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The spatial and temporal distribution of microalgae in the South China Sea: evidence from GIS-based analysis of 18S rDNA sequences LI LüYan1, HUANG QiaoJuan1, WU ShuHui1, LIN Duan2, CHEN JiaHui2 & CHEN YueQin1† 1

Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-Sen University, Guangzhou 510275, China; 2 South China Sea Environmental Monitoring Center, State Oceanic Administration, Guangzhou 510300，China

The purpose of this study was to estimate the spatial and temporal variation of microalgae in the South China Sea and to demonstrate the environmental factors controlling the diversity of microalgae by GIS (geographic information system)-based analysis of 18S rDNA sequences. Six 18S rDNA libraries were constructed from environmental samples collected at different sites in the study area, and more than 600 18S rDNA sequences were determined. The rDNA sequence data were then analyzed by DIVA-GIS software to display the spatial and temporal variation of phytoplankton’s composition. It was shown that the autotrophic eukaryotic plankton dominated over the heterotrophic cells in most of our clone libraries, and the dominating phytoplankton was Dinophyceae except for Bacillariophyta at the Xiamen harbor. The percentages of these two groups were controlled by water temperature and salinity. Our results also revealed that the species composition of Chlorophyta showed a close relationship with latitude, changing from Prasinophyceae at the high latitude to Trebouxiophyceae at the low latitude. Several newly classified picoplankton lineages were first uncovered in the South China Sea, including the pico-sized green alga Ostreococcus sp. and Picochlorum eukaryotum, and picobiliphytes, which was just discovered in 2007 with unknown affinities to other eukaryotes. Their spatial and temporal variation were also analyzed and discussed. microalgae, GIS-based analysis, 18S rDNA, South China Sea

Previous studies of microalgae assemblage in the South China Sea were mainly based on the phenotypic characteristics of algae. Their diversity was estimated mostly using the netz-phytoplankton data. For example, in the Bohai Sea[1,2] and the South China Sea[3], the phytoplankton was sampled using the standing net type III (mesh size 76 μm, the standard sampling tool in Chinese marine phytoplankton studies) with a vertical haul at each grid station. This method failed to count the phytoplankton smaller than 76 μm. For this reason, little has been known about the taxonomy and systematics of these microalgae in the South China Sea. On the other hand, the studies on free-living aquatic microorganisms[4,5] have found correlations between the assemblage composition

and environmental or geographic characteristics, such as salinity, depth, and latitude. Patterns in the spatial distribution of organisms provide important information about mechanisms that regulate the diversity of life and the complexity of ecosystems. Although microalgae dominate the photosynthetic biomass in many marine ecosystems, playing significant roles in global mineral cycles, their spatial diversification remains to be demonstrated. Genetic methodologies have revealed that past cul Received January 23, 2008; Accepted June 4, 2008 doi: 10.1007/s11427-008-0140-7 † Corresponding author (email: [email protected]) Supported by the National Natural Science Foundation of China (Grant No. U0631001), Funds from the Ministry of Education of China (Grant No. NCET-04-0788) and the Reserve Key Projects of Sun Yat-Sen University (IRT0447)

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ture-based studies missed most microbial diversity[4,6]. Phylogenetic information from ribosomal RNA (rRNA) genes directly amplified from the environment has led to the resurgence of pico-phytoplankton[7,8] and a tremendous diversity within marine micro-phytoplankton has been revealed from coastal and open ocean marine environments[9−13]. New species, genera, or even classes, such as the Pelagophyceae[14], Bolidophyceae[15], and the Picobiliphyate[16] have been described. For the past 20 years, the development and routine application of molecular-based techniques have made it possible to evaluate the biodiversity of aquatic microbial communities in detail, and in turn, offer great opportunities to understand how this parameter responds to various environmental stressors. The purpose of this study was to analyze the spatial and temporal variation of microalgae in the South China Sea, and to discuss the correlation between microalgae diversity and environmental factors by combining gene cloning and sequencing of eukaryotic rRNA genes and geographic information system (GIS) analysis. GIS is a useful technique in environmental modeling and assessment [17,18]. It can be used to analyze the distribution of organisms to elucidate geographic and ecological patterns. By importing biological distribution databases, using the latitude and longitude fields, GIS can be used to map and analyze biological distribution data.

