of environmental stress.47 In this study, the declining protists were mostly ciliates that often form relationships with bacterial communities that increase bacterial resilience to environmental stress.48,49 Some ciliates have been identified as important sources of regenerated nitrogen.50 In the presence of copper, some naturally associated bacteria can grow vigorously, using nitrogen compounds released by ciliates as an energy source.51 Therefore, the decline of ciliates will exacerbate effects on bacterial communities, which represent the basal element in the food web. Responses of Sedimentary Microbiota to Copper Were Consistent in both Laboratory and Field- Based Experiments. Although the species composition of microbiota, especially bacterial communities, are greatly influenced by geographical and environmental factors, the stress effects of contaminants on microbiota were similar. Nearly half (27 in 61) of the affected microbes in laboratory-based experiments were verified in field-based microcosms. This indicated that the current analysis pipeline of eDNA metabarcoding could reliably detect differences in community composition between two different environments. The difference between field and laboratory results may be explained by the experimental environment (temperature, light, habitat, etc.). Laboratory toxicity tests could not completely replace the field-based tests in the toxicity assessment, as field microcosms are closer to reflecting the natural environment than laboratory-based tests and more microorganisms can be found to be stressed by pollutants.

Table 1. Simple Parameters of Microbial Networks a

parameter

nodes network density network heterogeneity clustering coefficient connected components network diameter network centralization shortest paths characteristic path length avg number of neighbors

low copper (0 and 62.5 mg/kg)

moderate copper (125 and 250 mg/kg)

high copper (500 and 750 mg/kg)

199 0.011

198 0.011

161 0.012

0.596

0.568

0.504

0.006

0.004

0.017

11

9

12

15

20

21

0.025

0.025

0.026

26 672 (67%) 6.81

30 172 (77%) 7.869

13 624 (52%) 8.546

2.131

2.152

1.963

a

Nodes, number of OTUs; network density, how densely the network is populated with edges; network heterogeneity, tendency of a network to contain hub nodes; clustering coefficient, degree of clustering; connected components, components of the disconnected network; network diameter, largest distance between two nodes; network centralization, distribution of network density; shortest paths, shortest length between two nodes; characteristic path length, expected distance between two connected nodes; avg number of neighbors, average connectivity of a node in the network.

laboratory amphipod and chironomid tests were 105 and 123 mg/kg, respectively. The field microcosm SSD curve was more sensitive than the two laboratory SSD values, and the HC5 was 76 mg/kg (Figure 6C).

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DISCUSSION An important aim of ecological risk assessment is to protect the integrity and function of ecosystems. However, the majority of toxicity tests have considered only a few species that could be cultured in the laboratory. Here we demonstrated that eDNA metabarcoding allows measurement and assessment of community response of a wide spectrum of indigenous microorganisms to an environmental stressor. In addition, this approach can be used to assess functional aspects of ecosystems, such as the presence of denitrifying bacteria. Using a broader range of organisms not only enables a better understanding of the effects of a stress on the ecosystem but also provides more insights into which organisms are most sensitive to a particular stressor. Substitution of Dominant Species in Different Copper Treatments. Bacteria have evolved different mechanisms to overcome toxicity of copper and display different sensitivities to copper.35 Some bacteria reduce their exposure to metals through active uptake or biosorption by exopolysaccharides (EPS).36,37 In this study, Cyanobacteria composition changed between treatments, although their relative abundance remained similar across treatments. A similar response was also observed in other bacterial communities, including Verrucomicrobia, Proteobacteria, Spirochaetes, and Armatimonadetes. This suggests that relative abundance at the phylum level does not fully reflect the impact of pollutants on the ecological community and that species composition should be studied simultaneously. Substitution of 14475

DOI: 10.1021/acs.est.8b04903 Environ. Sci. Technol. 2018, 52, 14469−14479

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Environmental Science & Technology

Figure 6. Comparison between field microcosm and laboratory tests. (A) Shared responsive species; (B) response pattern at the class level; (C) species sensitivity distribution of macroinvertebrates and microbiota. Sediment copper criteria (HC5) was derived by the three-parameter log− logistic model.

