气孔导度对CO_2浓度变化的模拟及其生理机制

基于气孔运动的生理生化机制重点进行了气孔导度(gs)对CO2浓度变化的响应机制分析,并推导得到气孔导度(gs)对CO2浓度变化响应模型,并以9种植物进行了模型验证。结果表明:随着CO2浓度的升高,气孔导度会逐渐降低,且下降的幅度会随着CO2浓度的升高而逐渐减弱。气孔导度对CO2浓度(Cs)变化的响应模型可以表达为gs=gmax/(1+Cs/Cs0),其中式中gmax是最大气孔导度和Cs0是实验常数。该模型较好地模拟了气孔导度随CO2浓度变化的规律,模型参数具有明确的生理意义,与Jarvis模型和Ball-Berry模型相比,该模型如何实现多种环境因子的耦合有待进一步突破。另外,模型是在短期改变叶片CO2浓度的条件下得出的,在CO2浓度长期胁迫下的适用性也有待进一步确认。

Modeling the response of stomatal conductance to variable CO2 concentration and its physiological mechanism

As commonly recognized, global warming is mainly caused by the emission of carbon dioxide (CO2). Stomatal conductance (gs) plays an important role between the vegetation and atmosphere water and carbon exchanges, and therefore it is also a key model parameter in the water and carbon models. Accompanying with the elevated CO2 concentration, the response of stomatal conductance, correlating with transpiration and photosynthetic capacity, to elevated CO2 concentration is open to question. Stomatal conductance as an important index shows sensitive responses to elevated CO2 concentration, i.e., stomatal conductance decreases with elevated CO2 concentration and maintains lower intercellular CO2 pressure by 20% 30% than atmosphere CO2 concentration. We elucidated current understanding of the mechanisms that underlay the response of stomatal conductance to variable CO2 concentration, and an model that described the response of stomatal conductance to variable CO2 concentration was derived based on the physiological and biochemical mechanism of stomatal movement. By using Li-6400 portable photosynthesis system, stomatal conductance at leaf level was measured under controlled photosynthetic photons flux density (PPFD) and variable CO2 concentration conditions across 9 plant species, including crops such as maize (Zea mays), soybean (Glycine max), rice (Oryza sativa) in Northeast China, and woody plants such as slash pine (Pinus elliottii), Banana Shrub (Michelia figo), Orange (Citrus) in Southeast China and herbaceous plants such as Leymus chinensis, Agropyron mongolicum and Stipa grandis in Northwest China. Meanwhile, the model was verified using the dataset of 9 plant species at leaf level and at short-term. Both the observation and model results showed that the stomatal conductance gradually reduced with the increase of CO2 concentration, but this increasing trend would decrease with the gradually increase of CO2 concentration. The model could be expressed by gs=gmax/(1+Cs/Cs0), where gs is the stomatal conductance, Cs is the CO2 concentration, gmax is the maximum stomatal conductance, and Cs0 is the experimental constant. The model also showed that the responses of stomatal conductance to variable CO2 concentration were hyperbolic. The parameters, gmax and Cs0, were provided for the 9 plant species. This model could simulate the response of stomatal conductance to variable CO2 concentration well, with the specific physiological meaning of the model parameters. However, the impact of the model under elevated CO2 concentration should further be investigated on the water and carbon exchanges. The model only accounted for the variable CO2 concentration, and not for other different environmental factors. Compared with the Javis and Ball-Berry stomatal conductance models, breakthroughs in this model should be achieved in the response of the coupling different environmental factors in future. Furthermore, this model was only verified under the changing CO2 concentration in short-term, but the adaptability of this model under long-term of elevated CO2 concentration should also be confirmed.