Nonlinear models based on leaf architecture traits explain the
variability of mesophyll conductance across plant species
Abstract
Mesophyll conductance ( g m ) describes the efficiency with which CO
2 moves from substomatal cavities to chloroplasts.
Despite the stipulated importance of leaf architecture in affecting g m
, there remains a considerable ambiguity about how and whether anatomy
influences g m . This is, in part, because studies exploring the
relationship between leaf architecture and g m have often relied on
simple linear or exponential models to identify correlations. Here, we
employed non-linear machine learning models to more comprehensively
assess the relationship between ten leaf architecture traits and g m .
These models achieved excellent predictability of g m , which depended
on the leaf architecture traits considered as predictors. 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 . Additionally,
other leaf architecture traits, such as: leaf thickness, leaf density,
and chloroplast thickness emerged as important predictors of g m . We
found significant differences in the predictability between models
trained on different plant functional types (PFTs): those trained on
woody species could predict g m by anatomical traits on other woody
PFTs, ferns, and C 3 herbaceous plants, whereas the
converse did not hold in general. By moving beyond simple linear and
exponential models, our analyses demonstrated that a larger suite of
leaf architecture traits drive differences in g m than has been
previously acknowledged. These findings pave the way for modulating g m
by strategies that modify its leaf architecture determinants.