A Wide Range of Sensitivity at the Phylum Level to the Sediment Stressor Can Be Used to Support Ecological Risk Assessment. In our previous study,24 morphological sorting found that the survival and reproduction of two laboratory-bred snails declined with increasing copper concentrations. Not all the eDNA sequences have been annotated to morphologically identified species because of the incompleteness of the barcode database. However, Mollusca were also identified by eDNA metabarcoding to be the most sensitive metazoan to copper, and their total abundance (indicating population density) decreased with increasing copper. In addition, the dominant molluscan taxon Heterobranchia detected by eDNA metabarcoding was found to be more sensitive to copper than these two laboratory-bred snails. This suggests that eDNA metabarcoding can provide broader coverage of Mollusca taxa than conventional toxicity assessment based on morphology of only a few species, which can lead to a more reliable estimation of ecological risks.52 In addition to metazoans, eDNA metabarcoding also found that many microbiota were responsive to copper and some taxa, including those from Cyanobacteria, Fungi, Proteobacteria, and Protista, displayed a greater sensitivity to copper than macroinvertebrates. Most of the sensitive taxa (EC50 < 100 mg/kg) were bacteria, which indicated that the prokaryote community might be more sensitive to copper than the eukaryote community.53 This supports previous research that

meio- and microbiota are more sensitive to copper than the macroinvertebrates.45 Derivation of water and sediment quality guidelines for environmental stressors is very important for the protection of ecosystem health. The HC5 of field-based microcosm was slightly lower than the HC5 of the laboratory-based test, which suggests that the toxicity risk of copper may be underestimated by laboratory tests. The copper criterion based on the field microcosm metabarcoding (76 mg/kg) was very close to the current guideline, determined by the single-species toxicity test, which suggests that it is feasible to derive guidelines based on microbiota responses as shown by eDNA.54 These results suggested that the novel end points provided by eDNA metabarcoding, which is based on the entire assemblage in an ecosystem and includes effects of accessory factors, could be suitable for derivations of site- or regional- specific sediment criteria.55 Benthic Macroinvertebrates Sensitive to CopperContaminated Sediment Were Validated by Morphological Sorting. Over the past decades, scientists have conducted extensive research into the effects of copper on various macroinvertebrates. Here, the changes in abundance (mostly Chironomidae) and reproduction of metazoans (mostly Mollusca) in the field microcosms were assessed by morphological identification.24 More importantly, the adverse effects of copper on metazoans can also be detected by eDNA metabarcoding with less effort. In contrast to manually 14476

DOI: 10.1021/acs.est.8b04903 Environ. Sci. Technol. 2018, 52, 14469−14479

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Environmental Science & Technology Notes

processing macroinvertebrate samples and undertaking complex taxonomic identification, eDNA metabarcoding needs only a small amount of sediment for analysis, which can greatly reduce processing time.56 The observed consistency between molecular and morphological approaches illustrates the capacity for eDNA metabarcoding to provide ecologically relevant information that can be used to sediment toxicity assessment.45,57 Future Outlook for eDNA Metabarcoding. Although environmental DNA metabarcoding has unprecedented capacity to monitor the community composition in the field, significant improvement is still needed to quantify species abundance accurately in the future. Here relative abundance of sequence reads was used to estimate the relative abundance of a phylum, with the objective to identify copper-responsive species. Relative abundance can well explain changes in community composition and clearly reflects the succession of dominant OTUs/sequences. However, the changes of relative abundance of some taxa might be caused by the absolute richness itself or by changes in all other taxa. More and more studies have shown that the number of eDNA reads was correlated with species richness in the field.18 However, we propose the use of a quantitative PCR approach in the future to validate the metabarcoding approach used in study. Here only the sedimentary communities at the end of the experiment were analyzed. It is suggested that the effects of minor variation at the start of the experiment should be evaluated in future studies. In addition, the occurrence of network inference by use of spatial replicates makes it difficult to disentangle genuine biological interactions from niche preference effects. Real direct interactions still need further validation by experimental evidence.

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the Environmental Protection Public Welfare Scientific Research Project of China (201409040), National Natural Science Foundation for the Youth of China and Jiangsu Province (41807482 and BK20180331), Major Science and Technology Program for Water Pollution Control and Treatment (Grant 2017ZX07602002), and Jiangsu Environmental Monitoring Research Project (1802). X.Z. and J.Y. were supported by the Fundamental Research Funds for the Central Universities. We also thank Melbourne Water for funding the Aquatic Pollution Prevention Partnership’s involvement in this study.

