| Ecosystem Traits Linking Functional Traits to Macroecology | |
He, Nianpeng1,2 ; Liu, Congcong1,2; Piao, Shilong3; Sack, Lawren4; Xu, Li1; Luo, Yiqi5; He, Jinsheng3; Han, Xingguo6; Zhou, Guangsheng7; Zhou, Xuhui8; Lin, Yi9; Yu, Qiang10; Liu, Shirong11; Sun, Wei12; Niu, Shuli1; Li, Shenggong1; Zhang, Jiahui1; Yu, Guirui1,2
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| 2019-03-01 | |
| Source Publication | TRENDS IN ECOLOGY & EVOLUTION (IF:14.764[JCR-2019],18.601[5-Year]) |
| ISSN | 0169-5347 |
| Volume | 34Issue:3Pages:200-210 |
| Abstract | As the range of studies on macroecology and functional traits expands, integration of traits into higher-level approaches offers new opportunities to improve clarification of larger-scale patterns and their mechanisms and predictions using models. Here, we propose a framework for quantifying 'ecosystem traits' and means to address the challenges of broadening the applicability of functional traits to macroecology. Ecosystem traits are traits or quantitative characteristics of organisms (plants, animals, and microbes) at the community level expressed as the intensity (or density) normalized per unit land area. Ecosystem traits can inter-relate and integrate data from field trait surveys, eddy-flux observation, remote sensing, and ecological models, and thereby provide new resolution of the responses and feedback at regional to global scale. |
| WOS Keyword | LEAF CHLOROPHYLL CONTENT ; COLD-TEMPERATE FORESTS ; N-P STOICHIOMETRY ; PLANT ; TERRESTRIAL ; ASSOCIATION ; VARIABILITY ; PREDICTION ; WORLDWIDE ; CLIMATE |
| DOI | 10.1016/j.tree.2018.11.004 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Research Area | Environmental Sciences & Ecology ; Evolutionary Biology ; Genetics & Heredity |
| WOS Subject | Ecology ; Evolutionary Biology ; Genetics & Heredity |
| WOS ID | WOS:000459899000010 |
| Funding Project | National Key R&D Program of China[2016YFC0500202] ; program of Youth Innovation Research Team Project[LENOM2016Q0005] ; National Key R&D Program of China[2017YFA0604803] ; National Key R&D Program of China[2017YFC0504004] ; Natural Science Foundation of China[41671045] ; Natural Science Foundation of China[31770655] ; Natural Science Foundation of China[41571130043] |
| Publisher | ELSEVIER SCIENCE LONDON |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.igsnrr.ac.cn/handle/311030/49319 |
| Collection | 生态系统网络观测与模拟院重点实验室_生态系统综合研究中心 |
| Affiliation | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 3.Peking Univ, Coll Urban & Environm Sci, Dept Ecol, Beijing, Peoples R China 4.Univ Calif Los Angeles, Dept Ecol & Evolutionary Biol, Los Angeles, CA USA 5.Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA 6.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China 7.Chinese Acad Meteorol Sci, Beijing, Peoples R China 8.East China Normal Univ, Sch Ecol & Environm Sci, Shanghai, Peoples R China 9.Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & GIS, Beijing, Peoples R China 10.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Natl Hulunber Grassland Ecosyst Observat & Res St, Beijing, Peoples R China 11.Chinese Acad Forestry, Inst Forest Ecol Environm & Protect, Chinas State Forestry Adm, Key Lab Forest Ecol & Environm, Beijing, Peoples R China 12.Northeast Normal Univ, Minist Educ, Key Lab Vegetat Ecol, Changchun, Jilin, Peoples R China |
| Recommended Citation GB/T 7714 | He, Nianpeng,Liu, Congcong,Piao, Shilong,et al. Ecosystem Traits Linking Functional Traits to Macroecology[J]. TRENDS IN ECOLOGY & EVOLUTION,2019,34(3):200-210. |
| APA | He, Nianpeng.,Liu, Congcong.,Piao, Shilong.,Sack, Lawren.,Xu, Li.,...&Yu, Guirui.(2019).Ecosystem Traits Linking Functional Traits to Macroecology.TRENDS IN ECOLOGY & EVOLUTION,34(3),200-210. |
| MLA | He, Nianpeng,et al."Ecosystem Traits Linking Functional Traits to Macroecology".TRENDS IN ECOLOGY & EVOLUTION 34.3(2019):200-210. |
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| HeNP2019.pdf（2375KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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