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Microcystis Abundance is Predictable Through Ambient Bacterial Communities: A Data-Oriented Approach

JOURNAL OF ENVIRONMENTAL MANAGEMENT(2024)

Korea Res Inst Biosci & Biotechnol | K Water Res Inst | Natl Inst Environm Res

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Abstract
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 R2 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.
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Key words
Cyanobacterial harmful algal blooms,Microcystis,Prediction model,Multilayer perceptron,Bacterial community,Amplicon sequence variant
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要点】:本研究通过结合环境因素与细菌群落数据,利用多层感知器(MLP)机器学习模型,显著提高了对微囊藻(Microcystis)动态的预测准确度,揭示了微生物群落在预测蓝藻水华(cyanoHABs)中的重要性。

方法】:研究采用多层感知器(MLP)模型,将细菌群落与每周水质数据相结合,对韩国大忠水库和南海东河的微囊藻动态进行预测。

实验】:实验使用Daechung Reservoir和Nakdong River的数据集,模型性能以R2值表示,在结合细菌群落数据和环境因素的情况下,R2值提高至0.97,仅使用环境因素时R2值为0.78。通过模型的事后分析发现,氮源在微囊藻爆发中的作用比磷源更为关键,而细菌扩增序列变异体之间的贡献没有显著差异。