Niche Divergence-Based Identification of Microbial Indicators Tracking Cyanobacterial Bloom Succession: A Metagenomic and Machine Learning Framework

Abstract

Abstract Cyanobacterial harmful algal blooms (CyanoHABs) pose significant environmental and public health risks through toxin production, antibiotic resistance gene dissemination, and the disruption of biogeochemical cycles. Although extensive metagenomic investigations have explored microbial interactions between Cyanobacteria and coexisting members, the potential of incorporating functional indicators into early CyanoHAB monitoring systems remains underexplored. In this study, we employed metagenomic binning combined with machine learning approaches to analyze seasonal cyanobacterial succession and coexisting microbial communities across 19 sites in Erhai Lake across four seasons. We documented distinct niche shifts driven by the spatiotemporal heterogeneity in seasonal cyanobacterial succession alongside the formation of unique niches triggered by this asynchrony. Specialized microbial members (SN-MAGs) were identified as candidate bioindicators associated with early-stage cyanobacterial proliferation. Functional profiling showed r/K selection and metabolic adaptations in these SN-MAGs, including stress resistance and altered carbon metabolism, linked to cyanobacterial dynamics. Machine learning models leveraging these features were constructed and retrospectively evaluated. Cross-lake validation using an independent Lake Taihu dataset confirmed associations among these features and microbial community shifts preceding Microcystis proliferation. Our findings highlight the potential value of the dynamics of specific bacteria as a sensitive indicator of cyanobacterial dynamics. Moreover, we propose that these identified functional markers could potentially be developed into targeted molecular assays (e.g., qPCR assays), offering possible utility in predicting Microcystis population expansion.