1 Materials and methods 1.1 Oceanographic sampling Six environmental samples were collected during the summer cruises to different regions of the South China Sea. Figure 1 showed the map of our sampling sites, which was created by ArcGIS. Samples were collected on 0.45 μm filters from 2 L of seawater from the euphotic zone by passing through the 76 μm plankton net. The filters were preserved at −20℃ before subsequent analysis. 1.2 Experimental methods After the filters had been soaked in algal DNA extraction buffer (4% CTAB, 1.4 mol/L NaCl, 1% PVP, 100 mmol/L Tris-HCl, pH 8.0, 25 mmol/L EDTA, pH 8.0), nucleic acids were extracted as previously described [19]. Eukaryotic 18S rRNA genes were amplified by polymerase chain reaction (PCR) with eukaryote-specific primers 18N1(5′ -ACCTGGTTGATCCTGCCAGT-3′ ) 1122

and 18N11R (5′ -TGATCCTTCGCAGGTTCAC-3′ ) under previously described conditions[19]. The PCR products, 18S rDNA genes were collected to construct libraries by using the TA cloning kit (TaKaRa). The positive transformants of rDNA libraries were screened by PCR amplification and RFLP analysis. Representative clones of the library showing unique restriction fragment length polymorphism (RFLP) patterns were selected and the plasmids were extracted and purified for sequencing. The sequences of our library were sequenced by Invitrogen Biotech Co.Ltd. (Guangzhou, China). 1.3 Data analyses Preliminary taxonomic affiliation of the sequences was determined using BLASTN against the GenBank nr database (Sep. 2007). The sequences that passed chimeric screening were phylogenetically grouped and aligned using Clustal X 1.8 program. Phylogenetic relationships were inferred by the neighbor-joining method with PAUP v.4.0b10 and Bayesian analysis with MrBayes ver3.1. Bayesian analysis was performed using the GTR+I+G model with the following rate parameters: Prset statefreqpr=dirichlet(1, 1, 1, 1); Lset nst=6, rates =invgamma. Bayesian posterior probabilities were computed by running Markov chain Monte Carlo search for 3000000 generations by using the program default priors on model parameters. Trees were sampled every 100 generation with the first 25% discarded as burn in. The basic geographic data in the South China Sea (DEM, 1︰4000000) was extracted from the China marine information center. The geographic and biological data were analyzed under DIVA-GIS v5 (http://www. divagis.org/) and ArcGIS (ArcGIS 8.3, ESRI). DIVAGIS is a convenient geographic information system for the scientists who cannot afford generic commercial GIS software to the analysis of biodiversity data.

2 Result and discussion 2.1 Geographical position of our sampling sites and the distribution of autotrophic microorganisms The South China Sea extends from the equator to 22ºN, largely confined within the Tropic of Cancer. A basin as deep as 5000 m is located in its northeast and is bordered by broad shelves that are shallower than 100 m[20]. It has an extraordinary structural diversity and can be

LI Lu-Yan et al. Sci China Ser C-Life Sci | Dec. 2008 | vol. 51 | no. 12 | 1121-1128

divided into a number of geological divisions. In the software platform of Geographic Information System, we can clearly see the geographic condition of these sites (Figure 1), including continental shelf area (Site E1) and continental slope area (Site B19), subtropical mainland continental brim (Site 29), coastal waters of Hainan Island (Site N4) , tropical deep sea (Site 5) and equatorial shelf shallow sea (Site 37). To investigate the genetic biodiversity of small eukaryotes in the South China Sea, we collected samples from different representative areas of the South China

Sea. Six 18S rDNA libraries were constructed.More than 500 clones were screened by RFLP analysis in each library and over 100 clones for different patterns were sequenced. To display the diversity and variation of the main phytoplanktonic groups in each site, we applied DIVA-GIS software and ArcGIS 8.3 to analyze the spatial and temporal distribution of these major groups as shown in Figures 2 and 3. Figure 2 shows that autotrophic eukaryotic plankton dominated over the heterotrophic cells in our clone libraries except in site B19 (Figure 2). In the coastal sites (Sites 29, E1 and N4), the

Figure 1 Map of the sampling sites in the study, created by ArcGis 8.3 LI Lu-Yan et al. Sci China Ser C-Life Sci | Dec. 2008 | vol. 51 | no. 12 | 1121-1128

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percentages of autotrophic groups were 64.62%, 59.69% and 54.91%, respectively. A significant number of clones in our libraries were affiliated with uncharacterized marine eukaryotic groups. The percentage of unknown environmental clones was especially high from the samples in low latitude, for example, 55.92% in site 37 and 40.59% in Site 5. The sampling Site 37 is a well-characterized region of the equatorial shelf shallow sea in the Nansha Islands sea areas, where abundant nutrition is available. While Site 5 represents the tropical deep sea in the basin as deep as 4000 m. It seems that the tropical sea contains a much larger hidden world of microalgae than we thought. Site 29 is located at mainland brim (Xiamen harbor) with Jiulong River into which fresh water drains. In Site 29, the percentage of autotrophic groups was the highest, up to 64.62% and the percentage of unknown groups was lowest, 5.66%.

