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1

Effects of coastal upwelling on picophytoplankton distribution off the coast of Zhanjiang in South China Sea

Wu M-L., Wang Y-S., Wang D.-X., Dong J-D.

Oceanological and Hydrobiological Studies

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2014

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Vol. 43, No. 3

283--291

EN

Coastal upwelling occurred along the west coast of Guangdong in the northern South China Sea during the summer of 2006. The effects of upwelling on the vertical and horizontal distributions of Prochlorococcus and Synechococcus were investigated. A distinct vertical temperature difference between the surface water and water at a depth of 30 m was observed in the coastal upwelling region. There was a clear spatial variability of temperature, and an increasingly obvious horizontal gradient was created from the coast to offshore waters. Picophytoplankton communities observed from the coast to offshore waters were significantly different. In the coastal upwelling waters, the picophytoplankton community was dominated by Synechococcus within the euphotic zone. Prochlorococcus dominated the picophytoplankton community in the euphotic zone in the non-upwelling region. This difference in the picophytoplankton community structure was due to different hydrodynamics. The results of canonical correspondence analysis demonstrate that temperature, salinity, and phosphate concentration may be important factors affecting the distribution of Prochlorococcus and Synechococcus.

2

Monsoon-driven Dynamics of water quality by multivariate statistical methods in Daya Bay, South China Sea

Wu M-L., Wang Y-S., Sun C-C., Sun F-L., Cheng H., Wang Y-T., Dong J-D., Wu J.

Oceanological and Hydrobiological Studies

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2012

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Vol. 41, No. 4

66-76

EN

Eleven physicochemical parameters of data collected from 12 stations in Daya Bay in 2003 were analyzed by multivariate statistical analysis. Cluster analysis (CA) grouped data from 4 seasons into two groups, the northeast and southwest monsoon periods, representing different natural processes. During the northeast monsoon period, principal component analysis (PCA) and CA group the 12 monitoring sites into Cluster DA1 (S1, S2 and S6) and Cluster DA2 (S3-S5 and S7-S12). During the southwest monsoon period, PCA and CA group the 12 monitoring sites into Cluster WB1 (S1, S2, S7, S9 and S11) and Cluster WB2 (S3-S6, S8, S10, S11 and S12). The spatial heterogeneity within the bay was defined by different hydrodynamic conditions and human activities. These results may be valuable for achieving sustainable use of the coastal ecosystems in Daya Bay.

3

Monsoon-driven dynamics of environmental factors and phytoplankton in tropical Sanya Bay, South China Sea

Wu M-L., Zhang Y-Y., Dong J-D., Cai C-H., Wang Y-S., Long L-J., Zhang S.

Oceanological and Hydrobiological Studies

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2012

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Vol. 41, No. 1

57-66

EN

In this work, we analyze environmental (physical and chemical) and biological (phytoplankton) data obtained in Sanya Bay during four cruises, carried out in January, April, August, and October. The main objective of this study was to identify the key environmental factors affecting phytoplankton structure and bacterioplankton in the bay. Results suggest that spatial variations in the phytoplankton community and bacterioplankton biomass are the result of nutrients. Temporal variation in the abundance of bacterioplankton and phytoplankton are affected by a combination of physical and biological factors, such as temperature and nutrients. The silicate, phosphate, and nitrogen phytoplankton require for growth may be co-limited. Monsoon winds (a southwestern monsoon during summer and a northeastern monsoon during winter) play important roles in controlling the phytoplankton community and bacterioplankton abundance in Sanya Bay, northern South China Sea.

4

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

Wang Y-S., Sun C-C., Lou Z-P., Wang H., Mitchell B.G., Wu M-L., Sun Z-X.

Oceanological and Hydrobiological Studies

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2011

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Vol. 40, No.1

82-95

EN

Physicochemical and benthos data were collected from 12 marine monitoring stations in Daya Bay, during 2001-2004. 12 stations in Daya Bay could be grouped into three clusters: cluster 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 areas (stations S3, S4, S5 and S8); cluster III consisted of stations in the southwest, the middle and the northeast of the Bay (stations S7, S9, S10, S11 and S12). Calculation with bivariate correlations between benthos and major physicochemical factors showed that the density of benthos in all stations correlated positively with temperature, DO, pH, NH4-N, SiO3-Si, SiO3-Si /PO4-P and chlorophyll a and was negatively correlated with salinity, Secchi, COD, NO3-N, NO2-N, TIN, PO4-P, TIN/PO4-P and BOD5. Factor analysis showed that there were high positive loading salinity, Secchi and NH4-N of three clusters. Results revealed that temperature, DO, pH, SiO3-Si and SiO3-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.

