Key Laboratory of Reservoir Aquatic Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Toxins (Basel). 2022 Aug 2;14(8):530. doi: 10.3390/toxins14080530.
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 bloom toxicity responds to relevant physicochemical characteristics and zooplankton assemblages. As expected, the BART model achieved better performance in biomass and MCs concentration predictions than some comparative models, including random forest and multiple linear regression. The importance analysis via BART illustrated that the shade index was overall the best predictor of MCs concentrations, implying the predominant effects of light limitations on the MCs content of . Variables of greatest significance to the toxicity of also included pH and dissolved inorganic nitrogen. However, total phosphorus was found to be a strong predictor of the biomass of total and toxic . Together with the partial dependence plot, results revealed the positive correlations between protozoa and biomass. In contrast, copepods biomass may regulate the MC quota and concentrations. Overall, our observations arouse universal demands for machine-learning strategies to represent nonlinear relationships between harmful algal blooms and environmental covariates.
有毒蓝藻水华已成为人类和环境健康的严重全球性危害。大多数研究都集中在蓝藻组成与蓝藻毒素产生之间的关系上。然而，人们对影响蓝藻毒素危害的环境条件知之甚少。在这里，我们分析了一个独特的 22 个站点数据集，该数据集包括一个大型浅水湖中水质、蓝藻属、浮游动物组合以及微囊藻（MCs）配额和浓度的月度观测值。使用非负潜在因子（NLF）分析对 MCs 的缺失值进行了插补，结果达到了令人满意的准确性。此外，我们使用贝叶斯加法回归树（BART）来量化水华毒性如何响应相关物理化学特征和浮游动物组合。正如预期的那样，BART 模型在生物量和 MCs 浓度预测方面的性能优于一些比较模型，包括随机森林和多元线性回归。通过 BART 进行的重要性分析表明，阴影指数总体上是 MCs 浓度的最佳预测因子，这意味着光照限制对 MCs 含量的主导作用。对 的毒性最重要的变量还包括 pH 和溶解无机氮。然而，总磷被发现是总 和有毒 的生物量的强预测因子。与偏依赖图一起，结果揭示了原生动物和 的生物量之间的正相关关系。相比之下，桡足类生物量可能会调节 MC 配额和浓度。总的来说，我们的观察结果引起了对机器学习策略的普遍需求，以表示有害藻类水华与环境协变量之间的非线性关系。
