Influence of environmental changes on picophytoplankton and bacteria in Daya Bay, South China Sea

Influencia de cambios ambientales en picofitoplancton y bacterias en la bahía de Daya, mar de China Meridional

Mei-Lin Wu¹*, Yu-Tu Wang^1,2, You-Shao Wang^1,2, Fu-Lin Sun^1,2

¹ State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China. * Corresponding author. E-mail: mlwu@scsio.ac.cn

Received July 2014,
Accepted August 2014.

Resumen

Los cambios ambientales generados por las actividades humanas y los procesos naturales determinan las características de la distribución y abundancia de dos grupos de picofitoplancton (Synechococcus y picoeucariontes). Se evaluaron las comunidades de picofitoplancton y de bacterias con mayor (MaADN) y menor (MeADN) cantidad de ADN, identificadas mediante citometría de flujo, durante el periodo de transición intermonzónico en otoño en la bahía de Daya (mar de China Meridional). La abundancia de Synechococcus y picoeucariontes varió entre 2.16 x 10⁴ y 1.45 x 10⁵ cél mL^-1 y entre 0.78 x 10³ y 7.95 x 10³ cél mL^-1, respectivamente. La abundancia del grupo bacteriano de MaADN fue mayor en el agua superficial (media: 5.58 x 10⁵ cél mL^-1) que en el fondo de la bahía (media: 3.74 x 10⁵ cél mL^-1), con una diferencia significativa (n = 12, P = 0.05). No se observó una diferencia significativa entre la abundancia del grupo de MeADN en la superficie (media: 7.06 x 10⁵ cél mL^-1) y el fondo (media: 4.83 x 10⁵ cél mL^-1) (n = 12, P > 0.05). Un análisis de componentes principales mostró que ambos grupos de picofitoplancton (Synechococcus y picoeucariontes) y bacterias (MaADN y MeADN) se relacionaron positivamente con nutrientes (NO₃-N, NH₄-N y SiO₃-Si). Se identificaron tres subsistemas en la bahía: las partes oeste y este, las partes central y noroeste, y la boca y parte central.

Palabras clave: picofitoplancton, bacterias, citometría de flujo, bahía de Daya.

Abstract

Key words: picophytoplankton, bacteria, flow cytometry, Daya Bay.

INTRODUCTION

Coastal bays are very complex and fragile ecosystems affected by human activities and natural processes such as monsoons (Jickells 1998). Half of the world’s population now lives within 60 km of the coast. Coastal pollution often results in adverse conditions leading to the development of harmful algal blooms and/or eutrophication. This has resulted in an ecological unbalance, the loss of biodiversity, and the rapid reduction of biological resources (Wu et al. 2012). Moreover, there is a large input of pollutants to the coastal seas as a result of the land and ocean interaction in the coastal zone.

Daya Bay (South China Sea) is a special ecosystem under strong pressure or impact from natural phenomena (Southeast Asian monsoons) and anthropogenic activities (e.g., aquaculture, nuclear power plants) (Xu 1989, Wang et al. 2006, Wu and Wang 2007, Wang et al. 2008, Wang et al. 2009). Pollutants entering a bay system normally result from many transport pathways including wastewater, runoff effluents, land reclamation, recreation, and fish culture, as well as atmospheric deposition and climate change. This complex coastal system is the reason for the implementation of environmental monitoring programs intended to produce a better understanding and management of the ecosystems within it (Wu and Wang 2007; Wang et al. 2008; Wu et al. 2009, 2010).

Whether this ecosystem influenced by human activities and natural processes has a different spatial structure and how this has affected the picophytoplankton community and bacteria in the area is still unclear. This study was designed to investigate the physical and chemical properties and phytoplankton and bacterial abundances in this coastal ecosystem to identify whether the dynamics of the phytoplankton community and bacteria is associated with the autumn monsoon transition period.

