Oceanological and Hydrobiological Studies International Journal of Oceanography and Hydrobiology

Volume 40, Issue 1

ISSN 1730-413X (82–95) eISSN 1897-3191 2011

DOI: 10.2478/s13545-011-0009-4 Received: March 23, 2010 Original research paper Accepted: July 19, 2010

/PO4-P and chlorophyll a and was negatively correlated with salinity, Secchi, COD, NO3-N, NO2-N, TIN, PO4-P, TIN/PO4- Identification of water quality and P and BOD5. Factor analysis showed that there were high benthos characteristics in Daya Bay, positive loading salinity, Secchi and NH4-N of three clusters. Results revealed that temperature, DO, pH, SiO3-Si and SiO3- China, from 2001 to 2004 Si/PO4-P and chlorophyll a could also play an important role in determining the biomass of benthos in Daya Bay, especially near the Nuclear Power Plants, in the southern part and in the cage culture areas. You-Shao Wang1, 2, 3,∗, Cui-Ci Sun1, 2, Zhi-Ping 1 3 3 Lou , Haili Wang , B. Greg Mitchell , Mei-Lin INTRODUCTION Wu1, 2, Zong-Xun Sun1 China is a large coastal nation located along the western Pacific Ocean with 18,000 km of the 1Key Laboratory of Tropical Marine Environmental, mainland coastline, along which there are many large Dynamics, South China Sea Institute of Oceanology, Chinese and important bays. Daya Bay is one of the series of Academy of Sciences, Guangzhou 510301, China large and important gulfs along the southern coast of 2Marine Biology Research Station at Daya Bay, Chinese China. Academy of Sciences, Shenzhen 518121, China Daya Bay Nuclear Power Plant (DNPP) was the 3Scripps Institution of Oceanography, University of California, first commercial nuclear power plant and the largest San Diego, CA 92093-0218, USA foreign investment joint project in China since 1982, and marked the first step taken by China in the

development of large-capacity commercial nuclear power units (Zang 1993). The sea water from the Key words: water quality, benthos, anthropogenic Daya Bay Nuclear Power Plant has been discharged activity, multivariate statistical analysis, Daya Bay at a rate of about 95 m3 s-1 and a temperature of 65 (DYB), South China Sea since 1993, and the warm water is deposited into the southern area of Daya Bay. Another Nuclear Power℃ Abstract Plant – the Lingao Nuclear Power Plant (LNPP), located near the Daya Bay Nuclear Power Plant, has Physicochemical and benthos data were collected from 12 also been in operation since 2002. These changes marine monitoring stations in Daya Bay, during 2001-2004. 12 have also given some impacts on the ecological stations in Daya Bay could be grouped into three clusters: cluster environment of the southwestern part of Daya Bay I consisted of stations in the southern part of Daya Bay (stations S1, S2 and S6); cluster II consisted of stations in the cage culture (Bodergat et al. 2003; Wang et al. 2006, 2008; Zheng areas (stations S3, S4, S5 and S8); cluster III consisted of stations et al. 2001). Studies have also been conducted on the in the southwest, the middle and the northeast of the Bay bay’s overall ecology since 1982 (Han 1991; Pan & (stations S7, S9, S10, S11 and S12). Calculation with bivariate Cai 1996; Pan & Wang 1998; Wang et al. 2006, 2008). correlations between benthos and major physicochemical factors showed that the density of benthos in all stations correlated Tang et al. (2003) also applied “Advances Very High positively with temperature, DO, pH, NH4-N, SiO3-Si, SiO3-Si Resolution Radiometer” (AVHRR) data for studying the thermal plume from the power plant on Daya

∗ Bay. Wang et al. (2006) also used multivariate Corresponding author: [email protected]

Identification of water quality and benthos characteristics in Daya Bay, China, from 2001 to 2004| 83 statistical analysis for studying the water quality and Wang et al. 2006, 2008). Dapeng Cove, lying between phytoplankton characteristics in Daya Bay during the Lingao Nuclear Power Plant (LNPP) and the 1999-2002. Wu & Wang (2007) used chemometrics Marine Biological Research Station (MBRS) in the to evaluate anthropogenic effects in Daya Bay and southwestern part of Daya Bay, is about 4.5 km (N– found that increases in human activities alter the S) by 5 km (E–W). Daya Bay is located in a balance of nutrients in Chinese coastal waters. Wang subtropical region, and its annual mean air et al. (2008) summarized and discussed the changes temperature is 22 . The coldest months are January in the ecological environment of Daya Bay, and the and February, with a monthly mean air temperature trends in more than 20 years. Zooplankton of 15 , and the hottest℃ months are July and August, characteristics can be affected by many factors, for with a monthly mean air temperature of 28 . The example, the cooling system of nuclear power plants minimum℃ sea surface temperature occurs in winter has unfavorable thermal effects on the sea surface (15 ) and the maxima - in the summer and ℃the fall micro-layer zooplankton community structure (Yang (30 ) (Xu 1989; Wang et al. 2006, 2008). No major et al. 2005). The human activities were the main rivers℃ discharge into Daya Bay, but there are three factor to impact the ecological environment in Daya small℃ rivers (Nanchong River, Longqi River and Bay (Wang et al. 2006, 2008; Wu & Wang 2007; Wu Pengcheng River) that flow into Dapeng Cove. et al. 2009, 2010). The Pearl River is in the western part of Daya Water quality and benthos characteristics have Bay. The bay has diverse subtropical habitats also been investigated in other bays in the world including coral reefs, mangroves, rocky and sandy (Dauer & Alden 1995, Theodorou 1995, Hall et al. shores, mudflats, etc. The coral reefs and mangroves 2000). Dauer & Alden (1995) indicated that health of have special resource values and ecological benefits, benthic communities was inferentially related to and are very important to the sustainable social and trends observed in water quality conditions in the economical development in these subtropical coastal tributaries and the main-stem of Chesapeake Bay. areas (Wang et al. 2008). Coral reefs and mangrove Theodorou (1995) made an assessment of water areas have important relations to the regulation and quality in a coastal embayment (Phaleron Bay, optimization of the subtropical marine environments Greece), and found that the water column quality and have become the subject of much international and benthos are indistinguishable from those of the attention in recent years (Mumby et al. 2004, Pearson open Saronikos Gulf. Hall et al. (2000) studied a 2005). probabilistic ecological risk assessment of tributyltin in surface waters of the Chesapeake Bay Watershed. Field sampling and laboratory analysis Understanding the organisms' sublethal responses and drawing on experimental ecological studies will Locations for 12 monitoring stations at Daya Bay lead to improved prediction of benthic community’s are shown in Fig. 1 (Wang et al. 2006, 2008). responses and more reliable assessment of project Seawater samples were taken during 2001- 2004. A impacts. Quanta Water Quality Monitoring System This paper makes the first attempt at identifying (Hydrolab Corporation, USA) was employed to the relationships between the water quality and collect the data for temperature, pH, salinity and benthos characteristics in Daya Bay by multivariate depth of water in all stations. Sea water samples for statistics, based on the systematically investigated the analysis of nutrients and chlorophyll a were taken data in Daya Bay during 2001-2004. using 5-L GO FLO bottles at the surface and bottom layers, and other samples from various depths were MATERIALS AND METHODS collected according to the methods and sampling tools of “The specialties for oceanography survey” Study site (GB12763-91, China). Water samples from various The sampling area of Daya Bay is located at depths were analyzed for nitrate, nitrite and silicate º º º º with a SKALAR auto-analyzer (SkalarAnalytical B.V. 113 29′42″-114 49′42″E and 23 31′12″-24 50′00″N in SanPlus, Holand). Ammonium and phosphorus were Guangdong Province (Fig. 1). It covers an area of analyzed with oxidation methods using hypobromite 2 600 km with a width of about 15 km and a north– and molybdophosphoric blue, with a UV1601 south length of about 30 km, and about 60% of the spectrophotometer (SHIMADZU Corporation). area in the Bay is less than 10 m deep (Xu 1989; Dissolved oxygen (DO), 5-day biochemical oxygen

