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Distribution of phytoplankton in the East China Sea and the southern Yellow Sea in spring in relation to environmental variables and dimethylsulfide compounds
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Jiawei Zhang1, 2, 5, †, Yanghang Chen4, †, Xueyan Ren1, 2, Vishal Patil1, 2, Lin Sun1, 2, Xuesong Li1, 2, Junrong Liang1, 2, Jun Zhang1, 2, Yahui Gao1, 2, 3, *, Changping Chen1, 2, 3, *
Acta Oceanologica Sinica | 2022, 41(6) : 41 - 53
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Acta Oceanologica Sinica | 2022, 41(6): 41-53
• Dynamics of ecosystems and anthropogenic drivers in the Yellow Sea Large Marine Ecosystem •
Distribution of phytoplankton in the East China Sea and the southern Yellow Sea in spring in relation to environmental variables and dimethylsulfide compounds
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Jiawei Zhang1, 2, 5, †, Yanghang Chen4, †, Xueyan Ren1, 2, Vishal Patil1, 2, Lin Sun1, 2, Xuesong Li1, 2, Junrong Liang1, 2, Jun Zhang1, 2, Yahui Gao1, 2, 3, *, Changping Chen1, 2, 3, *
Affiliations
  • 1 School of Life Sciences, Xiamen University, Xiamen 361102, China
  • 2 Key Laboratory of the Coastal and Wetland Ecosystems of Ministry of Education, Xiamen University, Xiamen 361102, China
  • 3 State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
  • 4 Laboratory of Marine Biology and Ecology, Third Institute of Oceanography, State Oceanic Administration, Xiamen 361005, China
  • 5 Eco-Environmental Monitoring and Research Center, Pearl River Valley and South China Sea Ecology and Environment Administration, Ministry of Ecology and Environment, Guangzhou 510610, China
Published: 2022-06-25 doi: 10.1007/s13131-021-1913-0
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The coastal ecosystems are highly sensitive to climate change and are usually influenced by variations in phytoplankton communities and water physiochemical factors. In the present study, the phytoplankton community, chlorophyll a (Chl a) and their relationships with environmental variables and dimethylsulfide (DMS) and dimethylsulfoniopropionate (DMSP) were investigated in spring 2017 (March 24 to April 16) in the East China Sea (26.0°–33.0°N, 120.0°–128.0°E) and southern Yellow Sea (31.0°–36.0°N, 120.0°–125.0°E). The spatial distributions of phytoplankton species composition and cell density were investigated by qualitative and quantitative methods and were compared with historical data to study phytoplankton species succession in the survey area. The results showed that there were 275 phytoplankton species belonging to 90 genera and 6 phyla in the survey area, of which 208 species belonged to 62 genera of Bacillariophyta and 56 species belonged to 20 genera of Pyrrophyta. The dominant phytoplankton species were Skeletonema dohrnii, Chaetoceros vanheurckii and Prorocentrum donghaiense. The phytoplankton cell densities ranged from 0.06×104 cells/L to 418.73×104 cells/L, with an average value of 21.46×104 cells/L. In spring, the average ratio of Bacillariophyta/Pyrrophyta was 41.13 for the entire study area. The areas with high phytoplankton cell density were mainly distributed in the northern South Yellow Sea and offshore waters of the East China Sea. According to a canonical correspondence analysis among phytoplankton and environmental parameters, the water Chl a concentrations were notably consistent with phytoplankton cell density (P<0.001), and both showed significant negative correlations with salinity and nitrite (P<0.05) and significant positive correlations with dissolved oxygen and pH (P<0.001). There was a significant positive correlation between diatom (both in cell density and in dominant species) and DMS (P<0.05), which indicated that diatoms play a greater role in DMS production in this investigated area.

