Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay

JES-00632; No of Pages 13 J O U RN A L OF E N V I RO N ME N TA L S CI EN CE S X X (2 0 1 6 ) XX X–XXX

Available online at www.sciencedirect.com

ScienceDirect www.elsevier.com/locate/jes

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Yan Zhang1,4 , Lujun Chen1,3 , Renhua Sun2,5 , Tianjiao Dai2 , Jinping Tian1 , Wei Zheng3 , Donghui Wen2,⁎

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Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay

1. School of Environment, Tsinghua University, Beijing 100084, China 2. College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China 3. Zhejiang Provincial Key Laboratory of Water Science and Technology, Department of Environmental Technology and Ecology, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, Jiaxing 314050, China 4. Zhejiang Shuangyi Environmental Technology Development Co., Ltd., Jiaxing 314000, China

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AR TIC LE I NFO

ABSTR ACT

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Article history:

Anthropogenic activities usually contaminate water environments, and have led to the

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Received 11 May 2015

eutrophication of many estuaries and shifts in microbial communities. In this study, the

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Revised 30 October 2015

temporal and spatial changes of the microbial community in an industrial effluent receiving area

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Accepted 25 November 2015

in Hangzhou Bay were investigated by 454 pyrosequencing. The bacterial community showed

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Available online xxxx

higher richness and biodiversity than the archaeal community in all sediments. Proteobacteria

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Keywords:

Methanomicrobia were the two dominant archaeal classes in the effluent receiving area. PCoA

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Bacterial community

and AMOVA revealed strong seasonal but minor spatial changes in both bacterial and archaeal

Archaeal community

communities in the sediments. The seasonal changes of the bacterial community were less

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454 pyrosequencing

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Sediment

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dominated in the bacterial communities of all the samples; Marine_Group_I and

significant than those of the archaeal community, which mainly consisted of fluctuations in

Effluent receiving area

abundance of a large proportion of longstanding species rather than the appearance and

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disappearance of major archaeal species. Temperature was found to positively correlate with the

Hangzhou Bay

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Marine_Group_I; and might be the primary driving force for the seasonal variation of the microbial community. © 2016 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences.

Introduction

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Human development of coastal watersheds has dramatically increased environmental pressure on downstream estuarine and coastal ecosystems (Niemi et al., 2004). Increasing anthropogenic activities have resulted in declining water quality and eutrophication of many estuaries (Bouvy et al., 2010). Microbial communities, which play an important role in nutrient cycling and are exposed to changing environmental

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dominant bacteria, Betaproteobacteria, and negatively correlate with the dominant archaea,

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Published by Elsevier B.V.

conditions, are one of the most sensitive indicators of the deteriorating environments. Researchers have adopted microbial community investigation methods for monitoring or evaluating the environment and ecology of an estuary. For example, Sun et al. (2014) used pyrosequencing to investigate prokaryotic and eukaryotic microbes to monitor the environmental status of an estuary reservoir; Obiukwu and Otokunefor (2014) investigated the microbial community diversity of a refinery effluent, and water and sediments of

⁎ Corresponding author. E-mail: [email protected] (Donghui Wen). 5 Present address: Rural Energy & Environment Agency, Ministry of Agriculture, Beijing 100125, China.

http://dx.doi.org/10.1016/j.jes.2015.11.023 1001-0742/© 2016 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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1. Materials and methods

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1.1. Site description and sample collection

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The industrial structure, wastewater treatment and discharge of SYIA were described in our previous article (Zhang et al., 2014). The effluent discharge area and the sampling sites are shown in Fig. 1. Sampling was conducted in February, May,

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1.3. DNA extraction and pyrosequencing

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DNA was extracted from 0.5 g of each sediment sample with a Power soil DNA isolation kit (Mo Bio, Carlsbad, CA), following the manufacturer's instructions. The extracted DNA samples were stored at −20°C for further analysis. The bacterial and archaeal communities of each sample were analyzed by 454 pyrosequencing as previously described (Zhang et al., 2014). Briefly, bacterial and archaeal 16S rRNA genes were first amplified on an ABI9700 thermocycler (ABI, Foster City, USA) with corresponding barcoded primers and thermal programs. The adopted primers were 27F: AGAGTTTGATCCTGGCTCAG and 533R: TTACCGCGGCTGCTGGCAC for bacterial 16S rRNA; 344F: ACGGGGYGCAGCAGGCGCGA and 915R: GTGCTCCCCCGCCAA TTCCT for archaeal 16S rRNA. The amplicon libraries were generated by emulsion PCR after purifying the previous PCR products, and sequenced on a Roche GS-FLX Titanium Sequencer (Roche Diagnostics Corporation, Branford, CT) with the 454/ Roche B sequencing primer kit.

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1.4. Sequencing data processing and analysis

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Water temperatures were measured with a thermometer during sample collection. The pH, DO, and salinity of seawater were determined immediately after sampling using a pH meter (Thermo Orion 868), a DO meter (Thermo 3-star bench top), and a salinity meter (Mettler Toledo SG3-ELK, Switzerland), respectively. The COD, ammonia nitrogen (NH+4–N), nitrite nitrogen (NO−2 N), and nitrate nitrogen (NO−3 N) of seawater were detected by the alkaline potassium permanganate method, indophenol blue method, N-1-naphthyl-ethylenediamine method, and alkaline potassium persulfate digestion-zinc cadmium reduction method, respectively (AQSIQC and SAC, 2007). Sediment samples were dried with a freeze dryer (Christ Alpha 1-2 LD plus, German) and the water content was calculated by the weight decrement. Subsequently, 1 g portions of dried samples were extracted with 40 mL 2 mol/L KCl for 2 hr in a shaker. The extract was filtered through a 0.45 μm membrane filter; and the filtrate was used to analyze NH+4–N, NO−2 N, and NO−3 N using colorimetric methods (SEPA of China, 2002) with a UV–Vis spectrometer (Shimadzu UV2450, Japan). Total organic carbon (TOC) was measured with the K2Cr2O7– H2SO4 oxidation–reduction titration method (Gaudette et al., 1974). Total phosphorus (TP) in the sediments was measured using the ascorbic acid-molybdate blue method after 2 hr of combustion at 500°C and 16 hr of extraction with 1 M HCl (Zheng et al., 2013). The sediment pH was determined at a sediment/water ratio of 1/2.5 (w/w).

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1.2. Physical and chemical analysis

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August, and November in 2012. The surface sediments were collected using a surface sediment sampler (Van Veen, Hydro-Bios, Hiel-Holtenau, Germany). The overlying waters were sampled at the same time by a deepwater sampler. Sediments and waters were transported to the lab within 24 hr at 4°C, and then stored at − 70°C for further analysis.