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84 | You-Shao Wang, Cui-Ci Sun, Zhi-Ping Lou, Haili Wang, B. Greg Mitchell, Mei-Lin Wu, Zong-Xun Sun

Fig.1. Map for the monitoring stations in Daya Bay (Wang et al. 2006, 2008).

demand (BOD5), chemical oxygen demand (COD) 10 ml 90% acetone in the dark for 24 h in a and transparency were tested according to “The refrigerator and its concentration was determined specialties for marine monitoring” (GB17378.4-1998, with 10-AU Fluorometry (Turner Designs, USA). China). Two replicates of 1.5-l samples from the depths Benthos collection and analysis mentioned above were filtered through 47 mm Benthos analysis was also carried out at 12 GF/F filters and were immediately deep-frozen at stations according to “The specialties for 20 . At the end of the cruise, all filters were oceanography survey” (GB12763-91, China). After transported to the shore laboratory in liquid nitrogen. seawater samples were taken, two replicate box core Within℃ a week, the chlorophyll a was extracted in

Identification of water quality and benthos characteristics in Daya Bay, China, from 2001 to 2004| 85 samples were collected for benthic community of Daya Bay. Average salinity varied from 31.44 to analysis. Each replicate had a surface area of 0.05 m2 32.46, a higher average temperature was observed at (the core depth is about 20 cm) and was sieved on a stations S1 and S2 at the mouth of Daya Bay (Table 0.5 mm screen and relaxed in dilute isopropyl alcohol 1). Stratification, due to temperature and salinity for benthos analysis (Ning et al. 2005, Dauer & differences between the surface and bottom waters Alden 1995). The species number, abundance and within the bay, started to develop in June, became wet weight were recorded. For weight measurements the strongest from July to September, and BT-224S Sartorius Electronic Balance (Beijing disappeared in October when cold-water upwelling Sartorius Instrument & System Engineering Co., Ltd) took place, which affects the distribution of a variety was used. Sample analysis was carried out at the of nutrients, and thus it fluctuates in Daya Bay. South China Sea Institute of Oceanology, Chinese Temperature and salinity were uniformly distributed Academy of Sciences, China. with the depth from November to May in the following year. Based on the data measured from Statistical analysis 2001 to 2004, the highest surface and bottom water temperatures occurred in October and the lowest All statistical analysis methods were used ones - in January (Wang et al. 2006). according to Johnson & Wichern (1998). Kendall’s The average pH was quite uniform at all stations tau-b values were used to measure the degree of association among various variables with bivariate and ranged from 8.09 to 8.20 from 2001 to 2004 (Table 1). Dissolved oxygen (DO) in surface waters statistical analysis. Bivariate correlations between the were higher than in bottom waters at all stations. The biomass of benthos and major physical and nutrient average dissolved oxygen concentration in Daya Bay factors were calculated for all stations. Flexible-Beta is more uniform than the temperature ranging from cluster analysis was used between groups 6.97 mg dm-3 to 7.78 mg dm-3 (Table 1). Dissolved transforming the measures with Flexible-Beta oxygen content in spring and winter was higher than Distance. Factor analysis techniques were used to in summer and autumn. The highest dissolved investigate the various factors that are present in each oxygen content occurred in winter and the lowest of the three clusters identified by cluster analysis. one in summer. 5-day biochemical oxygen demand Factors were identified by the principal component (BOD5) levels in Daya Bay ranged from 1.27 to method with varimax rotation (using PROC X16 of 1.86 mg dm-3 , the highest BOD5 content occurred at the SAS system). Canonical correlation analysis was stations S3 and S8 (Table 1). Chemical oxygen carried out at stations S5, S6 and S11 at which demand (COD) ranged from 0.739 to 1.08 mg dm-3 benthos data were available, and the proportion of from 2001 to 2004 (Table 1). variation can be explained by the canonical variable. The lowest Secchi disk depth (1.96 m) was All statistical analysis programs are part of the recorded at station S3 at the southwest of Daya Bay, Statistical Analysis System (SAS 9.0) software and the highest Secchi disk depth (3.41 m) was package (SAS Institute Incorporation, 2002). recorded at station S6 in the center of Daya Bay. The high values of Secchi disk depth were in the southern RESULTS part of Daya Bay. (Table 1). Water quality and benthos data collected from Average concentrations of total dissolved 2001 to 2004 at 12 stations in Daya Bay are inorganic nitrogen (TIN), phosphorus (PO4-P) and summarized in Table 1, 2. In addition, physical, silica (SiO3-Si) in Daya Bay ranged between 3.799- -3 -3 nutrient and biological factors at stations S2, S3, S5, 5.420 μmol dm , 0.105-0.171 μmol dm and 19.32- -3 S7, S10 and S12, representing different functional sea 28.03 μmol dm (Table 1), respectively. Ratios of areas in Daya Bay, are selected in Fig. 2-4. TIN to PO4-P and PO4-P to SiO3-Si ranged between Spatial distribution of water temperature showed 40.11-52.85 and 234.19-1252.80. high values in the western part near the nuclear Mean chlorophyll a levels in Daya Bay ranged -3 power stations (near stations 4 and 5) with low values from 1.62 and 4.18 μg dm (Table 1), the higher in the southern parts of Daya Bay all the year from values were always found in autumn and summer. 2001 to 2004 (Table 1). The average temperature Station S8 in Daya Bay had the highest chlorophyll a levels (4.18 μg dm-3), followed by station S3 (4.16 mg ranges from 23.76 to 25.83 . The lowest average dm-3). temperature was observed at station S2 at the mouth ℃ ℃ About 200 species of benthos sampled from Daya www.oandhs.org

Table 1

Ranges and means of major physico-chemical and biological factors of 12 stations in Daya Bay from 2001 to 2004.

