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.

Accumulating evidence for trade-offs involving metabolic traits has demonstrated their importance in evolution of organisms. Metabolic models with different level of complexity have already been considered when investigating mechanisms that explain various metabolic trade-offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade-offs between: (i) kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into mechanisms underlying these five types of metabolic trade-offs obtained from constraint-based metabolic modelling, we emphasize the relation of metabolic trade-offs to the classical black box Y-model that provides conceptual explanation for resource acquisition-allocation trade-offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade-offs by relying on the toolbox provided by constraint-based metabolic modelling for single organisms and microbial communities.

Trade-offs between traits arise and reflect constraints imposed by the environment and physicochemical laws. Trade-off situations are expected to be highly relevant for sessile plants, which have to respond to changes in the environment to ensure survival. Despite increasing interest in determining the genetic and molecular basis of plant trade-offs, there are still gaps and differences with respect to how trade-offs are defined, how they are measured, and how their genetic architecture is dissected. The first step to fill these gaps is to establish what is meant by trade-offs. In this review we provide a classification of the existing definitions of trade-offs according to: (1) the measures used for their quantification, (2) the dependence of trade-offs on environment, and (3) whether data based on which they are inferred are from a single individual across different environments or a population of individuals in single or multiple environments. We then compare the approaches for quantification of trade-offs based on phenotypic, between-individual, and genetic correlations, and stress the need for developing further quantification indices particularly for trade-offs between multiple traits. Lastly, we highlight the genetic mechanisms underpinning trade-offs and experimental designs that facilitate their discovery in plants, with focus on usage of natural variability. This review also offers a perspective for future research aimed at identification of plant trade-offs, dissection of their genetic architecture, and development of strategies to overcome trade-offs, with applications in crop breeding.

Nitrogen (N) is fundamental to plant growth, development, and yield. Genes underlying N utilization and assimilation are well characterized, but mechanisms underpinning plasticity of different phenotypes to varying amounts of N in the soil remain elusive. Here, using Arabidopsis thaliana accessions, we dissected the genetic architecture of plasticity in early and late rosette diameter, flowering time and yield in response to three levels of N in soil. Genome-wide association analysis identified three significant associations for phenotypic plasticity, one for early rosette diameter and two for flowering time. We confirmed that the gene At1g19880, hereafter named as PLASTICITY OF ROSETTE TO NITROGEN 1 (PROTON1), encoding for a regulator of chromatin condensation 1 (RCC1) family protein, conferred plasticity of rosette diameter in response to changes in N availability. The altered plasticities were a result of faster development under limiting N, and correlated with the plasticity in the levels of primary metabolites. By using different growth conditions for a subset of accessions, we showed that plasticities of growth and flowering-related traits in response to N availability differed between the environmental cues, indicating decoupled genetic programs regulating these traits. Our findings provide a prospective for identification of genes that stabilize performance under fluctuating environments.