Picoplankton between 0.2 and 3 μm in diameter, including eukaryotic picophytoplankton, picozooplankton and heterotrophic bacteria, are essential components of plankton communities in all aquatic ecosystems (Flombaum et al. 2013; Mena et al. 2016). Picophytoeukaryotes,
The South China Sea (SCS), one of the largest marginal seas in the world, has rich biological resources (Jiang et al. 2014). The SCS is located in the subtropical and tropical northwest Pacific Ocean and the southern SCS is connected with the Andaman Sea and the Indian Ocean through the Straits of Malacca (SM), which are among the busiest international sea routes (Jiang et al. 2014; Wang et al. 2007). The northern SCS is characterized by significant environmental gradients owing to the discharge of the Pearl River and is affected by many types of physical forcing, such as monsoons, perennial cold cyclonic eddies, Taiwan Shoals, the Kuroshio Current and others (Morton & Blackmore 2001; Chen et al. 2006; Lin et al. 2011; Ning et al. 2004; Jiang et al. 2014). Waters of the SCS and the SM are significantly affected by the Asian monsoon.
Despite the large number of ecological researches on picoplankton in multifarious waters of the Pacific Ocean (Moon-van der Staay et al. 2000; Mackinson et al. 2015; Rii et al. 2016; Liang et al. 2017), the Atlantic Ocean (Schattenhofer et al. 2009; 2011; Zubkov et al. 2000), the Mediterranean Sea (Meador et al. 2010; Man-Aharonovich et al. 2010; Cerino et al. 2012) and the Arabian Sea (Fuchs et al. 1998), few studies have focused on the community structure of both eukaryotic and prokaryotic picoplankton in different marine regimes from the SM to the SCS along environmental gradients using the high-throughput sequencing technique.
The study area extends from the Straits of Malacca to the South China Sea (1°N–21°N, 96°E–114°E). Surface seawater samples were collected from 12 locations during a multidisciplinary cruise carried out in the Eastern Indian Ocean by R/V Shiyan 3, the South China Sea Institute of Oceanology, Chinese Academy of Sciences, from 14 to 19 April 2017 (Fig. 1). The surface water for phylogenetic analysis (2 l) was filtered through a polycarbonate filter with a pore size of 0.22 μm (Millipore Isopore, Bedford, USA) after pre-filtration through a polycarbonate filter with a pore size of 3 μm (Millipore Durapore, Bedford, USA). The filters (0.22 μm pore size) were snap frozen in liquid nitrogen and then stored at −80°C in the laboratory until DNA extraction. Seawater for nutrient analysis was filtered through polycarbonate filters with a pore size of 0.45 μm, collected in acid-washed polyethylene containers and immediately frozen (−20°C) until analysis.
Temperature, salinity and dissolved oxygen (DO) were measured at the sampling locations with a Sea-Bird SBE9 Conductivity-Temperature-Depth (CTD) profiler (USA). Chemical characteristics, including concentrations of nitrate (NO3-N), nitrite (NO2-N), ammonium (NH4-N), silicate (SiO3-Si) and phosphorus (PO4-P) were analyzed according to Jiang et al. (2014, 2015).
Total genomic DNA was extracted from the filters using the PowerWater DNA Isolation Kit (MoBio, Carlsbad, CA, United States) according to the manufacturer’s protocol. The DNA was stored at −80°C for subsequent analyses. The V4-V5 region of the 16S rRNA gene with the 515f/907r primer set (Xiong et al. 2014) and the V9 region of the 18S rRNA gene with the 1380f/1510r primer set (Amaral-Zettler et al. 2009) were selected for targeting amplicons. Next generation sequencing library preparations and Illumina MiSeq PE250 sequencing were conducted at the Novogene Company (Beijing, China). All sequences obtained in this study were deposited in the NCBI Sequence Read Archive (SRA) with accession numbers PRJNA492365 for the 16S rRNA gene and PRJNA492462 for the 18S rRNA gene.