MATERIALS AND METHODS

Study area

Sampling and analysis

Seawater samples were taken from the surface (0.5 m below the surface) and bottom (2 m above the bottom) at 12 stations (S1-S12) in January (winter), April (spring), August (summer), and November (autumn) 2012. Temperature, pH, and salinity at the surface and bottom depths were determined by a Quanta Water Quality Monitoring System (Hydrolab Corporation, USA). Seawater samples for the analysis of nutrients, chlorophyll α (μg L^-1), chemical oxygen demand (mg L^-1), and 5-day biochemical oxygen demand (mg L^-1) were taken using 5-L GO-FLO bottles. Water samples were analyzed for nitrate (NO₃-N, μmol L^-1), nitrite (NO₂-N, μmol L^-1), silicate (SiO₃-Si, μmol L^-1), ammonium (NH₄-N, μmol L^-1), phosphorus (PO₄-P, μmol L^-1), and total phosphorous (μmol L^-1) by spectrophotometry (GB 17378.42007 for Specifications for Oceanographic Survey, China). Dissolved oxygen (mg L^-1) was determined using Winkler titrations. Two replicate samples of 1.5 L from the surface and bottom depths were passed through 0.45-μm GF/F filters and the filtrate was deep-frozen immediately at -20 °C. At the end of the cruise, all filters were kept in liquid nitrogen and transported to a shore-based laboratory. Within a week after the sampling, chlorophyll α was extracted in 10 mL 90% acetone for 24 h in the dark, in a refrigerator, and the chlorophyll α concentration was determined with a 10AU fluorometer (Turner Designs, USA).

Picophytoplankton and bacteria defined by flow cytometry

Samples for picophytoplankton and bacteria were prefiltered through a 20-μm mesh netting. Triplicate samples were fixed with formaldehyde (2% final concentration) for 15 min in 2-mL cryotubes, quick-frozen in liquid nitrogen, and analyzed as soon as possible by a FACSCalibur flow cytometer (Becton Dickinson) equipped with a laser emitting at 488 nm in the laboratory. To estimate the abundance of the different groups, calibration of the cytometer flow rate was performed daily and a solution of 1-μm yellow-green latex beads (Polysciences, USA) was added to 0.5-mL subsamples as an internal standard. Abundances of picophytoplankton were calculated by the ratiometric method from the known amount of added beads, calibrated daily against the yellow-green beads.

The population of heterotrophic bacteria was also identified and enumerated by flow cytometry using a FACScanto flow cytometer (Becton Dickinson). Bacteria were then split into high DNA (HDNA) and low DNA (LDNA) groups using the differences in green fluorescence (Gasol et al. 1999).

Picophytoplankton and bacteria were only collected during the autumn sampling.

Air temperature, sea surface temperature, rainfall, and wind data were obtained from the Meteorological Bureau of Shenzhen Municipality (http://www.szmb.gov.cn/) and Hong Kong Observatory (http://www.hko.gov.hk).

Principal component analysis

Principal component analysis (PCA) is designed to transform the original variables into new, uncorrelated variables (axes), called the principal components, which are linear combinations of the original variables. The new axes lie along the directions of maximum variance (Shrestha and Kazama 2007). It reduces the dimensionality of the data set by explaining the correlation amongst a large number of variables in terms of a smaller number of underlying factors (principal components) without losing much information (Vega et al. 1998, Helena et al. 2000, Alberto et al. 2001, Li et al. 2009). In this study, PCA identified the seasonal changes of environmental factors and the interaction between environment and biology.

All mathematical and statistical computations were performed using MATLAB 2010a (Mathworks, Inc., USA).

RESULTS

Environmental factors

Air temperature, sea surface temperature, and wind data were obtained for Beijiao (Hong Kong, some 40 km from Daya Bay). Air temperature showed a clear seasonal variation, with the highest value (32.3 °C) recorded in July and August and the lowest (12.5 °C) in January (fig. 2a). Surface water temperature also showed a clear seasonal change, with minimum (16.8 °C) in February and maximum (27.5 °C) in October (fig. 2b). The prevailing winds (about 2.8 m s^-1) were southerly from May to November, and northerly to northwesterly from December to March (fig. 2c).

The PCA applied to the environmental factors distinguished three main groups (northeast monsoon period/winter, southwest monsoon period/summer, and monsoon transition period/spring and autumn) surrounding the first and second component axes, thus explaining 40.67% of the variance. The temperature, chlorophyll α, and phosphate loadings are positive in the first principal component (PC1), while salinity, SiO₃-Si, and NO₃-N are negative in PC1 (fig. 3). The PCA biplot based on PC1 and the second principal component (PC2) demonstrated the relationship between the monitoring seasons and environmental factors (fig. 3). The three main sampling seasons (northeast monsoon, southwest monsoon, and monsoon transition) clustered together. The northeast monsoon was associated with high salinity, the monsoon transition group occurs in the middle of PC1 and PC2, and the southwest monsoon showed its association with high temperature and chlorophyll α.

