BPP

BPP performs Bayesian inference under the multispecies coalescent using Markov Chain Monte Carlo to analyze multi-locus genomic sequence data for estimating species delimitation, species trees, divergence times (tau), and population sizes (theta).

Key Features:

• Multispecies Coalescent Model: Operates under the multispecies coalescent, explicitly accounting for gene tree discordance due to incomplete lineage sorting.
• Bayesian MCMC Inference: Employs Markov Chain Monte Carlo to sample posterior distributions of species trees, parameters, and delimitation configurations.
• Population Size Estimation (theta): Estimates effective population sizes (theta) from multi-locus sequence data.
• Species Divergence Time Estimation (tau): Estimates species divergence times (tau) to provide temporal context for speciation events.
• Species Tree Estimation: Estimates the species tree when a fixed guide tree is not provided.
• Species Delimitation: Performs species delimitation either with a fixed guide tree or by inferring delimitations jointly with phylogeny.
• Joint Analysis (A11) and Novel Priors: Conducts joint species delimitation and species-tree estimation using two novel priors that assign uniform probabilities across different numbers of delimited species.
• Analysis Mode A00: Estimation of divergence times and population sizes on a fixed phylogeny.
• Analysis Mode A01: Species tree estimation with predetermined species assignments and delimitations.
• Analysis Mode A10: Species delimitation using a fixed guide tree.
• Analysis Mode A11: Joint species delimitation and species-tree estimation where assignment, delimitation, and phylogeny are inferred together.

Scientific Applications:

• Speciation Analysis: Analyze multi-locus genomic datasets to infer speciation events and relationships among taxa.
• Demographic Inference: Estimate demographic parameters such as effective population sizes and historical population dynamics.
• Species Delimitation and Biodiversity Assessment: Resolve species boundaries to inform biodiversity studies and conservation decisions.
• Joint Inference of Assignment, Delimitation, and Phylogeny: Simultaneously infer species assignments, species delimitations, and species-tree topology in a unified Bayesian framework.
• Example Dataset: Demonstrated on a dataset of five nuclear loci from East Asian brown frogs using analysis modes A00, A01, A10, and A11.

Methodology:

Bayesian inference under the multispecies coalescent implemented via Markov Chain Monte Carlo; supports analysis modes A00, A01, A10, and A11, with A11 using two novel priors that assign uniform probabilities across numbers of delimited species.