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The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R 2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Every member of the ecological community is connected via a network of vital and complex relationships, called the web of life. To elucidate the ecological network and interactions among producers, consumers, and decomposers in the Daechung Reservoir, Korea, during cyanobacterial harmful algal blooms (cyanoHAB), especially those involving Microcystis, we investigated the diversity and compositions of the cyanobacterial (16S rRNA gene), including the genotypes of Microcystis (cpcBA-IGS gene), non-cyanobacterial (16S), and eukaryotic (18S) communities through high-throughput sequencing. Microcystis blooms were divided into the Summer Major Bloom and Autumn Minor Bloom with different dominant genotypes of Microcystis. Network analysis demonstrated that the modules involved in the different phases of the Microcystis blooms were categorized into the Pre-Bloom, Bloom, Post-Bloom, and Non-Bloom Groups at all sampling stations. In addition, the non-cyanobacterial components of each Group were classified, while the same Group showed similarity across all stations, suggesting that Microcystis and other microbes were highly interdependent and organized into cyanoHAB-related module units. Importantly, the Microcystis genotype-based sub-network uncovered that Pirellula, Pseudanabaena, and Vampirovibrionales preferred to interact with specific Microcystis genotypes in the Summer Major Bloom than with other genotypes in the Autumn Minor Bloom, while the copepod Skistodiaptomus exhibited the opposite pattern. In conclusion, the transition patterns of cyanoHAB-related modules and their key components could be crucial in the succession of Microcystis genotypes and to enhance the understanding of microbial ecology in an aquatic environment., (Copyright © 2019 Elsevier Ltd. All rights reserved.)