関連する実験動画
Updated: Jan 23, 2026

A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
ニュージーランドゼミの雑種形成の複雑な歴史を解き明かすことに失敗したゲノムデータ探索と仮説生成のための系統ネットワーク手法の使用
Mark Stukel1,2, Chris Simon1
1Ecology & Evolutionary Biology; University of Connecticut; Storrs, CT 06269 USA.
Abstract:
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, SNaQ, a gene tree-based method, and 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 dataset of over 500 nuclear Anchored Hybrid Enrichment genes along with mitochondrial genomes. This larger dataset 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 network 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.
関連する概念動画
Hypothesis: Accept or Fail to Reject?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
What is Evolutionary History?
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Phylogenetic Trees
Genomics
What is a Hypothesis?