phytoplankton  /  distribution  /  East China Sea  /  southern Yellow Sea  /  dimethylsulfoniopropionate
Jiawei Zhang, Yanghang Chen, Xueyan Ren, Vishal Patil, Lin Sun, Xuesong Li, Junrong Liang, Jun Zhang, Yahui Gao, Changping Chen. Distribution of phytoplankton in the East China Sea and the southern Yellow Sea in spring in relation to environmental variables and dimethylsulfide compounds[J]. Acta Oceanologica Sinica, 2022 , 41 (6) : 41 -53 . DOI: 10.1007/s13131-021-1913-0
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1 Introduction
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Phytoplankton are the main primary producers of marine ecosystems and account for nearly 95% of marine primary productivity (Koblentz-Mishke et al., 1970; Reynolds, 1984; Sun, 2011; Zhou, 2014) due to photosynthesis and the energy supply to other organisms (Falkowski et al., 1992; Noman et al., 2019), which are the foundations of marine ecosystems and food webs that sustain most oceanic life (Charlson et al., 1987; Sarmiento et al., 1988) and play a key role in marine ecosystems (Jia et al., 2014). Due to its short life cycle, phytoplankton is very sensitive to changes in environmental stress conditions, and the community structure of phytoplankton can reflect the nutritional status of marine ecosystems; therefore, phytoplankton are a critical indicator of changes in marine climates and environments (Norse, 1993; Hays et al., 2005; Torrisi and Dell’Uomo, 2006). For example, the eutrophication of seawater will lead to the miniaturization of phytoplankton, the different ratio of nitrogen to phosphorus will lead to the change of phytoplankton particle size structure, and the frequency of small-size diatom red tide will increase (Chen et al., 2010; Ma, 2018); the acidification of seawater and the decrease of pH will lead to the decrease of diatom species (Ma, 2018); the inhibition of cryptophytes and diatoms was more significant at high salinities (Van Meerssche and Pinckney, 2017). Especially, with changes in the global environment, the structure of phytoplankton communities has changed markedly, which has been indicated by the change of diatom predominance to dinoflagellates and cyanobacteria predominance, and the proportion of toxic and harmful algal blooms (HAB) is increasing (Anderson et al., 2002; Wu and Kow, 2002; Taylor et al., 2007; Aubry et al., 2012).
In recent years, with frequent human activities and the intensification of industrialization, global environmental and climate change have created unprecedented challenges (Lammers et al., 2013; Hoegh-Guldberg et al., 2018; Sekerc and Ozarslan, 2020). These anthropogenically induced changes have altered terrestrial and aquatic environments and ecosystems (Lammers et al., 2013). Emissions of industrial waste gas, fossil fuel burning, deforestation and wetland destruction have led to ocean acidification and global warming (Zeebe, 2012). Global warming directly influences the increment of temperature at the ocean surface, which not only causes increased water vapor in the atmosphere (Schaffer et al., 2000), leading to a greenhouse effect, but also causes glacial melting (Najjar et al., 2000, 2010) and sea level rise (Nick et al., 2013), and thereby changing the marine environment. The change of marine environment has an important impact on the marine ecosystem starting from phytoplankton, which is mainly manifested in the succession of phytoplankton community structure, physiological and ecological response, primary productivity and biogeochemical cycle (Findlay et al., 1999; Zhao et al., 2020).
On the other hand, phytoplankton produce an organic compound, dimethylsulfoniopropionate (DMSP), which is important for regulating the global climate and environment. DMSP is one of the main carbon and sulfur sources in the ocean (Stefels and van Leeuwe, 1998; Simó, 2001; Stefels et al., 2007). It also plays a role in osmotic pressure regulation (Kirst, 1990) and resistance to freezing (Nishiguchi and Somero, 1992; Karsten et al., 1996) of phytoplankton. Acrylic acid, one of its degradation products, can promote phytoplankton to resist predation (Wolfe and Steinke, 1996), while dimethylsulfide (DMS), another degradation product, plays an important role in climate regulation. DMSP, as the precursor of DMS, is decomposed by DMSP lyase (DLA) to form DMS. DMS released from seawater accounts for 95% of natural marine sulfur emissions and plays an important role in the global sulfur cycle. Charlson et al. (1987) proposed the CLAW hypothesis, which states that DMS has the function of regulating global climate. There may be a negative feedback process between DMS emission and climate change. With global warming, the surface temperature of seawater increases, which promotes phytoplankton productivity and thus accelerates DMS release. Recently, Deng et al. (2021) reported that DMS concentrations were affected by the pH of phytoplankton photosynthetic processes; when the pH exceeded 8.1, DMS degradation accelerated, and its concentration decreased. Andreae and Crutzen (1997) stated that if the DMS flux changes one time, the global average temperature will change by several degrees Celsius.
