1 Ecogenomics of Zooplankton Community Reveals Ecological2 Threshold of Ammonia Nitrogen3 Jianghua Yang,† Xiaowei Zhang,*,† Yuwei Xie,† Chao Song,† Jingying Sun,† Yong Zhang,‡

4 John P. Giesy,†,§,∥ and Hongxia Yu†

5†State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu

6 210023, China

7‡Jiangsu Environmental Monitoring Center, Nanjing, Jiangsu 210000, China

8§Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan S7N

9 5B3, Canada

10∥School of Biological Sciences, University of Hong Kong, SAR, China

11 *S Supporting Information

12 ABSTRACT: Communities of zooplankton can be adversely affected by contamination resulting from human activities. Yet13 understanding the influence of water quality on zooplankton under field-conditions is hindered by traditional labor-intensive14 approaches that are prone to incomplete or uncertain taxonomic determinations. Here, for the first time, an eco-genomic15 approach, based on genetic diversity in the mitochondrial cytochrome c oxidase I (COI) region of DNA of zooplankton was used16 to develop a site-specific, water quality criterion (WQC) for ammonia (NH3). Ammonia has been recognized as a primary17 stressor in the catchment of the large, eutrophic Tai Lake, China. Nutrients, especially NH3 and nitrite (NO3

−) had more18 significant effects on structure of the zooplankton community than did other environmental factors. Abundances of rotifers19 increased along a gradient of increasing concentrations of total ammonia nitrogen (TAN), while abundances of copepods and20 cladocera decreased. A novel, rapid, species sensitivity distribution (SSD) approach based on operational taxonomic units21 (OTUs) was established to develop a WQC for NH3. The WQC based on OTUs was consistent with the WQC based on the22 traditional morphology taxonomy approach. This genetics-based SSD approach could be a useful tool for monitoring for status23 and trends in species composition and deriving ecological criteria and an efficient biomonitoring tool to protect local aquatic24 ecosystems in virtually any aquatic ecosystem.

25 ■ INTRODUCTION

26 One of the major challenges in environmental management is27 to develop ecological thresholds to protect ecosystem and28 biodiversity from effects of chemical and physical stressors.29 Traditional methods of assessment of toxic potency are based30 on laboratory bioassays with single species,1,2 which investigate31 toxicity of chemical stressors to laboratory-bred, surrogate32 species. Toxicity data for multiple species can be used in33 probabilistic approaches to provide a relative ranking of34 sensitivities of species, by use of species sensitivity distributions35 (SSDs).3,4 The SSD approach, using data from standard36 laboratory species has been criticized by the misrepresentation

37of site-specific biodiversity and failure to acknowledge

38variations of site-specific conditions.5 Even if data derived in

39laboratory tests are available for multiple species, they might

40not be representative of species in an environment, especially

41when they are interacting among species and with the

42chemical−physical environment.

Received: November 7, 2016Revised: January 27, 2017Accepted: February 13, 2017Published: February 13, 2017

Article

pubs.acs.org/est

© XXXX American Chemical Society A DOI: 10.1021/acs.est.6b05606Environ. Sci. Technol. XXXX, XXX, XXX−XXX

jwp00 | ACSJCA | JCA10.0.1465/W Unicode | research.3f (R3.6.i12 HF02:4458 | 2.0 alpha 39) 2016/10/28 09:46:00 | PROD-JCAVA | rq_8155480 | 2/17/2017 12:07:54 | 8 | JCA-DEFAULT

43 As biological science has evolved, one of the major issues has44 been how to use large amounts of detailed genomic45 information, available from high through-put analyses in46 derivation of environmental standards and how to apply them47 to monitoring programs of status and trends of populations and48 communities. Ecogenomics technologies, such as metabarcod-49 ing which can characterize species composition by DNA,50 provide a rapid method of assessing biodiversity.6 Metabarcod-51 ing also provides a semiquantitative estimate of relative52 abundances because the amount of DNA from a species53 present in a sample is proportional to abundances of individuals54 of species.7−9 By metabarcoding of the mitochondrial CO155 region of DNA, genetic diversity of zooplankton can be56 characterized. Species compositions of zooplankton commun-57 ities, as determined by metabarcoding, were consistent with58 results based on traditional taxonomy based morphology.59 Furthermore, metabarcoding significantly improved identifica-60 tion of species, increased the number of zooplankton taxa61 observed, and successfully identified larvae of copepods.10