Range Factors S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 Mean Min-Max 18.00-29.85 14.50-29.95 16.00-32.30 16.20-31.80 16.00-34.00 15.10-30.45 17.90-29.85 15.40-32.30 15.40-31.15 15.05-31.70 14.70-31.50 15.20-30.70 Temperature ( ) Mean 23.86 23.76 24.49 24.78 25.63 23.91 23.86 24.48 24.16 24.28 24.21 23.95 Min-Max 28.30-34.40 28.35-33.81 26.90-33.51 27.32-33.58 25.02-34.50 30.17-33.91 28.16-34.67 26.60-33.68 27.73-34.30 27.29-34.22 27.61-34.46 28.37-33.55 Salinity (ppt) ℃ Mean 32.27 32.46 31.41 31.80 31.69 32.17 32.10 31.44 31.90 31.75 31.55 31.92 -3 Min-Max 5.36-9.11 5.63-8.81 6.66-9.57 5.04-8.66 6.23-7.76 5.22-7.71 6.40-9.61 6.25-11.04 6.11-9.68 6.51-8.74 6.34-8.64 6.22-8.93 DO (mg dm ) Mean 6.97 7.03 7.59 7.32 7.30 7.10 7.28 7.78 7.31 7.43 7.44 7.28 Min-Max 7.90-8.35 7.29-8.36 7.89-8.34 8.02-8.37 7.97-8.37 7.90-8.36 7.99-8.40 7.98-8.36 8.00-8.42 7.96-8.43 7.91-8.30 7.98-8.42 pH Mean 8.14 8.09 8.14 8.16 8.15 8.15 8.15 8.20 8.17 8.16 8.10 8.16 Min-Max 1.5-4.5 1.5-7.0 1.5-3.0 1.0-6.5 1.5-5.0 2.0-7.5 1.5-7.0 1.0-3.0 2.5-6.0 1.5-6.0 1.2-4.0 2.0-8.0 Secchi (m) Mean 3.23 2.89 1.96 2.88 2.96 3.41 3.22 1.86 3.09 2.84 2.64 3.12 -3 Min-Max .0.0-3.31 0.27-2.66 0.560-1.449 0.29-2.88 0.31-2.34 0.22-2.65 0.27-3.53 0.27-1.70 0.20-1.45 0.40-2.65 0.56-2.71 0.12-3.62 COD (mg dm ) Mean 1.02 0.948 0.949 0.856 0.780 0.913 0.899 0.908 0.739 0.755 1.01 1.08 -3 Min-Max 0.066-6.686 0.032-6.297 0.002-7.227 0.034-6.843 0.034-7.375 0.080-8.157 0.013-7.403 0.017-8.342 0.007-7.011 0.004-3.068 0.007-10.975 0.003-7.686 NO -N (μmol dm ) 3 Mean 2.747 2.745 2.202 1.909 2.061 2.354 2.088 1.739 1.351 1.621 1.947 2.153 -3 Min-Max 0-0.570 0.050-1.270 0-1.70 0.002-1.145 0.040-1.625 0.012-0.462 0.003-0.715 0.010-1.500 0.001-0.685 0.001-0.865 0-0.406 0-.0.900 NO -N (μmol dm ) 2 Mean 0.227 0.399 0.290 0.204 0.277 0.244 0.214 0.226 0.170 0.165 0.189 0.183 -3 Min-Max 0.109-7.611 0.028-3.670 0.080-4.770 0.040-4.932 0.030-5.589 0.330-4.907 0.315-4.084 0.014-5.025 0.034-4.836 0.027-7.250 0.037-5.336 0.040-4.418 NH -N (μmol dm ) 4 Mean 2.323 1.793 2.927 2.354 2.403 1.949 1.911 2.350 2.278 2.374 2.568 2.047 -3 Min-Max 0.190-14.36 0.070-9.805 0.080-9.910 0.080-9.387 0.077-9.188 0.079-12.771 0.039-11.025 0.041-8.095 0.042-7.154 0.032-9.552 0.044-13.249 0.047-9.879 TIN (μmol dm ) Mean 5.297 4.937 5.420 4.467 4.742 4.547 4.213 4.316 3.799 4.159 4.705 4.383 -3 Min-Max 0-0.550 0.050-0.720 0.090-0.570 0.012-0.518 0.015-0.535 0.008-0.619 0.003-0.354 0.010-0.226 0.001-0.323 0-0.257 0.004-0.389 0.006-0.279 PO -P(μmol dm ) 4 Mean 0.139 0.171 0.142 0.120 0.131 0.154 0.119 0.111 0.128 0.121 0.148 0.105 -3 Min-Max 9.647-30.06 8.766-38.44 6.275-39.25 4.808-35.66 6.567-43.54 5.248-33.75 7.150-34.92 2.022-35.93 8.182-36.17 5.248-40.44 8.180-47.20 36.92-2717.52 SiO -Si(μmol dm ) 3 Mean 19.32 21.98 20.06 19.02 21.02 21.11 21.28 22.70 20.63 21.38 28.03 20.46 Min-Max 12.85-217.32 11.59-114.43 9.780-95.66 6.630-90.92 6.179-225.00 8.391-102.35 8.600-166.71 3.359-114.58 6.980-72.68 11.97-119.57 4.168-170.02 8.15-148.56 TIN/PO -P 4 Mean 52.85 45.79 46.56 42.10 52.07 49.16 42.39 50.02 46.80 40.11 45.30 52.02 Min-Max 39.81-1533,58 48.88-4388.79 31.38-3802.54 45.17-1820.88 39.88-2092.74 64.77-4213.29 48.36-8693.54 56.69-729.45 29.50-16451.61 35.37-423.42 30.15-7369.72 36.93-2717.52 SiO -Si/ PO -P 3 4 Mean 288.65 475.16 386.71 319.23 387.44 448.17 765.98 345.58 1251.80 234.19 720.06 418.23 -3 Min-Max 0.60-3.26 0.43-6.98 0.40-11.00 0.40-8.84 0.33-4.69 0.40-7.67 0.17-7.85 0.58-10.20 0.67-6.99 0.49-11.16 0.77-10.48 0.69-6.10 Chlorophyll a (μg dm ) Mean 1.62 1.99 4.16 2.43 1.98 2.11 2.61 4.18 2.13 3.04 2.75 2.18 -3 Min-Max 0.49-4.35 0.49-3.56 0.76-3.24 0.54-3.70 0.62-2.96 0.55-4.19 0.46-5.24 0.40-4.38 0.51-2.82 0.99-3.84 0.65-3.26 0.52-5.24 BOD (mg dm ) 5 Mean 1.71 1.65 1.86 1.55 1.37 1.64 1.45 1.78 1.27 1.45 1.45 1.45 -2 Min-Max 0-115.54 0-115.80 0-758.00 0-197.74 0-150.00 0-192.70 0-948.20 0-324.10 0-1152.20 0-1236.60 0-529.14 0-835.00 Benthos (g m ) Mean 19.20 32.85 98.57 35.71 25.24 42.24 104.08 84.30 164.42 272.37 173.41 103.71 -2 Min-Max 0-17.70 0-5.20 0-8.00 0-102.54 0-10.40 0-15.00 0-28.00 0-2.00 0-5.20 0-6.00 0-15.50 0-15.00 Polychaeta (g m ) Mean 3.52 1.18 3.40 9.57 2.36 1.88 3.94 0.43 0.91 0.93 1.96 1.24 Min-Max 0-38.50 0-155.00 0-736.00 0-101.60 0-115.50 0-106.00 0-917.40 0-319.00 0-1147.00 0-1229.20 0-529.14 0-820.00 Mollusca (g m-2) Mean 4.68 24.84 92.34 15.35 18.09 29.08 95.58 80.16 159.54 257.32 170.38 97.56

