Ecotoxicology DOI 10.1007/s10646-015-1471-3

Spatial variation of phytoplankton community structure in Daya Bay, China Zhao-Yu Jiang1,2 • You-Shao Wang1,2 • Hao Cheng1 Jian-Dong Zhang1 • Jiao Fei1

•

Accepted: 28 April 2015 Ó Springer Science+Business Media New York 2015

Abstract Daya Bay is one of the largest and most important gulfs in the southern coast of China, in the northern part of the South China Sea. The phylogenetic diversity and spatial distribution of phytoplankton from the Daya Bay surface water and the relationship with the in situ water environment were investigated by the clone library of the large subunit of ribulose-1, 5-bisphosphate carboxylase (rbcL) gene. The dominant species of phytoplankton were diatoms and eustigmatophytes, which accounted for 81.9 % of all the clones of the rbcL genes. Prymnesiophytes were widely spread and wide varieties lived in Daya Bay, whereas the quantity was limited. The community structure of phytoplankton was shaped by pH and salinity and the concentration of silicate, phosphorus and nitrite. The phytoplankton biomass was significantly positively affected by phosphorus and nitrite but negatively by salinity and pH. Therefore, the phytoplankton distribution and biomass from Daya Bay were doubly affected by anthropic activities and natural factors. Keywords Phytoplankton community  Daya Bay  Large subunit of ribulose-1, 5-bisphosphate carboxylase (rbcL) gene  Redundancy analysis (RDA)  Environmental variable

& You-Shao Wang [email protected] 1

State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China

2

Daya Bay Marine Biology Research Station, Chinese Academy of Sciences, Shenzhen 518121, China

Introduction Phytoplankton, as the primary producer in the food web, plays an important role in the marine ecosystem. Phytoplankton populations and community structure directly or indirectly regulate the primary productivity in the ocean, which generate roughly half of the primary production worldwide (Field et al. 1998). Phytoplankton contributes massively to climate processes (Murtugudde et al. 2002) and biogeochemical cycles (Sabine et al. 2004; Roemmich and McGowan 1995), particularly the carbon cycle. More than 36.5 Gt of CO2 is captured each year by phytoplankton through photosynthesis in the oceans (Andersen et al. 1996; Antia et al. 2001; Bowler et al. 2009; Riebesell 2004). Because of its indisputable importance in marine ecosystem, the phytoplankton distribution has been widely studied in different aquatic ecosystems (Wang et al. 2006; Bernardi Aubry et al. 2013; Gasiu¯nait_e et al. 2005; Lo´pezFlores et al. 2009; Suikkanen et al. 2007; Suzuki et al. 1997). Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBis CO) is the key enzyme for fixing CO2 into organic cellular components during the Calvin–Benson cycle. The previous studies have been showed that several forms of the enzyme are existed in nature (Ashida et al. 2003; Watson et al. 1999). The form I of RuBisCO is found in all eukaryotic algae (with the exception of Dinoflagellates), cyanobacteria and chemoautotrophic proteobacteria (Tabita 1999). Certain photosynthetic bacteria and dinoflagellates have a form II type enzyme (Watson and Tabita 1997), while Forms III and IV are expressed by Archaea and certain bacteria (Eisen et al. 2002; Hanson and Tabita 2001; Kunst et al. 1997; Tabita 1999). Phylogenetic analyses of the large subunit of RuBisCO (rbcL) form I gene indicate four distinct lineages, named IA, IB, IC and ID, respectively. Form IA and IB rbcL are carried by some

