Generalizing Bayesian phylogenetics to infer shared evolutionary events.

Area of Science:

• Evolutionary biology
• Phylogenetics
• Genomics

Background:

• Biological diversification often involves multiple lineages diverging simultaneously.
• Existing phylogenetic methods assume independent, bifurcating divergences, limiting the analysis of such processes.
• Testing for shared divergence patterns has been challenging due to methodological constraints.

Purpose of the Study:

• To develop a novel Bayesian phylogenetic approach to infer shared and multifurcating divergences.
• To relax the assumption of independent lineage divergence in phylogenetic analyses.
• To jointly infer phylogenetic relationships, divergence times, and diversification patterns.

Main Methods:

• Introduced a Bayesian phylogenetic approach expanding tree topologies to include shared and multifurcating divergences.
• Relaxed the assumption of independent, bifurcating divergences.
• Applied the method to genomic data from Philippine gecko genera.

Main Results:

• The new method accurately infers shared and multifurcating divergence events.
• Performance is comparable to current methods when divergences are independent and bifurcating.
• Found evidence of shared divergence patterns in Philippine geckos, suggesting landscape changes drove speciation bursts.

Conclusions:

• The generalized phylogenetic tree space accommodates complex diversification processes.
• This approach enables new research into simultaneous diversification events across various life sciences.
• The study provides a robust framework for analyzing macroevolutionary patterns driven by shared events.