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(1) Giesy, J. P.; Hoke, R. A. Bioassessment of the toxicity of freshwater sediment. Verh. - Int. Ver. Theor. Angew. Limnol. 1991, 24, 2313−2321. (2) Le Jeune, A. H.; Charpin, M.; Deluchat, V.; Briand, J.-F.; Lenain, J.-F.; Baudu, M.; Amblard, C. Effect of copper sulphate treatment on natural phytoplanktonic communities. Aquat. Toxicol. 2006, 80 (3), 267−280. (3) Shaw, J. L.; Manning, J. P. Evaluating macroinvertebrate population and community level effects in outdoor microcosms: Use of in situ bioassays and multivariate analysis. Environ. Toxicol. Chem. 1996, 15 (5), 608−617. (4) Roussel, H.; Ten-Hage, L.; Joachim, S.; Le Cohu, R.; Gauthier, L.; Bonzom, J.-M. A long-term copper exposure on freshwater ecosystem using lotic mesocosms: Primary producer community responses. Aquat. Toxicol. 2007, 81 (2), 168−182. (5) Wang, F.; Goulet, R. R.; Chapman, P. M. Testing sediment biological effects with the freshwater amphipod Hyalella azteca: the gap between laboratory and nature. Chemosphere 2004, 57 (11), 1713−1724. (6) Burton, G. A. Assessing the toxicity of freshwater sediments. Environ. Toxicol. Chem. 1991, 10 (10), 1585−1627. (7) Pettigrove, V.; Hoffmann, A. A field-based microcosm method to assess the effects of polluted urban stream sediments on aquatic macroinvertebrates. Environ. Toxicol. Chem. 2005, 24 (1), 170−180. (8) Pettigrove, V.; Marshall, S.; Ryan, B.; Hoffmann, A. A field microcosm method to determine the impact of sediments and soils contaminated by road runoff on indigenous aquatic macroinvertebrates. In Highway and Urban Environment: Proceedings of the 8th Highway and Urban Environment Symposium, Morrison, G. M., Rauch, S., Eds. Springer Netherlands: Dordrecht, 2007; pp 385−398; DOI: 10.1007/978-1-4020-6010-6. (9) Gauss, J. D.; Woods, P. E.; Winner, R. W.; Skillings, J. H. Acute toxicity of copper to three life stages of Chironomus tentans as affected by water hardness-alkalinity. Environ. Pollut., Ser. A 1985, 37 (2), 149−157. (10) Gessner, M. O.; Tlili, A. Fostering integration of freshwater ecology with ecotoxicology. Freshwater Biol. 2016, 61 (12), 1991− 2001. (11) Schmitt-Jansen, M.; Veit, U.; Dudel, G.; Altenburger, R. An ecological perspective in aquatic ecotoxicology: Approaches and challenges. Basic Appl. Ecol. 2008, 9 (4), 337−345. (12) Relyea, R.; Hoverman, J. Assessing the ecology in ecotoxicology: a review and synthesis in freshwater systems. Ecology Letters 2006, 9 (10), 1157−1171. (13) Halstead, N. T.; Mcmahon, T. A.; Johnson, S. A.; Raffel, T. R.; Romansic, J. M.; Crumrine, P. W.; Rohr, J. R. Community ecology theory predicts the effects of agrochemical mixtures on aquatic biodiversity and ecosystem properties. Ecology Letters 2014, 17 (8), 932−941.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b04903.

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REFERENCES

Additional text, four tables, and 16 figures describing the sediment spiking method, taxonomy details of prokaryotes and eukaryotes recovered by eDNA, nonlinear fitting of each responsive taxon, repeatability of sequencing, UniFrac distance between copper treatments, network of bacteria communities, and comparison of morphology sorting and DNA metabarcoding on the responses of mollusk species to copper (PDF)

AUTHOR INFORMATION

Corresponding Author

*Telephone: (86)-25-89680623; e-mail: [email protected] or [email protected]. ORCID

Jianghua Yang: 0000-0002-2788-6425 Katherine Jeppe: 0000-0003-2284-5090 Xiaowei Zhang: 0000-0001-8974-9963 Author Contributions

All authors conceived and designed the experiments; J.Y.and K.J. performed the experiments. J.Y. performed 16S and 18S amplicon, multiparallel sequencing and analysis of the NGS data. J.Y. wrote the manuscript and K.J., V.P., and X.Z. revised the manuscript. All authors read and approved the final version of the manuscript. 14477

DOI: 10.1021/acs.est.8b04903 Environ. Sci. Technol. 2018, 52, 14469−14479

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DOI: 10.1021/acs.est.8b04903 Environ. Sci. Technol. 2018, 52, 14469−14479