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Cyanobacterial blooms represent a significant environmental problem, threatening aquatic ecosystems worldwide. Caused by the eutrophication of water bodies and global climate change, these blooms have altered freshwater ecosystems worldwide during recent decades. Although cyanobacterial blooms are typically caused by blue-green cyanobacteria, which derive their color from the phycocyanin pigment, other pigmented cyanobacterial blooms have been frequently observed in water bodies. These blooms pose a serious environmental threat to inland waters, endangering global public health and aquatic ecosystems. Therefore, comprehending the mechanism of color variation in cyanobacterial blooms is crucial for revealing the outbreak mechanism and implementing effective prevention and control measures. This study developed a human–machine interactive workflow for extracting cyanobacterial blooms and recognizing their colors based on the Forel-Ule index and Sentinel-2 MultiSpectral Instrument data. Using this workflow, the authors conducted spatiotemporal analysis and statistical analysis of bloom color for cyanobacterial blooms in four typical eutrophic lakes from 2019 to 2022. The findings indicated a declining trend in cyanobacterial blooms across the four studied lakes over the years, among which Hulun Lake experienced an annual increase in cyanobacterial blooms and emerged as the lake with the most severe outbreak of such blooms in 2022. The yellowing status of cyanobacterial blooms varied among the four lakes, with Taihu Lake and Dianchi Lake exhibiting a relatively high proportion of green-yellow and yellow cyanobacterial blooms, followed by Chaohu Lake, whereas Hulun Lake had the lowest occurrence. The workflow developed in this study was implemented in Google Earth Engine and provided an automated, integrated, and rapid monitoring solution for the long-term monitoring and color recognition of cyanobacterial blooms. [ABSTRACT FROM AUTHOR]

Cells of Microcystis are associated with heterotrophic bacteria and organized in colonies in natural environment, which are basic elements in the mass occurrence of cyanobacterial species. Analyzing these colonies by using metagenomics is helpful to understand species composition and relationship. Meanwhile, the difference in population abundance among Microcystis colonies could be used to recover genome bins from metagenome assemblies. Herein, we designed a pipeline to obtain high-quality genomes of mutualistic bacteria from single natural Microcystis colonies. Single colonies were lysed, and then amplified by using multiple displacement amplification to overcome the DNA quantity limit. A two-step assembly was performed after sequencing and scaffolds were grouped into putative bins based on their differential-coverage among species. We analyzed six natural colonies of three prevailing Microcystis species from Lake Taihu. Clustering results proved that colonies of the same species were similar in the microbial community composition. Eight putative population genome bins with wide bacterial diversity and different GC content were identified based on coverage difference among colonies. At the phylum level, proteobacteria was the most abundant besides cyanobacteria. Six of the population bins were further refined into nearly complete genomes (completeness > 90%). [ABSTRACT FROM AUTHOR]

• Cyanobacteria impacted plankton bio-diversity and resource use efficiency. • No clear relation between cyanobacteria and phytoplankton absolute abundances. • Cyanobacteria had opposite relation with phytoplankton and zooplankton bio-diversities. • Cyanobacteria increased total algae and cladocerans RUEs. • Cyanobacteria decreased phytoplankton and rotifers RUEs. Cyanobacterial blooms are global threats to freshwater ecosystem functioning, human health, and ecoservices. We assessed impacts of cyanobacterial bloom intensity on plankton ecosystem functioning using eukaryotic phytoplankton and zooplankton indicators and associated key physicochemical data collected from four seasons of two years at 24 evenly distributed sites in Lake Taihu that has year-around cyanobacterial blooms. Our analyses involved comparison of four site-groups with different bloom intensities and analyzing all sampling sites together using comparison, hierarchical partitioning analysis, generalized additive mixed model, and structural equation model. We found that cyanobacterial abundance positively associated with TP and temperature (negatively with TN:TP), while phytoplankton positively associated with TN. There was an inverse relation trend between relative abundances of phytoplankton and cyanobacteria, but there was no clear trend between absolute abundances of phytoplankton and cyanobacteria. Rotifers were most dominant when cyanobacteria were unabundant, while cladocerans presented higher abundance when cyanobacteria were in high abundance. Phytoplankton functional richness and species richness negatively and zooplankton functional richness and species richness positively associated with cyanobacterial bloom intensity. Cyanobacterial bloom intensity negatively associated with resource use efficiencies (RUEs) of phytoplankton and rotifers, and positively associated with RUE of cladocerans. Our analytical approach of integrating comparison of site-groups and analyzing all sites together uncovered how cyanobacterial bloom intensity shifted and altered physicochemical and biological conditions and plankton ecosystem functioning, and identified the mechanism and strength of the interactive linkages among physicochemical and biological indicators. Although our results may be different from oligotrophic lakes or reservoirs, our findings provide new insights in understanding the impacts of cyanobacterial bloom intensity on the dynamics of plankton communities and ecosystem functioning for polymictic eutrophic lakes, which may have broad application in enhancing the knowledge of this subject and provides the science base for managing polymictic eutrophic lake water quality and ecosystem functioning. [ABSTRACT FROM AUTHOR]