Revealing Physiochemical Factors and Zooplankton Influencing Microcystis Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning
文献类型：期刊论文
| 作者 | Wang, Xiaoxiao1,5 ; Wang, Lan3,4; Shang, Mingsheng5; Song, Lirong2; Shan, Kun5
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| 刊名 | TOXINS
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| 出版日期 | 2022-08-01 |
| 卷号 | 14期号:8页码:16 |
| 关键词 | Microcystis blooms microcystins nutrient zooplankton machine learning risk management Lake Taihu |
| DOI | 10.3390/toxins14080530 |
| 通讯作者 | Shan, Kun(shankun@cigit.ac.cn) |
| 英文摘要 | 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 Microcystis bloom toxicity responds to relevant physicochemical characteristics and zooplankton assemblages. As expected, the BART model achieved better performance in Microcystis 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 Microcystis. Variables of greatest significance to the toxicity of Microcystis also included pH and dissolved inorganic nitrogen. However, total phosphorus was found to be a strong predictor of the biomass of total Microcystis and toxic M. aeruginosa. Together with the partial dependence plot, results revealed the positive correlations between protozoa and Microcystis 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. |
| 资助项目 | National Natural Science Foundation of China[62072429] ; National Natural Science Foundation of China[51609229] ; National Natural Science Foundation of China[41701247] ; Chongqing Science and Technology Commission[cstc2019jscxgksbX0042] ; West Light Foundation of The Chinese Academy of Sciences[E1296001] ; key cooperation project of Chongqing Municipal Education Commission[HZ2021008] ; National Basic Research Program of China[2008CB418006] |
| WOS研究方向 | Food Science & Technology ; Toxicology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000845145200001 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/16518]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Shan, Kun |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst HydroBiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China 3.Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China 4.Chongqing Univ Educ, Sch Artificial Intelligence, Chongqing 400147, Peoples R China 5.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Key Lab Reservoir Aqut Environm, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Xiaoxiao,Wang, Lan,Shang, Mingsheng,et al. Revealing Physiochemical Factors and Zooplankton Influencing Microcystis Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning[J]. TOXINS,2022,14(8):16. |
| APA | Wang, Xiaoxiao,Wang, Lan,Shang, Mingsheng,Song, Lirong,&Shan, Kun.(2022).Revealing Physiochemical Factors and Zooplankton Influencing Microcystis Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning.TOXINS,14(8),16. |
| MLA | Wang, Xiaoxiao,et al."Revealing Physiochemical Factors and Zooplankton Influencing Microcystis Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning".TOXINS 14.8(2022):16. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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