Leaf water content contributes to global leaf trait relationships

Zhiqiang Wang, Heng Huang (), Han Wang, Josep Peñuelas, Jordi Sardans, Ülo Niinemets, Karl J. Niklas, Yan Li, Jiangbo Xie and Ian J. Wright
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Zhiqiang Wang: Southwest Minzu University
Heng Huang: University of California, Berkeley
Han Wang: Tsinghua University
Josep Peñuelas: CSIC, Global Ecology Unit, CREAF-CSIC-UAB
Jordi Sardans: CSIC, Global Ecology Unit, CREAF-CSIC-UAB
Ülo Niinemets: Estonian University of Life Sciences, Kreutzwaldi 1
Karl J. Niklas: Cornell University
Yan Li: Zhejiang A&F University
Jiangbo Xie: Zhejiang A&F University
Ian J. Wright: Western Sydney University

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Leaf functional traits are important indicators of plant growth and ecosystem dynamics. Despite a wealth of knowledge about leaf trait relationships, a mechanistic understanding of how biotic and abiotic factors quantitatively influence leaf trait variation and scaling is still incomplete. We propose that leaf water content (LWC) inherently affects other leaf traits, although its role has been largely neglected. Here, we present a modification of a previously validated model based on metabolic theory and use an extensive global leaf trait dataset to test it. Analyses show that mass-based photosynthetic capacity and specific leaf area increase nonlinearly with LWC, as predicted by the model. When the effects of temperature and LWC are controlled, the numerical values for the leaf area-mass scaling exponents converge onto 1.0 across plant functional groups, ecosystem types, and latitudinal zones. The data also indicate that leaf water mass is a better predictor of whole-leaf photosynthesis and leaf area than whole-leaf nitrogen and phosphorus masses. Our findings highlight a comprehensive theory that can quantitatively predict some global patterns from the leaf economics spectrum.

Date: 2022
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DOI: 10.1038/s41467-022-32784-1

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