Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
Plant Cell Environ. 2024 Dec;47(12):5158-5171. doi: 10.1111/pce.15059. Epub 2024 Aug 21.
Mesophyll conductance ( ) describes the efficiency with which moves from substomatal cavities to chloroplasts. Despite the stipulated importance of leaf architecture in affecting , there remains a considerable ambiguity about how and whether leaf anatomy influences . Here, we employed nonlinear machine-learning models to assess the relationship between 10 leaf architecture traits and . These models used leaf architecture traits as predictors and achieved excellent predictability of . Dissection of the importance of leaf architecture traits in the models indicated that cell wall thickness and chloroplast area exposed to internal airspace have a large impact on interspecific variation in . Additionally, other leaf architecture traits, such as leaf thickness, leaf density and chloroplast thickness, emerged as important predictors of . We also found significant differences in the predictability between models trained on different plant functional types. Therefore, by moving beyond simple linear and exponential models, our analyses demonstrated that a larger suite of leaf architecture traits drive differences in than has been previously acknowledged. These findings pave the way for modulating by strategies that modify its leaf architecture determinants.
叶肉导度（ ）描述了 从叶肉间隙向叶绿体转移的效率。尽管规定了叶片结构对 有影响，但对于叶片解剖结构如何以及是否影响 仍然存在相当大的模糊性。在这里，我们采用非线性机器学习模型来评估 10 种叶片结构特征与 之间的关系。这些模型将叶片结构特征作为预测因子，并实现了对 的出色预测能力。模型中叶片结构特征重要性的剖析表明，细胞壁厚度和暴露于内部气腔的叶绿体面积对 种间变异有很大影响。此外，其他叶片结构特征，如叶片厚度、叶片密度和叶绿体厚度，也成为 的重要预测因子。我们还发现，在不同植物功能类型的模型训练之间，预测能力存在显著差异。因此，通过超越简单的线性和指数模型，我们的分析表明，更大的叶片结构特征组合驱动了 之间的差异，这比以前所认识到的更为明显。这些发现为通过改变其叶片结构决定因素来调节 铺平了道路。
