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首页> 外文期刊>Harmful Algae >Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China
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Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China

机译：贝叶斯网络在三种蓝藻灌注湖中微囊虫风险评估中的微囊杆菌形态学

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摘要

Microcystis spp., which occur as colonies of different sizes under natural conditions, have expanded in temperate and tropical freshwater ecosystems and caused seriously environmental and ecological problems. In the current study, a Bayesian network (BN) framework was developed to access the probability of microcystins (MCs) risk in large shallow eutrophic lakes in China, namely, Taihu Lake, Chaohu Lake, and Dianchi Lake. By means of a knowledge-supported way, physicochemical factors, Microcystis morphospecies, and MCs were integrated into different network structures. The sensitive analysis illustrated that Microcystis aeruginosa biomass was overall the best predictor of MCs risk, and its high biomass relied on the combined condition that water temperature exceeded 24 degrees C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (= 1.0 mu g/L) was higher under interactive effect of temperature increase and nutrients (nitrogen and phosphorus) imbalance than that of warming alone. Likewise, data-driven model development using a naive Bayes classifier and equal frequency discretization resulted in a substantial technical performance (CCI = 0.83, K = 0.60), but the performance significantly decreased when model excluded species-specific biomasses from input variables (CCI = 0.76, K = 0.40). The BN framework provided a useful screening tool to evaluate cyanotoxin in three studied lakes in China, and it can also be used in other lakes suffering from cyanobacterial blooms dominated by Microcystis.
机译：微囊杆菌SPP。在自然条件下发生的不同尺寸的菌落发生，在温带和热带淡水生态系统中扩展，并引起了严重的环境和生态问题。在目前的研究中，开发了一个贝叶斯网络（BN）框架以进入中国大型浅层养殖湖中的微囊蛋白（MCS）风险的概率，即太湖，巢湖和滇池。通过知识支持的方式，物理化学因素，微阴茎形态和MCS集成到不同的网络结构中。敏感性分析表明，微阴压铜绿假单胞菌生物量总体上是MCS风险的最佳预测因子，其高生物量依赖于水温超过24摄氏度的组合条件，并且总磷在0.2mg / L以上。模拟情景表明，危险MCS（> =1.0μg/ L）的概率在温度升高和营养物质（氮和磷）不平衡的互动效果上较高，而不是单独加温。同样地，使用天真凸床分类器和等频率离散化的数据驱动模型开发导致大量技术性能（CCI = 0.83，k = 0.60），但是当模型从输入变量排除物种特定生物量时，性能显着降低（CCI = 0.76，k = 0.40）。 BN框架提供了一种有用的筛选工具，用于评估中国三个学习的湖泊中的氰毒素，也可以在其他患有由微囊囊主导的蓝藻绽放的湖泊中使用。

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  • 来源
    《Harmful Algae》 |2019年第3期|14-24|共11页
  • 作者

    Shan Kun; Shang Mingsheng; Zhou Botian; Li Lin; Wang Xiaoxiao; Yang Hong; Song Lirong;

  • 作者单位

    Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Big Data Min & Applicat Ctr Chongqing 400714 Peoples R China|Chinese Acad Sci CAS Key Lab Reservoir Environm Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China;

    Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Big Data Min & Applicat Ctr Chongqing 400714 Peoples R China|Chinese Acad Sci CAS Key Lab Reservoir Environm Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China;

    Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Big Data Min & Applicat Ctr Chongqing 400714 Peoples R China|Chinese Acad Sci CAS Key Lab Reservoir Environm Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China;

    Chinese Acad Sci Inst Hydrobiol State Key Lab Freshwater Ecol & Biotechnol Wuhan 430072 Hubei Peoples R China;

    Chinese Acad Sci CAS Key Lab Reservoir Environm Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China;

    Univ Reading Dept Geog & Environm Sci Reading RG6 6AB Berks England;

    Chinese Acad Sci Inst Hydrobiol State Key Lab Freshwater Ecol & Biotechnol Wuhan 430072 Hubei Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Bayesian network; Cyanobacterial blooms; Microcystis; Eutrophication; Microcystin; Climate warming; Lake Taihu; Lake Chaohu; Lake Dianchi;

    机译：贝叶斯网络;蓝藻绽放;微囊杆菌;富营养化;微囊藻;气候变暖;太湖湖;巢湖湖;滇池;

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