5

Effects of polycyclic aromatic hydrocarbons exposure on antioxidant system activities and proline content in Kandelia candel

Song H., Wang Y-S., Sun C-C., Wu M-L., Peng Y-L., Deng C., Li Q.P.

Oceanological and Hydrobiological Studies

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2011

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Vol. 40, No.3

9-18

EN

The antioxidant system effects of Kandelia candel were investigated under four different levels of PAH stress. The activities of antioxidant enzymes including superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD), the responses to the change of malondialdehyde (MDA) contents and the accumulation of proline in K. candel were determined. Our results suggested that the activities of SOD, CAT, POD increased significantly in leaves and roots of K. candel (p≤0.05) with the increase of the external PAH concentrations, while in stems, the activities of these antioxidant enzymes were all significantly inhibited (p≤0.01). We also observed an increase of MDA in leaves, stems and roots, and an obvious correlation between MDA content and PAH concentrations in three locations, which showed that the change of MDA content could be used as a biomarker of K. candel under PAH stress. The proline content was found remarkably enhanced in leaves, stems and roots. However, a significant inverse correlation was observed between the proline content and SOD (r=-0.99, p≤0.01), POD (r=-0.95, p≤0.05) activities in stems. This study suggested that the antioxidative system of K. candel has an obvious organ-dependent feature when exposed to PAH contamination as revealed by discriminant analysis (DA).

6

Demonstration of a new indicator for studying upwelling in the northern South China Sea

Lin L., Wang Y-S., Sun C-C., Li N., Wang H., Mitchell B.G., Wu M-L., Song H., Wu J-F.

Oceanologia

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2011

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No. 53 (2)

605-622

EN

In order to demonstrate that silicate can be used as an indicator to study upwelling in the northern South China Sea, hierarchical cluster analysis (CA) and principle component analysis (PCA) were applied to analyse the metrics of the data consisting of 14 physical-chemical-biological parameters at 32 stations. CA categorized the 32 stations into two groups (low and high nutrient groups). PCA was applied to identify five Principal Components (PCs) explaining 78.65% of the total variance of the original data. PCA found important factors that can describe nutrient sources in estuarine, upwelling, and non-upwelling areas. PC4, representing the upwelling source, is strongly correlated to silicate (SiO3-Si). The spatial distribution of silicate from the surface to 200 m depth clearly showed the upwelling regions, which is also supported by satellite observations of sea surface temperature.

7

Using chemometrics to identify water quality in Daya Bay, China

Wu M-L., Wang Y-S., Sun C-C., Wang H., Lou Z-P., Dong J-D.

Oceanologia

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2009

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No. 51 (2)

217-232

EN

In this paper, chemometric approaches based on cluster analysis, classical and robust principal component analysis were employed to identify water quality in Daya Bay (DYB), China. The results show that these approaches divided water quality in DYB into two groups: stations S3, S8, S10 and S11 belong to cluster A, which lie in Dapeng Cove, Aotou Harbor and the north-eastern part of DYB, where water quality is related mainly to anthropogenic activities. The other stations belong to cluster B, which lie in the southern, central and eastern parts of DYB, where the quality is related mainly to water exchange with the South China Sea. Cluster analysis yields good results as a first exploratory method for evaluating spatial difference, but it fails to demonstrate the relationship between variables and environmental quality on the one hand and the untreated data on the other. However, with the aid of suitable chemometric approaches, the relationship between samples or variables can be investigated. Classical and robust principal component analysis can provide a visual aid for identifying the water environment in DYB, and then extracting specific information about relationships between variables and spatial variation trends in water quality.

8

Multivariate statistical analysis of water quality and phytoplankton characteristics in Daya Bay, China, from 1999 to 2002

Wang Y-S., Lou Z-P., Sun C-C., Wu M-L., Han S-H.

Oceanologia

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2006

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No. 48 (2)

193-211

EN

This study analyzed seasonal physicochemical and phytoplankton data collected at 12 marine monitoring stations in Daya Bay from 1999 to 2002. Cluster analysis based on water quality and phytoplankton parameters measured at the 12 stations could be grouped into three clusters: cluster I - stations S1, S2, S7 and S11 in the southern part and the north-eastern part of Daya Bay; cluster II - stations S5, S6, S9, S10 and S12 in the central and north-eastern parts of Daya Bay; cluster III - stations S3, S4 and S8 in the cage culture areas in the south-western part of Daya Bay and in the north-western part of the Bay near Aotou harbor. Bivariate correlations between phytoplankton density and the major physical and nutrient factors were calculated for all stations. Factor analysis shows that there were high positive loadings of pH, TIN and the ratio of TIN to PO4-P in the three clusters, which indicates that all the stations in the three clusters were primarily grouped according to their respective nutrient conditions.