An information-theoretical approach to phylogeography

Area of Science:

• Evolutionary Biology
• Population Genetics
• Computational Biology

Background:

• Phylogeographic data analysis often relies on qualitative interpretations or null hypothesis testing, which can be prone to bias or lack biological relevance.
• Existing methods may not fully capture the complexity of evolutionary processes influencing genetic variation across populations.

Purpose of the Study:

• To develop and evaluate an alternative phylogeographic data analysis method using information theory.
• To quantify the probability of multiple evolutionary hypotheses given genetic data, moving beyond traditional hypothesis testing.

Main Methods:

• Augmented the model-selection procedure in the `ima` software with Akaike Information Criterion (AIC) scores and model probabilities.
• Applied information-theoretic metrics to rank 17 models representing various historical evolutionary processes for *Plethodon idahoensis*.

Main Results:

• Identified two models with high probability, both supporting population divergence with differing ancestral and descendent effective population sizes (theta).
• These top models differed in the inclusion of migration as a parameter, suggesting ambiguity in parameter space given the data.
• Simulation studies indicated that model selection performance varied depending on whether migration or incomplete lineage sorting was the primary evolutionary process.

Conclusions:

• Information-theoretic metrics provide a statistically rigorous and more flexible approach to phylogeographic analysis compared to conventional hypothesis testing.
• The method offers a nuanced understanding of evolutionary processes, acknowledging uncertainty and providing a spectrum of support for competing hypotheses.