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Plant Trait Networks: Improved Resolution of the Dimensionality of Adaptation
He, Nianpeng1,2; Li, Ying3; Liu, Congcong1; Xu, Li; Li, Mingxu; Zhang, Jiahui1; He, Jinsheng4,5; Tang, Zhiyao4,5; Han, Xingguo6; Ye, Qing7; Xiao, Chunwang8; Yu, Qiang9; Liu, Shirong10; Sun, Wei2; Niu, Shuli; Li, Shenggong; Sack, Lawren11; Yu, Guirui1
2020
发表期刊TRENDS IN ECOLOGY & EVOLUTION
ISSN0169-5347
卷号35期号:10页码:908-918
摘要Functional traits are frequently used to evaluate plant adaptation across environments. Yet, traits tend to have multiple functions and interactions, which cannot be accounted for in traditional correlation analyses. Plant trait networks (PTNs) clarify complex relationships among traits, enable the calculation of metrics for the topology of trait coordination and the importance of given traits in PTNs, and how they shift across communities. Recent studies of PTNs provide new insights into some important topics, including trait dimensionality, trait spectra (including the leaf economic spectrum), stoichiometric principles, and the variation of phenotypic integration along gradients of resource availability. PTNs provide improved resolution of the multiple dimensions of plant adaptation across scales and responses to shifting resources, disturbance regimes, and global change.
学科领域Ecology ; Evolutionary Biology ; Genetics & Heredity
DOI10.1016/j.tree.2020.06.003
收录类别SCI
语种英语
WOS关键词LEAF ECONOMICS ; FUNCTIONAL TRAITS ; HYDRAULIC TRAITS ; TRADE-OFFS ; SPECTRUM ; STRATEGIES ; TOLERANCE ; EVOLUTION ; TRANSPORT ; ECOLOGY
WOS研究方向Environmental Sciences & Ecology ; Evolutionary Biology ; Genetics & Heredity
WOS记录号WOS:000573227700014
出版者ELSEVIER SCIENCE LONDON
文献子类Review
出版地LONDON
EISSN1872-8383
资助机构National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31988102, 3191101545, 31800368, 31770655] ; Second Tibetan Plateau Scientific Expedition and Research Program [2019QZKK060602] ; Chinese Academy of Sciences Strategic Priority Research Program [XDA23080401, XDA19020302] ; Youth Innovation Research Project from Key Laboratory of Ecosystem Network Observation and Modeling, CAS ; US National Science FoundationNational Science Foundation (NSF) [1457279, 1951244]
作者邮箱henp@igsnrr.ac.cn ; lawrensack@ucla.edu ; yugr@igsnrr.ac.cn
作品OA属性hybrid
引用统计
被引频次:97[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ibcas.ac.cn/handle/2S10CLM1/21573
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, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
3.Northeast Normal Univ, Key Lab Vegetat Ecol, Minist Educ, Changchun, Peoples R China
4.Beijing Forestry Univ, Sch Ecol & Nat Conservat, Beijing, Peoples R China
5.Peking Univ, Coll Urban & Environm Sci, Dept Ecol, Beijing, Peoples R China
6.Peking Univ, Key Lab Earth Surface Proc, Minist Educ, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China
8.Chinese Acad Sci, Key Lab Vegetat Restorat & Management Degraded Ec, South China Bot Garden, Guangzhou, Peoples R China
9.Minzu Univ China, Coll Life & Environm Sci, 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, Key Lab Forest Ecol & Environm, Chinas State Forestry Adm, Inst Forest Ecol Environm & Protect, Beijing, Peoples R China
12.Univ Calif Los Angeles, Dept Ecol & Evolutionary Biol, Los Angeles, CA 90095 USA
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He, Nianpeng,Li, Ying,Liu, Congcong,et al. Plant Trait Networks: Improved Resolution of the Dimensionality of Adaptation[J]. TRENDS IN ECOLOGY & EVOLUTION,2020,35(10):908-918.
APA He, Nianpeng.,Li, Ying.,Liu, Congcong.,Xu, Li.,Li, Mingxu.,...&Yu, Guirui.(2020).Plant Trait Networks: Improved Resolution of the Dimensionality of Adaptation.TRENDS IN ECOLOGY & EVOLUTION,35(10),908-918.
MLA He, Nianpeng,et al."Plant Trait Networks: Improved Resolution of the Dimensionality of Adaptation".TRENDS IN ECOLOGY & EVOLUTION 35.10(2020):908-918.
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