Figure 2 The distribution of autotrophic or heterotrophic groups in the South China Sea, analyzed by ArcGis 8.3 and DIVA-GIS v5 (Site 5: 39.60% autotroph, 19.80% heterotroph, 40.59% unknown; Site 37: 25.31% autotroph , 18.78% heterotroph, 55.92% unknown; Site 29: 64.62% autotroph, 29.72% heterotroph, 5.66% unknown; Site B19: 7.97% autotroph, 66.67% heterotroph, 25.36% unknown; Site E1: 59.69% autotroph, 18.85% heterotroph, 21.47% unknown; Site N4: 54.91% autotroph, 13.29% heterotroph, 31.79% unknown.) 1124

2.2 The percentage of Bacillariophyta and Dinophyceae and their association with water temperature and salinity Previous studies on phytoplankton in the South China Sea with traditional methods showed that the average cell abundance of phytoplankton diatoms (the size > 76 μm) accounted for 75.8% and dinoflagellates only accounted for 10.80%[3]. However, the genetic biodiversity of small eukaryotes in this study showed that the Dinophyceae was the dominant group (Figure 3) in most sampling sites of this sea area, except that in Site 29 the percentage of Bacillariophyta was higher than Dinophyceae (Bacillariophyta 43.8% and Dinophyceae 27.7%). In N4, E1, B19 and 37, the percentages of Dinophyceae were all over 50% and the highest was in E1, about 77.19%. As shown in table 1, Gymnodiniale is the

Figure 3 The distribution of dominant phylum or class of algae in the South China Sea, analyzed by ArcGis 8.3 and DIVA-GIS v5 (Site 29: 27.73% Dinophyceae, 8.03% Chlorophyta, 43.80% Bacillariophyta; Site B19: 54.57% Dinophyceae, 27.27% Chlorophyta, 18.18% Bacillariophyta; Site E1: 77.19% Dinophyceae, 2.63% Chlorophyta, 0.88% Bacillariophyta; Site N4: 50.53% Dinophyceae, 6.32% Chlorophyta, 2.11% Bacillariophyta, 22.11% Haptophyceae; Site 5: 37.50% Dinophyceae, 27.50% Chlorophyta, 17.50% Streptophyta; Site 37: 51.61% Dinophyceae, 27.42% Chlorophyta, 12.90% Streptophyta, 3.23% Bacillariophyta. )

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main composition in all libraries, and Peridiniale and Prorocentrale were other dominated groups in most sites except for Gonyaulacale in Site 29. It was generally considered that high temperature and salinity would increase the percentage of dinophyceaes and restrain the growth of diatoms[3], and the ration of diatoms and dinophyceaes had been used to estimate the environment of sea[1]. This notion has been further demonstrated in this study. For instance, in Site 29 which is near the shore area with stream of fresh water and has a low level of water temperature and salinity, the percentage of diatom is higher (about 43.80%). While sites E1 and B19 are both in the Kuroshio area, where the surface seawater was as hot as up to 29.5℃ because of the Kuroshio current[3], high temperature and high salinity ocean water such as Kuroshio current restrain the

growth of diatoms and increase the abundance of dinophyceaes, and as a result, the percentages of dinophyceaes in these two sites were very high (the highest was in E1 of 77.19%) and the percentage of diatom was only 0.88%. Low ratio of dinophyceaes in previous work could be the reason that many species of dinophyceaes seemed easy to pass through the 76 μm size mesh when measured by the conventional methods with traditional method. 2.3 The temporal distribution of green algae in the South China Sea Chlorophyta was found in each sampling site and the species composition of this taxon showed obviously relationship with latitude. From the higher to the lower the species composition of Chlorophyta changed from

Figure 4 Space variation of picobliliphyte in the world. The percentage of picobliliphytes in the summer libraries were analyzed with a template of the world which had been provided by ArcGis 8.3. Table 1

