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

Abstract

Mesophyll conductance ((Formula presented.)) describes the efficiency with which (Formula presented.) moves from substomatal cavities to chloroplasts. Despite the stipulated importance of leaf architecture in affecting (Formula presented.), there remains a considerable ambiguity about how and whether leaf anatomy influences (Formula presented.). Here, we employed nonlinear machine-learning models to assess the relationship between 10 leaf architecture traits and (Formula presented.). These models used leaf architecture traits as predictors and achieved excellent predictability of (Formula presented.). 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 (Formula presented.). Additionally, other leaf architecture traits, such as leaf thickness, leaf density and chloroplast thickness, emerged as important predictors of (Formula presented.). 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 (Formula presented.) than has been previously acknowledged. These findings pave the way for modulating (Formula presented.) by strategies that modify its leaf architecture determinants.