中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

文献类型:期刊论文

作者Yan, Zhengbing; Guo, Zhengfei; Serbin, Shawn P.; Song, Guangqin; Zhao, Yingyi; Chen, Yang; Wu, Shengbiao; Wang, Jing; Wang, Xin2; Li, Jing2,3
刊名NEW PHYTOLOGIST
出版日期2021
卷号232期号:1页码:134-147
ISSN号0028-646X
关键词gas exchange leaf hyperspectral reflectance maximum carboxylation capacity multitrait covariance partial least-squares regression (PLSR) plant functional traits vegetation spectroscopy
DOI10.1111/nph.17579
文献子类Article
英文摘要Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V-c,V-max), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V-c,V-max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V-c,V-max), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V-c,V-max and the four morphological and biochemical traits (R-2 <= 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V-c,V-max relationships across three forest biomes into a single robust model for V-c,V-max (R-2 = 0.77), and also accurately estimated the four traits (R-2 = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V-c,V-max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V-c,V-max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.
学科主题Plant Sciences
电子版国际标准刊号1469-8137
出版地HOBOKEN
WOS关键词IMAGING SPECTROSCOPY ; PHYSIOLOGICAL TRAITS ; CO2 ASSIMILATION ; NITROGEN-CONTENT ; V-CMAX ; CANOPY ; PLANT ; REFLECTANCE ; VEGETATION ; TEMPERATE
WOS研究方向Science Citation Index Expanded (SCI-EXPANDED)
语种英语
出版者WILEY
WOS记录号WOS:000674041900001
资助机构National Natural Science Foundation of China [31922090, 31901086] ; Research Grants Council Early Career Scheme [27306020] ; Seed Fund for Basic Research [201905159005] ; Division of Ecology and Biodiversity PDF research award ; Next-Generation Ecosystem Experiments (NGEE Tropics) project - Office of Biological and Environmental Research in the Department of Energy, Office of Science ; United States Department of Energy [DE-SC0012704]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/26334]  
专题植被与环境变化国家重点实验室
作者单位1.Univ Hong Kong, Sch Biol Sci, Div Ecol & Biodivers, Pokfulam Rd, Hong Kong, Peoples R China
2.Brookhaven Natl Lab, Environm & Climate Sci Dept, Upton, NY 11973 USA
3.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Tsinghua Univ, Dept Earth Syst Sci, Key Lab Earth Syst Modelling, Minist Educ, Beijing 100084, Peoples R China
6.Tsinghua Univ, Joint Ctr Global Change Studies, Beijing 100084, Peoples R China
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GB/T 7714
Yan, Zhengbing,Guo, Zhengfei,Serbin, Shawn P.,et al. Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types[J]. NEW PHYTOLOGIST,2021,232(1):134-147.
APA Yan, Zhengbing.,Guo, Zhengfei.,Serbin, Shawn P..,Song, Guangqin.,Zhao, Yingyi.,...&Wu, Jin.(2021).Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types.NEW PHYTOLOGIST,232(1),134-147.
MLA Yan, Zhengbing,et al."Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types".NEW PHYTOLOGIST 232.1(2021):134-147.

入库方式: OAI收割

来源:植物研究所

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