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the effluent receiving area, the Okrika Sector of the Bonny estuary, and found changes in the microbial population density and disappearance of organisms resulting from the refinery effluent; Liu et al. (2014) determined the benthic microbial communities along the Pearl Estuary to identify the potential co-occurrence patterns among different microbial lineages and functional groups, as well as the influences of environmental factors on microbial community structures. China, the world's largest developing economy, is facing serious deterioration of estuarine and coastal water quality. One of the major contributors to China's economic growth are industrial parks (Yu et al., 2015), a large part of which are located in eastern coastal areas. The four largest industrial wastewater discharging provinces during 1995–2010, Jiangsu, Guangdong, Zhejiang, and Shandong (Geng et al., 2014), were all situated in China's coastal region. The enormous industrial wastewater discharge is considered to be one of the major causes for the coastal pollution. Hangzhou Bay, the inlet of the Qiantang River, has become one of the most seriously polluted water areas along China's coast. Surrounded by 9 industrial parks, Hangzhou Bay receives hundreds of millions of m3 of industrial wastewater every year, which contains about 100,000 ton chemical oxygen demand (COD) and more than 6000 ton ammonia (L. Liu et al., 2012). The seawater quality in Hangzhou Bay has been contaminated to a level inferior to Class IV, the worst level of the national seawater quality standard (GB 3097-1997). Nutrient levels and eutrophication (S. Gao et al., 2011; Qin et al., 2009), heavy metals (Fang and Wang, 2006; Zhang et al., 2008), major and trace elements (S. Liu et al., 2012), organochlorine pesticides (X. Gao et al., 2011), and polycyclic aromatic hydrocarbons (Chen et al., 2006) in the bay have been investigated in recent years. However, the impact of industrial effluent on the microbial community of the estuary has seldom been investigated. Previously, we investigated the similarity of archaeal and bacterial communities between WWTPs and the effluent receiving area of a fine-chemical industrial park, Shangyu Industrial Area (SYIA), located on the south bank of Hangzhou Bay (Zhang et al., 2014). However, the microbial community in the bay has not been studied in temporal and spatial resolution. This article is a follow-up research of our study in SYIA (Zhang et al., 2014). We aimed to investigate the spatial and temporal changes of the bacterial and archaeal communities in the sediments of the effluent receiving area near SYIA. Seasonal samples from different sites in the effluent receiving area were collected and further analyzed by pyrosequencing. Multivariate statistical analysis was conducted to explore the potential relationships between microbial ecology and environmental factors.

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QIIME software (Caporaso et al., 2010) was adopted for 174 converting pyrosequencing flowgrams to sequences and 175

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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subsequently analyzing them. Barcodes were removed from sequence reads initially, followed by filtering and denoising to eliminate low quality and ambiguous reads, i.e., the reads with ambiguous bases >0, sequence lengths < 200 bp, or average sequence quality < 25. The pre-treated sequences were scanned by Chimera-uchime to identify and remove putative chimeras subsequently. The remaining valid sequences were clustered into OTUs with a sequence identity threshold of 97% by Mothur software (Schloss et al., 2009). The diversity statistics and rarefaction were calculated after OTU clustering using Mothur, and the taxonomy was classified with the Silva database. To avoid bias in alpha diversity comparison caused by the disparity of sequence numbers between bacterial libraries and between archaeal libraries, the same number sequences in each pyrosequencing library were subsampled randomly by Mothur and alpha diversity indexes were calculated. Principal coordinate analysis (PCoA) and hierarchical clustering were conducted and plotted with the R program (R Development Core Team, 2008); and analysis of molecular variance (AMOVA), was conducted using Mothur. The heatmaps were also plotted with the R program. Redundancy analysis (RDA) was performed with Canoco 4.0. All original 454 sequence reads were archived at the NCBI Sequence Read Archive (SRA) under accession SRP039368.

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2. Results

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2.1. Seawater and sediment properties

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A total of 9 sediment samples and corresponding 9 water samples were selected and analyzed in this study. Sample names were denoted as “W site-month” for water or “S

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Fig. 1 – Map of the Shangyu Industrial Area and the sampling sites. (a) Location of the SYIA (in red); (b) map of the SYIA showing the effluent discharge area (in red); (c) the effluent discharge area showing the sampling sites (red spots; S2, S4, S5, and S6 means site 2, site 4, site 5, and site 6). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

site-month” for sediment, for example “W4-Feb” and “S4-Feb” respectively mean the water sample and sediment sample collected from site 4 in February. The properties of seawater samples are shown in Table S1. The temperature varied from 6.0 to 28.0°C with the change of seasons. Water pH ranged from 7.65 to 7.88; and DO ranged from 3.50 to 5.82 mg/L. Salinity significantly changed with the season, ranging from 1.69 to 5.50 psu. The COD of seawater varied from 2.58 to 4.00 mg/L. NO−3 N (2.05–3.23 mg/L) was the dominant inorganic N species in the water; NH+4–N was at lower levels (0.11–0.45 mg/L); and NO−2 N was at much lower levels (below 0.03 mg/L) in May, August, and November, but accumulated to 0.25–0.30 mg/L in February. The inorganic nitrogen, as the primary pollutant, placed this area into the worst level (inferior to Class IV) of seawater quality according to the National Seawater Quality Standard (GB3097-1997). The properties of sediment samples are shown in Table S2. Water content of the sediments ranged from 20.9% to 31.6%; and pH varied from 8.38 to 9.6. S4-Aug. and S2-Nov were found to have the lowest and highest TOC of 27.8 and 361.4 mg/L, respectively. The TOC of other sediment samples varied from 86.7 to 177.3 mg/L. The NH+4–N and NO−3 N levels of the sediments were 19.06–22.69 and 8.72–18.41 μg/g, respectively. NO−2 N remained at much lower levels in sediment samples, being not detected in four sediment samples and ranging from 0.04 to 0.17 μg/g in others. TP in the sediment samples varied from 0.38 to 0.54 mg/g. In general, the water and sediment properties of different sites were similar in the same season. However, both water and sediment properties changed in different seasons. Water temperature was the most unstable environmental factor, increasing from 6°C in February to 28°C in August, and then decreasing to 16°C in November. The water properties, e.g., DO, COD, and NO−2 N and the sediment properties, e.g., pH,

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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The 9 pyrosequencing libraries of bacterial 16S rRNA gene yielded 116,139 reads after quality filtering and removal of chimeric sequences from 146,513 raw reads. The average length of the sequences was 453 nucleotides with adaptor and barcode primers trimmed. The species richness, library coverage and diversity estimation were calculated for each library and shown in Table 1. Good's coverage of the bacterial 16S rRNA libraries of S4-Feb, S5-Feb, and S6-Feb was 0.74–0.79, while the coverage values of other libraries were all over 0.88, indicating that the libraries could well reflect the samples' bacterial communities. The Chao1 and the rarefaction curve (Fig. S1) demonstrated that the richness of bacteria in February samples was much higher than that in other seasons' samples. The bacterial biodiversity of the samples from February was higher than that of the samples from other seasons too, according to the Shannon index.