62 Thus, metabarcoding could be a useful biomonitoring tool to63 profile zooplankton communities on a large scale.64 Ammonia (NH3), one of several ubiquitous forms of65 nitrogen (N) that is considered to be one of the most66 important pollutants in aquatic environments.11 Ammonia can67 enter aquatic ecosystems from industrial and municipal68 effluents as well as runoff from agricultural and natural areas.69 Ammonia, especially in the un-ionized form (NH3

o) is very70 toxic to most aquatic organisms, including zooplankton and71 fish.12 Effects of human activities on the global nitrogen cycle72 makes understanding of toxicity of NH3 increasingly urgent.73 Over the past 30 years, the United States Environmental74 Protection Agency (U.S.EPA) has revised values for the WQC75 for TAN downward four times, in 1985, 1999, 2009, and76 2013.12

77 Ammonia causes both acute and chronic effects on78 zooplankton at both the individual and community levels.13,14

79 Yet understanding the influence of water quality on80 zooplankton under field-conditions is hindered by traditional81 labor-intensive approaches. Herein is presented a proposed82 method for combining newer genomic techniques with83 traditional approaches, including probabilistic assessments84 under field conditions. As an example, the proposed techniques85 were applied to develop a site-specific, WQC for a globally86 relevant contaminant, ammonia. First, effects of various87 environmental factors (e.g., nutrient factors) on the structure88 of zooplankton community by metabarcoding were statistically89 assessed in a large area of the catchment of Tai Lake, China.90 Second, metabarcoding was integrated with field based SSDs91 for zooplankton to develop a site-specific water quality criterion92 for TAN.

93 ■ MATERIALS AND METHODS94 Study Area and Water Sampling. The catchment of Tai95 Lake (Chinese: Taihu) is one of the most densely populated96 and developed areas in China.15 During the past 20 years,97 excessive nutrient loading by rapid industrialization and98 urbanization has caused rapid deterioration of water quality.16

99 In the present study, 69 sampling sites across the basin were100 sampled from 28 November, 2013 to 12 December 2013101 (Supporting Information (SI) Figure S1 and Table S1).102 Zooplankton communities assembled by metabarcoding had103 been previously compared to results of traditional taxonomic104 characterization based on morphology.10 Here, effects of

105environmental factors on the zooplankton community were106assessed by use of the same data set.107Water Chemistry. The methods for measurements of108environmental factors (chemical oxygen demand (CODMn),109total phosphorus (TP), phosphate (PO4

+), total nitrogen (TN,110all forms of nitrogen), nitrate (NO3

−), nitrite (NO2−), total

111ammonia nitrogen (TAN), biochemical oxygen demand112(BOD5) and Chlorophyll a (Chl a), Water temperature113(WT), pH, dissolved oxygen (DO), and trophic level index114(TLI)) are shown in the SI (Table S3)115Metabarcoding and Morphology Based Zooplankton116Biomonitoring. Two samples of zooplankton were collected117from each sampling site. One sample was used for118metabarcoding analysis while the other was used for119identification of zooplankton based on visual morphology.17−19

120Details of zooplankton community assembly by metabarcoding121protocol have been described previously.10 Briefly, zooplankton122was filtered through a 5-μm filter (Millipore) and total DNA123was isolated by use of the E.Z.N.A. water DNA kit (Omega).124The fragment of mitochondria CO1 was used to characterize125the zooplankton community.20 Products of polymerase chain126reaction (PCR) were sequenced by Ion Torrent PGM (Life127Technologies). Metabarcoding data were analyzed according to128the UPARSE pipeline,21 and OTUs were annotated by the129Statistical Assignment Package (SAP).22