The East China Sea (ECS) and Yellow Sea (YS) are important marginal seas in the Northwest Pacific Ocean (Chen, 2009). This area is one of the most typical continental shelf shallow seas in the world (Yu et al., 2017). The environmental status of this sea area is not only affected by inputs of land-based materials, but also is closely related to the water exchange process of the adjacent ocean. The main circulation system of the ECS is composed of the Taiwan Warm Current in the Kuroshio shelf area and the coastal current in the nearshore area (Guan, 1994; Chen, 2009). At the same time, large amounts of fresh water are transported to the ECS by runoff from of the Changjiang River, which has an important impact on the marine environment (Zhao et al., 2019a). The YS is a semiclosed shelf shallow sea. The warm current in the middle of the YS and the coastal currents on both sides are the main circulations in the YS. There are cold, seasonal water masses in the middle of the YS in summer (Guan, 1994; Zhang et al., 1996). These complex ocean circulations and abundant land-based material inputs affect the environment and ecosystem of the ECS and YS (Liu and Xu, 1963; Zhou et al., 2008; Chen, 2009; Chinese Society of Oceanography, 2015), which cause the marine environment to be eutrophic and have high primary productivity to produce more DMS, leading to significant impacts on global environmental change. Therefore, it is especially important to study phytoplankton in this sea area. After consulting the literature, we found that research on marine phytoplankton in the ECS and YS began in the late 1950s, and large volumes of data have been gathered (Zhu and Guo, 1959; Guo, 1963; Chen et al., 1980; Huang et al., 1984; Furuya et al., 1996; Chiang et al., 1999, 2004). To date, many studies by domestic and foreign scholars have covered many aspects, such as phytoplankton taxonomy, size fraction structure, floristic distribution, environmental adaptability and HAB (Lu and Goebel, 2001; Zhou et al., 2008; Song, 2010; Guo et al., 2011). Former studies have shown that the species composition and distribution of phytoplankton in the YS and ECS are largely controlled by the marine environment and oceanographic processes, such as the Yellow Sea Cold Water Mass, distribution of Changjiang River Diluted Water and invasion of Kuroshio into the ECS shelf area (Liu et al., 2012; Wang et al., 2014; Song et al., 2017; do Rosario Gomes et al., 2018; Zhao et al., 2018, 2019b). The community diversity of phytoplankton and the response of biogenic active gases to phytoplankton are the key areas of marine phytoplankton investigation in China.
At present, studies of DMS in this area are mainly focused on the concentration distribution, air sea flux estimation and relationship of phytoplankton with physical and chemical factors (Yang et al., 2015a; Liu et al., 2016; Jian et al., 2019). However, the contribution of phytoplankton community to DMS has been rarely studied. Moreover, most studies in China have concentrated on the local waters of Qingdao (Hu et al., 1995, 1997; Ma, 2004; Ma et al., 2004), Xiamen (Du et al., 1998; Jin et al., 2004), the Bohai Sea (Xu et al., 2019), the YS (Yang et al., 2015b; Liu et al., 2016), and the ECS (Yang et al., 2011), which have space coverage limitations. Few studies have examined the relationship between phytoplankton and DMS at the scale of the entire east shelf sea area. In this study, which is based on an investigation of the phytoplankton distributions in the ECS and the South YS (SYS) from March to April 2017, the chlorophyll a (Chl a) concentration and phytoplankton cell density, community structure and species diversity are studied. The aim of this study is to reveal the relationships between phytoplankton and environmental factors, and the DMS and DMSP concentrations across the entire ECS and SYS.
