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Reservoirs, heavily influenced by artificial management, often harbor phytoplankton assemblages dominated by cyanobacteria or dinoflagellates, triggering significant changes in aquatic ecosystems. However, due to limited sampling frequency and insufficient attention to species composition, the bloom processes and key characteristics of phytoplankton community structure have not been systematically elucidated. During the low-water level period when blooms are most likely to occur (June to September) in a tributary bay of the Three Gorges Reservoir, daily sampling was conducted to investigate phytoplankton community composition, identify significant environmental factors, and evaluate important structure characteristics of phytoplankton community. The results showed that Microcystis aeruginosa maintained a clear dominance for almost a month in stage 1, with low Shannon and evenness but a high dominance index. Phytoplankton total density and biomass decreased drastically in stage 2, but Microcystis aeruginosa still accounted for some proportion. The highest Shannon and evenness but the lowest dominance index occurred in stage 3. Peridiniopsis niei occurred massively in stage 4, but its dominant advantages lasted only one to two days. NH4-N was responsible for the dominance of Microcystis aeruginosa, while TP and PO4-P was responsible for the dominance of Peridiniopsis niei; however, precipitation contributed to their drastic decrease or disappearance to some extent. The TN: TP ratio could be considered as an important indicator to determine whether Microcystis aeruginosa or Peridiniopsis niei dominated the phytoplankton community. Throughout the study period, physiochemical factors explained more variation in phytoplankton data than meteorological and hydrological factors. Pairwise comparisons revealed an increase in average β diversity with stage progression, with higher β diversities based on abundance data than those based on presence/absence data. Repl had a greater effect on β diversity differences based on presence/absence data, whereas RichDiff had a greater effect on β diversity differences based on species abundance data. Co-occurrence networks for stage 1 showed the most complex structure, followed by stage 4, while the network for stage 3 was relatively sparse, although the overall community division remained compact. This study provides a useful attempt to explore the status and changes in phytoplankton community structure during the bloom process through high-resolution investigation. [ABSTRACT FROM AUTHOR]

As a typical lake in the early stage of eutrophication, the non-point source pollution (NSP) in Erhai Lake was highly concerned. Since 2017, protection and rescue actions have been implemented in the Erhai Lake basin which significantly improved the water quality of Erhai Lake. But little attention has been paid to the interception effects of these actions on NSP. Based on high resolution datasets, including refinement land use/land cover (LU/LC) data, China Meteorological Assimilation Driving Database for the SWAT (CMADS), and Advanced Land Observing Satellite-1 (ALOS-12) data, the interception effects of ecological ponds newly built in 2018 on NSP was discussed with the support of the soil and water assessment (SWAT) model. These three high resolution datasets can meet the needs for simulating NSP by SWAT model, which was conductive to better reveal the interception effects of ecological ponds on NSP. Besides, the pollution load of shrubbery on Total nitrogen (TN)/Total phosphorous (TP), which has never been involved in similar research works in the Erhai Lake basin, was measured. Compared the temporal and spatial variations in TN/TP concentration before and after 2018, the interception effects of ecological ponds on NSP in the Erhai Lake basin were revealed by a sharp decrease in TN/TP concentration and a delayed presence of annual peak value about 1 month late in TN concentration before and after 2018. The interception intensity of NSP was determined by the number and volume of ecological ponds in each sub-basin and their corresponding upstream.

Reservoirs, heavily influenced by artificial management, often harbor phytoplankton assemblages dominated by cyanobacteria or dinoflagellates, triggering significant changes in aquatic ecosystems. However, due to limited sampling frequency and insufficient attention to species composition, the bloom processes and key characteristics of phytoplankton community structure have not been systematically elucidated. During the low-water level period when blooms are most likely to occur (June to September) in a tributary bay of the Three Gorges Reservoir, daily sampling was conducted to investigate phytoplankton community composition, identify significant environmental factors, and evaluate important structure characteristics of phytoplankton community. The results showed that Microcystis aeruginosa maintained a clear dominance for almost a month in stage 1, with low Shannon and evenness but a high dominance index. Phytoplankton total density and biomass decreased drastically in stage 2, but Microcystis aeruginosa still accounted for some proportion. The highest Shannon and evenness but the lowest dominance index occurred in stage 3. Peridiniopsis niei occurred massively in stage 4, but its dominant advantages lasted only one to two days. NH 4 -N was responsible for the dominance of Microcystis aeruginosa , while TP and PO 4 -P was responsible for the dominance of Peridiniopsis niei ; however, precipitation contributed to their drastic decrease or disappearance to some extent. The TN : TP ratio could be considered as an important indicator to determine whether Microcystis aeruginosa or Peridiniopsis niei dominated the phytoplankton community. Throughout the study period, physiochemical factors explained more variation in phytoplankton data than meteorological and hydrological factors. Pairwise comparisons revealed an increase in average β diversity with stage progression, with higher β diversities based on abundance data than those based on presence/absence data. Repl had a greater effect on β diversity differences based on presence/absence data, whereas RichDiff had a greater effect on β diversity differences based on species abundance data. Co-occurrence networks for stage 1 showed the most complex structure, followed by stage 4, while the network for stage 3 was relatively sparse, although the overall community division remained compact. This study provides a useful attempt to explore the status and changes in phytoplankton community structure during the bloom process through high-resolution investigation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tan, Wang and Cai.)

Nitrogen (N) and phosphorus (P) affect the growth of phytoplankton in the waters. Nitrogen is an important part in forming protein in organisms, while phosphorus is the most essential element for growth and contributes in protein formation and cell metabolism in organisms. The activities from the catchment area that enter into the P.B Soedirman Reservoir can affect both concentration of N and P. The comparison total nitrogen (TN) and total phosphorus (TP) is the limiting factor to affect the growth of phytoplankton. Chrysophyta is one of division from phytoplankton that has function as an important component in the food chain and become as natural feed for fish. This research showed the abundance of Chrysophyta, relations of TN with the abundance of Chrysophyta; TP with the abundance of Chrysophyta; TN/TP with the abundance of Chrysophyta; TN, TP, TN/TP with the abundance Chrysophyta in the P.B Soedirman Reservoir, Banjarnegara Regency. This research used survey method with purposive sampling technique on 7 observation stations with 3 repeating. The relation of TN/TP with the abundance of Chrysophyta were analyzed used correlation regression - correlation analyzis. The results of the research showed that total abundance Chrysophyta was between 339-9570 ind/l, relation between TN/TP with the abundance of Chrysophyta with the degree was weak to very strong correlation. The most affection correlation was shown by of TN, TP, TN/TP collectively with the abundance of Chrysophyta with coefficient value (r) of 0,890 with the strength of the relation of 79,3%. The regression analysis of TN, TP, TN/TP collectively with the abundance of Chrysophyta was shown by the equation Y= – 80987,801 – 8499,931TN + 1,684TP + 406,373TN/TP.