An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets,Global Change Biology

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An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets
Global Change Biology ( IF 10.8 ) Pub Date : 2022-01-28 , DOI: 10.1111/gcb.16103
Julien Lamour ₁ , Kenneth J Davidson _{1,

2} , Kim S Ely ₁ , Gilles Le Moguédec ₃ , Andrew D B Leakey _{4,

5,

6} , Qianyu Li ₁ , Shawn P Serbin ₁ , Alistair Rogers ₁

Affiliation

Stomata play a central role in surface–atmosphere exchange by controlling the flux of water and CO₂ between the leaf and the atmosphere. Representation of stomatal conductance (g_sw) is therefore an essential component of models that seek to simulate water and CO₂ exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO₂ concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between g_sw and photosynthetic CO₂ assimilation (A). However, measurement of leaf-level g_sw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of g_sw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between g_sw and A was not linear, challenging the key assumption upon which optimality theory is based—that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in g_sw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g₀ and g₁. Our modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g₁ and other leaf traits. We also observed nonlinear behavior between A and g_sw in independent data sets that included data collected from attached and detached leaves, and from plants grown at elevated CO₂ concentration. We propose that this empirical modification of the USO model can improve the measurement of g_sw parameters and the estimation of plant and ecosystem-scale water and CO₂ fluxes.

中文翻译：

光合作用和气孔导度之间关系的改进表示可以更稳定地估计电导参数并提高不同数据集的拟合优度

气孔通过控制叶片和大气之间的水和CO ₂通量在地表-大气交换中发挥核心作用。因此，气孔导度 ( g _sw ) 的表示是寻求模拟植物和生态系统中的水和 CO _{2交换的模型的重要组成部分。}对于叶表面的给定环境条件（CO ₂浓度和蒸汽压不足或相对湿度），模型通常假设g _sw和光合 CO ₂同化 ( A ) 之间存在线性关系。然而，测量叶片水平g _sw对变化的响应曲线A是罕见的，特别是在热带地区，导致评估这一关键假设的数据有限。在这里，我们测量了g _sw和A对六种热带物种在不同叶子物候阶段的辐照度的响应。我们证明了g _sw和A之间的关系不是线性的，这挑战了最优性理论所基于的关键假设——即增水的边际成本是恒定的。我们的数据表明，增加A会导致g _{sw小幅增加}在低辐照度下，但在高辐照度下增加更大。我们重新制定了流行的统一气孔优化 (USO) 模型来解释这种现象，并使关键电导参数g ₀和g ₁的一致估计成为可能。我们对 USO 模型的修改提高了拟合优度并减少了偏差，从而能够在任何辐照度下可靠地估计电导参数。此外，我们的修改揭示了气孔坡度参数g ₁与其他叶片性状之间以前无法检测到的关系。我们还观察到A和g _sw之间的非线性行为在独立的数据集中，包括从附着和分离的叶子以及在升高的 CO ₂浓度下生长的植物收集的数据。我们建议这种对 USO 模型的经验修改可以改进g _sw参数的测量以及植物和生态系统规模的水和 CO ₂ 通量的估计。

更新日期：2022-01-28

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