2 Materials and methods
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2.1 Study area
From March 27 to April 15, 2017, the R/V Dongfanghong 2 scientific research vessel established 10 sections in the ECS (26.0°– 33.0°N, 120.0°–128.0°E) and the SYS (31.0°–36.0°N, 120.0°–125.0°E) to conduct a comprehensive hydrological, chemical and biological survey at 58 stations (Fig. 1).
2.2 Sample collection and phytoplankton species identification
Sample collection, processing and analysis of the phytoplankton community and Chl a concentration were carried out in accordance with General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China and Standardization Administration (2008). In this survey, seawater samples were collected by the R/V Dongfanghong 2 in the ECS and SYS. Seawater was obtained at 58 stations using a series of 12 L Niskin bottles attached to a Seabird 911 plus conductivity-temperature-depth (CTD) sensor rosette (SEA-BIRD, America). Salinity, temperature and water depth were measured using the Seabird 911 CTD equipment. Chl a samples were filtered with a Whatman GF/F glass fiber filter membrane (diameter 25 mm) with a pore size of 0.70 μm under a pressure of less than 15 kPa. To prevent Chl a decomposition, 1 mL of the MgCO3 suspension was added to the seawater samples before filtration. After filtration was completed, the filter membrane was folded in half, wrapped in aluminum foil and then frozen in liquid nitrogen. The Chl a concentration was determined by the fluorescence method (Parsons et al., 1984). The filters were extracted with 10 mL 90% acetone solution at low temperature (−20°C) for approximately 24 h and were then centrifuged at 4 500 r/min for 15 min, and the supernatant was determined by the Trilogy laboratory-type fluorescence analyzer 7200-000 (Turner Designs, America). A series of standard concentration gradient solutions were prepared with a pure Chl a product (Sigma, America) before sample determination. Fluorescence signals were measured using the Turner Designs system, and working curves were drawn according to the concentration and fluorescence signals.
Phytoplankton water samples (1 000 mL) from each representative layer was collected and poured into sample bottles. The samples were immediately fixed with Lugol’s iodine solution at a ratio of 1.5%. All samples were statically deposited for 24 h, and the supernatant was removed by a siphon. A 0.1 mL water sample was placed into a counting box that was used for identification (Round et al., 1990; Guo and Qian, 2003; Huang and Lin, 2012; Cheng et al., 2013a, b; Yang et al., 2016a, b), and counting was performed used an Olympus BH-2 microscope. Each sample was counted twice, and the number of specimens per microscopic examination was more than 500 individuals. There were no significant differences between the two microscopic examinations of the same sample.
The DMS determination method is described in Zhang et al. (2014). DMSP determinations were carried out by pretreatment, which converted 1:1 DMSP into DMS (Dacey and Blough, 1987; Yang et al., 2011). Particulate DMSP (DMSPp) was calculated as the difference between total DMSP and dissolved DMSP (DMSPd) (Xu et al., 2019). The DMSP determinations were carried out by pretreatment, which converted 1:1 DMSOt and DMSOd into DMS. The particulate dimethylsulfoxide (DMSOp) levels were derived from the differences between DMSOt and dissolved DMSO (DMSOd) (Kiene and Gerard, 1994; Spiese et al., 2009). The determination of sulfide-related data was completed in the Ocean University of China (unpublished), and they were used for correlation analysis.
2.3 Data processing and statistical analysis
The Shannon-Wiener diversity index (H'), Pielou evenness index (J) and dominance index (Y) were used to analyze the phytoplankton community structure. The Shannon-Wiener diversity index formula (Shannon, 1948; Shannon and Weaver, 1949) is as follows:
$ {H}^{{'}}=-\sum _{i=1}^{s}{P}_{i}{{\rm{l}}\mathrm{o}\mathrm{g}}_{2}{P}_{i}, $
in the formula, Pi is the ratio of the number of species i in the sample to the total number of samples, and S is the number of species in the sample.
