Which Phylogenetic Networks are Level-k Networks with Additional Arcs? Structure and Algorithms

TL;DR

This paper addresses identifying support networks within phylogenetic networks, not only trees, in the context of reticulate evolution. Extends Hayamizu's structure theorem to allow a direct-product characterization of the three families $\mathcal{A}_N$, $\mathcal{B}_N$, and $\mathcal{C}_N$, yielding closed-form counting formulas and connections to Fibonacci, Lucas, Padovan, and Perrin numbers. Provides linear-time algorithms for counting $|\mathcal{A}_N|$, $|\mathcal{B}_N|$, and $|\mathcal{C}_N|$ and linear-delay listing; also gives a linear-time method to obtain a minimum-reticulation support network by selecting an element from $\mathcal{C}_N$. Presents exact and heuristic exponential-time algorithms for the Level Minimization problem (minimum level), with practical performance for networks with moderate reticulations.

Abstract

Reticulate evolution gives rise to complex phylogenetic networks, making their interpretation challenging. A typical approach is to extract trees within such networks. Since Francis and Steel's seminal paper, "Which Phylogenetic Networks are Merely Trees with Additional Arcs?" (2015), tree-based phylogenetic networks and their support trees (spanning trees with the same root and leaf set as a given network) have been extensively studied. However, not all phylogenetic networks are tree-based, and for the study of reticulate evolution, it is often more biologically relevant to identify support networks rather than trees. This study generalizes Hayamizu's structure theorem for rooted binary phylogenetic networks, which yielded optimal algorithms for various computational problems on support trees, to extend the theoretical framework for support trees to support networks. This allows us to obtain a direct-product characterization of each of three sets: all, minimal, and minimum support networks, for a given network. Each characterization yields optimal algorithms for counting and generating the support networks of each type. Applications include a linear-time algorithm for finding a support network with the fewest reticulations (i.e., the minimum tier). We also provide exact and heuristic algorithms for finding a support network with the minimum level, both running in exponential time but practical across a reasonably wide range of reticulation numbers.