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叶片性状与气候和海拔的相关性预测——以中国贡嘎山为例。

Predictability of leaf traits with climate and elevation: a case study in Gongga Mountain, China.

机构信息

Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Shuangqing Road, Haidian District, Beijing 100084, China.

Joint Center for Global Change Studies (JCGCS), Shuangqing Road, Haidian District, Beijing 100875, China.

出版信息

Tree Physiol. 2021 Aug 11;41(8):1336-1352. doi: 10.1093/treephys/tpab003.

DOI:10.1093/treephys/tpab003
PMID:33440428
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8454210/
Abstract

Leaf mass per area (Ma), nitrogen content per unit leaf area (Narea), maximum carboxylation capacity (Vcmax) and the ratio of leaf-internal to ambient CO2 partial pressure (χ) are important traits related to photosynthetic function, and they show systematic variation along climatic and elevational gradients. Separating the effects of air pressure and climate along elevational gradients is challenging due to the covariation of elevation, pressure and climate. However, recently developed models based on optimality theory offer an independent way to predict leaf traits and thus to separate the contributions of different controls. We apply optimality theory to predict variation in leaf traits across 18 sites in the Gongga Mountain region. We show that the models explain 59% of trait variability on average, without site- or region-specific calibration. Temperature, photosynthetically active radiation, vapor pressure deficit, soil moisture and growing season length are all necessary to explain the observed patterns. The direct effect of air pressure is shown to have a relatively minor impact. These findings contribute to a growing body of research indicating that leaf-level traits vary with the physical environment in predictable ways, suggesting a promising direction for the improvement of terrestrial ecosystem models.

摘要

叶面积比(Ma)、单位叶面积氮含量(Narea)、最大羧化能力(Vcmax)和叶片内与环境 CO2 分压比(χ)是与光合作用功能相关的重要特征,它们沿着气候和海拔梯度表现出系统变化。由于海拔、压力和气候的共变,沿海拔梯度分离气压和气候的影响具有挑战性。然而,最近基于最优理论开发的模型提供了一种独立预测叶片特征的方法,从而可以分离不同控制因素的贡献。我们应用最优理论来预测在贡嘎山地区的 18 个地点的叶片特征变化。我们表明,这些模型平均解释了 59%的特征变异性,而无需进行特定地点或特定区域的校准。温度、光合有效辐射、蒸气压亏缺、土壤水分和生长季节长度都是解释观测模式所必需的。气压的直接影响被证明相对较小。这些发现有助于越来越多的研究表明,叶片水平的特征以可预测的方式随物理环境而变化,这为改进陆地生态系统模型提供了一个有前途的方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/c1c862602bef/tpab003f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/202981e67e39/tpab003f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/5bed149769ff/tpab003f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/7b7bc3b9accb/tpab003f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/e20a8d944251/tpab003f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/df7d724975ea/tpab003f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/c1c862602bef/tpab003f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/202981e67e39/tpab003f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/c726a6ceae34/tpab003f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/5bed149769ff/tpab003f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/7b7bc3b9accb/tpab003f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/e20a8d944251/tpab003f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/df7d724975ea/tpab003f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eee2/8454210/c1c862602bef/tpab003f7.jpg

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本文引用的文献

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2
An optimality-based model explains seasonal variation in C3 plant photosynthetic capacity.基于最优性的模型解释了 C3 植物光合能力的季节性变化。
Glob Chang Biol. 2020 Nov;26(11):6493-6510. doi: 10.1111/gcb.15276. Epub 2020 Sep 12.
3
Global response patterns of plant photosynthesis to nitrogen addition: A meta-analysis.
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Front Plant Sci. 2024 Nov 19;15:1484744. doi: 10.3389/fpls.2024.1484744. eCollection 2024.
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Spatial Metabolomic Profiling of Pinelliae Rhizoma from Different Leaf Types Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.采用基质辅助激光解吸电离质谱成像技术对不同叶型的半夏块茎进行空间代谢组学分析。
Molecules. 2024 Sep 7;29(17):4251. doi: 10.3390/molecules29174251.
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Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities.六种典型植被群落的叶片功能性状及其影响因素
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Front Plant Sci. 2023 Apr 3;14:1128227. doi: 10.3389/fpls.2023.1128227. eCollection 2023.
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