TASTI

TASTI infers species trees from gene trees using a maximum likelihood framework that accounts for ancestral population structure and is implemented as an R package.

Key Features:

• Maximum Likelihood Estimation: Uses a maximum likelihood approach tailored for scenarios involving ancestral population structure to infer species trees from gene trees.
• Handling Ancestral Population Structure: Explicitly models ancestral population structure that can skew gene tree frequencies during species tree inference.
• Supertree Scalability: Incorporates a supertree approach to scale inference to larger numbers of input taxa.
• Validation via Simulations: Performance validated through genetic simulations in three- and four-taxon settings, with accuracy improving as the number of input gene trees increases.
• Empirical Application: Applied to a six-species Afrotropical mosquito dataset.
• Implementation: Provided as an R package.

Scientific Applications:

• Resolving Conflicting Gene Trees: Inferring species trees from multilocus genomic datasets with conflicting signals across gene trees.
• Phylogenetics and Systematics: Reconstructing species relationships and evolutionary histories when ancestral population structure is present.
• Conservation Genetics: Supporting analyses of species relationships relevant to conservation decisions in taxa affected by ancestral structure.

Methodology:

Maximum likelihood estimation tailored to ancestral population structure; use of a supertree approach for scalability; validation via genetic simulations in three- and four-taxon settings; empirical application to a six-species Afrotropical mosquito dataset; implemented in R.