The Pielou Uniformity Index Formula (Pielou, 1969) is as follows:
$ J=\frac{{H}^{{'}}}{{{\rm{l}}\mathrm{o}\mathrm{g}}_{2}S}, $
where S is the number of species in the sample.
The dominance calculation formula (Lampitt et al., 1993) is as follows:
$ Y=\frac{{n}_{i}}{N}{f}_{i} , $
where ni is the number of species i in all samples; N is the number of species; and fi is the frequency of occurrence of the species at each survey station.
Statistical Product and Service Solutions 11.0 and Canoco 4.5 were used to analyze the correlations between dominant species and environmental factors. Correlation analyses among phytoplankton density, diversity index, Bacillariophyta cell density/Pyrrophyta cell density (B/P), DMSO and environmental factors in the SYS and ECS in spring 2017 were performed using the R Programming Language software. The relationships between the phytoplankton community structure and the main environmental factors were analyzed with CANOCO software. As we considered dominant species, only those taxa that were observed in >10% of all samples were included in the analyses (Chen, 2017; Leira and Sabater, 2005). Species data and environmental parameters were transformed by log10(x+1) and were then analyzed to obtain normal distributions. First, the species data were analyzed by detrended correspondence analysis to determine the appropriate ranking model. Then, corresponding ranking analysis was carried out, and the correlation among different stations, different species and environmental factors was analyzed by a double sequence diagram.
3 Results
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3.1 Characteristics of temperature and salinity in seawater
The surface seawater temperatures of the SYS and the ECS are shown in Fig. 2a. The temperature range in this investigation is 6.91°C to 12.32°C for the SYS and 10.91°C to 23.17°C for the ECS, with average values of 9.40°C for the SYS and 16.98°C for the ECS. The average sea surface temperature of the entire investigated sea area is 13.97°C. It can be clearly observed that the surface temperature of this sea area is different from north to south and gradually rises from north to south. The water temperature gradually rises from northwest to southeast in the SYS and from north to south in the ECS.
The average salinity of the whole eastern shelf sea area in this investigation is 32.60 compared to 30.82 in the Changjiang River Estuary and 34.38 in the open sea area. The salinity range in the SYS is 25.81 to 33.84, with an average of 32.23, and that in the ECS is 28.22 to 35.10, with an average of 32.84. The surface salinities of the SYS and ECS fluctuate greatly from 28.81 to 35.10, and the lowest value appears near the Changjiang River Estuary and gradually increases (Fig. 2b).
3.2 Distribution of Chl a concentration
The mean Chl a concentration in the investigated sea area is 1.097 μg/L and ranges from 0.028 μg/L to 8.805 μg/L. The high-value areas mainly occur in the southern Shandong Peninsula of the YS and offshore areas of the ECS. The Chl a concentration at the Stations E3 (8.805 μg/L), A2 (7.691 μg/L) and S2 (7.416 μg/L) are relatively high. The lowest Chl a concentration is 0.028 μg/L at the Station E7. The Chl a concentration decreases from alongshore to offshore (Fig. 3a). The peak Chl a values are relatively higher in the southern waters of the Shandong Peninsula of the YS and offshore waters of Zhejiang and Fujian in the ECS (35.0°–37.0°N, 27.0°–31.0°N, 121.0°–124.0°E) (Fig. 3a).
3.3 Species composition in phytoplankton
In total, 275 species of phytoplankton belonging to 90 genera and 6 phyla of algae were identified in this study, including 208 species belonging to 62 genera of Bacillariophyta, accounting for 75.636% of the total number of species; 56 species, 20 genera, and 20.364% for Pyrophyta; 4 species, 4 genera, and 1.455% for Cyanophyta; 4 species, 2 genera, and 1.455% for Prymnesiophyta; 2 species, 1 genus, and 0.727% for Chlorophyta; and 1 species, 1 genus, and 0.363% for Cryptophyta (Fig. 4).
