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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Abstract: 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.
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Key words:
- phytoplankton /
- distribution /
- East China Sea /
- southern Yellow Sea /
- dimethylsulfoniopropionate
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Figure 1. Map of the location of phytoplankton survey stations in the East China Sea and the southern Yellow Sea.
Figure 2. Distributions of sea surface temperature (°C, a) and salinity (b) in the investigated areas of the East China Sea and southern Yellow Sea.
Figure 3. Distributions of Chl a concentration (a), Bacillariophyta cell density/Pyrrophyta cell density (B/P) (b) and phytoplankton cell density (c) in the East China Sea and the southern Yellow Sea.
Figure 5. Distributions of phytoplankton dominant species in the East China Sea and the southern Yellow Sea. a. Skeletonema dohrnii; b. Chaetoceros vanheurckii; and c. Prorocentrum donghaiense.
Figure 6. Phytoplankton species diversity in the East China Sea and the southern Yellow Sea. a. Shannon-Wiener index; b. Pielou’s index; and c. species number.
Figure 7. Correlation analysis of phytoplankton, environmental factors and sulfide in seawater. a. Correlation analysis of phytoplankton diversity with temperature, salinity and nutrients; b. correlation analyses of phytoplankton diversity with DO, pH, Chl a concentration and dimethyl sulfide concentration. Density represents phytoplankton density; S-W, Shannon-Wiener diversity index;
${\rm{N}}{\rm{H}}^+_{4}\text{-}{\rm{N}}$ , ammonium nitrogen;${\rm{N}}{\rm{O}}^-_{2}\text{-}{\rm{N}}$ : nitrate nitrogen;${\rm{P}}{\rm{O}}^{3-}_{4}\text{-}{\rm{N}}$ , phosphate phosphorus;${\rm{S}}{\rm{i}}{\rm{O}}^{2-}_{3}\text{-}{\rm{S}} {\rm{i}}$ , silicate silicon; B/P, Bacillariophyta cell density/Pyrrophyta cell density; DO, dissolved oxygen; Chl a: Chlorophyll a concentration; pH, potential of hydrogen; DMS, dimethylsulfide; DMSPd, dissolved dimethylsulfoniopropionate; DMSPp, particle dimethylsulfoniopropionate; DMSOd, dissolved dimethyl sulfoxide; DMSOp, particle dimethyl sulfoxide. Blue numbers indicate negative correlations; red numbers, positive correlations. * denotes P<0.05; **, P<0.01; and ***, P<0.001. The coordinate axis value in the figure indicates the parameters of the diagonal.
Figure 8. Biplot of the canonical correspondence analysis results for dominant phytoplankton cell density (a), sampling stations (b) and environmental variables (→). Numbers with letters represent the relevant species: SP1, Bacteriastrum hyalinum; SP2, Chaetoceros castracanei; SP3, C. curvisetus; SP4, C. debilis; SP5, C. densus; SP6, C. diadema; SP7, C. lorenzianus; SP8, C. muelleri; SP9, C. pelagicus; SP10, C. pseudocurvisetus; SP11, C. vanheurckii; SP12, Cyclotella striata; SP13, Frustulia interposita var. chinensis; SP14, Leptocylindrus danicus; SP15, Melosira moniliformis; SP16, Nitzschia closterium; SP17, N. longissima; SP18, Paralia sulcata; SP19, P. donghaiense; SP20, P. micans; SP21, P. minimun; SP22, Pseudo-nitzschia delicatissima; SP23, Skeletonema costatum; SP24, S. dohrnii; SP25, S. marinol; SP26, S. munzelii; SP27, Thalassiosira rotula; SP28, Thalassiosira sp.; and SP29, Trichodesmium hildebrandtii. Chl a represents Chlorophyll a concentration; Density, sampling station phytoplankton density.
Table 1. Dominant species of phytoplankton in the East China Sea and the southern Yellow Sea
Dominant species Cell density
/(104 cells·L-1)Frequency
(fi)Dominance
(Y)Skeletonema dohrnii 8.05 0.34 0.03 Chaetoceros vanheurckii 1.94 0.38 0.14 Prorocentrum donghaiense 0.68 0.77 0.02 
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Table 2. Comparisons of the average Chl a concentration in this study and other investigations in the Yellow Sea (YS) and the East China Sea (ECS)
Investigation area Investigation time Average Chl a concentration/(μg·L−1) References SYS, ECS 2017 1.10 this study SYS, northern of ECS 2011 1.43 Wen et al. (2012) YS, ECS 2006–2007 1.17 Wang (2011) SYS, ECS 2000–2001 0.75 Huang et al. (2006) Note: SYS is the abbreviation of the South Yellow Sea.
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Table 3. Comparison of species number, cell density and dominant species of the same survey area in this study and other studies
Sampling time Survey area Species
numberCell density/
(104 cells·L−1)Dominant species References April, 2017 26°–36°N, 120°–128°E 275 21.46 Skeletonema dohrnii, Chaetoceros vanheurckii, Prorocentrum donghaiense this study May, 2011 25°00'–33°30'N,
120°00'–127°30'E193 6.69 Prorocentrum dentatum, Pseudo-nitzschia delicatissim, Skeletonema sp., Paralia sulcata, Prorocentrum minimum, Gymnodinium lohmanni, Coscinodiscus sp., Chaetoceros curvisetus Zhao et al. (2015) April, 2009 30°30'–32°00'N,
121°00'–123°30'E64 58.22 Skeletonema dohrnii, Paralia sulcata, Thalassionema nitzschioides, Pseudo-nitzschia pungens Sun and Tian (2011) May, 2008 26°50'–34°07'N,
120°50'–123°59'E155 26.63 Prorocentrum dentatum, Skeletonema costatum, Scrippsiella trochoidea, Paralia sulcata Tian et al. (2010) May, 2007 26°50'–34°00'N,
120°50'–124°00'E144 7.89 Pseudo-nitzschia delicatissim, Prorocentrum dentatum, Skeletonema costatum, Thalassiosira rotula Tan et al. (2009) June–July, 2006 25°00'–39°00'N,
118°00'–129°00'E136 0.84 Thalassiosira scrotiformis, Pseudo-nitzschia pungens Zhou (2014); Zhou et al. (2015) June, 2006 26°00'–34°00'N,
121°00'–126°00'E130 14.70 Prorocentrum dentatum, Karenia mikimotoi, Pseudo-nitzschia pungens, Pseudo-nitzschia. delicatissima Wang et al. (2008) May, 2005 30°30'–32°30'N,
121°00'–123°30'E92 36.18 Karenia mikimotoi, Skeletonema costatum, Prorocentrum dentatum Luan (2007) May, 2002–June, 2005 (Spring) 27°00'–32°00'N,
122°00'–123°30'E162 – Paralia sulcate, Nitzschia spp., Pseudo-nitzschia spp., Pseudo-nitzschia delicatissima, Pseudo-nitzschia pungens, Thalassiosira spp., Thalassiosira rotula, Skeletonema costatum, Coscinodiscus spp., Chaetoceros spp. Xie (2007) April, 1998 23°30'–33°00'N,
118°30'–128°00'E– 0.002 Chaetoceros lorenzianus, Noctiluca scintillans Luo et al. (2007) 1960, 1971 27°–34°N,
the west of 127°E– – Rhizosolenia styliformis, Guinardia cylindrus, Climacodium biconcavum Chen et al. (1980) Note: – represents no data.
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