Unverified paper record
Incorporating photosynthetic acclimation improves stomatal optimisation models
bioRxiv · 10 Jul 2023 · 10.1101/2023.07.07.548137
Abstract
Stomatal opening in plant leaves is regulated through a balance of carbon and water exchange under different environmental conditions. Accurate estimation of stomatal regulation is crucial for understanding how plants respond to changing environmental conditions, particularly under climate change. A new generation of optimality-based modelling schemes determines instantaneous stomatal responses from a balance of trade-offs between carbon gains and hydraulic costs, but most such schemes do not account for biochemical acclimation in response to drought. Here, we compare the performance of seven instantaneous stomatal optimisation models with and without accounting for photosynthetic acclimation. Using experimental data from 38 plant species, we found that accounting for photosynthetic acclimation improves the prediction of carbon assimilation in a majority of the tested models. Non-stomatal mechanisms contributed significantly to the reduction of photosynthesis under drought conditions in all tested models. Drought effects on photosynthesis could not accurately be explained by the hydraulic impairment functions embedded in the stomatal models alone, indicating that photosynthetic acclimation must be considered to improve estimates of carbon assimilation during drought. Summary Statement Accounting for photosynthetic acclimation improves the predictions of carbon assimilation in all the stomatal optimization models evaluated. The influence of drought on photosynthesis cannot be fully explained by the hydraulic impairment function of the stomatal models alone.
Plant phenotyping relevance
7種類の気孔最適化モデルを比較評価し、光合成順化を組み込んだモデルの炭素同化予測性能を検証しているため、植物生理形質の計算的推定手法の技術検証が中心です。
abstractHere, we compare the performance of seven instantaneous stomatal optimisation models with and without accounting for photosynthetic acclimation.
abstractaccounting for photosynthetic acclimation improves the prediction of carbon assimilation in a majority of the tested models.
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