-2 Min-Max 0-50.40 0-6.80 0-3.00 0-30.40 0-12.20 0-9.70 0-5.00 0-0.10 0-40.20 0-65.90 0-1.15 0-0.80 Echinodermata (g m ) Mean 5.71 0.81 0.30 7.73 2.04 1.63 0.56 0.0125 2.71 5.15 0.072 0.64

-2 Min-Max 0-24.00 0-4.92 0-22.00 0-20.00 0-34.50 0-1.99 0-2.80 0-6.50 0-16.40 0-51.00 0-13.70 0-11.70 Crustacean (g m ) Mean 4.65 0.67 2.53 2.12 2.73 0.23 0.44 1.05 0.50 6.96 0.99 1.42

-2 Min-Max 0-6.50 0-80.00 0 0-15.00 0-0.40 0-126.60 0-9.00 0-45.70 0-3.30 0-32.00 0 0-38.50 Others (g m ) Mean 0.64 5.32 0 0.94 0.025 9.41 0.56 3.20 0.21 2.00 0 2.84

Identification of water quality and benthos characteristics in Daya Bay, China, from 2001 to 2004| 87

Table 2 Bay have been studied since 1982 (Jiang et al. 1990, Wang et al. 2008). Benthos in Daya Bay can be Species numbers of benthos in Daya Bay from 2001 to divided into six communities: 2004. (I) the community of Marphysa bell oculata- Species 2001 2002 2003 2004 Terebellidesstroemi-Eurythoëparvecaruuculata- Polychaeta 50 38 27 27 Typhocarcinus sp.; Mollusca 17 25 20 33 (II) the community of Listriolobusbrevirostris- Crustacean 8 10 11 11 Amphiopluslaevis-Schiszasterlacunosus; Echinodermata 5 11 7 8 (III) the community of Veremolpamicra- Others 3 8 3 5 Mabellarcaconsociata-aphiaundulata- Total 83 92 68 84 amphiopluslaevis; (IV) the community of Onuphis sp.-Philineargentata- Ampelisca spp.;

Fig. 2. Changes in annual mean (a) pH, (b) temperature ( ), (c) dissolved oxygen (mg dm-3), (d) salinity (PSU) and (e) chemical oxygen demand (mg dm-3) at selected stations in Daya Bay from 2001 to 2004. ℃ www.oandhs.org

88 | You-Shao Wang, Cui-Ci Sun, Zhi-Ping Lou, Haili Wang, B. Greg Mitchell, Mei-Lin Wu, Zong-Xun Sun

Fig. 3. Changes in (a) TIN, (b) phosphorus (P) and (c) silica (Si) at selected stations in Daya Bay from 2001 to 2004 (μmol dm-3).

-3 -3 Fig. 4. Changes in (a) TIN to PO4-P ratios, (b) SiO3-Si to PO4-P ratios, (c) chlorophyll a (μg dm ) and (d) BOD5 (mg dm ) at selected stations in Daya Bay from 2001 to 2004.

(V) the community of Loveniasubcarinata- demonstrating the annual succession (Table 2) Prionospiomalmgreni-Schizasterlacunosus.; (Dauer et al. 1995, Currie et al. 1999). (VI) the community of Veremolpamicra- Table 1 presents the temporal trends of major Eurythoëparvecaruuculata-linopherus hirsute- water quality and benthos parameters at 12 Listriolobusbrevirostris-Protaankyrabidentata(Jiang monitoring stations in Daya Bay during the study et al. 1990). period from 2001 to 2004. The polychaeta, mollusca, echinodermata and A statistically significant decreasing trend in pH crustacean were the main benthos communities in was observed at all stations (Fig. 2a) This result was Daya Bay with a variation of dominant species similar to those observed in Port Shelter of Hong

Identification of water quality and benthos characteristics in Daya Bay, China, from 2001 to 2004| 89