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chemoautotrophic bacteria, most cyanobacteria and all green algae (Corredor et al. 2004; Wawrik et al. 2003). While, form IC has been found in some photosynthetic bacteria as well as hydrogen oxidizers, and many non-green algae carry form ID rbcL (Corredor et al. 2004; Wawrik et al. 2003). Daya Bay (22.310 1200 –22.500 0000 N, 114.290 4200 –114.490 4200 E) is one of the largest and most important gulfs in the southern coast of China, in the northern part of the South China Sea. Daya Bay is a semi-enclosed bay with an area of 600 km2 and an average depth of about 11 m (Sun et al. 2011). As a subtropical bay, the annual mean air temperature in Daya Bay is 22 °C. No major rivers discharge into Daya Bay, and most of its water originates from the South China Sea. In recent years, the surrounding area is a rapid economic development district in Guangdong Province, China. There are present of petrochemical, plastic, printing and other industries as well as harbors (Song et al. 2004). The marine aquaculture has been one of the important industries in this area. In addition, Daya Bay Nuclear Power Plant (DNPP) and Lingao Nuclear Power Plant (LNPP) have been operated since 1993 and 2003, respectively (Wang et al. 2008). Based on the above-mentioned environmental importance and complexity, Daya Bay has been arousing the interest of researchers. Several studies on phytoplankton in Daya Bay have been carried out since the 1980s (Mo¨llmann et al. 2009; Lapointe and Clark 1992; Howarth et al. 2002), but the previous studies obtained the phytoplankton sample by the shallow water net-III, some small phytoplankton, such as ultraphytoplankton, can escape through the net. Additionally, it is difficult to identify small phytoplankton by microscopy directly. In the present study, the rbcL gene and DNA library technique were introduced to analyze the phytoplankton community in Daya Bay. Two objectives were pursued in parallel to (1) characterize the diversity and spatial distribution of phytoplankton, and (2) identify the potential environmental factors influencing the phytoplankton distribution in Daya Bay.

Fig. 1 Sampling stations in Daya Bay

0.45 lm pore-size polycarbonate filters. The filters were used for Chl a analysis and the filtrates for dissolved nutrient analysis. Samples were frozen in -20 °C in the dark until analysis. Physical and chemical analysis

Materials and methods

Temperature, salinity and pH of the stations were measured with a Quantar Water Quality Monitoring System (The Hach Company, Loveland, CO, USA). The characteristics, including concentrations of nitrate (NO3-N), nitrite (NO2N), ammonium (NH4-N), silicate (SiO3-Si) and phosphorus (PO4-P) were carried out using an Auto Analyzer 3 (AA3; Seal Analytical Inc., Mequon, Wisconsin, USA) and Chl a were analyzed according to Wang et al. (2008, 2006, 2011). All sample analyses were carried out within 2 weeks of the end of this cruise in our laboratory.

Sample collection

DNA extraction and PCR amplification

Samples were collected at 6 stations during the Cruise on 23 July 2013 (Fig. 1). Seawater samples were taken using 5 L GO FLO bottles at surface layer according to the protocols of ‘‘The specialties for oceanography survey’’ (GB12763-91, China). After each sample collection, the surface water for phylogenetic analysis (1 L) was filtered onto 0.2 lm pore-size polycarbonate filter (Millipore Isopore, Bedford, USA). The filters were snap frozen in liquid nitrogen and then stored at -20 °C in the Lab until DNA extraction. The subsamples for chlorophyll a (Chl a) and nutrient variables measurement was filtered through

The 0.2 lm pore-size polycarbonate filters were cut into small pieces and transferred into the extraction tubes. DNA was extracted from the filters using the EZNA Water DNA Kit (Omega Bio-Tek, Inc., Doraville, GA, USA) according to the manufacturer’s instructions. The precipitated DNA was resuspended in 20 lL of TE buffer (10 mM Tris–HCl, 1 mM Na2EDTA, pH 8.0). A 554 bp fragment of rbcL form ID gene was amplified by the primer set of 50 GATGATGARAAYATTAACTC-30 and 50 -ATTTGDCCACAGTGDATACCA-30 (Wawrik and Paul 2004). Primer sequences are stated using the International Union of

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Spatial variation of phytoplankton community structure in Daya Bay, China

Pure and Applied Chemistry (IUPAC) degeneracies. The PCR mixtures (50 lL) contained 1 lM of each primer, 0.2 mM dNTP, 2.5 U Taq DNA polymerase (Takara Shuzo Co. Ltd., Otsu, Japan), 19 PCR buffer and 100 ng DNA template. The PCR program included an initial denaturation at 95 °C for 3 min, followed by 35 cycles of denaturation at 95 °C for 60 s, annealing at 52 °C for 60 s, and extension at 72 °C for 90 s, and a final elongation at 72 °C for 15 min. PCR amplification was performed on PTC-200 cyclers (Bio-Rad, Hercules, CA, USA). Cloning and screening of clone libraries

