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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
A new method for handling missing species in diversification analysis applicable to randomly or nonrandomly sampled
Natalie Cusimano1, Tanja Stadler, Susanne S Renner
1Systematic Botany and Mycology, University of Munich, Menzinger Str. 67, 80638 Munich, German.
Systematic Biology
|February 16, 2012
Summary
Chronograms are used to study diversification rates, but incomplete and nonrandom species sampling poses a challenge. A new method, CorSiM (Corrected Species Sampling), simulates missing data to accurately model diversification even with biased sampling.
Area of Science:
- Phylogenetics
- Evolutionary Biology
- Computational Biology
Background:
- Chronograms from molecular dating are crucial for inferring diversification rates and their temporal changes.
- Incomplete and often nonrandom species sampling in phylogenies presents a significant limitation for accurate diversification analyses.
- Existing methods like the γ statistic and birth-death likelihood analysis are suitable for random sampling but lack objective approaches for nonrandomly sampled phylogenies.
Purpose of the Study:
- To introduce CorSiM, a novel automated method for fitting diversification models to phylogenies with nonrandom species sampling.
- To address the limitations of current diversification analysis methods when faced with biased species representation.
- To provide a tool that simulates missing phylogenetic splits, enabling robust model-fitting.
Main Methods:
- CorSiM simulates missing phylogenetic splits using a constant rate birth-death model.
- The method allows users to specify whether species sampling is random or nonrandom.
- Simulated complete trees are then used for subsequent diversification model-fitting analyses, differing from prior methods relying on incomplete tree null distributions.
Main Results:
- CorSiM accurately models diversification rates in phylogenies with both random and nonrandom species sampling.
- In a nonrandomly sampled Araceae clade, CorSiM detected an increase in diversification rate, unlike classic methods that favored a constant rate.
- CorSiM significantly reduces Type I errors in diversification analysis, though Type II errors remain a consideration.
Conclusions:
- CorSiM offers a robust and automated solution for diversification modeling with nonrandomly sampled phylogenies.
- The method enhances the accuracy of evolutionary rate estimations, particularly in datasets with biased species representation.
- CorSiM is implemented as an R package, facilitating its application to large-scale phylogenetic datasets.
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