hide
Free keywords:
-
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.