Statistical analysis Sequences were analyzed by the Distance-Based OTU and Richness (DOTUR) Program (Schloss and Handelsman 2006). The program has been widely used to reveal the biological diversity (Cao et al. 2012; Li et al. 2013). In the present study, the distance cut-off was set at 5 % in amino acid sequences for operational taxonomic units (OTUs). The richness estimators (Chao1 and Shannon) and diversity index (Simpson) were firstly introduced to evaluate the RBCL amino acids sequences from this study and were simultaneously generated by DOTUR. The geographic distribution of phylogenetic structure phytoplankton in Daya Bay was investigated by the online software UniFrac (http://bmf.colorado.edu/unifrac/ index.psp) using the principal coordinates analysis (PCoA) and Jack-knife environmental clusters (Lozupone et al. 2006), which employs the genetic distances to evaluate the community similarity based on the amino acid sequences data. To determine the relationship between environmental factors and phytoplankton community structures, we conducted the multivariate statistical analysis by redundancy analysis (RDA), which is a linear method of direct ordination (ter Braak and Smilauer 2002). RDA was performed using CANOCO 4.5 for Windows (Biometris, Wageningen, Netherlands). Pearson correlation coefficient analysis was also evaluated with SPSS 13.0 for Windows (SPSS Inc., Chicago, IL, USA) to relate the physiochemical variables and the diversity and richness indices of the amino acid sequences and the concentration of Chl a.

The PCR products were loaded in agarose gel to check the size and purified using Bioteke multifunctional DNA purification kits (Bioteke corp., Beijing, China). The purified DNA was ligated into pMD 18-T Vector (Takara Shuzo Co. Ltd., Otsu, Japan) and subsequently transformed into E. coli DH5a cell (Tiangen Biotech Co. Ltd., Beijing, China) according to the manufacturer’s instructions. White colonies were then picked and streaked onto individual plates containing 100 lg mL-1 ampicillin. To screen for the presence of clones with the correct insert size, clones were determined by PCR amplified with the primer set M13-47 (50 CGCCAGGGTTTTCCCAGTCACGAC-30 ) and RV-M (50 AGCGGATAACAATTTCACACAGG-30 ). The PCR mixtures (25 lL) contained 0.4 lM of each primer, 0.1 mM dNTP, 1.25 U Taq DNA polymerase (Takara Shuzo Co. Ltd., Otsu, Japan), 19 PCR buffer. The PCR program included an initial denaturation at 95 °C for 5 min, followed by 31 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 60 s, and a final elongation at 72 °C for 10 min. The number of clones selected for each sample was 96 for the form ID primer set. The number of clones with correct insert size of station S1, S4, S7, S8, S10 and S14 were 85, 87, 90, 84, 88 and 92, respectively. The patterns with correct insert size were selected for sequencing with M13 primer using an ABI 3730 DNA Sequencer at the Beijing Genomics Institute (Shenzhen, China).

Results

Phylogenetic analysis

Environmental parameters of study area

The partial rbcL gene sequences were translated into amino acids and translations were aligned with a representative sample of rbcL sequences available in GenBank. The amino acid sequences were introduced to generate phylogenetic tree, which was constructed using MEGA 5.0 software (Oxford Molecular group, Oxford, UK) with neighbor-joining method. The evolutionary distances were computed using the neighbor-joining method with bootstrap analyses for 1000 replicates (Saitou and Nei 1987; Tamura et al. 2011).

The basic environmental variables at each sampling site were summarized in Table 1. The temperature was higher at site S4 than the other sites, while the lowest was observed at site S1. Compared to other sampling sites, higher salinity and concentrations of SiO3-Si and NO3-N were detected at site S1, but that of NO2-N was lowest at this site. The concentrations of PO4-P and NO2-N at site S8 was the highest, while that of PO4-P lowest at site S7. The concentration of NH4-N was the highest at site S4, whereas lowest at site S10. The pH value was highest at site S10,

Nucleotide sequence accession numbers The form ID rbcL gene sequences retrieved in this study were deposited in the GenBank with the accession numbers KJ471582-KJ472107.