130Biodiversity. Shannon, Simpson, and Pielou indicies were131estimated using the relative abundance of each OTU by use of132the “Vegan” package (version 2.2-1) in R software (R version1333.1.2 (2014−10−31)). Beta diversity was estimated by134computing unweighted UniFrac distances between samples.23

135Ecotoxicity Data Set. Ecotoxicity data of NH3 were136downloaded from the ECOTOX database (https://cfpub.epa.137gov/ecotox/) and other studies (see SI, Table S3). Concen-138trations of TAN associated with specific toxicities were139normalized and adjusted to pH 7.0 and temperature = 20 °C140(eqs 1 and 2) before the WQC was derived by the SSD141model.12

= − −= Tlog(TAN ) log(TAN ) 0.036(20 )T 20 T 142(1)

++

+− −⎛⎝⎜

⎞⎠⎟TAN TAN

0.04891 10

6.951 10pH 7 pH 7 7.204 pH pH 7.204

143(2)

144Statistical Analyses. Relationships between various envi-145ronmental factors were analyzed by use of Spearman146correlations in the R language. To evaluate their associations147with structures of zooplankton communities, environmental148factors were transformed (ln (x + 1)) and normalized for use in149the Mantel test.24 Redundancy and principal component150analyses (PCA) were employed ordinate samples according151to various types of water bodies by use of the “Vegan” package152(version 2.2−1) in R. Data on species were transformed to153unweighted UniFrac distances between samples by QIIME154(version 1.9.1+dfsg-lbiolinux4) before PCA analysis.23 Relative155contributions of environmental factors to explain variance of156zooplankton communities were determined using forward157selection, distance-based, linear modeling (distLM) in158PERMANOVA.25 To identify sensitive OTUs, structures of159taxonomic communities were related to each environmental160factor by use of quantile regression (p < 0.1)26 in R with the161package “quantreg”. The 50% effective concentration (EC50) of162TAN was calculated by use of quantile regression. Ranking of163sensitivities of OTUs to TAN were calculated using the EC50 by

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164 a SSD model developed with a bootstrapping method. The165 WQC (HC5 and HC10) for TAN was derived using the166 normalized TAN by a 3 parameter log−logistic model.

167 ■ RESULTS168 Characteristics of Environmental Factors. Nutrients,169 including NO3

−, NO2−, TAN, TN, PO4

+ and TP exhibited170 significant and positive correlations (Spearman’s test (r2 > 0.6,171 p < 0.001). BOD5 was strongly correlated (r

2 = 0.91, p < 0.001)172 with COD. Transparency of water (a measure of turbidity),173 numbers of algal cells, DO and pH were weakly (r2 < −0.3, p <174 0.05) and negatively correlated to concentrations of nutrients.175 Water temperature was weakly and negatively correlated with176 DO, algal density, transparency, BOD5, and COD and

f1 177 positively correlated with concentrations of nutrients (Figuref1 178 1).

179 Zooplankton Community Structure Altered by Nu-180 trient Factors and TAN. Composition of zooplankton181 communities, determined by metabarcoding, varied among

f2 182 habitats with distinct nutrient profiles (Figure 2). Redundancy183 analysis showed that 33.1% of overall variability of zooplankton184 community composition was explained by the first two185 principal components (RDA1 and RDA2).186 Effects of nutrients and TAN on zooplankton were greater187 than were other environment factors. RDA1(24.7% variance188 explained) was mostly explained by nutrient factors and TAN,189 whereas RDA2 (8.6% variance explained) was mainly explained190 by other variables. The Mantel test showed most nutrient191 factors were strongly associated (mantel’s R > 0.2, p < 0.05)192 with structures of zooplankton communities. TAN, NO2

−, and193 WT had a significant effect on structures of communities of194 cladocera. Nitrate, NO2

−, TN, PO4+, and TAN had a significant

195 effect on structures of copepod communities (Figure 1).196 Structures of communities based on either metabarcoding or197 traditional morphological taxonomy showed that diversity of

