Distinguishing Phylogenetic Level-2 Networks with Quartets and Inter-Taxon Quartet Distances
Niels Holtgrefe, Elizabeth S. Allman, Hector Baños, Leo van Iersel, Vincent Moulton, John A. Rhodes, Kristina Wicke
TL;DR
The paper addresses identifiability for semi-directed level-2 phylogenetic networks using quartet data, and proves that a canonical form $N^{c}$ exactly captures when two networks can be distinguished by displayed quartets. It introduces a NANUQ-type inter-taxon distance $d_N$ and shows its decomposition into a weighted sum of quartet metrics plus an error term, establishing circular decomposability for outer-labeled planar bloblets. The authors provide a constructive identifiability framework and show that NANUQ distances differentiate canonical forms, paving the way for statistically consistent quartet-based inference under the Network Multispecies Coalescent. These results lay theoretical groundwork for practical inference pipelines and highlight rich connections between displayed quartets, circular decomposable metrics, and canonical network forms.
Abstract
The inference of phylogenetic networks, which model complex evolutionary processes including hybridization and gene flow, remains a central challenge in evolutionary biology. Until now, statistically consistent inference methods have been limited to phylogenetic level-1 networks, which allow no interdependence between reticulate events. In this work, we establish the theoretical foundations for a statistically consistent inference method for a much broader class: semi-directed level-2 networks that are outer-labeled planar and galled. We precisely characterize the features of these networks that are distinguishable from the topologies of their displayed quartet trees. Moreover, we prove that an inter-taxon distance derived from these quartets is circular decomposable, enabling future robust inference of these networks from quartet data, such as concordance factors obtained from gene tree distributions under the Network Multispecies Coalescent model. Our results also have novel identifiability implications across different data types and evolutionary models, applying to any setting in which displayed quartets can be distinguished.