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Z.-Y. Jiang et al. Table 1 Environmental characteristics of the samples from Daya Bay Site

Temperature (°C)

Salinity (%)

PO4-P (lM)

SiO3-Si (lM)

NO3-N (lM)

NO2-N (lM)

NH4-N (lM)

Chl a (lg/L)

pH

S1

28.44

33.47

0.59

3.61

2.93

0.06

1.23

9.47

8.33

S4

30.10

32.78

0.91

3.60

1.64

0.14

1.31

15.47

8.35

S7

28.69

33.21

0.41

2.77

0.57

0.14

1.13

6.14

8.45

S8

29.65

29.39

1.53

1.25

0.64

0.50

1.06

57.31

7.97

S10

28.92

32.22

0.38

2.26

0.64

0.14

0.99

5.70

8.48

S14

29.67

31.66

1.00

2.94

1.50

0.21

1.03

7.53

8.32

but lowest at site S8. The highest Chl a concentrations were recorded at site S8. Molecular diversity of form ID RBCL amino acid sequences A total of 526 sequences from Daya Bay were combined for analysis, and 116 OTUs were identified based on 5 % cutoff for the amino acid sequences (Table 2). Within each clone library, the number of OTU recovered was variable from 8 to 27. OTU numbers and Shannon index of sites S7 and S8 were relatively higher, while lowest one at site S14. The Chao 1 demonstrated that the richness at site S1 was greater than the other sites. However, the low Simpson index was observed in this study. Community analyses of the phytoplankton assemblages A few groups were identified in the UniFrac PCoA analysis based on phylogenetic diversity of form ID RBCL amino acid sequences (Fig. 2a). The first principal coordinate (P1) explained 73.60 % of the total community variability. The weighted UniFrac PCoA analysis indicated that sites S8 and S14 separated from other sites with larger discrepancy, while the other four sites grouped together with higher support values, while site S7 was separated from the other

Table 2 Clone libraries for the form ID RBCL amino acid sequences from Daya Bay and diversity index analyses

Sample location

Selected clone

OTUs (95 %)

Shannon

Chao 1

Simpson

S1

85

21

1.81

56

0.33

S4 S7

87 90

19 25

1.69 2.21

38.5 45

0.35 0.2

S8

84

27

2.85

45.2

0.71

S10

88

16

1.49

29.75

0.37

S14

92

8

0.53

23

0.79

Total

526

116

10.58

237.45

2.75

1.76

39.58

0.46

Average

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Fig. 2 Weighted UniFrac PCA and Jackknife Environment Clusters analyses based on form ID RBCL amino acid sequences from Daya Bay

87.67

19.33

Spatial variation of phytoplankton community structure in Daya Bay, China Fig. 3 Unrooted phylogenetic tree based on an analysis of the c derived amino acid sequences from rbcL gene sequences in this study and representative database sequences. Bootstrap analysis was based on 1000 replicates. Bootstrap values less than 50 % were not shown. Scale bar indicates 2 % sequence divergence. Numbers in parentheses refer to the number of clones assigned to an OTU

three sites. Similar classification was revealed in the UniFrac environment clustering analysis (Fig. 2b). Samples from sites S14 and the other sites were distinguished separately. Phylogenetic diversity of form ID RBCL amino acid sequences In general, all amino acid sequences in this study could be divided into seven phylogenetic groups except three sequences (S1-43, S1-67 and S4-22) (Fig. 3; Table 3). Of these, the bulk of the phytoplankton in our sampling region was composed of Diatom (33 OTUs; 231 clones), Eustigmatophyte (21 OTUs; 200 clones) and Prymnesiophyte (43 OTUs; 70 clones), which accounted for 82.8 and 97.1 % of the OTUs and clones observed in total, respectively. Raphidophyte, Dictyochophyte, Cryptophyte, Chrysophyte and unidentified sequences were also obtained. Diatoms and Prymnesiophytes were observed throughout the sampling sites. No Eustigmatophytes were observed at site S14, and Raphidophytes were only obtained at site S8. Relationships between environmental factors and phytoplankton distribution RDA was performed to discern possible linkages between environmental factors and phytoplankton distribution (Fig. 4). Each environmental variable in the RDA biplot was represented by an arrow and the length of the individual arrow indicated how much variance was explained by that variable. Eigenvalues (indicating strength of the model) for the first two multivariate axes were 0.752 and 0.164, respectively. The sum of all canonical eigenvalues was 1.000. The first canonical axis was positively correlated with temperature, PO4-P and NO2-N and the second canonical axis was positively correlated with PO4-P and NO2-N but negatively correlated with the other environmental variables. The result of RDA was in accord with the UniFrac community classification. Sites S8 and S14 separated from other sites, while sites S1, S4 and S10 were closed to each other and positively correlated with pH, salinity, NH4-N and SiO3-Si. The concentration of NO2-N positively influenced on the phytoplankton composition at site S8. The phytoplankton distribution at sites S7 and S14 were