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t1:1 t1:3 t1:2

Raw reads

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S2-Aug

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S2-Nov

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S5-Feb

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S6-Feb

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Table 1 – Coverage, diversity and richness of 16S rRNA gene pyrosequencing libraries.

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Across all 9 libraries, 94 different bacterial classes were found in total. The main classes (relative abundance > 1%) in 9 samples are exhibited in Fig. 2a. The dominant bacteria in all samples were classified in phylum Proteobacteria, of which Betaproteobacteria with relative abundance of 16.55–45.75% in different samples was the most dominant. Following Betaproteobacteria, Gammaproteobacteria, Alphaproteobacteria, and Deltaproteobacteria accounting for 6.68– 21.75%, 5.95–28.38%, and 0.82–11.63% respectively, were the other three dominant classes. These four classes accounted for 51.07–80.73% in different samples. A total of 532 bacterial genera were found across all sediment samples. The main genera (relative abundance >1%) are shown in Fig. 3a. Separated from other samples, the right four samples in Fig. 3a, i.e., S2-May, S4-Nov, S4-May, and S2-Aug, presented their abundant genera at the top right corner of the figure, which were Sediminibacterium, Flavobacterium, Cloacibacterium, Acinetobacter, Rhodobacter, Undibacterium, Burkholderia, Novospingobium, and Curvibacter. The other five samples displayed common abundant genera like Nitrospira, Acidiferrobacter, Thiobacillus, and Pseudomonas.

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OTU

Coverage

Chao1

Shannon index

2964 (1995) 2665 (2037) 2893 (2197) 958 (958) 2394 (1115) 1734 (806) 2580 (1254) 3119 (1502) 2924 (1335) 986 (825) 801 (675) 845 (683) 723 (540) 1626 (1069) 1025 (563) 1360 (687) 1039 (857) 678 (678)

0.79 (0.70) 0.75 (0.69) 0.74 (0.67) 0.88 (0.88) 0.93 (0.84) 0.95 (0.89) 0.93 (0.83) 0.95 (0.81) 0.91 (0.80) 0.93 (0.91) 0.94 (0.93) 0.94 (0.92) 0.97 (0.95) 0.93 (0.88) 0.98 (0.94) 0.98 (0.93) 0.93 (0.91) 0.93 (0.93)

5701 (4608) 5797 (4926) 6016 (5138) 1729 (1729) 4250 (2472) 3256 (1810) 4280 (2616) 4257 (2776) 5103 (3186) 1603 (1450) 1410 (1250) 1530 (1282) 1208 (943) 2803 (2078) 1465 (952) 1839 (1210) 1676 (1465) 1166 (1166)

7.20 (6.98) 7.09 (6.94) 7.35 (7.19) 5.22 (5.22) 5.80 (5.59) 4.68 (4.53) 6.25 (6.03) 6.81 (6.53) 5.96 (5.71) 4.59 (4.55) 4.03 (4.00) 4.07 (4.03) 4.59 (4.55) 5.43 (5.31) 4.39 (4.31) 4.59 (4.50) 4.9 (4.85) 4.28 (4.28)

⁎ The numbers in the parentheses are the subsampled sequence number or the indexes calculated from the subsamples.

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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Acidimicrobiia Acidobacteria Actinobacteria Alphaproteobacteria Anaerolineae Bacilli Betaproteobacteria Clostridia Cytophagia Deltaproteobacteria Elusimicrobia Epsilonproteobacteria Flavobacteria Gammaproteobacteria Gemmatimonadetes Holophagae Ignavibacteria Nitrospira Planctomycetacia Sphingobacteria Others Unclassified

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Group_C3 Halobacteria Marine_Benthic_Group_A Marine_Benthic_Group_B Marine_Group_I Methanobacteria Methanomicrobia Miscellaneous_Crenarchaeotic_Group Soil_Crenarchaeotic_Group Terrestrial_group Thermoplasmata Thermoprotei Others Unclassified

Fig. 2 – Dominant classes in the samples. (a) Bacterial classes; (b) archaeal classes. The classes with abundance <1% were assigned into “Others”. Sequences that could not be classified into a known group were assigned into “Unclassified”.

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S6-Feb, S4-Feb, and S5-Feb, however, exhibited more abundance of genera Porticoccus, Methylotenera, Limnobacter, Marinicella, and some others, which made them differ from S4-Aug and S2-Nov. The PCoA (Fig. 4a) revealed that 9 samples were separated into three clusters: 3 samples from February strongly clustered together; S2-Nov and S4-Aug drifted away; and S2-May, S4-Nov, S2-Aug, and S4-May assembled at another corner. The same result was revealed by hierarchical cluster analysis (Fig. S2). In general, the bacterial community in February was found to be different from the other seasons' samples. However, other samples did not exhibit significant seasonal changes in the bacterial community. On the other hand, the bacterial communities of the sediment samples also did not display distinct differences between sampling sites. Using AMOVA to evaluate the differences in bacterial communities

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between seasons, the result was Fs of 1.5567 and p-value of 0.096, indicating that bacterial communities of the sediments changed seasonally, though the seasonal change was not significant (α = 0.05). Using AMOVA to evaluate the differences in bacterial communities between sites, the result was Fs of 0.9192 and p-value of 0.575, indicating that bacterial communities did not significantly vary between sites. In order to further track the seasonal change of the sediment bacterial communities, the shared OTUs among the bacterial libraries of four seasons from site 4 were analyzed. As shown in Fig. 5a, the majority of the OTUs in each library were seasonally specific, and were not shared by other samples. Among pairs of samples, S4-May & S4-Aug shared 504 OTUs, which was much lower than that between any other two samples (656–690 shared OTUs). All the four libraries shared 193 OTUs, which included 21.2%, 19.1%, 25.8%,

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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a) Bacterial genera

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Candidatus_Nitrososphaera Candidatus_Methanomethylophilus Methanosphaera Methanocorpusculum Rice_Cluster_I Methanocella Methanothermobacter Methanofollis Methanogenium Halogranum Unclassified Candidatus_Nitrosopumilus Candidatus_Nitrosoarchaeum Methanosarcina Methanomethylovorans Methanosaeta Methanoculleus Methanobrevibacter Methanolinea Methanimicrococcus Methanoregula Methanobacterium Methanospirillum Methanosphaerula Methanomassiliicoccus Cenarchaeum Candidatus_Parvarchaeum Methanolobus OM60NOR5_clade Natronolimnobius Halolamina Methanococcoides ANME−3