The software package QIIME 1.9.1 (Caporaso et al. 2010) was used for 16S and 18S rRNA gene data analysis. The forward and reverse reads were derived from the original DNA fragments, which were merged by using FLASH (Magoč & Salzberg 2011) and assigned to a sample based on a barcode and truncated by cutting off the barcode and primer sequence. Sequences were grouped into operational taxonomic units (OTUs) using the clustering program VSEARCH 1.9.6 (Edgar 2010) at 97% sequence identity. Rarefaction analysis was conducted using the original detected OTUs. The taxonomic assignment was performed by the RDP classifier at a confidence threshold of 0.8 (Wang et al. 2007).
The Shannon index, the Chao1 index and Good’s coverage of samples were determined as described by Schloss et al. 2009. Weighted and unweighted UniFrac, Bray–Curtis and principal coordinate analysis (PCoA) were performed by QIIME. The unweighted pair group method with arithmetic mean (UPGMA) clustering was conducted by unweighted and weighted UniFrac based on the protocol published by Caporaso et al. 2010.
Heatmap and redundancy analysis (RDA) was conducted using the R statistical package to determine the correlation between community composition and environmental parameters (Zhang et al. 2014).
Network analysis was performed according to Qin et al. (2010). Forty most abundant OTUs of picoplankton communities were selected and Spearman correlations were calculated. In order to reduce the network complexity, correlations
After quality filtering of the raw reads, a total of 901199 16S rRNA gene sequences (on average 75100 reads per sample) and 1 238 847 18S rRNA gene sequences (on average 103237 reads per sample) from 12 samples were obtained and clustered into 1914 OTUs and 3931 OTUs for 16S and 18S rRNA genes (97% cutoff), respectively. The Shannon index indicated that eukaryotic picoplankton a-diversity was significantly higher (independent t-test and paired t-test;
Sequencing information and diversity index analyses
Site | OTU | Chao1 | Shannon | Coverage | ||||
---|---|---|---|---|---|---|---|---|
16S | 18S | 16S | 18S | 16S | 18S | 16S | 18S | |
S1 | 1000 | 2341 | 977.50 | 2641.87 | 5.20 | 8.87 | 0.997 | 0.993 |
S2 | 1092 | 1920 | 1045.07 | 2233.97 | 6.38 | 7.91 | 0.997 | 0.994 |
S3 | 891 | 2069 | 1050.14 | 2496.52 | 4.32 | 7.78 | 0.996 | 0.993 |
S4 | 995 | 1593 | 957.65 | 1909.62 | 6.26 | 7.50 | 0.998 | 0.995 |
S5 | 762 | 1791 | 738.70 | 2226.80 | 4.20 | 7.60 | 0.997 | 0.993 |
S6 | 746 | 1872 | 781.84 | 2334.47 | 4.50 | 6.94 | 0.997 | 0.993 |
S7 | 991 | 2174 | 969.97 | 2615.01 | 5.30 | 7.94 | 0.997 | 0.993 |
S8 | 972 | 2388 | 936.86 | 2728.43 | 5.17 | 8.45 | 0.997 | 0.993 |
S9 | 878 | 1803 | 875.60 | 2270.83 | 4.90 | 5.98 | 0.997 | 0.993 |
S10 | 822 | 2146 | 811.78 | 2405.77 | 4.96 | 8.71 | 0.997 | 0.995 |
S11 | 693 | 1978 | 672.34 | 2301.63 | 4.31 | 7.20 | 0.998 | 0.994 |
S12 | 753 | 2111 | 746.50 | 2448.65 | 4.48 | 7.22 | 0.997 | 0.994 |
In total, 34 prokaryotic phyla and 55 eukaryotic phyla were detected. The top five bacterial phyla accounted for more than 97.7% of the total abundance.
The results of the principal coordinate analysis (PCoA) based on weighted UniFrac distances revealed that most differences could be attributed to geographical location (Fig. 3). The first two PCoA axes explained more variance in the abundant prokaryotic communities (84.43%; Fig. 3a) than in the eukaryotic communities (42.36%; Fig. 3b). Samples from the SCS and site S1 were grouped together (upper side and right side of the PCoA plots, respectively), while samples from four other sampling sites of the SM were separated.
Based on the composition of the top 40 OTUs, samples were clustered into two groups (Fig. 4). The top 40 OTUs of the 16S RNA gene at sampling sites S3, S4 and S5 were clustered together into a subgroup and the remaining OTUs were included in another group (Fig. 4a).