Kong (Yung et al. 2001). The higher and lower values Bivariate correlations between benthos biomass were found in winter and summer of 2001 at the and major physical and nutrient factors were selected stations. calculated for all stations. The density of benthos at All nutrient parameters remained relatively all stations correlated positively with temperature, unstable during the study period at the selected DO, pH, NH4-N, SiO3-Si, SiO3-Si/PO4-P, stations (Fig. 3). chlorophyll a and negatively correlated with salinity, The concentrations of TIN showed significant Secchi, COD, NO3-N, NO2-N, TIN, PO4-P, increasing trends (Fig. 3a), and PO4-P and SiO3-Si TIN/PO4-P, BOD 5. Such a relationship between showed significant decreasing trends at all stations nutrients and benthos was also found in the Lower (Fig. 3b, c). The ratios of TIN to PO4-P increased Chesapeake Bay (Dauer & Alden 1995). from 2001 to 2004 (Fig. 4a), and the ratios of SiO3-Si Cluster analysis based on the major water quality to PO4-P remained relatively stable except for 2002 parameters measured (the first column of Table 2) (Fig. 4b). The levels of chlorophyll a increased at first revealed that the 12 monitoring stations could be and then decreased (Fig. 4c) at the selected stations in grouped into three clusters. Flexible-beta cluster Daya Bay from 2001 to 2004, and the highest value analysis was used and the corresponding dendrogram recorded at station S3 was in the cage culture areas using the Flexible-beta method between groups (Wu & Wang 2007, Wu et al. 2009). The polychaeta, transforming measured with Flexible-beta distance, mollusca, echinodermata and crustacean were the and the result of the Flexible-beta cluster analysis was dominant benthos groups in Daya Bay (Wang et al. shown in Fig. 6. 2008). There were also trends for the different levels of benthos decreasing from 2001 to 2004, which are presented in Fig. 5. The mean biomass of benthos at stations S2 and S5 were lower than others from 2001 to 2004 in Fig. 5.

Fig. 6. Results of the Flexible-beta method for cluster analysis showing the three clusters of all stations.

Factor analysis techniques were used to investigate the various factors that are present in each of the three clusters identified by cluster analysis. Factors were identified by the principal component method with varimax rotation. Eigenvalues and cumulative proportions of the correlation matrix are presented in Table 3. The first four eigenvalues (λ1–

λ4) of the correlation matrix of the clusters, using the Fig. 5. Changes in mean biomass of benthos at selected -2 principle component method in factor analysis, are stations of Daya Bay from 2001 to 2004 (g m ). also shown in Table 4. www.oandhs.org

90 | You-Shao Wang, Cui-Ci Sun, Zhi-Ping Lou, Haili Wang, B. Greg Mitchell, Mei-Lin Wu, Zong-Xun Sun

Table 3

Factor loadings (after varimax rotation) of the first two factors for Cluster I, II and III. Cluster I Cluster II Cluster III F1 F2 F1 F2 F1 F2 Temperature ( ) -0.07402 0.99726 -0.91594 0.17441 0.41463 -0.84822 Salinity (ppt) 0.08681 -0.99622 -0.86609 -0.23685 -0.79031 0.58854 DO (mg dm-3) ℃ -0.95422 0.29911 0.90756 -0.23741 0.66559 -0.56114 pH 0.10302 0.99468 0.34421 -0.88746 -0.95259 -0.18645 Secchi (m) -0.08829 0.99609 -0.97061 -0.00703 -0.81068 0.51100 COD (mg dm-3) 0.99806 -0.06229 0.96300 0.13467 0.30587 0.37842 -3 NO3-N (μmol dm ) 0.71192 -0.70226 -0.11188 0.98878 0.09577 0.63140 -3 NO2-N (μmol dm ) -0.34213 -0.93965 0.04398 0.88400 0.13031 0.98026 -3 NH4-N (μmol dm ) 0.85008 0.52665 0.50845 0.85305 0.73916 -0.65671 TIN (μmol dm-3) 0.96037 -0.27873 0.24975 0.96593 0.69021 0.18550 -3 PO4-P(μmol dm ) -0.67938 -0.73379 -0.03668 0.99788 0.90839 -0.09308 -3 SiO3-Si(μmol dm ) -0.83054 -0.55696 0.32481 -0.39576 0.99190 0.01782

TIN/PO4-P 0.72370 0.69012 -0.11296 0.06295 -0.09709 0.01522

SiO3-Si/ PO4-P -0.92164 -0.38804 -0.01222 0.79633 0.13335 0.24584 -3 BOD5 (mg dm ) 0.99133 0.13138 0.98990 0.09895 0.17930 0.25444 Chlorophyll a (mg m-3) -0.99963 -0.02708 0.99220 0.02532 0.33479 -0.16112 Cumulative % of variance explained 54.46 45.54 42.63 38.56 37.04 23.78

Table 4

The first four eigenvalues (λ1-λ4) of the correlation matrix of the clusters using the principle component method in factor analysis. Eigenvalues (Proportion)

λ1 λ2 λ3 λ4 Cluster I 9.64627694 (0.6029) 6.35372306 (0.3971) 0 0 Cluster II 6.97431928 (0.4359) 6.11283888 (0.3821) 2.91284184 (0.1821) 0 Cluster III 7.31970370 (0.4575) 4.71267099 (0.2945) 2.34265154 (0.1464) 1.62497376 (0.1016)

In order to investigate the association between a contributed to temperature, salinity, pH, DO, Secchi, physical-chemical and biological variable, canonical COD, SiO3-Si, BOD5, chlorophyll a and all the correlation analysis was carried out for stations S5, S6 biomass of benthos. The third canonical variable and S11, for which benthos data were available. The involved a contrast between temperature, NO2-N, proportion of variation can be explained by the NH4-N, TIN/PO4-P, SiO3-Si/PO4-P and the canonical variable, and the correlation coefficients biomass of polychaeta and echinodermata. between individual parameters and their canonical At station S6 at the south of Daya Bay, two pairs variables for stations S5, S6 and S11 are shown in of significant canonical variables were also identified, Table 5. which accounted for 100% of the variations. The At station S5, near the DNPP and LNPP in the correlations between the physico-chemical/biological southwestern part of Daya Bay, three pairs of parameters and their corresponding factors are also significant canonical variables were identified, which presented in Table 5. The first canonical variable of accounted for 100% of the variations. The the physico-chemical parameters involved a contrast correlations between the physico-chemical/biological between temperature, salinity, DO, Secchi, NO3-N, parameters and their corresponding factors are NO2-N, PO4-P, TIN/PO4-P. The second canonical presented in Table 5. The first canonical variable of variable chiefly included pH, COD, NO3-N, NH4-N, the physico-chemical parameters involved a contrast PO4-P, SiO3-Si, TIN/PO4-P, SiO3-Si/PO4-P, BOD5 between DO, NO3-N, NO2-N, TIN, PO4-P, SiO3-Si and all the biomass of benthos. and SiO3-Si/PO4-P, biological parameters associated At station S11 in the northeastern part, near the strongly with the biomass of crustacean and others. biggest cage culture area—the Fanhe harbor in Daya The second canonical variable was chiefly Bay (http//hzsin.gov.cn), one pair of significant