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Relative abundance of community (%)

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Sediminibacterium Flavobacterium Cloacibacterium Acinetobacter Rhodobacter Undibacterium Burkholderia Novosphingobium Curvibacter Thermomonas Exiguobacterium Faecalibacterium Azoarcus Aeromonas Limnohabitans Vogesella Aquabacterium Flexibacter Arenimonas Gaetbulibacter Nitrosomonas Algoriphagus Pedobacter Lutibacter Rubrivivax Hydrogenophaga Nitrosococcus Nitrospira Acidiferrobacter Thiobacillus Pseudomonas Thauera Dechloromonas Porticoccus Perlucidibaca Methylotenera Limnobacter Marinicella Albidiferax Shewanella Unclassified Others Rhodococcus Escherichia−Shigella Paludibacterium Brachymonas

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Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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2.3. Diversity of archaea

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The 9 pyrosequencing libraries of archaeal 16S rRNA genes yielded 97,820 reads after quality filtering and removal of chimeric sequences from 122,433 raw reads. The average length of the sequences was 510 nucleotides with adaptor and barcode primers trimmed. The species richness, library coverage and diversity estimation were calculated for each library and shown in Table 1. Good's coverage values of the archaeal 16S rRNA libraries were all over 0.93, indicating that the libraries could adequately reflect the samples' archaeal communities. The Chao1 and the rarefaction curve (Fig. S3) demonstrated that the richness of S4-May was the highest of all samples, while S2-May and S2-Aug were the two samples with the lowest richness of archaeal community. Also, S4-May was the sample possessing the highest archaeal biodiversity, according to the Shannon index. Across all 9 libraries, 30 different archaeal classes were found in total. The main classes (relative abundance > 1%) in each sample are exhibited in Fig. 2b. Marine_Group_I dominated in the majority of sediment samples, including S4-Feb, S5-Feb, S6-Feb, S2-Aug, S4-Aug, and S4-Nov. Another abundant archaeal class was Methanomicrobia, which dominated in S2-May, S4-May, and S2-Nov. These two classes accounted for 62.84%–83.92% in different samples. Thermoplasmata, Halobacteria, Miscellaneous_Crenarchaeotic_Group, and

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b) Archaeal communities 1

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Thermoprotei were the other main archaeal classes in all sediment samples. A total of 40 archaeal genera were found across all sediment samples. The main genera (relative abundance >1%) are shown in Fig. 3b. Candidatus_Nitrosopumilus, Candidatus_Nitrosoarchaeum, Methanosarcina, Mehanomethylovorans, and Methanosaeta were five main archaeal genera with high relative abundance in all sediment samples. S2-May and S4-May possessed higher abundance of several genera than the other samples, e.g., Methanoculleus, Methanobrevibacter, and Methanolinea. More archaeal genera with relative abundance over 1% were found in S4-May and S2-Nov than in the other samples. The PCoA (Fig. 4b) revealed strong clustering of samples from the same season, especially the samples from February, May, and August. The same phenomenon was revealed by hierarchical cluster analysis (Fig. S4). Sampling season seems to be a primary factor driving the change of archaeal communities. Sampling site did not exhibit enough influence to interfere with the impact of sampling season. Using AMOVA to evaluate the differences in archaeal communities between the four seasons, the result was Fs of 7.1385 and p-value less than 0.001. And the p-values of AMOVA between any two seasons were all less than 0.001, which indicated significant seasonal change in the archaeal communities in the sediments. Use AMOVA to evaluate the differences in archaeal communities between sampling sites, the result was Fs of 0.8354 and p-value of 0.635, which indicated that the archaeal communities did not significantly vary between sampling sites. The shared OTUs among the four archaeal libraries from site 4 were analyzed as shown in Fig. 5b. Seasonally specific OTUs accounted for the largest fractions in S4-Feb, S4-May, and, S4-Aug, while less seasonally specific OTUs were detected in S4-Nov, S4-May and S4-Aug who shared 501 OTUs, much higher than that shared by any other two libraries, e.g., S4-Feb and S4-Nov only shared 280 OTUs. All four libraries shared 163 OTUs, which held 51.8%, 39.1%, 59.4%, and 69.6% of total sequences from S4-Feb, S4-May, S4-Aug, and S4-Nov, respectively. As shown in Table 3, the shared OTUs were identified in 11 classes, in which Marine Group I was the most abundant.

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and 21.5% of the total sequences from S4-Feb, S4-May, S4-Aug, and, S4-Nov, respectively. As shown in Table 2, the shared OTUs were identified in 21 classes, of which Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria were the most abundant. RDA results (Fig. 6a) demonstrated that temperature accounted for the greatest amount of variability for the bacterial community. It exhibited a strong positive correlation with the most abundant bacterial class, Betaproteobacteria. TP exhibited a strong positive correlation with Gammaproteobacteria but a strong negative correlation with Alphaproteobacteria, respectively. NH+4–N and pH, however, showed weak correlations with the main bacterial classes.

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Fig. 4 – Community analysis with PCoA based on Bray–Curtis distance. Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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a) Bacterial libraries

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Fig. 5 – Shared OTUs analysis of the 16S rRNA gene libraries of four seasons' samples from site 4.

Table 2 – Shared OTUs of bacterial 16S rRNA libraries of four seasons' samples from site 4. Class

Shared OTU

S4-Feb

S4-May

S4-Aug

S4-Nov

t2:5 t2:6 t2:7 t2:8 t2:9 t2:10 t2:11 t2:12 t2:13 t2:14 t2:15 t2:16 t2:17 t2:18 t2:19 t2:20 t2:21 t2:22 t2:23 t2:24 t2:25 t2:26

Acidimicrobiia Acidobacteria Actinobacteria Alphaproteobacteria Betaproteobacteria Chlorobia Cytophagia Deltaproteobacteria Flavobacteria Fusobacteriia Gammaproteobacteria Gemmatimonadetes Holophagae Ignavibacteria KD4-96 Nitrospira Planctomycetacia SC3-20 Sphingobacteriia Thermoleophilia Thermotogae Unclassified

8 8 1 30 33 1 1 16 3 1 22 9 4 3 3 8 4 1 2 2 1 32

t2:27

Total

92 35 1 218 500 8 22 123 26 3 207 65 33 9 10 25 18 1 25 26 3 327 1777 (21.2%) ⁎

55 22 5 1891 581 7 28 101 16 12 127 16 11 5 10 35 14 1 160 18 1 176 3292 (19.1%)

208 85 2 1027 1205 16 9 299 30 1 624 203 56 43 11 95 9 6 8 34 5 443 4419 (25.8%)

75 61 5 536 1393 5 4 135 31 6 743 20 13 6 14 37 18 32 162 26 3 243 3568 (21.5%)

t2:28

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t2:4

193

⁎ The numbers in the parentheses are percentages of the shared reads in the total reads of each library.

