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Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks
Li, Hongbin; Hou, Guoxiang; Dakui, Feng; Xiao, Bangding; Song, Lirong; Liu, Yonyding; Hou, GX, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
2007-06-01
Source PublicationECOLOGICAL INFORMATICS
ISSN1574-9541
Volume2Issue:2Pages:184-192
AbstractLake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.; Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.
SubtypeArticle
KeywordAlgal Dynamics Algal Bloom Neural Network Ph Sensitivity Analysis
DepartmentChinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China; Huazhong Univ Sci & Technol, Dept Ocean Sci & Engn, Wuhan 430074, Peoples R China
Subject AreaEcology
DOI10.1016/j.ecoinf.2007.03.007
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEcology
WOS IDWOS:000249940600015
WOS KeywordCROSS-VALIDATION ; MODEL ; BLOOMS ; ANNA
Citation statistics
Cited Times:25[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/152342/8402
Collection期刊论文
Corresponding AuthorHou, GX, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
Affiliation1.Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China
2.Huazhong Univ Sci & Technol, Dept Ocean Sci & Engn, Wuhan 430074, Peoples R China
Recommended Citation
GB/T 7714
Li, Hongbin,Hou, Guoxiang,Dakui, Feng,et al. Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks[J]. ECOLOGICAL INFORMATICS,2007,2(2):184-192.
APA Li, Hongbin.,Hou, Guoxiang.,Dakui, Feng.,Xiao, Bangding.,Song, Lirong.,...&Hou, GX, Chinese Acad Sci, Inst Hydrobiol, Wuhan 430072, Peoples R China.(2007).Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks.ECOLOGICAL INFORMATICS,2(2),184-192.
MLA Li, Hongbin,et al."Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks".ECOLOGICAL INFORMATICS 2.2(2007):184-192.
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