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A niche model to predict Mierocystis bloom decline in Chaohu Lake, China
  • ISSN号:0254-4059
  • 期刊名称:《中国海洋湖沼学报:英文版》
  • 时间:0
  • 分类:X52[环境科学与工程—环境工程] S718.5[农业科学—林学]
  • 作者机构:[1]Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China, [2]Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • 相关基金:Supported by the National Basic Research Program of China (973 Program) (No. 2008CB418002), the National Major Programs of Water Body Pollution Control and Remediation (Nos. 2009ZX07106-001, 2009ZX07104-005), and the National Natural Science Foundation of China (No. 30830025)
中文摘要:

Cyanobacterial blooms occur frequently in lakes due to eutrophication. Although a number of models have been proposed to forecast algal blooms, a good and applicable method is still lacking. This study explored a simple and effective mathematical-ecological model to evaluate the growth status and predict the population dynamics of Microcystis blooms. In this study, phytoplankton were collected and identified from 8 sampling sites in Chaohu Lake every month from July to October, 2010. The niche breadth and niche overlap of common species were calculated using standard equations, and the potential relative growth rates of Microcystis were calculated as a weighted-value of niche overlap. In July, the potential relative growth rate was 2.79 (a.u., arbitrary units) but then rapidly declined in the following months to -3.99 a.u. in September. A significant correlation (R=0.998, P<0.01) was found in the model between the net-increase in biomass of Microcystis in the field and the predicted values calculated by the niche model, we concluded that the niche model is suitable for forecasting the dynamics of Microcystis blooms. Redundancy analysis indicated that decreases in water temperature, dissolved oxygen and total dissolved phosphorus might be major factors underlying bloom decline. Based on the theory of community succession being caused by resource competition, the growth and decline of blooms can be predicted from a community structure. This may provide a basis for early warning and control of algal blooms.

英文摘要:

Cyanobacterial blooms occur frequently in lakes due to eutrophication. Although a number of models have been proposed to forecast algal blooms, a good and applicable method is still lacking. This study explored a simple and effective mathematical-ecological model to evaluate the growth status and predict the population dynamics of Microcystis blooms. In this study, phytoplankton were collected and identified from 8 sampling sites in Chaohu Lake every month from July to October, 2010. The niche breadth and niche overlap of common species were calculated using standard equations, and the potential relative growth rates of Microcystis were calculated as a weighted-value of niche overlap. In July, the potential relative growth rate was 2.79 (a.u., arbitrary units) but then rapidly declined in the following months to -3.99 a.u. in September. A significant correlation (R=0.998, P〈0.01) was found in the model between the net-increase in biomass of Microcystis in the field and the predicted values calculated by the niche model, we concluded that the niche model is suitable for forecasting the dynamics of Microcystis blooms. Redundancy analysis indicated that decreases in water temperature, dissolved oxygen and total dissolved phosphorus might be major factors underlying bloom decline. Based on the theory of community succession being caused by resource competition, the growth and decline of blooms can be predicted from a community structure. This may provide a basis for early warning and control of algal blooms.

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期刊信息
  • 《中国海洋湖沼学报:英文版》
  • 中国科技核心期刊
  • 主管单位:中国科学院 中国科协
  • 主办单位:中国海洋与湖沼学会
  • 主编:曾呈奎
  • 地址:中国青岛南海路7号
  • 邮编:266071
  • 邮箱:cjol@ms.qdio.ac.cn
  • 电话:0532-82898754
  • 国际标准刊号:ISSN:0254-4059
  • 国内统一刊号:ISSN:37-1150/P
  • 邮发代号:2-581
  • 获奖情况:
  • 第一、二届全国优秀科技期刊二等奖,中科院优秀期刊二等奖
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,美国化学文摘(网络版),英国农业与生物科学研究中心文摘,荷兰文摘与引文数据库,美国地质文献预评数据库,美国剑桥科学文摘,美国科学引文索引(扩展库),美国生物科学数据库,英国科学文摘数据库,英国动物学记录,日本日本科学技术振兴机构数据库,中国中国科技核心期刊
  • 被引量:349