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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a) Bacterial communities

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RDA results (Fig. 6b) revealed that temperature accounted for the greatest amount of variability for the archaeal community. It exhibited a strong negative correlation with Marine_Group_I and Halobacteria. NO−3 N showed a strong negative correlation with classes Methanobacteria, Group_C3, and Methanomicrobia. However, TOC showed a strong positive correlation with Soil_Crenarchaeotic_Group and Marine_Benthic_Group_B but negative correlation with Thermoplasmata and Marine_Benthic_Group_A.

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Fig. 6 – Redundancy analysis (RDA) of communities as affected by sediment properties. (a) Bacterial communities. Abbreviation in figure: Acidimi., Acidimicrobiia; Acidoba., Acidobacteria; Alphapr., Alphaproteobacteria; Deltapr., Deltaproteobacteria; Gemmati., Gemmatimonadetes; Nitrosp., Nitrospira; Unclass., Unclassified. (b) Archaeal communities. Abbreviation in figure: Halobac. Halobacteria; M.B.G._A, Marine_Benthic_Group_A; M.B.G._B, Marine_Benthic_Group_B; M.C.G., Miscellaneous_Crenarchaeotic_Group; Methan., Methanobacteria; Methno., Methanomicrobia;M.G._I, Marine_Group_I; S.C.G., Soil_Crenarchaeotic_Group; Thermop., Thermoplasmata; Unclass., Unclassified.

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3. Discussion

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Anthropogenic activities may release massive amounts of pollutants and lead to a deterioration of environmental and ecological quality. In our study area, the seawater quality has become inferior to Class IV of the Seawater Quality Standard (GB3097-1997), indicating the negative influence of human activities on this industrial effluent receiving area. The microbial community showed a response to the variation of environmental factors as well as the influence of pollutant

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input. In this study, we investigated the seasonal change of bacterial and archaeal communities at different sites of the effluent receiving area, and analyzed the potential relationship between the microbial community and environmental factors. From the evaluation of both bacterial and archaeal libraries (Table 1), bacterial communities in the sediments were found to possess higher richness and biodiversity than archaeal communities according to their Chao1 and Shannon indices, which is consistent with the microbial communities of other surface sediments (Lee et al., 2013; Lin et al., 2014).

486 485 484 483 482 481 480 479 478 477 476 475 474 473 472 471 470 469

Bacterial phylogenetic assignment yielded 45 bacterial phyla, 94 bacterial classes, and 532 bacterial genera, and revealed a high level of bacterial diversity. Accounting for 52.2%–80.8% of the total bacteria, Proteobacteria was the predominant bacterial phylum in the sediments of this effluent receiving area of Hangzhou Bay, which is the same dominant bacterial phylum in the sediments of the nearby Yangtze River estuary (Feng et al., 2009; Huang et al., 2014). The average abundance of Betaproteobacteria and Gammaproteobacteria in all the 9 libraries were the highest,

497

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

487 488 489 490 491 492 493 494 495 496 498 499 500 501 502 503 504 505 506

10

521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549

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oxidation. All the identified ammonia oxidizing archaea, for example Nitrosopumilus maritimus SCM1 (Martens-Habbena et al., 2009) and Nitrososphaera viennensis EN76 (Tourna et al., 2011), belong to Thaumarchaeota. The second most dominant class was Methanomicrobia, a group in phylum Euryarchaeota, which was once discovered as the dominant archaea in shelf sediments in Alaska (Hamdan et al., 2013). Halobacteria, habituated to high-salinity aquatic environments (Dorador et al., 2010; Moune et al., 2003; Yan et al., 1998), and Thermoplasmata, regarded as methanogenic archaea (Iino et al., 2013) and found in shallow marine areas (Schneider et al., 2013), were the other main archaeal classes detected in all the sediments. Different from our study, the dominant archaea in the Jiulong River estuary were identified as Miscellaneous Crenarchaeotal Group (MCG), whose ecological functions are still unknown (Li et al., 2012). MCG also was also the main archaeal component in the sediments at the depth range of 0– 50 cm in Pearl River Estuary (Jiang et al., 2011). According to the work by Kubo et al. (2012), MCG was abundant, diverse and widespread in marine sediments, and did not vary significantly between seep and non-seep sites. The dominant archaea in this study, Thaumarchaeota, also predominated across all surface sediments of the Caspian Sean (Mahmoudi et al., 2015) and deep-sea sediments in Iheya North and Iheya Ridge (Zhang et al., 2015). The shared OTU analysis of the four seasons' libraries of site 4, however, revealed that the shared archaeal 16S rRNA OTUs rather than the shared bacterial ones constituted higher percentages of the total sequences in each corresponding library (Tables 2 and 3). This revealed that the archaeal community in the sediment, rather than the appearance and disappearance of major archaeal species, showed seasonal shifts in the abundance of a large proportion of longstanding species. One of the central goals of microbial ecology is to explore the relationships between environmental factors and microbial communities, to further understand how environment and microbial communities affect each other (Hollister et al., 2010). The pH, temperature, water contents, organic matters, and total phosphorus have been reported to be important drivers of microbial communities in sediments (Fagervold et al., 2014; Hollister et al., 2010; Shao et al., 2011; Wang et al., 2014). Among the environmental factors in this

