Assessing the robustness of SNaQ to violations induced by high-level phylogenetic networks
- 1University of Wisconsin - Madison
Abstract
Phylogenetic networks extend the traditional tree model to capture reticulate evolutionary processes such as gene flow and hybridization. Among available inference tools, SNaQ is a widely used quartet-based method that offers a computationally efficient, statistically grounded approach to network estimation, but is limited to level-1 networks, in which reticulation cycles do not overlap. This assumption is a statistical requirement for identifiability rather than a reflection of biological reality, as many evolutionary scenarios, particularly those involving extensive or closely spaced gene flow, are expected to produce level-2 or higher networks. How SNaQ performs when this assumption is violated remains poorly understood. Here, we systematically evaluate SNaQs performance on simulated non-level-1 networks. Because existing network comparison metrics such as hardwired cluster dissimilarity are not true distances beyond level-1, we introduce complementary measures: hybrid cluster compatibility, blob compatibility, and tree-of-blobs comparison, to more directly assess structural recovery. We find that while SNaQ does not recover the exact topology of non-level-1 networks, it reliably infers the circular order of taxa and frequently recovers a tree of blobs compatible with the true network, suggesting the level-1 constraint acts as a form of regularization against overfitting. Recovery of reticulation signal is strongly tied to inheritance proportion and the user-specified maximum number of reticulations, with SNaQ behaving as a conservative estimator that favors strong, well-supported events over finer-scale or overlapping signals. These results clarify the strengths and limits of quartet-based network inference under model misspecification and offer practical guidance for applying SNaQ to complex reticulate histories.
Related
- Assessing the impact of phylogenetic reconstruction error on genetic reassortment inference in segmented viruses
- Best Matches in Phylogenetic Networks
- Exact Counts of Binary Phylogenetic Networks with Four Reticulations
- The nearest neighbor interchange proximity measure for the tree-basedness of rooted binary phylogenetic networks
- The parental parsimony problem on binary, tree-child phylogenetic networks
BibTeX
@misc{justison2026assessing,
title = {{Assessing the robustness of SNaQ to violations induced by high-level phylogenetic networks}},
author = {J. A. JUSTISON and G. Ballen and R. Ceja and C. Solis-Lemus and C. Acosta-Cortes and R. Ceja},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.01.748505},
url = {https://www.biorxiv.org/content/10.64898/2026.09.01.748505v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Assessing the robustness of SNaQ to violations induced by high-level phylogenetic networks
AU - J. A. JUSTISON
AU - G. Ballen
AU - R. Ceja
AU - C. Solis-Lemus
AU - C. Acosta-Cortes
AU - R. Ceja
PY - 2026
DA - 2026/09/01
PB - bioRxiv
DO - 10.64898/2026.09.01.748505
UR - https://www.biorxiv.org/content/10.64898/2026.09.01.748505v1
AB - Phylogenetic networks extend the traditional tree model to capture reticulate evolutionary processes such as gene flow and hybridization. Among available inference tools, SNaQ is a widely used quartet-based method that offers a computationally efficient, statistically grounded approach to network estimation, but is limited to level-1 networks, in which reticulation cycles do not overlap. This assumption is a statistical requirement for identifiability rather than a reflection of biological reality, as many evolutionary scenarios, particularly those involving extensive or closely spaced gene flow, are expected to produce level-2 or higher networks. How SNaQ performs when this assumption is violated remains poorly understood. Here, we systematically evaluate SNaQs performance on simulated non-level-1 networks. Because existing network comparison metrics such as hardwired cluster dissimilarity are not true distances beyond level-1, we introduce complementary measures: hybrid cluster compatibility, blob compatibility, and tree-of-blobs comparison, to more directly assess structural recovery. We find that while SNaQ does not recover the exact topology of non-level-1 networks, it reliably infers the circular order of taxa and frequently recovers a tree of blobs compatible with the true network, suggesting the level-1 constraint acts as a form of regularization against overfitting. Recovery of reticulation signal is strongly tied to inheritance proportion and the user-specified maximum number of reticulations, with SNaQ behaving as a conservative estimator that favors strong, well-supported events over finer-scale or overlapping signals. These results clarify the strengths and limits of quartet-based network inference under model misspecification and offer practical guidance for applying SNaQ to complex reticulate histories.
ER -