The main genus of the microalgae in the South China Sea

Site

Latitude

Dinophyceae

Chlorophyta

29

24.55

Prasinophyceae

Coscinodiscophyceae

B19

19.95

Prasinophyceae

Bacillariophyceae

E1

19.08

Gymnodiniale Gonyaulacale Gymnodiniale Peridiniale Gymnodiniale

Prasinophyceae

Coscinodiscophyceae

N4

18

Bacillariophyceae

5

11

Prasinophyceae Trebouxiophyceae Trebouxiophyceae

37

5.5

Gymnodiniale Peridiniale Gymnodiniale Peridiniale Gymnodiniale Peridiniale Prorocentraledinophyceae

Trebouxiophyceae Chlorophyceae

Streptophyta

Bryophyta Zygnemophyceae Bryophyta Zygnemophyceae

Bacillariophyta

Haptophyceae

Chrysochromulina campanulifera

Coscinodiscophyceae

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Prasinophyceae to Trebouxiophyceae (Table 1). The dominant species of Chlorophyta in most sampling sites were Ostreococcus sp. and Picochlorum eukaryotum, which were pico-sized alga (less than 3 μm). In the previous investigations of phytoplankton in the Nansha Island sea area, no remarkable relationship between the cell abundance and Chl-a was revealed[21]. Our result supported this finding. Notably, in the site of tropical sea area (Site 5 and 37) we found Streptophyta clones. They were Bryophyta and Zygnemophyceae populations. Site 5 is in the lime sand island area of the South China Sea, while Site 37 is in the coral reefs area[22]. The Bryophyta might be brought to surface by upwelling from reefs.

study), however, no obvious relationship with latitude was revealed (Figure 4). To further demonstrate the temporal distributions of picobiliphytes with latitude, we constructed a phylogenetic tree with picobiliphytes 18S rDNA sequences from different geographic regions (Figure 6), and no correlation with the place or latitude was found. These may suggest that picobiliphytes were eurytopic alga in the ocean; they were ignored only because of their very small sizes.

2.4 The nano- and pico-planktonic alga firstly uncovered in the tropical shallow sea area of the South China Sea Figure 2 clearly showed that there was 22% Haptophyceae in Site N4, a site in the coastal water of the tropical island, and most of which were Chrysochromulina campanulifera, but no Haptophyceae sequences was found in other sites. The Haptophyta represents a major lineage of chlorophyll a+c algae. Most known haptophytes occur as planktonic forms in coastal and oceanic environments. Only 4 species of Chrysochromulina have been reported in China[23] because of the small size and hard to culture. All of them were collected in Jiaozhou Bay and never found in the South China Sea before. Chrysochromulina was the first discovery in tropical marine areas. Another new taxon firstly uncovered in the coastal water of tropical sea was Picobiliphyte. Picobiliphyte was a marine picoplanktonic algal group, just discovered in 2007[16] with unknown affinities to other eukaryotes. The inferred presence of a phycobiliprotein-containing plastid in picobiliphytes is in good agreement with their putative sister relationship to Cryptophytes, which also contain phycobiliproteins. We found three clones of picobiliphytes with identical sequences in the library of Site E1. Comparing our result with those in other sites of the world with ArcGIS (ArcGIS 8.3, ESRI) (Figure 5), it was shown that Picobiliphyte sequences were detected mostly in summer, from June to September. Picobiliphytes have been found in a variety of marine systems, including the European coast[16], the North Atlantic[16], the Arctic Ocean[10] and tropical marine area (this

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Figure5 Temporal variation of picobliliphyte in each month of a year. The black symbols were our sites in the South China Sea, the grey ones were other site from which surveyed full-length 18S rDNA sequences with high similarity to picobliliphyte. HE = Helgoland, RA = Roscoff ASTAN, BL = Blanes Bay, Z59 = Norwegian Sea, NW= Canada Basin of the Arctic Ocean.

3 Conclusion Using a molecular approach, this study of small phytoplankton suggested that there has been significant diversity of small phytoplankton in the South China Sea and that their composition showed distinct spatial variation. We are not yet able to propose testable hypotheses to explain the patterns of their distribution. However, there are some hints to elucidate the biological and physical factors controlling the presence or absence of phytoplankton. The GIS used in the Oceanology study provided a new approach for spatial modeling via integration of many important geographic and biological features. As a result, more accurate representations of location and spatial extent were revealed. With more hydrological data and biostatistics database development, we can further understand the biogeography of microphytoplankton.

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Figure 6 Phylogenetic tree of Picobiliphytes based on full-length 18S rRNA sequence by Bayesian analysis. Numbers on the tree represent the values of posterior probability values.

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