Diversity and distribution analysis of eukaryotic communities in the Xiangshan Bay, East China sea by metabarcoding approach.

Eukaryotic communities play an important role in the coastal ecosystem of Xiangshan Bay, a narrow semi-closed bay famous for fisheries and marine farming. However, information on the diversity and composition of eukaryotic communities in Xiangshan Bay remains unclear. In this study, the metabarcoding approach was utilized to comprehensively investigate the eukaryotic plankton community structure and dominant taxa, particularly eukaryotic microalgae, in the Xiangshan Bay over a period of four months in 2018. The results showed that the three major phyla were Arthropoda, Chlorophyta, and Bacillariophyta. The richness indices revealed that species richness peaked in February and was at its lowest in May. Diversity indices showed that the samples collected in May had the lowest diversity. Centropages was detected in the samples of all months, however, its highest dominance was observed in the samples collected in February. In addition, compared to other months, a greater proportion of eukaryotic microalgae was witnessed in March. The three eukaryotic algae with highest abundances in March were Cyclotella, Prorocentrum, and Thalassiosira. Moreover, high diversity of pico-sized (0.2-2.0 µm) phytoplankton (which are often easily missed by microscopy) was discovered in this study by using metabarcoding approach. This study highlights the strength and significance of the metabarcoding approach to uncover a large number of eukaryotic species which remains undetectable during application of conventional approaches. The findings of this study reveals that the eukaryotic community structure varies noticeably in both time and space throughout sampling period, with temperature being the most important environmental factor influencing these changes. This study lays a solid foundation to understand eukaryotic plankton composition, temporal and spatial dynamics and the distribution mechanism of eukaryotic plankton community in Xiangshan Bay, providing theoretical reference for further studies related to marine ecology.

[Effects of environmental factors on ß diversity of zooplankton community in thermal discharge seawaters near Guohua Power Plant in Xiangshan Bay, Zhejiang, China].

Zooplankton samples were seasonally collected at 10 stations in thermal discharge seawaters near Guohua Power Plant in Xiangshan Bay. The abundance data from these samples were pooled and further combined with field environmental factors, then generalised dissimilarity modelling (GDM) was used to explore the effects of environmental factors on ß diversity of zooplankton community. The results showed that altogether 95 species of zooplankton belonging to 14 taxa were found. In these taxa, small zooplankton with 62.6% of abundance was the main taxa, while copepods dominated in adult groups, which abundance accounted for 35.3%. According to Whittaker's definition and additive partition, a diversity accounted for 36.3% and ß diversity 63.7%. Environmental factors explained 43.8% of ß diversity, and geographical distance between sampling sites had no effect on ß diversity. However, there were still 19.9% of ß diversity remained to be explained. After GDM fitting, there were nine environmental variables affecting zooplankton ß diversity and explaining 68.8% of ß diversity. The variables contributing to ß diversity from high to low were seasonal water temperature, dissolved oxygen, seawater temperature increment, conductivity, suspended particulate matter, salinity, transparency, water depth and redox potential, respectively. Seasonal water temperature, dissolved oxygen and seawater temperature increment were the most important factors for driving ß diversity changes, and accounted for 23.9%, 13.7% and 9.7% of absolute contribution to the interpretable portion of the ß diversity, respectively. When seasonal water temperature, dissolved oxygen and seawater temperature increment were below 25 °C, greater than 5 mg · L(-1) and over 1 °C, respectively, ß diversity rapidly increased with the increasing variable gradients. Furthermore, other predictors had little effect on ß diversity.