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which consist of bacteria groups with versatile degradation capacities and aerobic autotrophic ammonia oxidizing bacteria (AOB) (Kowalchuk and Stephen, 2001; Purkhold et al., 2000). In another sequencing study by our group focusing on Betaproteobacterial AOB using the same samples (Zhang et al., submitted), Nitrosomonas was the main AOB group in Betaproteobacteria, while Nitrosospira was detected in only three samples with relative abundance less than 2.3% in all Betaproteobacterial AOB. Consistently, Nitrosomonas in Betaproteobacteria was detected in all samples in this study, while Nitrosospira was not detected in any samples. Nitrosomonas is the dominant AOB in most wastewater treatment plants (Gao et al., 2013; Limpiyakorn et al., 2011; Zhang et al., 2011), and is considered a potential bio-indicator species for pollution or freshwater/wastewater input into coastal environments (Cao et al., 2012). Nitrospira has been recognized as the numerically dominant nitrite-oxidizing bacteria responsible for the second step of aerobic nitrification (Fujitani et al., 2013). The existence of Nitrospira (average abundance 1.84%) coupled with AOB groups ensured the functioning of nitrification in this area. In addition, another main bacterial class in the sediments, Flavobacteria, belongs to the phylum Bacteroidetes, and has been found to be abundant in wastewater treatment systems (Zhu et al., 2013), exhibiting good resistance to pollutants and toxicity. However, in contrast to its high abundance in this area (average fraction of 5.1%), Flavobacteria was not detected in the surface sediment of the nearby Yangtze River estuary (Feng et al., 2009). Sphingobacteriia, with average abundance of 2.70%, is a group of Gram-negative, non-spore-forming rod-like bacteria containing high quantities of sphingophospholipids in their cells (Schmidt et al., 2012), and was identified as one of the main bacterial genera responsible for organic pollutant removal (Shangguan et al., 2015). Sphingobacteriia has been detected in the South China Sea and East China Sea (Du et al., 2006). Archaeal phylogenetic assignment yielded 32 archaeal classes and 48 archaeal genera. The most dominant class was Marine_Group_I, one of the major phylogenetic groups of ocean archaea, classified in the third archaeal phylum Thaumarchaeota (Brochier-Armanet et al., 2008), and typically comprises 50% and more of the total microbial community in the dark ocean (Karner et al., 2001; Teira et al., 2004). This phylum comprises the archaea with the ability of ammonia

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Table 3 – Shared OTUs of archaeal 16S rRNA libraries of four seasons' samples from site 4. Shared OTU

S4-Feb

S4-May

S4-Aug

S4-Nov

t3:5 t3:6 t3:7 t3:8 t3:9 t3:10 t3:11 t3:12 t3:13 t3:14 t3:15

AK8 Group C3 Halobacteria Marine_Group_I Marine_Benthic_Group_A Marine_Benthic_Group_B Methanobacteria Methanomicrobia Miscellaneous_Crenarchaeotic_Group Soil_Crenarchaeotic_Group Thermoplasmata

1 5 10 86 1 5 1 19 21 2 12

17 16 89 3834 3 23 1 76 83 16 322

t3:16

Total

163

4480 (51.8%) ⁎

32 137 53 1716 11 133 3 3048 283 300 275 5991 (39.1%)

5 80 416 11,177 140 22 3 1544 332 21 3004 16,744 (59.4%)

9 22 65 2877 1 37 3 260 115 408 106 3903 (69.6%)

t3:17

U

Class

⁎ The numbers in the parentheses are percentages of the shared reads in the total reads of each library.

Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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Acknowledgments

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This study was supported by a General Project granted by the National Natural Science Foundation of China (No. 51178002). The authors thank Zhongyuan Zheng, Jing Zhang, Zhichao Li, and Chengfeng Zhang from Peking University, Cong Liu from Tsinghua University, and Yin Zhang from Shanghai Normal University for helping in the sample collection.

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In this industrial effluent receiving area, the bacterial community showed higher richness and biodiversity than the archaeal community in sediments. The phylum Proteobacteria predominated in the bacterial communities; while classes Marine_ Group_I and Methanomicrobia were the two dominant archaeal groups in this area. Both bacterial and archaeal communities presented strong seasonal but minor spatial changes; and the seasonal change in the archaeal community was more significant than that of the bacterial community. Instead of the appearance and disappearance of major archaeal species, the archaeal community in the sediment shifted seasonally in the abundance fluctuations of a large proportion of longstanding species. Temperature was found to positively correlate with the dominant bacteria, Betaproteobacteria, and negatively correlate with the dominant archaea, Marine_Group_I; and may be the primary driving force for the seasonal variation of microbial communities.

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Bouvy, M., Arfi, R., Bernard, C., Carre, C., Got, P., Pagano, M., et al., 2010. Estuarine microbial community characteristics as indicators of human-induced changes (Senegal River, West Africa). Estuar. Coast. Shelf Sci. 87 (4), 573–582. Brochier-Armanet, C., Boussau, B., Gribaldo, S., Forterre, P., 2008. Mesophilic crenarchaeota: Proposal for a third archaeal phylum, the Thaumarchaeota. Nat. Rev. Microbiol. 6 (3), 245–252. Cao, H., Hong, Y., Li, M., Gu, J., 2012. Community shift of ammonia-oxidizing bacteria along an anthropogenic pollution gradient from the Pearl River Delta to the South China Sea. Appl. Microbiol. Biotechnol. 94 (1), 247–259. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, Elizabeth K., et al., 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7 (5), 335–336. Chen, Z., Gao, X., Song, Z., Mai, B., 2006. Distribution and source identification of polycyclic aromatic hydrocarbons in tidebeach surface sediments of Hangzhou Bay. China Environ. Sci. 26 (2), 233–237. Dorador, C., Vila, I., Remonsellez, F., Imhoff, J.F., Witzel, K., 2010. Unique clusters of Archaea in Salar de Huasco, an athalassohaline evaporitic basin of the Chilean Altiplano. FEMS Microbiol. Ecol. 73 (2), 291–302. Du, H., Jiao, N., Hu, Y., Zeng, Y., 2006. Diversity and distribution of pigmented heterotrophic bacteria in marine environments. FEMS Microbiol. Ecol. 57 (1), 92–105. Fagervold, S., Bourgeois, S., Pruski, A., Charles, F., Kerherve, P., Vetion, G., et al., 2014. River organic matter shapes microbial communities in the sediment of the Rhone prodelta. ISME J. 8 (11), 2327–2338. Fang, J., Wang, K., 2006. Spatial distribution and partitioning of heavy metals in surface sediments from Yangtze Estuary and Hangzhou Bay, People's Republic of China. Bull. Environ. Contam. Toxicol. 76 (5), 831–839. Feng, B., Li, X., Wang, J., Hu, Z., Meng, H., Xiang, L., et al., 2009. Bacterial diversity of water and sediment in the Changjiang estuary and coastal area of the East China Sea. FEMS Microbiol. Ecol. 70 (2), 236–248. Fujitani, H., Aoi, Y., Tsuneda, S., 2013. Selective enrichment of two different types of Nitrospira-like nitrite-oxidizing bacteria from a wastewater treatment plant. Microbes Environ. 28 (2), 236–243. Gao, J., Luo, X., Wu, G., Li, T., Peng, Y., 2013. Quantitative analyses of the composition and abundance of ammonia-oxidizing archaea and ammonia-oxidizing bacteria in eight full-scale biological wastewater treatment plants. Bioresour. Technol. 138C (6), 285–296. Gao, S., Chen, J., Jin, H., Wang, G., Lu, Y., Li, H., et al., 2011a. Characteristics of nutrients and eutrophication in the Hangzhou Bay and its adjacent waters. J. Mar. Sci. 29 (3), 36–46. Gao, X., Zhou, G., Hu, S., Cao, A., 2011b. Distribution of OCPs in surface intertidal sediments of Hangzhou Bay. Acta Sci. Circumst. 31 (2), 322–327. Gaudette, H.E., Flight, W.R., Toner, L., Folger, D.W., 1974. An inexpensive titration method for the determination of organic carbon in recent sediments. J. Sediment. Res. 44, 249–253. General Administration of Quality Supervision, Inspection and Quarantine of China and Standardization Administration of China, 2007a. Specifications for oceanographic survey — Part 4: Survey of chemical parameters in sea water. Standards Press of China, Beijing. Geng, Y., Wang, M., Sarkis, J., Xue, B., Zhang, L., Fujita, T., et al., 2014. Spatial–temporal patterns and driving factors for industrial wastewater emission in China. J. Clean. Prod. 76, 116–124.