Bacillariophyta and Pyrrophyta were the main phylum of phytoplankton species in the survey area. The areas with high B/P ratios were generally consistent with the phytoplankton cell densities and distributions of the dominant population (Figs 3b, c). In spring, the average B/P ratio was 41.13 in the entire study area, of which the average for the SYS was 16.31, with the lowest value at Station B5 (0.71) and highest value (231.00) at Station D5; the average value in the ECS was 57.44, of which the highest was 550.86 (Station E3) and the lowest was 0.10 (Station E5).
3.4 Phytoplankton cell density
The phytoplankton cell densities ranged from 0.06×104 cells/L to 418.73×104 cells/L, with an average value of 21.46×104 cell/L. The highest cell density was 418.73×104 cells/L at Station E3, and the lowest cell density was 0.06 ×104 cells/L at Station F3. The phytoplankton cell density in the northern SYS and offshore waters of the ECS were high (Fig. 3c). The average diatom cell density was 18.52×104 cells/L, while the highest cell density was 417.96×104 cells/L (Station E3), and the lowest cell density was 0.02×104 cells/L (Station F3).
3.5 Dominant species of phytoplankton
Skeletonema dohrnii was the dominant species in spring 2017. Its average cell density was 8.05×104 cells/L, which accounted for 37.51% of the total phytoplankton cell density. The maximum cell density was 262.50×104 cells/L at Station E3 (Fig. 5a); Chaetoceros vanheurckii had an average cell density of 1.94×104 cells/L, which accounted for 9.04% of the total density, and the maximum cell density was 70.50×104 cells/L at Station E3 (Fig. 5b); Prorocentrum donghaiense had an average cell density of 0.68 ×104 cells/L (3.17%) and a maximum of 6.60×104 cells/L at Station B6 (Fig. 5c). The distributions of the dominant phytoplankton species during this voyage were generally consistent with the overall cell density distribution, which was mainly concentrated in the northern part of the SYS and offshore areas of the ECS. The high-value areas for S. dohrnii and C. vanheurckii were mainly distributed in the offshore areas of Zhejiang and Fujian in the ECS (Stations E3 and S2), and the high-value areas for P. donghaiense were distributed in the eastern part of the SYS (Figs 5a–c). The frequency and dominance of the dominant species are shown in Table 1.
3.6 Community structure of phytoplankton
The average Shannon-Wiener index for the phytoplankton community in spring in the SYS and ECS was 2.965, and the average Pielou’s index was 0.668. The species number in the SYS ranged from 20 to 25, and the species number in the ECS coastal waters was much higher (35 to 45). In the ECS, the phytoplankton Shannon-Wiener and Pielou’s indexes both showed a trend of being low in coastal areas and high in offshore areas (Fig. 6).
4 Discussion
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4.1 Distribution characteristics of Chl a
In spring 2017, the Chl a concentration in the investigated sea area was characterized by high alongshore values and low offshore values (Fig. 3a). Compared with historical data from the same season, the Chl a concentrations in the YS and the ECS were different (Table 2). The Chl a concentrations in spring 2017 were significantly higher than those in 2000–2001 (Huang et al., 2006), similar to those in 2006–2007 (Wang, 2011) and lower than those in 2011 (Wen et al., 2012). These results may be attributed to the hydrological differences among the investigated sea areas. In spring, the cold water mass of the YS has not yet formed (Li et al., 2012). Because of the mixing action, large amounts of nutrients are carried to the upper and middle seawater and phytoplankton multiply in large numbers, leading to the occurrence of areas with high Chl a concentrations in the SYS. This result is consistent with that of Cho et al. (1994). Due to the influence of the Kuroshio subsurface water and Taiwan Warm Current on the eastern and southern parts of the ECS (Wen et al., 2012), the concentration of Chl a is low in the open sea. In spring, the coastal areas of the ECS are controlled by fresh water with low transparency and high nutrient levels (Li and Luan, 1998), as well as by the influence of coastal upwelling, which forms a high-value area in the southern part of the Changjiang River Estuary near the Zhoushan Islands and Minjiang River Estuary.