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Z.-Y. Jiang et al. Table 3 Distribution of RBCL phylotypes detected at individual station in the DNA library Site

Diatoms

Raphidophytes

Dictyochophytes

Eustigmatophytes

Prymnesiophytes

Cryptophytes

Chrysophytes

OTUs

Clones

OTUs

Clones

OTUs

Clones

OTUs

Clones

OTUs

Clones

OTUs

Clones

OTUs

Clones

S1

3

13

0

0

0

0

6

57

8

10

0

0

2

3

S4

2

14

0

0

1

1

5

58

6

8

0

0

4

5

S7

8

41

0

0

1

1

4

29

9

12

2

3

2

4

S8

11

48

1

1

0

0

2

3

12

31

1

1

0

0

S10

2

24

0

0

0

0

4

53

7

8

0

0

3

3

S14

7

91

0

0

0

0

0

0

1

1

0

0

0

0

Total

33

231

1

1

2

2

21

200

43

70

3

4

11

15

Fig. 4 Redundancy analysis (RDA) ordination plots for environmental parameters and phytoplankton communities

influenced by the eight environmental characteristics slightly. Pearson correlation coefficient analysis between environmental variables and Chl a concentration and phytoplankton diversity were also analyzed (Table 4). It was interesting to find that the Chl a concentration strongly positively correlated with PO4-P and NO2-N, while significantly negatively correlated with salinity and pH value (Table 4).

Discussion In this study, seven phylogenetic groups of phytoplankton were detected (Fig. 3; Table 3). The dominant species of phytoplankton were diatoms and eustigmatophytes, which accounted for 81.9 % of all the clones of rbcL genes. Diatoms are single-celled photoautotrophic eukaryotes which

123

are responsible for at least 25 % of the global carbon dioxide fixation (Smetacek 1999; Zimmermann et al. 2011). Diatoms, enclosed within frustules, are the dominant group of phytoplankton in the modern ocean (Rabosky and Sorhannus 2009). They live in limnic, marine, and terrestrial ecosystems as well as in aerosols (Zimmermann et al. 2011). They contribute to around 40 % of oceanic primary productivity and over 50 % of organic carbon burial in marine sediments (Falkowski et al. 2004; Rabosky and Sorhannus 2009). The Eustigmatophyta is a very small division with only 6 genera and 12 described species (Volkman et al. 1999). They occur nearly ubiquitously in all types of waters from fresh to marine habitats. It has been shown that diatoms and dinoflagellates exhibited predominance in Daya Bay (Sun et al. 2006). While no dinoflagellates were obtained in this study, since that dinoflagellates, unlike other photosynthetic eukaryotes, express only a Form II RubisCO (La Du et al. 2002). In the present study, we employed the form ID rbcL gene to research on the phytoplankton in Daya Bay. Prymnesiophytes were estimated to contribute at 37.1 % of total OTUs from the form ID RBCL amino acid, but 13.3 % of the total clones (Table 3). This indicated that Prymnesiophytes were widely spread and wide varieties lived in Daya Bay, whereas the quantity was limited. The previous studies has demonstrated that prymnesiophytes are one of the most abundant nanoplanktonic groups (in the 2–20 lm size range) in marine environment, from tropical to polar waters (Andersen et al. 1996; Suzuki et al. 1997). There were only two phytoplankton groups (Diatoms and Prymnesiophytes) and 8 OTUs from 92 selected clones found at site S14, which located in the Fanhe Harbor. The Fanhe Harbor is a long and narrow waterway. A great deal of pollutant from terrigenous source material and boats and ships, in addition, the waters exchange rate is slow. The reasons mentioned above lead to the degradation of phytoplankton community at site S14. In case of Daya Bay, the highest Chl a concentration and phytoplankton diversity was recorded at site S8. The site is located in the fish and shellfish cage culture areas and had the lowest pH value