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study, temperature was found to be the vital one, positively correlated with the dominant bacteria, Betaproteobacteria, and negatively correlated with the dominant archaea, Marine_Group_I (Fig. 6). Therefore, water temperature may be the primary driver for the variation in the microbial community. Consistently, the differences among the communities from different seasons were significant, compared to those from different sampling sites, based on PCoA (Fig. 4) and hierarchical cluster analysis (Fig. S2 and Fig. S4). In Hangzhou Bay, Macrozoobenthos (Zhou et al., 2009), Zooplankton (Wang et al., 2008), and Phytoplankton (Zhou et al., 2010) have been studied, however, microbial communities had not previously been studied temporally and spatially. Small variations in water properties of the samples were found between different sites (Table S1). Temperature was the environmental factor with the biggest variation and correlated with the change of the abundance of archaeal and bacterial groups, so it may be the reason for seasonal changes in the microbial community. In order to explore the long term influence of industrial effluent discharge on the microbial ecology of the coastal bay area, further research on the comparison of microbial communities between the contaminated area and a background area is anticipated.

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E

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E

D

P

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O

F

hypersaline ponds in Mediterranean salterns. FEMS Microbiol. Ecol. 44 (1), 117–130. Niemi, G., Wardrop, D., Brooks, R., Anderson, S., Brady, V., Paerl, H., et al., 2004. Rationale for a new generation of indicators for coastal waters. Environ. Health Perspect. 112 (9), 979–986. Obiukwu, C.E., Otokunefor, T.V., 2014. Microbial community diversity of a water body in the Niger Delta as affected by refinery effluent. Univ. Microbiol. Res. 2 (1), 1–14. Purkhold, U., Pommerening-Roser, A., Juretschko, S., Schmid, M.C., Koops, H.P., Wagner, M., 2000. Phylogeny of all recognized species of ammonia oxidizers based on comparative 16S rRNA and amoA sequence analysis: implications for molecular diversity surveys. Appl. Environ. Microbiol. 66 (12), 5368–5382. Qin, M., Cai, Y., Wang, X., Wei, Y., Ren, M., Ge, C., 2009. Analysis and assessment on eutrophication in Hangzhou Bay. Mar. Environ. Sci. 28 (S1), 53–55. R Development Core Team, 2008. R: A Language and Environment for Statistical Computing. R foundation for statistical computing, Vienna, Austria (http://www.R-project.org). Schloss, P.D., Westcott, S.L., Ryabin, T., Hall, J.R., Hartmann, M., Hollister, E.B., et al., 2009. Introducing mothur: open-source, platform-independent, community-supported software for sescribing and comparing microbial communities. Appl. Environ. Microbiol. 75 (23), 7537–7541. Schmidt, V.J., Wenning, M., Scherer, S., 2012. Sphingobacterium lactis sp nov and Sphingobacterium alimentarium sp nov., isolated from raw milk and a dairy environment. Int. J. Syst. Evol. Microbiol. 62, 1506–1511. Schneider, D., Arp, G., Reimer, A., Reitner, J., Daniel, R., 2013. Phylogenetic analysis of a microbialite-forming microbial mat from a hypersaline Lake of the Kiritimati Atoll, Central Pacific. PLoS ONE 8 (6), e66662. Shangguan, H., Liu, J., Zhu, Y., Tong, Z., Wu, Y., 2015. Start-up of a spiral periphyton bioreactor (SPR) for removal of COD and the characteristics of the associated microbial community. Bioresour. Technol. 193, 456–462. Shao, K., Gao, G., Qin, B., Tang, X., Wang, Y., Chi, K., et al., 2011. Comparing sediment bacterial communities in the macrophyte-dominated and algae-dominated areas of eutrophic Lake Taihu, China. Can. J. Microbiol. 57 (4), 263–272. State Environmental Protection Administration of China, 2002. Monitoring and Analysis Method of Water and Wastewater. fourth ed. China Environmental Science Press, Beijing. Sun, Z., Li, G., Wang, C., Jing, Y., Zhu, Y., Zhang, S., et al., 2014. Community dynamics of prokaryotic and eukaryotic microbes in an estuary reservoir. Sci. Rep. 4, 6966. Teira, E., Reinthaler, T., Pernthaler, A., Pernthaler, J., Herndl, G.J., 2004. Combining catalyzed reporter deposition-fluorescence in situ hybridization and microautoradiography to detect substrate utilization by bacteria and archaea in the deep ocean. Appl. Environ. Microbiol. 70 (7), 4411–4414. Tourna, M., Stieglmeier, M., Spang, A., Koenneke, M., Schintlmeister, A., Urich, T., et al., 2011. Nitrososphaera viennensis, an ammonia oxidizing archaeon from soil. Proc. Natl. Acad. Sci. U. S. A. 108 (20), 8420–8425. Wang, S., Dong, H., Hou, W., Jiang, H., Huang, Q., Briggs, B.R., et al., 2014. Greater temporal changes of sediment microbial community than its waterborne counterpart in Tengchong hot springs, Yunnan Province, China. Sci. Rep. 4, 7479. Wang, X., Wei, Y., Qin, M., Yang, Y., Ren, M., 2008. Studies on variation trend of diversity of zooplankton in ecology monitoring area of Hangzhou Bay. Mar. Environ. Sci. 27, 67–71. Yan, B., Zhang, X., Liang, D., Xu, D., Liu, Y., Xhang, W., 1998. Mechanism and modes of microbial minerogenesis of polymetallic nodules on the ocean floor. Acta Geol. Sin.-Engl. Ed. 72 (3), 282–290. Yu, F., Han, F., Cui, Z., 2015. Evolution of industrial symbiosis in an eco-industrial park in China. J. Clean. Prod. 87, 339–347.

