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Updated: Jan 23, 2026

A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
Using Phylogenetic Network Methods for Genomic Data Exploration and Hypothesis Generation Fails to Untangle a
Mark Stukel1,2, Chris Simon1
1Ecology & Evolutionary Biology, University of Connecticut, 75 N. Eagleville Rd, Unit 3043, Storrs, CT 06269-3043, USA.
None:
Rapid species radiations make hybridization among species more likely. Detecting and reconstructing hybridization is therefore critical for understanding species relationships in many cases. We explored the relative performance of two phylogenetic network methods, species networks applying quartets (SNaQ), a gene tree-based method, and Phylogenetic Network Estimation using SiTe patterns (PhyNEST), a site pattern-based method, in evaluating the plausibility of proposed past hybridization hypotheses. As our study system, we used the New Zealand cicada genera Kikihia and Maoricicada. Previous phylogenomic work on these two species radiations suggested multiple hybridization events in response to changing landscapes and climate. We generated hypotheses for specific hybridization events based on observed hybrid mating songs and patterns of mito-nuclear discordance from previous studies. We tested our hypotheses using the D-statistic and a phylogenomic data set of over 500 nuclear Anchored Hybrid Enrichment genes along with mitochondrial genomes. This larger data set provided stronger support for some of our hybridization scenarios but not all. Using these same data, we inferred phylogenetic networks using SNaQ and PhyNEST to determine whether the two methods recovered plausible networks with respect to our hypothesized hybridization events. We found that both SNaQ and PhyNEST recovered an extensive history of reticulate evolution in New Zealand cicadas, which broadly matched our predictions. We suggest that differences between networks inferred by the two network programs may result from using site patterns versus gene trees as input data or reflect other differences in the inference methods. Finally, we discuss considerations for users applying these methods to targeted enrichment data and suggest improvements for network method developers.
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