Spatial variation of phytoplankton community structure in Daya Bay, China Table 4 Correction statistical analyses of environmental variables with the Chl a concentration and phytoplankton diversity

Environmental variables Temperature Salinity PO4-P

Chl a

OTUs

Shannon

Chao 1

0.41

-0.24

-0.14

-0.48

0.55

-0.87*

-0.15

-0.33

0.21

-0.79

0.14

0.28

-0.03

0.78

SiO3-Si

-0.72

-0.35

-0.51

0.12

-0.46

NO3-N

-0.28

-0.21

-0.30

0.40

-0.09

NO2-N

0.86*

Simpson

0.93**

NH4-N

-0.13

pH

-0.95**

0.35

0.50

-0.05

0.07

0.27

0.15

0.56

-0.49

-0.40

-0.52

-0.25

-0.66

* Correlations are significant at P \ 0.05 ** Correlations are significant at P \ 0.01

(Table 1). The concentrations of PO4-P and NO2-N were higher at this site than the other sites (Table 1), indicating that the release of amount of nutrients from fish farming stimulated the growth of phytoplankton. The relationship between the phytoplankton community and environmental parameters has been shown various in different waters (Bernardi Aubry et al. 2013; Gasiu¯nait_e et al. 2005; Gregorio et al. 2012; Jiang et al. 2014; Lin et al. 2012; Lo´pez-Flores et al. 2009; McCarthy et al. 1977; Pomati and Nizzetto 2013; Suikkanen et al. 2007; Sun et al. 2006; Suzuki et al. 1997; Vera et al. 2012). Suikkanen et al. (2007) interpreted that dissolved inorganic nitrogen (DIN) concentration was the most important factors to shape the phytoplankton community structure in the open northern Baltic Sea. Yang and Jiao (2004) have found that a strong influence of currents and water column structure on picoplankton distribution in the Nansha Islands area of the South China Sea. Sun et al. (2006) mentioned that the temperature and hydrodynamics in conjunction with the pattern of nutrients (DIN, DIP and the N/P) availability and depletion affected the phytoplankton composition. Based on the RDA analysis, the pH and salinity and concentration of SiO3-Si, PO4-P and NO2-N could shape the community structure of phytoplankton (Fig. 4). Site S4 was adjacent to the nuclear power plants, and the temperature of this site was higher than the other sites for an effect of thermal water discharge from the nuclear power plants, but the phytoplankton community of site S4 was not obviously influenced by the temperature (Fig. 4). This was consistent with the report by Sun et al. (2011). Based on the Pearson correlation coefficient analysis, PO4-P and NO2-N had a significant positive impact on the phytoplankton biomass, but significant negative impact influence by salinity and pH value (Table 4). With the progressive increase in anthropic activities, including more domestic sewage and industrial waste water discharge as well as nutrient enrichment and toxins derived from the cage culture of fish and seashells, the nutrients concentration is increasing. The nutrients have impacts on the

growth and distribution of phytoplankton. In a word, nutrients from frequent anthropic activity and land-based pollution have a positive influence on the phytoplankton diversity and biomass, while the salinity and pH of the natural seawater has a negative influence. The ecosystem of Daya Bay was a compound ecosystem, doubly driven by anthropic activities and natural factors. In this study, rbcL gene was firstly used as a biomarker to investigate the community structure and distribution of phytoplankton in Daya Bay. The phytoplankton richness was higher in aquaculture district, while the northeastern part appeared the ecosystem degradation in Daya Bay. Diatoms and eustigmatophytes were the dominant phytoplankton species in the study area. The community structure of phytoplankton was shaped by pH and salinity and the concentration of SiO3-Si, PO4-P and NO2-N. The phytoplankton biomass was significantly positively affected by PO4-P and NO2-N but negatively by salinity and pH. Therefore, the phytoplankton distribution and biomass were affected by anthropic activities and natural factors in Daya Bay. Acknowledgments This research was supported by, the National Natural Science Foundation of China (Nos. 41430966 and 41176101), the key projects in the National Science & Technology Pillar Program in the Eleventh Five-year Plan Period (No. 2012BAC07B0402), the Knowledge Innovation Programs of the Chinese Academy of Sciences (No. KSCX2-SW-132) and State Key Laboratory of Tropical Oceanography (No. LTOZZ1402). Conflict of interest of interest.

The authors declare that they have no conflict

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