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Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China.
Harmful Algae. 2019 03; 83:14-24.HA

Abstract

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 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/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 naïve 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.

Authors+Show Affiliations

Big Data Mining and Applications Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; CAS Key Lab on Reservoir Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China. Electronic address: shankun@cigit.ac.cn.Big Data Mining and Applications Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; CAS Key Lab on Reservoir Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.Big Data Mining and Applications Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; CAS Key Lab on Reservoir Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.CAS Key Lab on Reservoir Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; University of Chinese Academy of Sciences, Beijing 100049, China.Department of Geography and Environmental Science, University of Reading, Whiteknights, Reading, RG6 6AB, UK.State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: lrsong@ihb.ac.cn.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

31097252

Citation

Shan, Kun, et al. "Application of Bayesian Network Including Microcystis Morphospecies for Microcystin Risk Assessment in Three Cyanobacterial Bloom-plagued Lakes, China." Harmful Algae, vol. 83, 2019, pp. 14-24.
Shan K, Shang M, Zhou B, et al. Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China. Harmful Algae. 2019;83:14-24.
Shan, K., Shang, M., Zhou, B., Li, L., Wang, X., Yang, H., & Song, L. (2019). Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China. Harmful Algae, 83, 14-24. https://doi.org/10.1016/j.hal.2019.01.005
Shan K, et al. Application of Bayesian Network Including Microcystis Morphospecies for Microcystin Risk Assessment in Three Cyanobacterial Bloom-plagued Lakes, China. Harmful Algae. 2019;83:14-24. PubMed PMID: 31097252.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China. AU - Shan,Kun, AU - Shang,Mingsheng, AU - Zhou,Botian, AU - Li,Lin, AU - Wang,Xiaoxiao, AU - Yang,Hong, AU - Song,Lirong, Y1 - 2019/01/25/ PY - 2018/09/02/received PY - 2018/12/12/revised PY - 2019/01/09/accepted PY - 2019/5/18/entrez PY - 2019/5/18/pubmed PY - 2020/2/23/medline KW - Bayesian network KW - Climate warming KW - Cyanobacterial blooms KW - Eutrophication KW - Lake Chaohu KW - Lake Dianchi KW - Lake Taihu KW - Microcystin KW - Microcystis SP - 14 EP - 24 JF - Harmful algae JO - Harmful Algae VL - 83 N2 - 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 °C and total phosphorus was above 0.2 mg/L. Simulated scenarios suggested that the probability of hazardous MCs (≥1.0 μg/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 naïve 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. SN - 1878-1470 UR - https://www.unboundmedicine.com/medline/citation/31097252/Application_of_Bayesian_network_including_Microcystis_morphospecies_for_microcystin_risk_assessment_in_three_cyanobacterial_bloom_plagued_lakes_China_ DB - PRIME DP - Unbound Medicine ER -