198zooplankton was dependent on comprehensive nutrition level199TLI. In locations with higher TLI, diversities of cladocera and200 f3copepods were significantly less (Figure 3A), while diversities201of rotifers were greater (Figure 3B).202Correlations between Ammonia and the Structure of203the Zooplankton Community. Compared to nutrient204factors, ammonia was a major determinant of compositions of205zooplankton communities in various regions of Tai Lake.206Approximately 34% of variation in dissimilarity of composition207of zooplankton communities was explained by measured208 t1environmental factors (Table 1). In decreasing order of209influence, these factors were TAN, numbers of algal cells, and210WT, which together explained 25.7% of variation in211composition of the zooplankton community (Table 1; Forward212selection sequential tests) and TAN was a major contributor213explaining approximately 15% of variation. RDA1 was214significantly, negatively correlated with TAN (r2 = −0.78, p <2150.001).216Ammonia was significantly associated with differences among217structures of communities of zooplankton in the catchment of218Tai Lake. PCA analysis demonstrated structures of zooplankton219communities were distinct between samples of lesser (<0.5 mg/220L) and greater concentrations of TAN (>0.5 mg/L) (SI Figure221S2). Relative abundances of copepods and cladocera were222inversely proportional to concentrations of TAN, whereas total223abundance of rotifers increased along a gradient of increasing224concentrations of TAN (SI Figure S3). Diversity of the rotifer225community was directly proportional to concentration of TAN,226whereas diversity of Bosmina sp (cladocera) was inversely227proportional (see SI, Figure S4). Proportions of sensitive228OTUs, those of which abundance was inversely proportional to229concentrations of TAN, (p < 0.1 in quantile test) were inversely230 f4proportional to concentrations of TAN (Figure 4E).231Sensitivities of Various Zooplankton Groups to Total232Ammonia Nitrogen. There were thirty-nine zooplankton233OTUs classified as being “sensitive” (quantile test, p < 0.1) to234TAN, which represented 32.1% of CO1 sequences in the235metabarcoding data set (Figure 4 and SI Figure S5). The most

Figure 1. Relationships between environmental variables andcomposition of the zooplankton community. Pairwise comparisonsof environmental factors with a color gradient denoting Spearman’scorrelation coefficients. Taxonomic groups were related to eachenvironmental factors by mantel test. TN, all forms of nitrogen; NO3,nitrate; NO2, nitrite; TAN, all forms of ammonia; PO4, orthophos-phate; TP, total phosphorus; BOD5, Five-day Biochemical OxygenDemand; CODMn, Chemical Oxygen Demand; algal, numbers ofalgal cells; DO, dissolved oxygen, and WT, water temperature.

Figure 2. Ordination of the zooplankton community with environ-mental variables. Blue vectors point to the direction of the increase fora given variable so that water with similar environmental variableprofiles or zooplankton community are localized in similar positions inthe diagram. A: the similarity of sample. B the relationship betweenRDA1 and TAN.

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236 sensitive OTUs were copepods and cladocera. More than 40%237 of OTUs classified as cladocera were sensitive to TAN, and238 they represented two-thirds of the total number of cladocera239 reads in metabarcoding (Figure 4G, I). For copepods,240 approximately 17% of copepod OTUs were sensitive to TAN,241 and they represented 40% of the total number of copepod242 reads. Only one OTU, representing 2.6% of the rotifer reads243 was found to be sensitive to TAN, although rotifers represented244 more than half (55%) of the total number of OTUs in the245 zooplankton community (Figure 4G, I).246 BLAST against the database of local taxa, demonstrated that247 sensitive OTUs in metabarcoding data set belong to six species248 (Bosmina sp, Ceriodaphnia cornuta, Schmackeria inpinus,249 Sinocalanus dorrii, Macrothrix sp, and Keratella quadrala).250 Bosmina sp and Sinocalanus dorrii represented 21 and 10251 sensitive OTUs, respectively, that were abundant and exhibited252 frequent occurrences (Figure 4C, D).253 Both metabarcoding data and traditional morphological254 monitoring showed most of the sensitive taxa were copepoda255 or cladocera (Figure 4H, G). Laboratory-based toxicity tests256 showed that copepoda and cladocera were more sensitive to257 TAN than were species of rotifer (Figure 4F). Seven taxonomic258 groups identified by traditional morphological identification,259 Bosmina sp, Ceriodaphnia cornuta, Schmackeria inpinus,

