Empirical modelling of submersed macrophytes in Yangtze lakes
文献类型：期刊论文
| 作者 | Wang, HZ; Wang, HJ; Liang, XM; Ni, LY; Liu, XQ; Cui, YD |
| 刊名 | ECOLOGICAL MODELLING
 |
| 出版日期 | 2005-11-10 |
| 卷号 | 188期号:2-4页码:483-491 |
| 关键词 | key-time models submersed macrophytes Yangtze shallow lakes biomass transparency thresholds |
| ISSN号 | 0304-3800 |
| 通讯作者 | Wang, HZ, Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China |
| 中文摘要 | 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. |
| 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. |
| WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
| 学科主题 | Ecology |
| 类目[WOS] | Ecology |
| 研究领域[WOS] | Environmental Sciences & Ecology |
| 关键词[WOS] | WATER TRANSPARENCY ; SIMULATION-MODEL ; BIOMASS ; COMMUNITIES ; VEGETATION ; DYNAMICS ; PATTERNS ; COVER ; DEPTH ; STATE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000233188700019 |
| 公开日期 | 2010-10-13 |
| 源URL | [http://ir.ihb.ac.cn/handle/152342/9110]  |
| 专题 | 水生生物研究所_中科院水生所知识产出（2009年前）_期刊论文 |
| 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 |
| 推荐引用方式 GB/T 7714 | Wang, HZ,Wang, HJ,Liang, XM,et al. Empirical modelling of submersed macrophytes in Yangtze lakes[J]. ECOLOGICAL MODELLING,2005,188(2-4):483-491. |
| APA | Wang, HZ,Wang, HJ,Liang, XM,Ni, LY,Liu, XQ,&Cui, YD.(2005).Empirical modelling of submersed macrophytes in Yangtze lakes.ECOLOGICAL MODELLING,188(2-4),483-491. |
| MLA | Wang, HZ,et al."Empirical modelling of submersed macrophytes in Yangtze lakes".ECOLOGICAL MODELLING 188.2-4(2005):483-491. |
入库方式： OAI收割
来源：水生生物研究所
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