School of Biological Sciences, University of Utah, Salt Lake City, UT, USA.
Warnell School of Forestry and Natural Resources, University of Georgia, E Green Street, Athens, GA, USA.
Tree Physiol. 2019 Aug 1;39(8):1416-1427. doi: 10.1093/treephys/tpz038.
Modeling stomatal control is critical for predicting forest responses to the changing environment and hence the global water and carbon cycles. A trait-based stomatal control model that optimizes carbon gain while avoiding hydraulic risk has been shown to perform well in response to drought. However, the model's performance against changes in atmospheric CO2, which is rising rapidly due to human emissions, has yet to be evaluated. The present study tested the gain-risk model's ability to predict the stomatal response to CO2 concentration with potted water birch (Betula occidentalis Hook.) saplings in a growth chamber. The model's performance in predicting stomatal response to changes in atmospheric relative humidity and soil moisture was also assessed. The gain-risk model predicted the photosynthetic assimilation, transpiration rate and leaf xylem pressure under different CO2 concentrations, having a mean absolute percentage error (MAPE) of 25%. The model also predicted the responses to relative humidity and soil drought with a MAPE of 21.9% and 41.9%, respectively. Overall, the gain-risk model had an MAPE of 26.8% compared with the 37.5% MAPE obtained by a standard empirical model of stomatal conductance. Importantly, unlike empirical models, the optimization model relies on measurable physiological traits as inputs and performs well in predicting responses to novel environmental conditions without empirical corrections. Incorporating the optimization model in larger scale models has the potential for improving the simulation of water and carbon cycles.
建立气孔控制模型对于预测森林对环境变化的响应，从而预测全球水碳循环至关重要。已证明，一种基于性状的气孔控制模型，在优化碳收益的同时避免水力风险，能够很好地应对干旱。然而，该模型对大气 CO2 变化的响应性能尚未得到评估，而大气 CO2 由于人类排放而迅速增加。本研究通过生长室中的盆栽水桦（Betula occidentalis Hook.）幼苗，测试了增益风险模型预测气孔对 CO2 浓度响应的能力。还评估了该模型预测气孔对大气相对湿度和土壤水分变化响应的能力。增益风险模型预测了不同 CO2 浓度下光合作用同化、蒸腾速率和叶片木质部压力，平均绝对百分比误差（MAPE）为 25%。该模型还预测了相对湿度和土壤干旱的响应，MAPE 分别为 21.9%和 41.9%。总体而言，增益风险模型的平均绝对百分比误差（MAPE）为 26.8%，而标准气孔导度经验模型的 MAPE 为 37.5%。重要的是，与经验模型不同，优化模型依赖于可测量的生理性状作为输入，在无需经验校正的情况下，能够很好地预测对新环境条件的响应。在更大规模的模型中纳入优化模型，有可能改善水碳循环的模拟。
