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A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

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Split Probabilities and Species Tree Inference Under the Multispecies Coalescent Model.

Elizabeth S Allman¹, James H Degnan², John A Rhodes³

¹Department of Mathematics and Statistics, University of Alaska Fairbanks, P.O. Box 756660, Fairbanks, AK, 99775, USA.

Bulletin of Mathematical Biology

|November 12, 2017

Summary

Topological summaries of gene trees offer fast species tree inference. Split invariants derived from gene tree probabilities can identify the rooted species tree topology for 5+ taxa.

Keywords:

Multispecies coalescent model Species tree identifiability Split probability

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Area of Science:

Phylogenetics
Computational Biology
Evolutionary Biology

Background:

Species tree inference from gene trees is crucial for understanding evolutionary relationships.
Topological summaries of gene trees offer computational advantages over traditional methods.
The multispecies coalescent model is a standard framework for gene tree analysis.

Purpose of the Study:

To investigate the utility of split probabilities from gene trees for inferring species tree topology.
To explore the mathematical properties of splits within the multispecies coalescent model.
To determine if rooted species tree information can be recovered from unrooted gene tree splits.

Main Methods:

Analysis of split probabilities under the multispecies coalescent model.
Investigation of split consensus methods.
Identification and application of split invariants (polynomial relationships between split probabilities).

Main Results:

Split probabilities under the multispecies coalescent model contain information about species tree topology.
Split invariants were derived and analyzed.
It was demonstrated that rooted species tree topology can be identified from split probabilities for trees with 5 or more taxa, with a potential exception for 6 taxa.

Conclusions:

Topological summaries of gene trees, specifically split probabilities, are valuable for species tree inference.
Split invariants provide a powerful tool for analyzing gene tree data.
This approach offers a way to infer rooted species trees, overcoming limitations of unrooted methods for larger datasets.