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Published on: February 5, 2014
Clades and clans: a comparison study of two evolutionary models
Sha Zhu1, Cuong Than, Taoyang Wu
1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK, sha.joe.zhu@gmail.com.
The Yule-Harding-Kingman (YHK) and proportional to distinguishable arrangements (PDA) models show log-convex clade size distributions, meaning extreme clade sizes are more probable. A critical value determines when YHK or PDA models favor specific clade sizes in evolutionary trees.
Area of Science:
- Evolutionary biology
- Phylogenetics
- Mathematical biology
Background:
- Binary tree generating models like Yule-Harding-Kingman (YHK) and proportional to distinguishable arrangements (PDA) are crucial in evolutionary biology.
- Understanding clade size distributions is vital for macro-evolutionary insights, hypothesis testing, and phylogenetic analyses.
Purpose of the Study:
- To analyze and compare clade size distributions under the YHK and PDA models.
- To determine the probabilistic behavior of clade sizes in random binary trees generated by these models.
- To extend these findings to binary unrooted trees.
Main Methods:
- Mathematical analysis of clade size distributions.
- Proof of log-convexity for distributions under YHK and PDA models.
- Derivation of a critical value to compare clade probabilities between the two models.
Main Results:
- Clade size distributions under both YHK and PDA models are proven to be log-convex.
- A critical value κ(n) is identified for n ≥ 4, indicating when YHK is more probable than PDA for a given clade size k (1 < k < κ(n)) and vice versa (κ(n) < k < n).
- Similar log-convexity and comparative results are established for clan sizes in binary unrooted trees.
Conclusions:
- The log-convexity of clade size distributions implies a higher likelihood of very large or very small clades.
- The identified critical value provides a quantitative basis for comparing evolutionary dynamics between the YHK and PDA models.
- The findings offer valuable tools for phylogenetic inference and understanding macro-evolutionary patterns.
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