260Sinocalanus dorrii, Copepod nauplii, Cyclops larvae, and261Calanoida larvae, were sensitive to TAN (SI Figure S6). Four262species identified by both metabarcoding and morphological263monitoring (Bosmina sp, Ceriodaphnia cornuta, Schmackeria264 f5inpinus, and Sinocalanus dorrii) were sensitive to TAN (Figure265 f55).266Derivation of a Site-Specific Water Quality Criterion267of TAN Based on Sensitive OTUs. The f-SSDs for TAN,268based on species identified by use of morphological taxonomy269or OTUs identified by use of metabarcoding were more270sensitive than were laboratory-bred, surrogate zooplankton271 f6(Figure 6 and SI Table S5). There were 16 zooplankters for272which data on toxicity of NH3 were available in the ECOTOX273database (see SI, Table S3). HC5 and HC10 derived from the274SSD, based on tests done under laboratory conditions, were 2.4275and 2.9 mg/L, respectively. In the field, 7 of the 76 taxonomic276groups (9.2%) identified by use of morphology were sensitive277to TAN. The HC5 for concentrations of TAN measured in Tai278Lake and the SSD developed for species identified in Tai Lake279by morphological taxonomy was 1.1 mg TAN/L. There were280291 zooplankton OTUs detected in more than a third of281samples of which 13.4% were sensitive to TAN. Values for HC5

282and HC10 derived from the SSD based on OTUs were 1.4 and2832.9 mg TAN/L, respectively. Results of both traditional lab-

Figure 3. Effects of eutrophication on diversity of zooplankton in the Tai Lake ecosystem: (A) Shannon diversity of clodocera and copepoda; (B)Shannon diversity of rotifer. The upper diagram is based on species determined by use of visual inspection of morphology, whereas the lowerdiagram is based on OTUs determined by metabarcoding. p-values are indicated for each regression axis.

Table 1. Distance-Based, Linear Modelling Results of Zooplankton Community Composition against 13 Predictor Variables inthe Full Analysis (9999 Permutations)a

marginal tests forward selection sequential tests

predictor variable pseudo-F variation explained P pseudo-F variation explained cumulative variation explained P

+TAN 11.5508 14.955 0.005 11.5508 14.955 14.955 0.005**+algal.density 7.1576 9.308 0.005 7.0789 7.944 22.899 0.005**+WT 8.5149 11.131 0.005 3.2025 2.821 25.72 0.005**+Chl.a 3.4607 3.94 0.01 3 2.518 28.238 0.005**+PO4 10.2423 13.384 0.005 2.539 1.919 30.157 0.01*+DO 3.049 3.302 0.015 2.1764 1.463 31.62 0.015+NO3 6.9639 9.041 0.005 1.7033 0.879 32.499 0.065+TN 10.8596 14.113 0.005 1.6569 0.3969 32.8959 0.09+NO2 8.4262 11.014 0.005 1.3922 0.3245 33.2204 0.185+TP 3.5807 4.124 0.015 1.1126 0.3029 33.5233 0.32+pH 2.4763 2.401 0.02 1.0588 0.1999 33.7232 0.395+CODMn 2.5555 2.527 0.02 0.8238 0.1438 33.867 0.65+BOD5 2.6869 2.735 0.03 0.6381 0.1082 33.9752 0.85

aBold means significantly correlated with community structure at P < 0.01.

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284 based toxicity tests and field metabarcoding monitoring data285 demonstrated that copepods were more susceptible to adverse286 effects of NH3 than were cladocera and copepoda.

