Topological phenotypes constitute a new dimension in the phenotypic space of leaf venation networks

TL;DR

This paper introduces a novel topological phenotypic trait for leaf venation networks, providing a new dimension for phenotypic analysis that enhances leaf identification and understanding of network development.

Contribution

It presents a new topological metric for leaf venation networks, demonstrating its utility in species identification and linking topological traits to developmental noise effects.

Findings

Topological traits improve leaf identification accuracy.

Diverse topological network traits found within a single plant family.

Topological information complements geometric traits in phenotypic analysis.

Abstract

The leaves of angiosperms contain highly complex venation networks consisting of recursively nested, hierarchically organized loops. We describe a new phenotypic trait of reticulate vascular networks based on the topology of the nested loops. This phenotypic trait encodes information orthogonal to widely used geometric phenotypic traits, and thus constitutes a new dimension in the leaf venation phenotypic space. We apply our metric to a database of 186 leaves and leaflets representing 137 species, predominantly from the Burseraceae family, revealing diverse topological network traits even within this single family. We show that topological information significantly improves identification of leaves from fragments by calculating a "leaf venation fingerprint" from topology and geometry. Further, we present a phenomenological model suggesting that the topological traits can be explained by noise effects unique to specimen during development of each leaf which leave their imprint on the final network. This work opens the path to new quantitative identification techniques for leaves which go beyond simple geometric traits such as vein density and is directly applicable to other planar or sub-planar networks such as blood vessels in the brain.