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Application of Bayesian network including Microcystis morphospecies for microcystin risk assessment in three cyanobacterial bloom-plagued lakes, China
Shan, Kun1,3; Shang, Mingsheng1,3; Zhou, Botian1,3; Li, Lin2; Wang, Xiaoxiao3; Yang, Hong4; Song, Lirong2,5
2019-03-01
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.
关键词Bayesian network Cyanobacterial blooms Microcystis Eutrophication Microcystin Climate warming Lake Taihu Lake Chaohu Lake Dianchi
DOI10.1016/j.hal.2019.01.005
发表期刊HARMFUL ALGAE
ISSN1568-9883
卷号83页码:14-24
收录类别SCI
WOS记录号WOS:000470940300002
语种英语