287 ■ DISCUSSION288 Nutrients, especially TN, NO3

− and NO2−, and TAN had more

289 significant effects on communities of zooplankton than did

290other environmental factors in the Tai Lake basin. The291catchment of Tai Lake, situated in the lower reaches of the292Yangtze River, is one of the most densely populated and293developed areas in China.15 After decades of intensive input of294nutrients from industrial and agricultural sources, most surface295waters in the Tai Lake basin have become hypereutrophic,296which has resulted in numerous large algae blooms recent297years.27,28 Eutrophication is still one of the most serious

Figure 4. Phylogenetic distribution of assignable components ofsensitive OTUs: (A) Tree diagram of representative sequences foreach OTU. Distance was measured as number of base substitutions persite, based on the Kimura two-parameter (K2P) method. Asterisksindicate species based on morphology that were found to be sensitiveto TAN; (B) Profiles of distribution of each OTU. The color indicatesweighting by relative abundances of reads. For instance 0.01 means thenumber of reads for that taxon accounts for 1% of total number ofreads; (C) Relative abundance; (D) Frequencies of occurrences. (E)Linear regression between TAN and proportion of sensitive OTUs;(F) Sensitivities of copepoda, cladocera, and rotifera to TAN intraditional toxicity test under laboratory conditions; (G) Proportionsof OTUs, based on metabarcoding, affected by TAN; (H) Number ofsensitive species determined by visual inspection of morphology; (I)Proportions of sequences in metabarcoding data affected by TAN.

Figure 5. Comparison of metabarcoding and morphological monitoring data in four species sensitive to ammonia. The 50% effective concentration(EC50) for TAN was calculated by use of the quantile regression. Colors represent type of water body (yellow, Tai Lake; red, Small Lake; green,River; blue, reservoir).

Figure 6. Species sensitivity distribution for TAN based on variousevaluation methodologies. HC10 for SSD based on species determinedby visual morphology was not available because only 9.2% species werefound to be sensitive to TAN. For OTUs based on metabarcodingdeemed sensitive to TAN were those for which abundances of OTUsdecreased along an increasing gradient of concentrations of TAN andthe coefficient <0.1 by the quantile test (see SI, Figure S6). Speciesidentified by visual morphology deemed to be sensitive to TAN arethose for which the density of species decreased along an increasinggradient of concentrations of TAN and the coefficient <0.1 by thequantile test (see SI, Figure S7). Acute toxicity data of ammonia/TANwere from several previous studies (for details see SI, Table S3).Chronic toxicity data were calculated by the acute to chronic ratio(8.507). In order to better compare the laboratory toxicity data andfield monitoring data, all the TAN were normalized and adjusted topH 7.0 and temperature = 20 °C.

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298 environmental problems confronted by local environmental299 managers.29 The results reported here demonstrated that TAN300 and nutrient factors (TN, NO3, NO2

−, and phosphate) had301 significant effects on compositions of zooplankton commun-302 ities. As one of the most important pollutants in aquatic303 environments, TAN was more toxic to and had a greater304 influence on zooplankton than did other nutrient factors.30,31

305 The results of the variation partitioning analysis suggest TAN is306 a major stressor on zooplankton in the Tai Lake basin.307 Other evidence of TAN being a major stressor for308 zooplankton is that the diversities and abundances of rotifer309 were directly proportional to TAN. Some laboratory toxicity310 studies found some rotifers, such as Brachionus rubens and311 Brachionus rotundiformis to be more tolerant of NH3 than some312 cladocerans, such as Moina micrura and the copepod Acartia313 tonsa.32−34 Tolerance of rotifers allows them survive relatively314 great concentrations of NH3. In the present study, abundances315 of copepods and cladoceras were inversely proportional to316 concentrations of TAN. However, both abundances and317 diversities of rotifers were directly proportional to concen-318 trations of TAN. Although it is difficult to conclude that TAN319 was the sole cause of the shift observed in the zooplankton320 community, effects of NH3 on zooplankton in Tai Lake basin321 cannot be ignored. Rotifers exhibited the greatest diversity and322 accounted for more than half of OTUs observed, of which only323 one was sensitive to TAN. In contrast, more than 30% of the324 copepoda and cladocera reads were sensitive to TAN.325 Identification of species, based on morphological taxonomy,326 suggested that only copepods and cladocera were sensitive to327 TAN. So, together ecogenomics and morphology-based328 monitoring of diversity of zooplankton provided empirical329 evidence of apparent sensitivities of these species.330 Other stressors could potentially affect the response of the331 zooplankton to nutrient stressors, for example, via food web.332 This is always a limitation of the apparent effects threshold333 analysis. Laboratory studies of individual species also lack334 realism and potential effects of multiple stressors. But335 application of the two approaches simultaneously, as we have336 done in this study allows an interpretation of the effects of337 accessory factors as well as additional stressors on the critical338 stressor. This approach is analogous to the water-effects ratio339 approach often applied to individual contaminants.35 This340 allows a simultaneous correction for mitigating effects of341 speciation and dissipation as well as potential supra-additive342 effects of additional stressors. Results of previous studies have343 indicated that other contaminations, such as metals and344 pesticides have ecological risk to benthic, and the eutrophica-345 tion are still the most serious environmental problem in this346 area.36,37 So, this study assessed whether ammonia was likely347 the critical contaminant by comparing results of laboratory- and348 field-based assessments. The fact that the results indicated349 similar thresholds for ammonia based on either the laboratory350 or fields-based results indicates that ammonia was not greatly351 affected by mitigating or synergistic interactions with other352 environmental factors or stressors.353 Metabarcoding also provides a useful method for determin-354 ing relative sensitivities of individual species to specific355 pollutants. Although traditional laboratory toxicity tests allow356 researchers to control experimental conditions, they are often357 criticized for lacking ecologically relevance.5 Laboratory toxicity358 tests are also severely limited by the few surrogate species359 available for testing.38 Species richness response curves derived360 from field monitoring data (f-SSDs) are more ecologically

