Using Phylogenetic Network Methods for Genomic Data Exploration and Hypothesis Generation Fails to Untangle a Confusing History of Hybridization in New Zealand Cicadas

生物 系统发育网络 系统发育树 进化生物学 网状进化 系统发育学 网状的 推论 系统基因组学 核基因 溯祖理论 交配 鉴定(生物学) 计算生物学 遗传算法 生殖隔离
作者
Mark Stukel,Chris Simon
出处
期刊:Systematic Biology [Oxford University Press]
卷期号:75 (5): 967-983
标识
DOI:10.1093/sysbio/syag006
摘要

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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