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学科主题: Ecology
题名: Empirical modelling of submersed macrophytes in Yangtze lakes
作者: Wang, HZ; Wang, HJ; Liang, XM; Ni, LY; Liu, XQ; Cui, YD
通讯作者: Wang, HZ, Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
关键词: key-time models ; submersed macrophytes ; Yangtze shallow lakes ; biomass ; transparency thresholds
刊名: ECOLOGICAL MODELLING
发表日期: 2005-11-10
DOI: 10.1016/j.ecolmodel.2005.02.006
卷: 188, 期:2-4, 页:483-491
收录类别: SCI
文章类型: Article
部门归属: Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China; Chinese Acad Sci, Grad Sch, Beijing, Peoples R China
WOS标题词: Science & Technology ; Life Sciences & Biomedicine
类目[WOS]: Ecology
研究领域[WOS]: Environmental Sciences & Ecology
摘要: Submersed macrophytes in Yangtze lakes have experienced large-scale declines due to the increasing human activities during past decades. To seek the key factor that affects their growth, monthly investigations of submersed macrophytes were conducted in 20 regions of four Yangtze lakes during December, 2001-March, 2003. Analyses based on annual values show that the ratio of Secchi depth to mean depth is the key factor (50% of macrophyte biomass variability among these lakes is statistically explained). Further analyses also demonstrate that the months from March to June are not only the actively growing season for most macrophytes, but the key time the factor acts. Five key-time models yielding higher predictive power (r(2) reaches 0.75,0.76,0.77,0.69 and 0.81) are generated. A comparison between key-time models and traditional synchronic ones indicates that key-time models have higher predictive power. Analyses of transparency thresholds during macrophyte growing season and the limitations of the models are presented. The models and other results may benefit the work concerning submersed macrophyte recovery in Yangtze lakes. (c) 2005 Elsevier B.V. All rights reserved.
英文摘要: Submersed macrophytes in Yangtze lakes have experienced large-scale declines due to the increasing human activities during past decades. To seek the key factor that affects their growth, monthly investigations of submersed macrophytes were conducted in 20 regions of four Yangtze lakes during December, 2001-March, 2003. Analyses based on annual values show that the ratio of Secchi depth to mean depth is the key factor (50% of macrophyte biomass variability among these lakes is statistically explained). Further analyses also demonstrate that the months from March to June are not only the actively growing season for most macrophytes, but the key time the factor acts. Five key-time models yielding higher predictive power (r(2) reaches 0.75,0.76,0.77,0.69 and 0.81) are generated. A comparison between key-time models and traditional synchronic ones indicates that key-time models have higher predictive power. Analyses of transparency thresholds during macrophyte growing season and the limitations of the models are presented. The models and other results may benefit the work concerning submersed macrophyte recovery in Yangtze lakes. (c) 2005 Elsevier B.V. All rights reserved.
关键词[WOS]: WATER TRANSPARENCY ; SIMULATION-MODEL ; BIOMASS ; COMMUNITIES ; VEGETATION ; DYNAMICS ; PATTERNS ; COVER ; DEPTH ; STATE
语种: 英语
WOS记录号: WOS:000233188700019
ISSN号: 0304-3800
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ihb.ac.cn/handle/152342/9110
Appears in Collections:中科院水生所知识产出(2009年前)_期刊论文

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作者单位: 1.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
2.Chinese Acad Sci, Grad Sch, Beijing, Peoples R China

Recommended Citation:
Hong-Zhu Wang; Hai-Jun Wang; Xiao-Min Liang; Le-Yi Ni; Xue-Qin Liu; Yong-De Cui.Empirical modelling of submersed macrophytes in Yangtze lakes,ECOLOGICAL MODELLING,2005,188(2-4):483-491
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