biostudies-literatureWang XWest Light Foundation of The Chinese Academy of SciencesNational Basic Research Program of ChinaChongqing Science and Technology CommissionNational Natural Science Foundation of Chinakey cooperation project of Chongqing Municipal Education Commission530https://www.ebi.ac.uk/biostudies/studies/S-EPMC9413751biostudies-literatureUnknown14(8)Toxic cyanobacterial blooms have become a severe global hazard to human and environmental health. Most studies have focused on the relationships between cyanobacterial composition and cyanotoxins production. Yet, little is known about the environmental conditions influencing the hazard of cyanotoxins. Here, we analysed a unique 22 sites dataset comprising monthly observations of water quality, cyanobacterial genera, zooplankton assemblages, and microcystins (MCs) quota and concentrations in a large-shallow lake. Missing values of MCs were imputed using a non-negative latent factor (NLF) analysis, and the results achieved a promising accuracy. Furthermore, we used the Bayesian additive regression tree (BART) to quantify how <i>Microcystis</i> bloom toxicity responds to relevant physicochemiToxinsRevealing Physiochemical Factors and Zooplankton Influencing <i>Microcystis</i> Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning.PMC941375151609229620724292008CB418006cstc2019jscx-gksbX0042HZ202100841701247E1296001Song LShan KShang MWang XWang LfalseRevealing Physiochemical Factors and Zooplankton Influencing <i>Microcystis</i> Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning.Toxic cyanobacterial blooms have become a severe global hazard to human and environmental health. Most studies have focused on the relationships between cyanobacterial composition and cyanotoxins production. Yet, little is known about the environmental conditions influencing the hazard of cyanotoxins. Here, we analysed a unique 22 sites dataset comprising monthly observations of water quality, cyanobacterial genera, zooplankton assemblages, and microcystins (MCs) quota and concentrations in a large-shallow lake. Missing values of MCs were imputed using a non-negative latent factor (NLF) analysis, and the results achieved a promising accuracy. Furthermore, we used the Bayesian additive regression tree (BART) to quantify how <i>Microcystis</i> bloom toxicity responds to relevant physicochemi2022-01-01T00:00:00Z2022 Aug2025-08-30T03:14:01.077Z2025-02-19T00:34:04.286ZS-EPMC94137513600619210.3390/toxins14080530