The unified stomatal optimization (USO) series models have shown obvious biases in predicting stomatal conductance (g_sw) of tropical rainforest trees, especially under low vapor pressure deficit (VPD) and irradiance conditions. However, few studies have focused on the structural problems of USO series models themselves. 

In a study published in Agricultural and Forest Meteorology, researchers from Xishuangbanna Tropical Botanical Garden (XTBG) and their collaborators presented a new method combining the Farquhar photosynthesis model and random forest (RF) algorithm to investigate the diurnal variation of stomatal responses in leaves of six tropical tree species in southern China. 

The researchers conducted experiments on sunlit leaves of six tropical tree species at XTBG during the dry seasons of 2021 and 2023. They developed a new coupled interface for the Farquhar photosynthesis model and the stomatal conductance model. Instead of using Ball–Berry–Leuning (BBL), USO and its derivative, they employed a data-driven random forest algorithm trained with extensive diurnal gas exchange and micrometeorological data.  

The results showed that when vapor pressure deficit (VPD) and radiation intensity were low, USO models and their derivatives significantly overestimated g_sw. This overestimation was mainly attributed to the assumption of linear relationship between g_sw and net assimilation rate (An), and the problem of unbounded g_sw at low VPD. Due to the negative correlation between intracellular:atmospheric CO₂ concentration ratio (Ci/Ca) and irradiance, the relationship between gsw and An is actually nonlinear.   

 The relationship between intracellular:atmospheric CO2 concentration ratio (Ci/Ca) and irradiance indicates that Ci/Ca is higher at lower irradiance and decreases and stabilizes at higher irradiance.  

Based on this finding, a monthly average value of minimum VPD during daytime was used to determine an empirical coefficient for representing g_sw as a finite value at low VPD. 

"The revised model significantly improved the prediction accuracy of g_sw at low VPD while maintaining the responsiveness of g_sw at high VPD,” said ZHANG Jiaolin of XTBG. 

Contact 

ZHANG Jiaolin  Ph.D Principal Investigator 

Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303, China
E-mail: zjl@xtbg.org.cn
Published: 5 June 2024