Bayesian phylogenetic methods provide a set of tools to efficiently evaluate large linguistic datasets by reconstructing phylogenies—family trees—that represent the history of language families. These methods provide a powerful way to test hypotheses about prehistory, regarding the subgrouping, origins, expansion, and timing of the languages and their speakers. Through phylogenetics, we gain insights into the process of language evolution in general and into how fast individual features change in particular. This article introduces Bayesian phylogenetics as applied to languages. We describe substitution models for cognate evolution, molecular clock models for the evolutionary rate along the branches of a tree, and tree generating processes suitable for linguistic data. We explain how to find the best-suited model using path sampling or nested sampling. The theoretical background of these models is supplemented by a practical tutorial describing how to set up a Bayesian phylogenetic analysis using the software tool BEAST2.
We used Bayesian evolutionary analysis to study linguistic data and infer phylogenetic trees of language evolution. Languages were encoded as binary strings indicating the presence or absence of members of cognate classes, the equivalence of classes of words with similar meaning, and shared ancestry. These strings formed the alignment data used to compute the posterior likelihood of a tree with respect to Bayes’ formula. Informative priors are crucial for testing hypotheses regarding the age of common ancestry and divergence times and should include as much available information as possible. Here, we investigated the birth–death process as a method to construct tree priors specifically suitable for modeling the evolution of cognate data. To test these models, we will use a dataset of the languages from Vanuatu, an island nation featuring world’s highest language density.
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