Identification of water quality and benthos characteristics in Daya Bay, China, from 2001 to 2004| 91

Table 5

Results of canonical correlation analysis for stations S5, S6 and S11. Correlations between PHYCHEM, BIO and their canonical variables. S 5 S6 S11 Canonical variate Canonical correlation Canonical correlation Canonical correlation 1 1.000000 1.000000 1.000000 2 1.000000 1.000000 3 1.000000 PHYCHEM 1 PHYCHEM 2 PHYCHEM 3 PHYCHEM 1 PHYCHEM 2 PHYCHEM 3 PHYCHEM 1 PHYCHEM 2 PHYCHEM 3 Temperature -0.2231 0.6933 -0.6852 1.0000 0.0011 0.0000 1.0000 0.0000 0.0000 Salinity -0.3477 0.9203 0.1792 -0.9999 0.0117 0.0000 -1.0000 0.0000 0.0000 pH -0.0430 -0.9942 0.0990 0.3700 -0.9290 0.0000 1.0000 0.0000 0.0000 DO -0.7913 -0.5792 0.1959 0.9841 0.1775 0.0000 -1.0000 0.0000 0.0000 Secchi -0.2288 0.9665 -0.1164 0.9999 -0.0132 0.0000 -1.0000 0.0000 0.0000 COD 0.4805 -0.7395 0.4714 -0.1371 0.9906 0.0000 1.0000 0.0000 0.0000

NO3-N 0.8928 0.3007 -0.3354 -0.7538 0.6571 0.0000 1.0000 0.0000 0.0000

NO2-N 0.6801 -0.0930 -0.7272 -0.9113 -0.4118 0.0000 1.0000 0.0000 0.0000 NH4-N 0.9619 -0.2670 -0.0589 0.4613 0.8873 0.0000 1.0000 0.0000 0.0000 TIN 0.9719 -0.0389 -0.2320 -0.3501 0.9367 0.0000 1.0000 0.0000 0.0000

PO4-P 0.9105 0.2160 -0.3526 -0.6806 -0.7326 0.0000 1.0000 0.0000 0.0000 SiO3-Si -0.5119 -0.7155 -0.4755 -0.4930 -0.8701 0.0000 1.0000 0.0000 0.0000

TIN/PO4-P -0.2660 -0.3068 -0.9138 0.6338 0.7735 0.0000 1.0000 0.0000 0.0000 SiO3-Si / PO4-P 0.5429 -0.1157 -0.8318 -0.3177 -0.9482 0.0000 1.0000 0.0000 0.0000

BOD5 0.4178 -0.8248 0.3810 0.0565 0.9984 0.0000 1.0000 0.0000 0.0000 BIO 1 BIO 2 BIO3 BIO1 BIO2 BIO3 BIO1 BIO2 BIO3 Chlorophyll a 0.2864 -0.9329 0.2182 0.0481 -0.9988 0.0000 1.0000 0.0000 0.0000 Polychaeta 0.1211 0.7023 0.7015 0.4659 0.8848 0.0000 0.0000 0.0000 0.0000 Mollusca 0.4112 -0.9111 0.0284 -0.0256 -0.9997 0.0000 0.0000 0.0000 0.0000 Echinodermata -0.1914 0.7764 0.6005 0.3390 0.9408 0.0000 0.0000 0.0000 0.0000 Crustacean 0.6237 0.6835 -0.3793 0.0983 0.9952 0.0000 0.0000 0.0000 0.0000 Others -0.7013 -0.6497 0.2933 0.2915 -0.9566 0.0000 0.0000 0.0000 0.0000

canonical variables were also identified, which (Wang et al. 2006). During that time, the thermocline accounted for 100% of the variations. The temperature gradient averaged 0.5 – 1 m-1. The correlations between the physico-chemical/biological thermocline depths changed from 6 m to 10 m, and parameters and their corresponding factors are its thickness was about 2 – 4 m in Daya Bay℃ (Wang et presented in Table 5. The first canonical variable of al. 2006). The minimum water temperature at the the physico-chemical parameters consisted of all bottom was about 23 in June of each year. The physico-chemical parameters and chlorophyll a. thermocline disappeared from September to the The first canonical variable of the following March due to℃ mixing (Wang et al. 2006). physicochemical parameters could be accounted for The DO, BOD5 and COD changed from 2001 to by different factors, while the second canonical 2004 indicating that the seawater of DYB was also variable was accounted for by different factors at within the First Class of National Seawater Quality stations S5 and S6, and the first canonical variable of Standards for China (GB3097-1997). the physicochemical parameters could be accounted Inorganic nitrogen and phosphorus levels were for by different factors only at station S11. For the within the National First Class Water Quality biological parameters, the first canonical variable all Standards for China from 2001 to 2004 (Wang et al. consisted of the biomass of crustaceanexcept for 2008). NH4-N (about 49%) and NO3-N (about 43%) station S11. The second canonical variable had all are the dominant total dissolved inorganic nitrogen positive association with the biomasses of polychaeta, (TIN) forms, they are accounting for about 90% of echinodermata and crustacean at stations S5 and S6. the TIN in these years, and NO2-N is only about 8%. The NO3-N content is lower relative to NH4-N, DISCUSSION revealing the thermodynamic imbalance between NH4-N, NO2-N and NO3-N. Biological activity may The temperature data suggest that Daya Bay is be the main factor influencing the balance (Huang et affected by the East Guangdong upwelling and a al. 2003; Wang et al. 2006, 2008), and there were thermocline developing between June and August different degrees of transformation of NH4-N for the www.oandhs.org

