Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, Korea.
Department of Earth System Science, Tsinghua University, Beijing, China.
Glob Chang Biol. 2020 Nov;26(11):6493-6510. doi: 10.1111/gcb.15276. Epub 2020 Sep 12.
The maximum rate of carboxylation (V ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating V at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant V based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in V has not been fully investigated. Here, we adapt an optimality-based model to simulate daily V (V at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal V field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant V across the 10 sites, with a medium root mean square error of 12.3 μmol m s , which suggests that seasonal changes in V are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.
最大羧化速率（V ）是决定植物光合作用能力的重要叶片特征。现有的大尺度估算 V 的方法主要依赖于与叶片氮/叶绿素含量或高光谱反射率等指标的经验关系，或依赖于总初级生产力或太阳诱导荧光的复杂反演模型。一种基于植物通过协调环境和生长来优化资源投资的假设的新机制方法，已被证明可以根据平均生长季节环境条件准确预测 C3 植物的 V 。然而，最优理论解释 V 季节性变化的能力尚未得到充分研究。在这里，我们通过纳入影响当前植物功能的前期环境和与植物功能协调的动态光吸收的影响，改编了一种基于最优理论的模型，以模拟每日 V （在标准化温度为 25°C 时的 V ）。然后，我们使用来自不同生态系统的 10 个地点的季节性 V 实地测量值来评估模型性能。总体而言，该模型解释了 10 个地点的 C3 植物 V 季节性变化的约 83%，中值均方根误差为 12.3 μmol m s ，这表明 V 的季节性变化与植物最优功能一致。我们表明，未能解释对前期环境的适应或与动态光吸收的协调会极大地降低估计精度。我们的结果表明，基于最优理论的方法可以准确再现冠层光合潜力的季节性变化，并表明将这种理论纳入下一代基于特征的陆地生物圈模型将提高全球光合作用的预测能力。