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C

T

Hamdan, L., Coffin, R., Sikaroodi, M., Greinert, J., Treude, T., Gillevet, P., 2013. Ocean currents shape the microbiome of Arctic marine sediments. ISME J. 7 (4), 685–696. Hollister, E.B., Engledow, A.S., Hammett, A.M., Provin, T.L., Wilkinson, H.H., Gentry, T.J., 2010. Shifts in microbial community structure along an ecological gradient of hypersaline soils and sediments. ISME J. 4 (6), 829–838. Huang, Z., Xie, B., Yuan, Q., Xu, W., Lu, J., 2014. Microbial community study in newly established Qingcaosha Reservoir of Shanghai, China. Appl. Microbiol. Biotechnol. 98 (23), 9849–9858. Iino, T., Tamaki, H., Tamazawa, S., Ueno, Y., Ohkuma, M., Suzuki, K., et al., 2013. Candidatus Methanogranum caenicola: a novel methanogen from the anaerobic digested sludge, and proposal of Methanomassiliicoccaceae fam. nov and Methanomassiliicoccales ord. nov., for a methanogenic lineage of the class Thermoplasmata. Microbes Environ. 28 (2), 244–250. Jiang, L., Zheng, Y., Chen, J., Xiao, X., Wang, F., 2011. Stratification of archaeal communities in shallow sediments of the Pearl River Estuary, Southern China. Anton Leeuw. Int. J. G. 99, 739–751. Karner, M.B., DeLong, E.F., Karl, D.M., 2001. Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature 409 (6819), 507–510. Kowalchuk, G.A., Stephen, J.R., 2001. Ammonia-oxidizing bacteria: A model for molecular microbial ecology. Annu. Rev. Microbiol. 55, 485–529. Kubo, K., Lloyd, K.G., Biddle, J.F., Amann, R., Teske, A., Knittel, K., 2012. Archaea of the Miscellaneous Crenarchaeotal Group are abundant, diverse and widespread in marine sediments. ISME J. 6 (10), 1949–1965. Lee, J.W., Kwon, K.K., Azizi, A., Oh, H.M., Kim, W., Bahk, J.J., et al., 2013. Microbial community structures of methane hydrate-bearing sediments in the Ulleung Basin, East Sea of Korea. Mar. Pet. Geol. 47, 136–146. Li, Q., Wang, F., Chen, Z., Yin, X., Xiao, X., 2012. Stratified active archaeal communities in the sediments of Jiulong River estuary, China. Front. Microbiol. 3, 311. Limpiyakorn, T., Sonthiphand, P., Rongsayamanont, C., Polprasert, C., 2011. Abundance of amoA genes of ammonia-oxidizing archaea and bacteria in activated sludge of full-scale wastewater treatment plants. Bioresour. Technol. 102 (4), 3694–3701. Lin, L., Wu, L., Cheng, T., Tu, W., Lin, J., Yang, T., et al., 2014. Distributions and assemblages of microbial communities along a sediment core retrieved from a potential hydrate-bearing region offshore southwestern Taiwan. J. Asian Earth Sci. 92 (SI), 276–292. Liu, J., Yang, H., Zhao, M., Zhang, X., 2014. Spatial distribution patterns of benthic microbial communities along the Pearl Estuary, China. Syst. Appl. Microbiol. 37 (8), 578–589. Liu, L., Huang, X., Cao, W., Yang, Y., 2012a. Pollution load characteristics of the Hangzhou Bay and its surrounding areas. Ocean Dev. Manag. 5, 108–112. Liu, S., Liu, Y., Yang, G., Qiao, S., Li, C., Zhu, Z., et al., 2012b. Distribution of major and trace elements in surface sediments of Hangzhou Bay in China. Acta Oceanol. Sin. 31 (4), 89–100. Mahmoudi, N., Robeson II, M.S., Castro, H.F., Fortney, J.L., Techtmann, S.M., Joyner, D.C., et al., 2015. Microbial community composition and diversity in Caspian Sea sediments. FEMS Microbiol. Ecol. 91 (1), 1–11. Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre, J.R., Stahl, D.A., 2009. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 461 (7266), 976–981. Moune, S., Caumette, P., Matheron, R., Willison, J.C., 2003. Molecular sequence analysis of prokaryotic diversity in the anoxic sediments underlying cyanobacterial mats of two

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Please cite this article as: Zhang, Y., et al., Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay, J. Environ. Sci. (2016), http://dx.doi.org/10.1016/j.jes.2015.11.023

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bacteria and archaea in Chongming eastern intertidal sediments. Appl. Microbiol. Biotechnol. 97 (18), 8351–8363. Zhou, Y., Long, H., Yu, J., 2009. Assessment on environmental quality in tideland of Hangzhou Bay by macrozoobenthos pollution index. Mar. Environ. Sci. 28, 473–481. Zhou, Y., Zhao, C., Gao, Y., Long, H., Yu, J., 2010. Variation and distribution characteristics of phytoplankton in ecology-monitoring area of Hangzhouwan Bay from 2005 to 2008. Mar. Environ. Sci. 28, 28–35. Zhu, X., Tian, J., Liu, C., Chen, L., 2013. Composition and dynamics of microbial community in a zeolite biofilter-membrane bioreactor treating coking wastewater. Appl. Microbiol. Biotechnol. 97 (19), 8767–8775.

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Zhang, C., Gao, X., Song, Z., Cheng, X., 2008. Heavy metal distribution and pollution assessment in the surface sediments of Hangzhou Bay. J. Fudan Univ. (Nat. Sci.) 47 (4), 535–540. Zhang, J., Sun, Q., Zeng, Z., Chen, S., Sun, L., 2015. Microbial diversity in the deep-sea sediments of Iheya North and Iheya Ridge, Okinawa Trough. Microbiol. Res. 177, 43–52. Zhang, T., Ye, L., Tong, A., Shao, M., Lok, S., 2011. Ammonia-oxidizing archaea and ammonia-oxidizing bacteria in six full-scale wastewater treatment bioreactors. Appl. Microbiol. Biotechnol. 91 (4), 1215–1225. Zhang, Y., Chen, L., Sun, R., Dai, T., Tian, J., Liu, R., et al., 2014. Effect of wastewater disposal on the bacterial and archaeal community of sea sediment in an industrial area in China. FEMS Microbiol. Ecol. 88 (2), 320–332. Zheng, Y., Hou, L., Liu, M., Lu, M., Zhao, H., Yin, G., et al., 2013. Diversity, abundance, and activity of ammonia-oxidizing

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