Nutrient distribution

The surface and bottom distributions of SiO₃-Si increased from the northern part to the mouth of Daya Bay (fig. 4a, b). The distribution of PO₄-P, however, was opposite to that of SiO₃-Si (fig. 4c, d). The spatial distribution of NO₃-N showed that the concentration decreased from the eastern to the western part of the bay (fig. 4e, f).

Biological response

Picophytoplankton

The picophytoplankton community in Daya Bay was mainly composed of Synechococcus and picoeukaryotes. Synechococcus abundance ranged from 2.16 x 10⁴ to 1.45 x 10⁵ cell mL^-1 (fig. 5a, b). There was no general difference in Synechococcus abundance between the surface and bottom waters in the bay, although there was a trend towards higher abundance at S3 and S11. There were no significant differences (P > 0.05) in picophytoplankton abundance between the surface and bottom depths. Synechococcus abundance was highest at S3.

The abundance of picoeukaryotes was lower than that of Synechococcus in the bay. The abundance of picoeukaryotes ranged from 0.78 x 10³ to 7.95 x 10³ cell mL^-1 (fig. 5c, d). The spatial distribution of picoeukaryote abundance was similar to that of Synechococcus.

Bacterial abundance in surface water was generally high in the southwest part of the bay (S3), reaching 2.73 x 10⁶ cell mL^-1 (fig. 6a). The lowest abundances of 4.51 x 10⁵ cell mL^-1 occurred in the central, southern, and eastern parts of the bay (S1, S2, S7, and S12). Bacterial abundance in bottom water was comparatively higher than in surface water except at S3 and S4 (fig. 6b). There were no significant differences in total bacterial abundance between the surface and bottom depths (n = 12, P = 0.07). Bacterial communities observed at the monitoring stations were characterized by one LDNA population and one HDNA population. The lowest LDNA abundance was observed in the central part of the bay (fig. 6c, d). No significant differences were observed in LDNA abundances between surface (mean: 7.06 x 10⁵ cell mL^-1) and bottom (mean: 5.88 x 10⁵ cell mL^-1) waters (P > 0.05). High HDNA abundances were observed in the western and northern parts of the bay (fig. 6e, f). In general, the abundance of HDNA bacteria in surface water (mean: 5.88 x 10⁵ cell mL^-1) was greater than in bottom water (mean: 3.74 x 10⁵ cell mL^-1), with significant difference (P = 0.05). No significant difference (P > 0.05) between LDNA and HDNA abundances was observed in surface and bottom water.

Principal component analysis

The PCA for picophytoplancton vs environmental factors and bacteria vs environmental factors was used to identify key environmental variables that could explain the picophytoplankton (Synechococcus and picoeukaryotes) and bacterial (HDNA and LDNA) abundances, respectively. For picophy-toplankton, PC1, which explained 49.21% of the variation of the environmental data, was highly correlated with Synechococcus and nutrients (fig. 7a). PC2 explained 21.24% of the variation and was highly correlated with PO4-P and NO₂-N. Both Synechococcus and picoeukaryotes were positively related to PC1 and negatively related to PC2. From the score plot, the spatial distribution of the samples can be observed clearly (fig. 7b). Three stations (S3, S11, and S12) were located in the western and eastern parts of the bay. The scores of these stations were positive in PC1. The two stations (S8 and S9) located around the central and northwest parts of the bay clustered. The scores of the rest of the stations (S1, S2, and S4-S7) located around the mouth and in the central part of the bay were negative and positive in PC2.

For bacteria, the first two principle components explained 50.39% of the variance in the environmental data (fig. 8a). PC1 was positively associated with SiO₃-Si, NO₃-N, NH₄-N, HDNA, and LDNA, and explained 27.81% of the total variance in the original variables. PC2 was associated with NO₂-N and PO₄-P, and explained 22.58% of the total variance. Both HDNA and LDNA were positively related to PC1 and PC2. The spatial pattern is similar to the PCA results for picophytoplankton and environmental factors (fig. 8b).