92 | You-Shao Wang, Cui-Ci Sun, Zhi-Ping Lou, Haili Wang, B. Greg Mitchell, Mei-Lin Wu, Zong-Xun Sun different bay regions. Concentrations of NH4-N at salinity were increasing from 2001 to 2004 at the stations S3, S8 and S11 in the cage culture areas for selected six stations. The chemical oxygen demand fish and seashell, as well as near the Fanhe harbor in had demonstrated a statistically significant decreasing Daya Bay, were higher than at other stations. The trend. The 5-day bio-chemical oxygen demand has a marine water quality of Daya Bay is better than in statistically significant increasing trend (Fig. 4d), with Port Shelter of Hong Kong (Yung et al. 2001) and in the highest BOD5 always at stations S3 and S8 of the the Pearl River (Xu et al. 2005), and worse than in cage culture areas, and this water environment was Sanya Bay (Huang et al. 2003, Dong et al. 2010). not suitable for marine zooplankton (Wang et al. Stations S8 and S3 were in the cage culture areas 2003). for fish and seashell in Daya Bay, and near the Aotou The warm waste water was one of the reasons harbor and the Pengcheng River in Dapeng Cove of that could directly affect the density of plankton and Daya Bay. There were higher nutrient concentrations benthos at stations S4 and S5 near the nuclear power in the areas of stations S8 and S3 than in the other plants in Daya Bay (Zheng et al. 2001, Wang et al. areas, and the biomass of benthos was always lower. 2008). Assuming that the temperature of waste water These results indicate that nutrients were the main from the nuclear power plant to be 1°C warmer than factors influencing chlorophyll a and phytoplankton the surrounding seawater, then the area of Daya Bay in Daya Bay (Sommer et al. 2002) and the biomass of affected by this warmer water was about 5.51 km2 benthos (Josefson & Rasmussen 2002). There is also (Zeng et al. 2002). The lower biomass of benthos a risk that higher nutrients will deteriorate the was found at stations S1, S2 and S6 in the southern environment for the growth of benthos (Dauer & part ofDaya Bay, where temperatures might be lower Conner 1980). (~1°C compared with the other areas), and the result The biomass of benthos varied seasonally, the is similar to that obtained by Brey and Gerdes (1997) maximum occurred in winter. Although the main who worked in the Antarctic. These results indicated species of benthosin Daya Bay demonstrate a minor that the temperature was an important factor for the change trend from 83 species to 93 species in 2001- benthic growth, especially in Daya Bay (Wang et al. 2004 (Wang et al. 2008), the annual mean biomass of 2008). The same results were also obtained in the benthos was gradually decreasing from 154.11 g m-2 areas of stations S3 and S8 (Table 1). Stations S3 and to 90.72 g m-2 from 2001 to 2004, and the value of S8 in the cage culture areas with higher nutrients than 34.65 g m-2 in 2003 is the lowest level since 1982 in the other areas, are not good environments for (Wang et al. 2008). These results indicate that one marine zooplankton and benthos (Wang et al. 2003, reason might be the fact that the temperatures in Wang et al. 2006). Nutrients and temperature could 2002–2003 were almost the lowest at all stations – play an important role in determining the density of this may have been due to the El Niño phenomenon phytoplankton, the biomass of zooplankton and of (Chao et al. 1996, Chen et al. 2005), and another benthos in Daya Bay (San Diego-McGlone et al. reason might be the anthropogenic activities as the 1995; Edwards et al. 2003; Gil et al. 2002; Wang et al. main factor for influencing the ecological 2003; Wang et al. 2006, 2008). Shtereva et al. (1999) environment in Daya Bay (Wang et al. 2006, 2008; pointed out to the high level of nutrients and the Wu & Wang 2007; Wu et al. 2009), such as the capacity of the ecosystem to produce and maintain nuclear power plants (Wang et al. 2006, 2008) and high phytoplankton biomass, and it also affected the marine aquaculture (~13298 ha cage culture area) biomass of benthos (Gil et al. 2002, Wang et al. 2003, (Wu & Wang 2007; Wu et al. 2009, 2010). Wang et al. 2008). Compared with other investigated All selected stations have also an increasing trend stations, the warm water from the nuclear power in the water temperature from 2001-2004 (Fig. 2b), plants affected the biomass of benthos at stations S4 especially in 2004. Climate change scenarios for the and S5 (Table 1 and Fig. 4). The results indicated that year 2100 indicate a significant increase in the air the warm water from the Daya Bay Nuclear Power temperature (by 2.3 – 4.5°C) and the greatest threat Plant (since 1993) and Lingao Nuclear Power Plant to the environment of the Gulf (Kont et al. 2003). (which was put into full production in 2003) had The highest value appeared especially near the greatly influenced the ecological processes and the nuclear power stations in a different year. In 2002- environment in this region, particularly plankton and 2003, the temperature was almost the lowest one at benthos were directly affected as marine organisms all stations, probably effected by El Niño (Chao et al. (Zheng et al. 2001; Li & Cai 2001; Wang et al. 2006, 1996, Chen et al. 2005). Dissolved oxygen and 2008).

Identification of water quality and benthos characteristics in Daya Bay, China, from 2001 to 2004| 93

