[C19] Ecology and Enviromment

[C19-04] Global morphospace occupation patterns of leaf vein network structures

*Noshita Koji¹, Kohei Iwamasa² (1. Kyushu University (Japan), 2. Turing Inc. (Japan))

Keywords:

phenotypic diversity,plant phenotying,phenome science,morphometrics,image analysis

Despite substantial variation in leaf vein architectures among angiosperms, a typical hierarchical network pattern is shared within clades. Functional demands (e.g., hydraulic conductivity, transpiration efficiency, and tolerance to damage and blockage) constrain the network structure of leaf venation, generating a biased distribution in the morphospace. Although network structures and their diversity are crucial for understanding angiosperm venation, previous studies have relied on simple morphological measurements and their derived statistics to quantify phenotypes. To better understand the morphological diversities and constraints on leaf vein networks, we developed a phenotyping workflow to quantify vein networks and identified leaf venation-specific morphospace patterns in our previous study (Iwamasa and Noshita 2023). It is extended to enable its application to large datasets of leaf vein images. The method involves four processes: leaf image acquisition using a feasible system, leaf vein segmentation based on a deep neural network model, network extraction as an undirected graph, and network feature calculation. By dimensionality reduction, we found characteristic distribution patterns in the space between PC1 and PC3, PC2 and PC4, and PC1 and PC10, which were the results of the principal component analysis of the vein structure features. For example, the U-shaped one-dimensional distribution pattern found in PC1 and PC3, which is aligned with the Pareto front that optimizes transport efficiency, construction cost, and robustness to damage as predicted by the earlier theoretical study, is a pattern found in five species of five genera in the previous study. This pattern was validated as universal with these larger datasets. We also show the relationship between these features and environmental data.