Redundancy analysis (RDA) was performed to identify possible correlations between environmental factors and picoplankton distribution (Fig. 5). Each environmental variable in the RDA biplot was represented by an arrow and the length of an individual arrow indicated how much variance was explained by that variable. The RDA analysis based on the prokaryotic picoplankton community composition was consistent with PCoA (Fig. 3a). Sampling sites S2, S3, S4 and S5 are located on the right-hand side of the RDA plot. However, the RDA models for eukaryotic picoplankton assemblages indicated that temperature and DO (statistically significant environmental factors;
The RDA analysis revealed that salinity (
As the whole networks of the total OTUs are too complicated to display, only a very limited number of key OTUs with higher abundance were considered. The top 40 OTUs were examined (Fig. 6). All curves of the network connectivity fitted to power-law model (
In the present study, the Illumina MiSeq sequencing technology was employed to describe the diversity and biogeography of picoplankton (including prokaryote and eukaryote) communities from the SM to the SCS. The comparison of the community structure based on OTU percentages indicated a divergent distribution pattern of individual picoplanktic taxa.
The results showed that
Being important autotrophic picoprokaryotes,
Five phyla –
In the present study, the weighted UniFrac PCoA indicated that picoplankton communities of prokaryotes and eukaryotes from the SM and the SCS belong to different groups. Samples from the SCS and site S1 were grouped together and placed on the opposite side of the four other sampling sites in the PCoA plots. Jiang et al. (2015) mentioned that geographical proximity is an important factor affecting the structure of phytoplankton communities from the Pearl River estuary to the SCS. Although site S1 was located in the SM mouth facing the Indian Ocean, the picoplankton communities exhibited characteristics of the open sea, such as the SCS in this study. It has been reported that sampling sites located near islands had a similar bacterial community (Ling et al. 2012). The distance between land and a sampling location may play an important role in the distribution of eukaryotic ultraplankton (Jiang et al. 2014). Therefore, the geographical location could influence the structure of picoplankton communities from the SM to the SCS.
RDA was conducted to further explore the relationships between the environmental factors and the community structure of prokaryotic and eukaryotic picoplankton. With regard to prokaryotes, the distribution of the sampling sites in the UniFrac PCoA analysis was consistent with the RDA results. Prokaryotic samples were clustered into two groups: the strait area (sampling sites S2–S5) and open waters (sampling sites S1, S6–S12). Temperature (
In terms of eukaryotic picoplankton, there was a discrepancy between UniFrac PCoA and RDA. This indicated that other variables, in addition to the eight selected environmental factors, may also contribute to the community cluster patterns, such as grazing. Further research is needed to identify specific factors. Environmental factors (temperature, DO, salinity, grazing and nutrient concentrations) can significantly affect the eukaryotic plankton community in the natural environment (Bernardi Aubry et al. 2013; Jiang et al. 2014; Suikkanen et al. 2007; Suzuki et al. 1997; Wu et al. 2014; De Vargas et al. 2015). Wu et al. (2014) reported that temperature and irradiance affected the picoeukaryotic distribution at the surface and 60 m sampling depth in the SCS (
Complicated networks were constructed by a variety of species interacting with each other in the complex marine ecosystem (Montoya et al. 2006). The marine ecosystem performs the system-level functions (such as biogeochemical cycling, ecosystem stability) owing to these complicated network interactions, and the functions could not be fulfilled by individual populations (Zhou et al. 2010). Community network models were built to demonstrate the interactions between species. The determined competition or cooperation relationships between species were based on nutrients, space, material and information (Zhang et al. 2014). Although ecological networks are very important to the ecosystem, there are few researches on networks of the picoplankton community. Based on Fig. 6, we found that the network size and structure of the prokaryotic community was significantly different from that of the eukaryotic community. In general, the interaction of bacterioplankton was relatively close compared to eukaryotic picoplankton. The results also showed that the cooperation dominated the relationships of both prokaryotic and eukaryotic communities. This is in line with the previous study (Zhang et al. 2014), which has reported that positive connections dominated the interactions between taxa in the DNA-based networks of the total bacterioplankton in the SCS.
In the current study, we focused on the diversity and biogeographic patterns of picoplankton communities, including both prokaryotic and eukaryotic groups from the Straits of Malacca (SM) to the South China Sea (SCS). The results suggested that