Algal Interaction-Mediated Biogenic Volatiles Enable Accurate Algal Bloom Prediction

Algal volatile organic compounds (AVOCs) act as real-time metabolic signals that enable accurate bloom prediction in single-species systems. However, interspecies interactions reshape algal growth dynamics across bloom stages, and evaluating how AVOC-based warning approaches perform under algal interactions is crucial. Here, proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) was employed to profile AVOCs from monocultures and cocultures of Microcystis aeruginosa and Chlorella vulgaris, and machine learning models were established to predict algal density. Coculture conditions, which inhibited C. vulgaris but stimulated M. aeruginosa, induced AVOCs putatively associated with fatty acid β-oxidation and carotenoid degradation, aligning with reported oxidative stress and intercellular signaling responses. These AVOCs (n = 1268) exhibited remarkable predictive power for algal density under interspecies interactions, with an extreme gradient boosting model achieving high accuracy (R²: 0.96). Shapley additive explanation analysis identified two categories of predictive AVOCs: fundamental metabolism-associated (e.g., methanethiol and dimethylamine, 22.66%) and interactions-associated (e.g., isophorone and (3E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), 6.97%) AVOCs. Interaction-associated AVOCs, particularly terpenoids known to participate in stress signaling and interspecific communication, exhibited a 123% increase in predictive importance. Transcriptomic analyses suggested that fundamental metabolism-associated AVOCs were functionally connected to photosynthesis and ribosomal pathways, whereas interaction-associated AVOCs were more closely related to carotenoid and fatty acid metabolic processes. The significant correlation between DMNT content and algal biomass was validated in a natural lake. These findings provide a biologically grounded framework for real-time bloom prediction by incorporating interaction-mediated volatiles into early warning strategies.