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Phylogenetic networks extend the phylogenetic tree structure and allow for modeling vertical and horizontal evolution in a single framework. Statistical inference of phylogenetic networks is prohibitive and currently limited to small networks. An approach that could significantly improve phylogenetic network space exploration is based on first inferring an evolutionary tree of the species under consideration, and then augmenting the tree into a network by adding a set of "horizontal" edges to better fit the data. In this paper, we study the performance of such an approach on networks generated under a birth-hybridization model and explore its feasibility as an alternative to approaches that search the phylogenetic network space directly (without relying on a fixed underlying tree). We find that the concatenation method does poorly at obtaining a "backbone" tree that could be augmented into the correct network, whereas the popular species tree inference method ASTRAL does significantly better at such a task. We then evaluated the tree-to-network augmentation phase under the minimizing deep coalescence and pseudo-likelihood criteria. We find that even though this is a much faster approach than the direct search of the network space, the accuracy is much poorer, even when the backbone tree is a good starting tree. Our results show that tree-based inference of phylogenetic networks could yield very poor results. As exploration of the network space directly in search of maximum likelihood estimates or a representative sample of the posterior is very expensive, significant improvements to the computational complexity of phylogenetic network inference are imperative if analyses of large data sets are to be performed. We show that a recently developed divide-and-conquer approach significantly outperforms tree-based inference in terms of accuracy, albeit still at a higher computational cost.
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This content will become publicly available on March 7, 2026
Misspecification Strikes: ASTRAL can Mislead in the Presence of Hybridization, even for Non-Anomalous Scenarios
ASTRAL is a powerful and widely used tool for species tree inference, known for its computational speed and robustness under incomplete lineage sorting. The method has often been used as an initial step in species network inference to provide a backbone tree structure upon which hybridization events are later added to such a tree via other methods. However, we show empirically and theoretically, that this methodology can yield flawed results. Specifically, we demonstrate that under the Network Multispecies Coalescent model – including non-anomalous scenarios – ASTRAL can produce a tree that does not correspond to any topology displayed by the true underlying network. This finding highlights the need for caution when using ASTRAL-based inferences in suspected hybridization cases.
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- Award ID(s):
- 2331660
- PAR ID:
- 10609948
- Editor(s):
- Falush, Daniel
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Molecular Biology and Evolution
- ISSN:
- 0737-4038
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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