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

Published version
Peer-reviewed

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Abstract

Mesophyll conductance ( g m gm ) describes the efficiency with which CO 2 CO2 moves from substomatal cavities to chloroplasts. Despite the stipulated importance of leaf architecture in affecting g m gm , there remains a considerable ambiguity about how and whether leaf anatomy influences g m gm . Here, we employed nonlinear machine-learning models to assess the relationship between 10 leaf architecture traits and g m gm . These models used leaf architecture traits as predictors and achieved excellent predictability of g m gm . 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 g m gm . Additionally, other leaf architecture traits, such as leaf thickness, leaf density and chloroplast thickness, emerged as important predictors of g m gm . 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 g m gm than has been previously acknowledged. These findings pave the way for modulating g m gm by strategies that modify its leaf architecture determinants.

Description

Publication status: Published

Keywords

impurity‐based feature importance, machine learning

Journal Title

Plant Cell Environ

Conference Name

Journal ISSN

0140-7791
1365-3040

Volume Title

Publisher

Wiley
Sponsorship
Novo Nordisk Foundation (via University of Copenhagen) (NNF 21OC0068884)