Transformations to simplify phylogenetic networks

TL;DR

This paper investigates methods for transforming complex phylogenetic networks into simpler trees, focusing on a consistency condition, and finds that the LSA tree method uniquely satisfies this property among several approaches.

Contribution

The paper introduces and analyzes the consistency condition for network-to-tree transformations and identifies the LSA tree method as the only method satisfying this criterion.

Findings

LSA tree method satisfies the consistency condition.

Several common methods do not satisfy the consistency condition.

Introduction of a new method that also does not satisfy the condition.

Abstract

The evolutionary relationships between species are typically represented in the biological literature by rooted phylogenetic trees. However, a tree fails to capture ancestral reticulate processes, such as the formation of hybrid species or lateral gene transfer events between lineages, and so the history of life is more accurately described by a rooted phylogenetic network. Nevertheless, phylogenetic networks may be complex and difficult to interpret, so biologists sometimes prefer a tree that summarises the central tree-like trend of evolution. In this paper, we formally investigate methods for transforming an arbitrary phylogenetic network into a tree (on the same set of leaves) and ask which ones (if any) satisfy a simple consistency condition. This consistency condition states that if we add additional species into a phylogenetic network (without otherwise changing this original network) then transforming this enlarged network into a rooted phylogenetic tree induces the same tree on the original set of species as transforming the original network. We show that the LSA (lowest stable ancestor) tree method satisfies this consistency property, whereas several other commonly used methods (and a new one we introduce) do not. We also briefly consider transformations that convert arbitrary phylogenetic networks to another simpler class, namely normal networks.