4.2 Phytoplankton community structure, interannual variations and their relationship with environmental factors
The investigation of phytoplankton in the eastern shelf area shows that phytoplankton are mostly concentrated in the Changjiang River Estuary and adjacent sea areas (Table 3). The number of phytoplankton species in this survey area increased from 2002 to 2011. Diatoms and dinoflagellates are the most important groups in the investigated sea area. In spring, the upwelling of the ECS coast leads to increased water temperatures and nutrients, and the rapid growth of phytoplankton near the coast, which forms a high-density phytoplankton accumulation area.
The dominant species in previous surveys were mainly Pseudo-nitzschia sp., Thalassiosira sp., Skeletonema costatum, Prorocentrum dentatum and Chaetoceros sp. (Table 3). The dominant species of phytoplankton obtained in this survey were different from those shown in the historical data. It is suspected that, in this investigation of P. donghaiense, this species is the same as the P. dentatum mentioned in past studies because many scholars have mistakenly identified P. donghaiense as P. dentatum in the past (Chen et al., 2015; Qi and Wang, 2003). Skeletonema dohrnii and C. vanheurckii exhibit greater dominance in coastal areas and form dominant populations (Fig. 5), which may be due to the increased temperatures and nutrient levels in spring that are affected by upwelling along the Fujian (Jiang and Wang, 2018; Wang et al., 2016) and Zhejiang (Duan et al., 2014) coasts.
Correlation analyses among phytoplankton cell density, diversity index, B/P and sulfide and environmental factors in the SYS and the ECS in the spring of 2017 were performed using the R Programming Language software. There was a significant negative correlation between phytoplankton density and salinity (P<0.05), and phytoplankton density decreased with increasing salinity (Fig. 7a). There was a significant correlation between phytoplankton density and nitrite (P<0.05); generally, nutrients affect the distribution and community structure of phytoplankton (Fig. 7a). There were significant correlations between phytoplankton density and dissolved oxygen, and pH and Chl a concentration (P<0.001) (Fig. 7a). In this study, the correlation between phosphate, nitrate and phytoplankton density is not significant, because the nutrients in the coastal waters in spring increase and the upwelling is significant. Nutrients are not the main factor limiting the growth and reproduction of phytoplankton. When the environmental conditions are suitable, phytoplankton grow and reproduce in large quantities. The ratio of Si, N and P will affect the phytoplankton community structure. According to the historical data over the years (Luan, 2007; Tan et al., 2009; Tian et al., 2010), when the environmental conditions are suitable in spring, the growth and reproduction of diatoms will be promoted first. Skeletonema and Chaetoceros will often become the dominant species or red tide species in the sea area, which may increase the production of DMSPp.
4.3 The relationship of DMS with the phytoplankton community structure
In a review of previous findings, it was found that little attention has been paid to the relationship between the phytoplankton community structure and DMS in the entire sea area during the same season (Yang et al., 2016; Jian et al., 2018, 2019). The relationship of DMS with the phytoplankton community structure in the investigated sea areas was analyzed in the present study. Our results showed that there was a significant correlation between phytoplankton density and DMS (P<0.05), a significant positive correlation with DMSPp and DMSOp (P<0.001), and a significant positive correlation between B/P ratio and DMS and DMSPp (P<0.05) (Fig. 7b). Pearson correlation analysis found that diatom cells density showed significant positive correlations with DMS (P<0.05) and DMSPp (P<0.01). At the same time, through Canonical correspondence analysis, we found that the dominant species of phytoplankton (C. curvisetus, C. debilis, C. diadema, C. muelleri, C. vanheurckii, Leptocylindrus danicus, and S. dohrnii) with high DMSP contents were positively correlated with dimethyl sulfur compounds (Fig. 8), indicating that diatoms may play an important role in DMS production in the investigated sea area. These results can be attributed to the fact that phytoplankton are the main contributors to marine DMS and that DMSPp is mainly derived from phytoplankton cells (Archer et al., 2013; Thariath et al., 2019). DMSPd is produced through natural secretion, cytolysis during virus lysis, grazing by zooplankton or cell rupture (Jian et al., 2019; Stefels et al., 2007; Wolfe et al., 2000; Zubkov et al., 