Detecting Regular Sound Changes in Linguistics as Events of Concerted Evolution

TLDR

Concerted evolution describes parallel changes at multiple sites, and in linguistics it manifests as regular sound change where a phoneme consistently shifts to another across many words. The authors aim to develop a general statistical model that detects concerted changes in aligned sequence data and apply it to regular sound changes in the Turkic language family. The model analyzes aligned linguistic sequences to identify parallel phoneme shifts, inferring dated phylogenies and applicable to any discrete element undergoing parallel change. The model uncovered over 70 historical regular sound changes in Turkic, improved phylogenetic inference four‑fold, produced reliable dates, and showed change timings follow a Poisson process with networks matching linguistic expectations.

Abstract

BackgroundConcerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family.ResultsLinguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations.ConclusionsWe demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group.