Likelihood-Based Tests of Species Tree Hypotheses

Area of Science:

• Systematics and evolutionary biology
• Computational phylogenetics
• Bioinformatics

Background:

• Likelihood-based tests are fundamental in phylogenetics for evaluating evolutionary hypotheses.
• Existing methods for testing gene trees are not directly applicable to species tree inference.
• There is a need for robust statistical frameworks to test species tree topologies.

Purpose of the Study:

• To develop and present novel likelihood-based statistical tests for species tree topologies.
• To adapt existing gene tree testing procedures for species tree models.
• To provide a computational tool for applying these tests to phylogenomic data.

Main Methods:

• Derivation of likelihood-based tests analogous to single-locus gene tree tests.
• Utilizing multispecies coalescent models and heuristic algorithms for maximum likelihood estimation.
• Inputting gene tree topologies into species tree models for hypothesis testing.

Main Results:

• Development of analogous likelihood-based approaches for species tree topology testing.
• Demonstration of test applications, limitations, and considerations using simulated and empirical data.
• Introduction of the SpeciesTopoTestR R package for formal species topology testing.

Conclusions:

• The new methods extend likelihood-based hypothesis testing to the species tree level.
• SpeciesTopoTestR provides a practical tool for phylogenomic analysis and species relationship inference.
• These advancements enhance the statistical rigor of species tree reconstruction and hypothesis evaluation.