2002). DMSP is decomposed by DMSP lyase or bacteria after entering seawater to produce DMS (Alcolombri et al., 2014, 2015; Caruana and Malin, 2014). According to the available references, it is known that diatom is not main DMSP producer at individual species level, compared with other phytoplankton groups such as Pyrrophyta and Haptophyta (Jian et al., 2019). In previous investigations of this area, researchers have paid more attention to the correlations between Chl a concentration, dinoflagellates, environmental factors and DMS (Yang et al., 2000; Wu et al., 2017; Xu et al., 2019; Qu et al., 2020), while the relationships between the phytoplankton community structure (especially diatoms) and DMSP have received little attention, which may have ignored the contribution of diatoms to dimethylsulfide in the sea area. Many studies have shown that diatoms dominate phytoplankton communities in the YS and ECS in spring (Guo, 2012; Tian et al., 2010; Xiao et al., 2013; Zhou et al., 2015). In this study, we found that the concentration of DMSPp was higher in the stations with higher diatom density, for example, the density of S. dornii in Station S2 accounts for 80.71% of the total density, and its DMSPp concentration is the highest in the investigated sea area; the density of S. dornii and C. vanheurckii in Station E3 accounted for 79.53% of the total density, and the DMSPp concentration in Station E3 ranking only second to in Station S2. Some stations with higher dinoflagellate density had lower DMSP concentration, for example, the concentration of DMSPp in stations (Stations B5 and FJ3) with lower B/P ratio was at lower level. This indicates that the DMSP production of some diatoms is not lower than that of dinoflagellates. It also states that diatoms can also make great contributions to the overall production of dimethylsulfide in waters when the diatom cell density is very high in seawater, and these results are consistent with those of Jiao et al. (2003).
5 Conclusions
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In the spring survey of 2017, 275 species of phytoplankton were identified in the SYS and ECS, and both phytoplankton taxa and abundances were dominated by diatoms followed by dinoflagellates. The high phytoplankton abundances were mainly present in the coastal waters of Fujian and Zhejiang, which were affected by upwelling of Fujian and Zhejiang coastal waters and were rich in nutrients.
Phytoplankton play an important role in DMS production and are the main source of dimethylsulfide compounds in the ocean. In particular, the correlations between the cell density and dominant species of diatom and dimethylsulfide species were significantly positive (P<0.05). Although some studies have shown that the DMS yield from diatom cells is not high, the contribution to DMS by diatoms cannot be ignored under the high cell density conditions of the sea. Our results demonstrated that diatoms play a greater role in the production of dimethylsulfide in the ESC and SYS.
Acknowledgements
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We thank the captain and crew of the R/V Dongfanghong 2 for their help and cooperation during the cruises. We would like to express our sincere thanks to Guipeng Yang of China Ocean University for providing DMS, DMSP and other related data, and to Yu Xin for providing nutrition data. We also thank the laboratory staff who helped collect samples during the expedition.
Funding
Less
  • The National Key Research and Development Program of China under contract Nos 2016YFA0601302 and 2018FY100202.
References
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Article Info
doi: 10.1007/s13131-021-1913-0
  • Receive Date：2021-02-19
  • Online Date：2025-11-21
  • Published：2022-06-25
Article Data
Affiliations
History
  • Received：2021-02-19
  • Accepted：2021-07-29
Funding
The National Key Research and Development Program of China under contract Nos 2016YFA0601302 and 2018FY100202.
Affiliations
    1 School of Life Sciences, Xiamen University, Xiamen 361102, China
    2 Key Laboratory of the Coastal and Wetland Ecosystems of Ministry of Education, Xiamen University, Xiamen 361102, China
    3 State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
    4 Laboratory of Marine Biology and Ecology, Third Institute of Oceanography, State Oceanic Administration, Xiamen 361005, China
    5 Eco-Environmental Monitoring and Research Center, Pearl River Valley and South China Sea Ecology and Environment Administration, Ministry of Ecology and Environment, Guangzhou 510610, China

Corresponding:

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chencp@xmu.edu.cn
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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