The results of the correlation analysis revealed salinity, Secchi depth, NO3-N and NO2-N) that not only the temperature, DO, pH, SiO3-Si, accounted for 23.78% of the data variance. SiO3-Si/PO4-P, chlorophyll a, but also salinity, Secchi Table 3 shows the corresponding factor loading depth, NO3-N, NO2-N, TIN, TIN/PO4-P, BOD 5 in three clusters. It should be noted that NO3-N and could play an important role in determining the NH4-N were important factors among stations in the biomass of benthos in Daya Bay (Dauer&Alden three clusters, whereas concentrations of individual 1995). The results are different from those obtained nutrient factors (i.e. NH4-N, NO2-N, NO3-N, TIN with multivariate statistical analysis that studied the and PO4-P) were more important in Cluster II. These water quality and phytoplankton characteristics in results were similar to the research on spatial Daya Bay from 1999 to 2002 (Wang et al. 2006). characterization of nutrient dynamics in the Bay of Cluster I consisted of stations S1, S2 and S6 in the Tunis (Souissi et al. 2000), on long-term changes in southern part of Daya Bay, where there are more the water quality and phytoplankton characteristics in effects from the Pearl River and South China Seas Port Shelte (Yung et al. 2001) and also on the water (Xu 1989), such as the East Guangdong upwelling quality and phytoplankton characteristics in Daya Bay (Xu 1989; Wang et al. 2006, 2008). Cluster II (Wang et al. 2006), which showed that nutrients were consisted of stations S3, S8 and S11 in the cage apparently more important factors among stations in culture areas in the southwestern part, the different clusters. northwestern part near the Aotou harbor and the These results are different from the research northeastern part near the Fenhe harbor of Daya Bay. reported for canonical correlation analysis about the The fish farming in Daya Bay has increased from the water quality and phytoplankton characteristics in annual production of about 100 tons (∼440 ha cage Daya Bay (Wang et al. 2006). The canonical culture area) in 1988 to approximately 60,000 tons correlation analysis for stations S5, S6 and S11 (∼14,000 ha cage culture area) in 2005, nearly 600- suggested also that benthos communities could be fold growth during the past 17 years (Wu et al. considered as bioindicators of water quality in Daya, 2009b).Cluster III consisted of stations S4, S5, S7, S9, particularly subjected to anthropogenic disturbance S10 and S12 in the southwestern, the middle and the (Casazza 2002). northeastern parts of Daya Bay. The results of cluster analysis could also reflect the different CONCLUSIONS functional areas of Daya Bay. These results are different from those reported for the water quality The results of the present study indicated that the and phytoplankton characteristics in Daya Bay by biomass of benthos at all stations correlated Wang et al. (2006), and also indicated that human positively with temperature, DO, pH, NH4-N, SiO3- activities were the main factor affecting the ecological Si, SiO3-Si/PO4-P and chlorophyll a, whereas environment in Daya Bay (Wang et al. 2008; Wu & negatively correlated with salinity, Secchi depth, Wang 2007; Wu et al. 2009, 2010). COD, NO3-N, NO2-N, TIN, PO4-P, TIN/PO4- In each cluster, more than 50% of the data Pand BOD5 by calculation of bivariate correlations variance could be explained by the first two principle between benthos and major physical and nutrient factors. All stations could be grouped into three components. In general, NO3-N, NH4-N and TIN are the most important factors in differentiating the clusters. Cluster I consisted of stations S1, S2, and characteristics of the three clusters as it is evident S6, which were in the southern part of Daya Bay. from the factor loadings. Cluster I with factor 1 Cluster II consisted of stations S3, S4, S8 and S5 in the cage culture areas in the southwestern part, the (positively with COD, NH4-N, NO3-N, TIN, northwestern part near the Aotou harbor and the TIN/PO4-P and BOD5) and factor 2 (positively with northeastern part near the Fenhe harbor of Daya temperature, pH, Secchi and NO2-N) accounted for 45.54% of the data variance. Cluster II with factor 1 Bay. Cluster III consisted of stations S7, S9, S10, S11 and S12 in the southwestern, the middle and the (positively with DO, COD, NO3-N, BOD5 and northeastern parts of Daya Bay. The results also Chlorophyll a) and factor 2 (positively with NH4-N, suggest that nutrients and benthos are good NO2-N, NO3-N, TIN, PO4-P and SiO3-Si/PO4-P) accounted for 38.36% of the data variance. Cluster environmental indicators that can quickly reflect the changing water quality in Daya Bay. As a multi-type III with factor 1 (positively with DO, NO3-N, TIN, ecosystem, Daya Bay seems to be driven mainly by PO4-P and SiO3-Si) and factor 2 (positively with human activities (Wang et al. 2008). The results

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94 | You-Shao Wang, Cui-Ci Sun, Zhi-Ping Lou, Haili Wang, B. Greg Mitchell, Mei-Lin Wu, Zong-Xun Sun revealed that the temperature and nutrients could Dong J.D., Zhang Y.Y., Zhang S., Wang Y.S., Yan Z.H., Wu also play an important role in determining the M.L., 2010, Identification of temporal and spatial variations of water quality in Sanya Bay, China by three-way principal component biomass of benthos in Daya Bay. analysis, Environmental Earth Sciences, 60: 1673-1682 The warm water from the Nuclear Power Plants Edwards V.R., Tett P., Jones K.J., 2003, Changes in the yield of and waste water from the cage culture areas had chlorophyll a from dissolved available inorganic nitrogen after an greatly influenced the ecological processes and the enrichment event-applications for predicting eutrophication in coastal waters, Continental Shelf Research, 23(17): 1771-1785 environment in this region according to changes in Gil J., Valdes L., Moral M., Sanchez R., Garcia-Soto C., 2002, the biomass of benthos and water quality at different Mesoscale variability in a high-resolution grid in the Cantabrian Sea stations in Daya Bay. Particularly the benthos was (southern Bay of Biscay), May 1995, Deep Sea Research Part I: directly affected as marine organisms, thus there is a Oceanographic Research Paper, 49(9): 1591-1607 need for more research on the waste warm water Hall L.W., Scott M.C., Killen W.D., Unger M.A., 2000, A probabilistic ecological risk assessment of tributyltin in surface waters of from the Nuclear Power Plants and from cage the Chesapeake Bay watershed, Human and Ecological Risk culture areas affecting the regional ecosystem of assessment, 6(1): 141-179 Daya Bay (Wang et al. 2006, Wu et al. 2010). Huang L., Tan Y., Song X., Huang X., Wang H. et al., 2003, The Furthermore, the development of fish-farming in status of the ecological environment and a proposed protection strategy in Sanya Bay, Hainan Island, China, Marine Pollution Bulletin, Daya Bay should be controlled in the future (Wu et 47(7-12): 180-186 al. 2009). Huang Y., Ou Q., Li J., Qian H., 1993, Comparison of zooplankton from Dapeng Bay and Daya Bay and relationship between zooplankton ACKNOWLEDGEMENTS and red tide, Marine Science Bulletin, 12(2): 46-21, (in Chinese) Jiang J., Li R., Zheng F., Lu L., Wu Q., Xu H., Huang X., 1990, Anaysis of benthos community structure in Daya Bay. In: Third This research was supported by the Knowledge Institute of Oceanology, State Oceanic administration (eds) Innovation Programs of the Chinese Academy of Collections of papers on marine ecology in the Daya Bay (II), Sciences (No. KZCX2-YW-Q07-02, No. KSCX2- p. 282-289. China Ocean Press, Beijing, China YW-Z-1024 and No. KSCX2-SW-132), the key Johnson R.A., Wichern D.W., 1998, Applied multivariate statistical projects in the National Science & Technology Pillar analysis, Prentice Hall, New York, pp. 816 Josefson A.B., Rasmussen B., 2002, Nutrient retention by benthic Program in the Eleventh Five-year Plan Period (No. macrofaunal biomass of Danish Estuaries: importance of nutrient Load 2009BADB2B0606), the National Natural Science and residence time, Estuarine, Coastal and Shelf Science, 50(2): Foundation of China (No. 41076070), the project of 205-216 Knowledge Innovation Program of South China Sea Kont A., Jaagu S.J., Aunap R., 2003, Climate change scenarios and the effect of sea-level rise for Estonia, Global Planet. Change, 36(1-2): Institute of Oceanology (LYQ200701) and the 1-15 National 908 project (No. 908-02-04-04). 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