Knowledge Management System Of Institute Of Botany,CAS
| Predicting ecosystem productivity based on plant community traits | |
He, Nianpeng1,2; Yan, Pu1; Liu, Congcong; Xu, Li; Li, Mingxu; Van Meerbeek, Koenraad3,4; Zhou, Guangsheng5; Zhou, Guoyi6; Liu, Shirong7; Zhou, Xuhui8; Li, Shenggong1; Niu, Shuli1; Han, Xingguo9 ; Buckley, Thomas N.; Sack, Lawren11; Yu, Guirui1
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| 2023 | |
| 发表期刊 | TRENDS IN PLANT SCIENCE
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| ISSN | 1360-1385 |
| 卷号 | 28期号:1页码:43-53 |
| 摘要 | With the rapid accumulation of plant trait data, major opportunities have arisen for the integration of these data into predicting ecosystem primary productivity across a range of spatial extents. Traditionally, traits have been used to explain physiological productivity at cell, organ, or plant scales, but scaling up to the ecosystem scale has remained challenging. Here, we show the need to combine measures of community-level traits and environmental factors to predict ecosystem productivity at landscape or biogeographic scales. We show how theory can extend the production ecology equation to enormous potential for integrating traits into ecological models that estimate productivity-related ecosystem functions across ecological scales and to anticipate the response of terrestrial ecosystems to global change. |
| 学科领域 | Plant Sciences |
| DOI | 10.1016/j.tplants.2022.08.015 |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS关键词 | LEAF TRAITS ; BIG-LEAF ; NITROGEN ; CLIMATE ; CO2 ; PHOTOSYNTHESIS ; STOICHIOMETRY ; EFFICIENCY ; GROWTH ; LEVEL |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) |
| WOS记录号 | WOS:000923202700001 |
| 出版者 | CELL PRESS |
| 文献子类 | Review |
| 出版地 | CAMBRIDGE |
| EISSN | 1878-4372 |
| 资助机构 | Second Tibetan Plateau Scientific Expedition and Research Program [2019QZKK060602] ; CAS Project for Young Scientists in Basic Research [YSBR-037] ; National Natural Science Foundation of China [42141004, 31988102] ; National Science and Technology Basic Resources Survey Program of China [2019FY101304] ; National Science Foundation [1457279, 1951244] ; US Department of Agriculture National Institute of Food and Agriculture (Hatch Project) [1016439] ; Division Of Integrative Organismal Systems ; Direct For Biological Sciences [1951244] Funding Source: National Science Foundation |
| 作者邮箱 | henp@igsnrr.ac.cn ; lawrensack@ucla.edu ; yugr@igsnrr.ac.cn |
| 作品OA属性 | Green Published, hybrid |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ibcas.ac.cn/handle/2S10CLM1/29104 |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Northeast Forestry Univ, Ctr Ecol Res, Harbin 150040, Peoples R China 4.Katholieke Univ Leuven, Dept Earth & Environm Sci, Div Forest Nat & Landscape, Leuven, Belgium 5.Katholieke Univ Leuven, KU Leuven Plant Inst, Leuven, Belgium 6.Chinese Acad Meteorol Sci, Haidian Dist, Beijing, Peoples R China 7.Nanjing Univ Informat Sci & Technol, Inst Ecol, Sch Appl Meteorol, Nanjing, Peoples R China 8.Chinese Acad Forestry, Inst Forest Ecol Environm & Protect, Key Lab Forest Ecol & Environm, Chinas State Forestry Adm, Beijing, Peoples R China 9.East China Normal Univ, Sch Ecol & Environm Sci, Shanghai, Peoples R China 10.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China 11.Buckley, Thomas N.] Univ Calif Davis, Dept Plant Sci, Davis, CA USA 12.Univ Calif Los Angeles, Dept Ecol & Evolutionary Biol, Los Angeles, CA 90095 USA |
| 推荐引用方式 GB/T 7714 | He, Nianpeng,Yan, Pu,Liu, Congcong,et al. Predicting ecosystem productivity based on plant community traits[J]. TRENDS IN PLANT SCIENCE,2023,28(1):43-53. |
| APA | He, Nianpeng.,Yan, Pu.,Liu, Congcong.,Xu, Li.,Li, Mingxu.,...&Yu, Guirui.(2023).Predicting ecosystem productivity based on plant community traits.TRENDS IN PLANT SCIENCE,28(1),43-53. |
| MLA | He, Nianpeng,et al."Predicting ecosystem productivity based on plant community traits".TRENDS IN PLANT SCIENCE 28.1(2023):43-53. |
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| Predicting_ecosystem（1609KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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