361relevant, since they assess indigenous species in the presence of362relevant accessory, environmental factors.38,39 The option of363using field data for deriving WQC is permitted by the European364Water Framework Directive and has been recommended by the365U.S.EPA for suspended sediment benchmarks and nutrient366criteria.40,41 Although f-SSDs are useful, traditional identifica-367tions of species, based on morphology are costly and time-368consuming, which limits application of f-SSDs. They are also369limited by simultaneous exposures to multiple stressors. Here a370novel approach, field based OTUs sensitivity distribution (f-371OSD), was demonstrated to derive ecological criteria for372pollutants, which combines the advantage of ecologically373relevant by f-SSDs and the advantage of species identification374by ecogenomics.7,42 Which OTUs were sensitive to TAN were375determined and used to derive a criterion of TAN in f-OSD.376The HC5 (1.4 mg TAN/L), derived by use of OTUs was377consistent with that derived based on field monitoring and378identification of species based on morphology (1.1 mg TAN/379L). These results provide information for confirming the380threshold for TAN in the realistic environment, while381integrating all of the conditions and potential effects of other382environmental variables.383Metabarcoding provided more comprehensive data on384biodiversity than did traditional taxonomy, which allowed385detection of slight responses of biota and made development of386the site-specific criterion for TAN more accurate and reliable.43

387Resolution of thresholds for effects is a function of the number388of taxa considered. For instance, if 10 species are included389resolution of the assessment would be no better than 10%.390Also, selection of species used to generate the SSD has a391significant effect on accuracies of assessments. Since it is392assumed that the entire range of possible sensitivities is393covered, if this is not true, a systematic bias would be394introduced. Use of the HC5 as a measure of de minimus risk is395questioned because, based on LC50 data would mean that 50%396of individuals of 5% of species would be expected to die. If397those species were cornerstone species of ecosystems, that398could result in a loss of ecosystem services. This information399along with understanding of the structure and functions of400ecosystems can then be used to make judgments of the401likelihood that critical ecosystem services might be adversely402affected.403In this study, 291 OTUs of various species of zooplankton404were identified by use of DNA metabarcoding, whereas405traditional visual identification of species identified 76 taxa.406Furthermore, since derivation of WQC by use of the SSD407model depends on number of sensitive species, using OTUs408instead of “species” provides more data points than provided by409use of the morphological method. Using metabarcoding, 39410OTUs (13.4%) were sensitive to TAN, whereas only seven taxa411(9.2%) were identified to be sensitive to TAN by use of visual412taxonomy. Furthermore, four species were identified to be413sensitive to TAN were also found to be sensitive to TAN by414identification as OTUs. EC50s of the four species were415consistent between the two approaches. Overall, the f-OSD416strategy demonstrated here minimized some of the limitations417of identification of species when developing site specific WQC418by use of the apparent effect threshold method, which is based419on synoptic collection of information on species and420concentrations of contaminants in the field.44

