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Reticulation, data combination, and inferring evolutionary history: an example from Danthonioideae (Poaceae)
Michael D Pirie1, Aelys M Humphreys, Nigel P Barker
1Institute for Systematic Botany, University of Zurich, CH-8008 Zurich, Switzerland. mpirie@sun.ac.za
Systematic Biology
|June 8, 2010
Summary
Conflicting gene trees can mislead evolutionary history studies. A new method, duplicating taxa, allows combining all data for accurate phylogenetic analyses, resolving reticulations and revealing true evolutionary patterns.
Area of Science:
- Evolutionary Biology
- Phylogenetics
- Genomics
Background:
- Conflicting gene trees pose challenges in inferring species-level evolutionary history.
- Current methods often exclude conflicting data or taxa, potentially losing valuable information.
Purpose of the Study:
- To present an alternative method for handling conflicting gene trees in phylogenetic analyses.
- To demonstrate how conflicting gene trees can lead to divergent conclusions about evolutionary history.
Main Methods:
- Developed a novel approach involving duplicating conflicting taxa within the data matrix.
- Applied this method to analyze phylogenetic relationships and evolutionary history in the genus Cortaderia.
- Utilized both chloroplast DNA (cpDNA) and nuclear ribosomal DNA (nrDNA) data.
Main Results:
- The new method allows the incorporation of all available data in phylogenetic analyses, accommodating reticulations.
- Inferences of morphological evolution in Cortaderia differed significantly between cpDNA (multiple origins) and nrDNA (single origin) trees.
- Biogeographic history reconstructions also varied, suggesting few dispersals (nrDNA) versus several (cpDNA) between southern continents.
Conclusions:
- Past hybridization events likely explain the observed gene tree conflicts, linking morphology with unrelated chloroplast lineages.
- Inconsistencies highlight the limitations of analyzing gene trees separately.
- The proposed method of duplicating taxa offers a robust approach to integrating conflicting data for more reliable evolutionary inferences.
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