期刊
TREE PHYSIOLOGY
卷 39, 期 8, 页码 1416-1427出版社
OXFORD UNIV PRESS
DOI: 10.1093/treephys/tpz038
关键词
CO2 concentration; drought; gain-risk optimization model; gas exchange; hydraulic risk; stomatal control
类别
资金
- NSF [1450650, 1714972, 1802880]
- David and Lucille Packard Foundation
- University of Utah Global Change and Sustainability Center
- USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme, Ecosystem Services and Agro-ecosystem Management [2018-67019-27850]
- USDA National Institute of Food and Agriculture Postdoctoral Research Fellowship [2018-67012-28020]
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.
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