A polynomial invariant for a new class of phylogenetic networks

TL;DR

This paper introduces a new polynomial invariant for a class of phylogenetic networks called separable networks, generalizing tree invariants to distinguish complex evolutionary histories involving reticulation events.

Contribution

It extends a complete polynomial invariant from trees to separable phylogenetic networks, enabling characterization of networks with the same leaves and reticulations.

Findings

The polynomial invariant distinguishes networks with identical leaf and reticulation counts.

The invariant applies to internally multi-labelled networks, capturing reticulation information.

Without leaf labels, the polynomial's variable count simplifies to reticulations plus two.

Abstract

Invariants for complicated objects such as those arising in phylogenetics, whether they are invariants as matrices, polynomials, or other mathematical structures, are important tools for distinguishing and working with such objects. In this paper, we generalize a complete polynomial invariant on trees to a class of phylogenetic networks called separable networks, which will include orchard networks. Networks are becoming increasingly important for their ability to represent reticulation events, such as hybridization, in evolutionary history. We provide a function from the space of internally multi-labelled phylogenetic networks, a more generic graph structure than phylogenetic networks where the reticulations are also labelled, to a polynomial ring. We prove that the separability condition allows us to characterize, via the polynomial, the phylogenetic networks with the same number of leaves and same number of reticulations by considering their internally labelled versions. While the invariant for trees is a polynomial in Z[x_1,..., x_n,y] where n is the number of leaves, the invariant for internally multi-labelled phylogenetic networks is an element of Z[x_1,..., x_n,lambda_1,...,lambda_r,y], where r is the number of reticulations in the network. When the networks are considered without leaf labels the number of variables reduces to r+2.