DISCUSSION

Daya Bay is located in a tropical region. The winds change direction from southwest to northeast or vice versa. The wet (southwest) monsoon brings clean air into the region from June to October. Conversely, the dry (northeast) monsoon predominates from November through April. To a great extent, the seasonal changes of the hydrodynamics in the bay are determined by Southeast Asian monsoons. Lower temperature and high salinity water intrudes into the bay along the bottom from the South China Sea under the influence of the weak southwest monsoon from May to September (Han 1998, Ji and Huang 1990, Wu et al. 2010). On the contrary, in Daya Bay the water column is vertically mixed under the influence of the northeast monsoon (Chen and Li 1996). In the study, the hydrodynamics in autumn shows interesting transit characteristics, from stratification in summer to well-mixed conditions in winter.

Spatially heterogeneous microbial communities were a regular feature of the subtropical embayment investigated in this study. However, two types of sites (marine aquaculture and coastal water) encapsulated the heterogeneity within the bay. The structure of microbial communities within the two distinct groups appeared likely to be driven by a combination of three prominent characteristics of this environment: highly localized marine aquaculture, physical forcing due to wind convection, and complex bay topography.

Generally, Prochlorococcus dominates in the subtropical oligotrophic oceans (Goericke and Welschmeyer 1993, Campbell et al. 1994), whilst Synechococcus is usually more abundant under intermediate nutrient conditions (Liu et al. 1997). Synechococcus is more abundant in plume-influenced and coastal waters, while Prochlorococcus was dominant in the oligotrophic water of the Mississippi River plume and its adjacent waters (Liu et al. 2004). Synechococcus cell density typically ranges from 10² to 10⁵ cells mL^-1 in temperate estuaries and often exceeds 10⁶ cells mL^-1 in subtropical regions (Wang et al. 2011). Synechococcus is the most abundant group in various coastal ecosystems, including Chesapeake Bay (Wang et al. 2011), San Francisco Bay (Ning et al. 2000), and Florida Bay (Phlips et al. 1999). In this study, Prochlorococcus was found in very low abundances. On the contrary, Synechococcus dominated in the bay (fig. 5). Nutrient availability might determine Synechococcus growth (Chen et al. 2007). The scores for S3, S11, and S12 are mainly due to Synechococcus, picoeukaryotes, NO₃-N, and SiO₃-Si in PC2. In fact, Synechococcus abundance at these stations is higher than that at the rest of the stations (fig. 7). The scores for these stations located in the western and eastern parts of the bay are different from those at the mouth and northern part of the bay in PC1.

In addition, the similar spatial pattern between the LDNA and HDNA groups may indicate that they may be better adapted to productive coastal waters, hence suggesting a niche partitioning between the similar bacterial groups. However, bacterial abundance in the eastern, northern, and western parts of the bay is slightly higher than in the rest of the areas (fig. 8). Our results suggest that the HDNA and LDNA groups may possess their own environmental niche with favorable conditions for them. The spatial differences in variability in bacterial abundance (HDNA and LDNA) may be due to system-specific changes in environmental parameters.

In summary, despite the important role of picophytoplankton and heterotrophic bacteria in the microbial dynamics of coastal bays, there is still a critical lack of information on their community composition and dynamics. Abiotic environments may determine the dynamics of phytoplankton and bacteria. Generally, abundances of picophytoplankton and heterotrophic bacteria have a similar spatial distribution. A significant positive correlation was observed between nutrients and Synechococcus and picoeukaryotes in the present work. Picophytoplankton abundance is likely to be driven by bottom-up processes (nutrient) and top-down processes (grazing). Nutrients may control the spatial distribution of bacteria. HDNA and LDNA bacterial groups may possess their own environmental niche with favorable conditions for them. Thus, biological activities may be due to system-specific changes in environmental parameters.

ACKNOWLEDGMENTS

This research was supported by the National Natural Science Foundation of China (projects No. 31270528 and No. 41206082); the Key Laboratory for Ecological Environment in Coastal Areas, State Oceanic Administration (No. 201211); and Key Laboratory of Marine Ecology and Environmental Science and Engineering, State Oceanic Administration (MESE-2013-02). The authors thank Jianlin Zhang for the flow cytometry analyses, the staff of the Marine Biology Research Station at Daya Bay (Chinese Academy of Sciences) for providing support and help, and the information system of China Ecosystem Research Network.

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