The ecological province based on phytoplankton species composition is important to understanding the interplay between environmental parameters and phytoplankton species composition. The aim of this study was to establish phytoplankton species composition ecological pattern thus elucidate the relationship between environmental factors and the phytoplankton species composition in the ecological provinces. Phytoplankton samples were collected from 31 stations in Yellow Sea(121.00?–125.00?E, 32.00?–39.22?N) in November 2014. The samples were enumerated and identified with the Uterm?hl method under an optical inverted microscope-AE2000 with magnifications of 200 × or 400 ×. In the present study, a total of 141 taxa belonging to 60 genera of 4 phyla of phytoplankton were identified, among them 101 species of 45 genera were Bacillariophyta, 36 species of 11 genera were Dinophyta, 3 species of 3 genera were Chrysophyta and 1 species of 1 genera was Chlorophyta. The study area was divided into 4 ecological provinces according to an unsupervised cluster algorithm applied to the phytoplankton biomass. A T-S(Temperature-Salinity) scatter diagram depicted with data of water temperature and salinity defined by environmental provinces matched well with the ecological provinces. The results of Canonical Correspondence Analysis(CCA) indicated that the phytoplankton species composition was mainly correlated with temperature, salinity and silicate concentration in the studied area. A method of establishing ecological provinces is useful to further understanding the environmental effects on the marine phytoplankton species composition and the consequent marine biogeochemistry.
LI XiaoqianFENG YuanyuanLENG XiaoyunLIU HaijiaoSUN Jun
The seasonal variations in phytoplankton community structure were investigated for the Sanggou Bay(SGB) and the adjacent Ailian Bay(ALB) and Lidao Bay(LDB) in Shandong Peninsula, eastern China. The species composition and cell abundance of phytoplankton in the bay waters in spring(April 2011), summer(August 2011), autumn(October 2011), and winter(January 2012) were examined using the Utermhl method. A total of 80 taxa of phytoplankton that belong to 39 genera of 3 phyla were identified. These included 64 species of 30 genera in the Phylum Bacillariophyta, 13 species of 8 genera in the Phylum Dinophyta, and 3 species of 1 genus in the Phylum Chrysophyta. During the four seasons, the number of phytoplankton species(43) was the highest in spring, followed by summer and autumn(40), and the lowest number of phytoplankton species(35) was found in winter. Diatoms, especially Paralia sulcata(Ehrenberg) Cleve and Coscinodiscus oculus-iridis Ehrenberg, were predominant in the phytoplankton community throughout the study period, whereas the dominance of dinoflagellate appeared in summer only. The maximum cell abundance of phytoplankton was detected in summer(average 8.08 × 103 cells L-1) whereas their minimum abundance was found in autumn(average 2.60 × 103 cells L-1). The phytoplankton abundance was generally higher in the outer bay than in the inner bay in spring and autumn. In summer, the phytoplankton cells were mainly concentrated in the south of inner SGB, with peak abundance observed along the western coast. In winter, the distribution of phytoplankton cells showed 3 patches, with peak abundance along the western coast as well. On seasonal average, the Shannon-Wiener diversity indices of phytoplankton community ranged from 1.17 to 1.78(autumn > summer > spring > winter), and the Pielou's evenness indices of phytoplankton ranged from 0.45 to 0.65(autumn > spring > summer > winter). According to the results of canonical correspondence analysis, phosphate level was the major factor that limited the occurrence of P. su
YUAN MingliZHANG CuixiaJIANG ZengjieGUO ShujinSUN Jun
Spatial distribution of phaeopigment and size-fractionated chlorophyll a(Chl a) concentrations were examined in relation to hydrographic conditions in the northern South China Sea(NSCS) during a survey from 20 August to 12 September, 2014. The total Chl a concentration varied from 0.006 to 1.488 μg/L with a mean value of 0.259±0.247(mean±standard deviation) μg/L. Chl a concentration was generally higher in shallow water(<200 m) than in deep water(>200 m), with mean values of 0.364±0.311 μg/L and 0.206±0.192 μg/L respectively. Vertically, the maximum total Chl a concentration appeared at depths of 30–50 m and gradually decreased below 100 m. The size-fractionated Chl a concentrations of grid stations and time-series stations(SEATS and J4) were determined, with values of pico-(0.7–2 μm), nano-(2–20 μm) and micro- plankton(20–200 μm) ranging from 0.001–0.287(0.093±0.071 μg/L), 0.004–1.149(0.148±0.192 μg/L) and 0.001–0.208(0.023±0.036 μg/L), respectively. Phaeopigment concentrations were determined at specifi c depths at ten stations, except for at station A9, and varied from 0.007 to 0.572(0.127±0.164) μg/L. Nano-and pico-plankton were the major contributors to total phytoplankton biomass, accounting for 50.99%±15.01% and 39.30%±15.41%, respectively, whereas microplankton only accounted for 9.39%±8.66%. The results indicate that the contributions of microplankton to total Chl a biomass were less important than picoplankton or nanoplankton in the surveyed NSCS. Diff erent sized-Chl a had similar spatial patterns, with peak values all observed in subsurface waters(30–50 m). The summer monsoon, Kuroshio waters, Zhujiang(Pearl) River plume, and hydrological conditions are speculated to be the factors controlling the abundance and spatial heterogeneity of Chl a biomass in the NSCS.
