Abstract: To clarify the spatial and temporal variations of phytoplankton community structure of Taihu Lake during the early stage of the fishing ban and their key environmental driving factors, we investigated water environmental factors and phytoplankton community structure at 18 sampling sites in Taihu Lake in May (spring), August (summer), November (autumn) and December (winter) of 2020. A total of 130 phytoplankton species from 7 phyla and 67 genera were identified. Chlorophyta (65 species) was the dominant group, followed by Bacillariophyta (30 species) and Cyanophyta (20 species). The dominant species throughout the year included Microcystis sp., Dolichospermum sp., and Pseudanabaena sp. Dominant species differed across seasons and regions, but the dominance of Microcystis sp. was the greatest. There was significant seasonal variation in phytoplankton density and biomass, and no spatial difference. The mean density and biomass were the highest in summer, with (1.14±1.04)×10⁸ cells·L^-1 and 14.67±9.64 mg·L^-1, respectively. Phytoplankton density was the lowest in autumn, with (0.32±0.55)×10⁸ cells·L^-1, while the lowest biomass of 2.49±4.35 mg·L^-1 in winter. Redundancy analysis showed that nine water quality indicators, including water temperature, total nitrogen, turbidity, chemical oxygen demand, nitrite, water depth, pH, conductivity and dissolved oxygen, significantly influenced the structure of phytoplankton community. Based on the evaluation results of dominant species, diversity index and comprehensive trophic state index, the water quality of Taihu Lake was between moderately and heavily polluted throughout the year. The study can provide scientific information for understanding the current status of phytoplankton community structure and the evaluation of ecosystem health of the Taihu Lake. It also provides basic data support for the impact of the implementation of fishing ban on lake ecosystem.

Key words: phytoplankton, fishing ban, spatial and temporal characteristics, environmental factor, water quality assessment