Nonlinear models based on leaf architecture traits explain the variability of mesophyll conductance across plant species.

Abstract

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 asleaf thickness, leaf densityand 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.