421The threshold for effects of TAN, derived by use of field-422OSD was slightly less than that derived by use of the SSD based423on laboratory toxicity data. This does not mean the SSD based

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424 on results of laboratory bioassays is not acceptable for deriving425 criteria, but the field-OSD, which is based on the entire426 assemblage in an ecosystem and includes effects of accessory427 factors, is more suitable for derivations of site- or regional-428 specific WQCs.45 Most species used to develop current a429 criterion for NH3 are based on laboratory toxicity data for non-430 native species. However, there is insufficient data for toxicity of431 ammonia to native species for which protection is sought for.432 The threshold for effects of TAN derived by field-OSD closely433 matched that derived by field-species method. While the WQC434 for TAN derived based on OTUs developed by metabarcoding435 of the zooplankton community during the present study436 demonstrates a promising approach, since zooplankton might437 not be the most sensitive taxa, more sensitive species should be438 considered, such as mollusks12,46 and fishes47 should also be439 considered. Balance between f-OSD/SSD and laboratory440 toxicity testing is also needed in derivation of WQC. The F-441 OSO/SSD approach is limited because it requires that the442 contaminant already be in the environment and thus cannot be443 applied to all compounds.444 A novel approach (f-OSDs) for development of ecologically445 relevant, site-specific, WQC to protect aquatic organisms from446 effects of TAN based on the quantifiable changes of biodiversity447 of zooplankton populations was presented. The f-OSDs448 approach incorporates advantages of environmental relevance449 from field-based SSD and the advantages of comprehensive,450 high throughput, high sensitivity, and low cost in species451 identification from metabarcoding.44,48 This method also452 allowed for rapid assessment of which species/OTUs were453 more sensitive to a particular contaminant. Once calibrated to a454 local environment, f-OTUs can be used for rapid screening to455 understand the status and trend in structures of aquatic456 ecosystem. While, in this study the ecogenomic approach was457 used to gain information on effects of TAN on zooplankton,458 the technology can be expanded to be used to monitor for459 effects on other groups of organisms, including phytoplank-460 ton,49,50 insects,51 and fish.52 It might even be possible to461 monitor for effects on terrestrial animals or aquatic birds.53 This462 will be particularly important for studying endangered or463 threatened species. Finally, use of a more focused assessment of464 abundances of specific genes as indicators of function or general465 health of species might be possible.

466 ■ ASSOCIATED CONTENT467 *S Supporting Information468 The Supporting Information is available free of charge on the469 ACS Publications website at DOI: 10.1021/acs.est.6b05606.

470 Additional information as noted in the text (PDF)

471 ■ AUTHOR INFORMATION472 Corresponding Author473 *Phone: (86)-25-89680623; e-mail: zhangxw@nju.edu.cn,474 howard50003250@yahoo.com.475 ORCID476 Xiaowei Zhang: 0000-0001-8974-9963477 Notes478 The authors declare no competing financial interest.

479 ■ ACKNOWLEDGMENTS480 For support, we thank Jiangsu Province Funds for Distin-481 guished Young Scientists (BK20130015) and Major Science

482and Technology Program for Water Pollution Control and483Treatment (Grant#2012ZX07506-003 and 2012ZX07101-007-48401). X.Z. was supported by the Fundamental Research Funds485for the Central Universities and the Collaborative Innovation486Center for Regional Environmental Quality. J.P.G. was487supported by the “High Level Foreign Experts” program488(#GDT20143200016) funded by the State Administration of489Foreign Experts Affairs, the P.R. China to Nanjing University490and the Einstein Professor Program of the Chinese Academy of491Sciences. J.P.G. was also supported by the Canada Research492Chair program and a Distinguished Visiting Professorship in493the School of Biological Sciences of the University of Hong494Kong. We also thank Prof G. Allen Burton, Jr. from University495of Michigan for his advice and editing of English grammar.

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