Phylogenetic trees are a central tool for studying language evolution and have wide implications for understanding cultural evolution as a whole. For example, they have been the basis of studies on the evolution of musical instruments, religious beliefs and political complexity. Bayesian phylogenetic methods are transparent regarding the data and assumptions underlying the inference. One of these assumptions—that languages change independently—is incompatible with the reality of language evolution, particularly with language contact. When speakers interact, languages frequently borrow linguistic traits from each other. Phylogenetic methods ignore this issue, which can lead to errors in the reconstruction. More importantly, they neglect the rich history of language contact. A principled way of integrating language contact in phylogenetic methods is sorely missing. We present contacTrees, a Bayesian phylogenetic model with horizontal transfer for language evolution. The model efficiently infers the phylogenetic tree of a language family and contact events between its clades. The implementation is available as a package for the phylogenetics software BEAST 2. We apply contacTrees in a simulation study and a case study on a subset of well-documented Indo-European languages. The simulation study demonstrates that contacTrees correctly reconstructs the history of a simulated language family, including simulated contact events. Moreover, it shows that ignoring contact can lead to systematic errors in the estimated tree height, rate of change and tree topology, which can be avoided with contacTrees. The case study confirms that contacTrees reconstructs known contact events in the history of Indo-European and finds known loanwords, demonstrating its practical potential. The model has a higher statistical fit to the data than a conventional phylogenetic reconstruction, and the reconstructed tree height is significantly closer to well-attested estimates. Our method closes a long-standing gap between the theoretical and empirical models of cultural evolution. The implications are especially relevant for less documented language families, where our knowledge of past contacts and linguistic borrowings is limited. Since linguistic phylogenies have become the backbone of many studies of cultural evolution, the addition of this integral piece of the puzzle is crucial in the endeavour to understand the history of human culture.

Language evolution is traditionally described in terms of family trees with ancestral languages splitting into descendent languages. However, it has long been recognized that language evolution also entails horizontal components, most commonly through lexical borrowing. For example, the English language was heavily influenced by Old Norse and Old French; eight per cent of its basic vocabulary is borrowed. Borrowing is a distinctly non-tree-like process--akin to horizontal gene transfer in genome evolution--that cannot be recovered by phylogenetic trees. Here, we infer the frequency of hidden borrowing among 2346 cognates (etymologically related words) of basic vocabulary distributed across 84 Indo-European languages. The dataset includes 124 (5%) known borrowings. Applying the uniformitarian principle to inventory dynamics in past and present basic vocabularies, we find that 1373 (61%) of the cognates have been affected by borrowing during their history. Our approach correctly identified 117 (94%) known borrowings. Reconstructed phylogenetic networks that capture both vertical and horizontal components of evolutionary history reveal that, on average, eight per cent of the words of basic vocabulary in each Indo-European language were involved in borrowing during evolution. Basic vocabulary is often assumed to be relatively resistant to borrowing. Our results indicate that the impact of borrowing is far more widespread than previously thought.

Abstract Understanding how languages change is important not only for the reconstruction of protolanguages and for estimating diversification dates (i.e. the dates when languages split), but also for the inference of evolutionary trees (or phylogenetic networks) of language families. We propose a parametric model of language change that addresses lexical polymorphism (two or more words for a given basic meaning) based on what is known about how languages change. Under our model, changes of state in lexical characters occur only due to semantic shift or borrowing, leading to (potentially brief) periods in which polymorphism is present. Across a wide range of model conditions, we find that a simple and natural modification to the maximum parsimony (MP) criterion (which seeks the tree with the fewest number of changes) to allow it to handle polymorphic characters has the best accuracy, substantially improving on well‐known Bayesian methods based on appearances and disappearances of words. We also provide a new analysis of Indo–European that takes polymorphism into account, finding support for a previous tree (Nakhleh et al., 2006) and a new tree that differs from the previous tree in the relationship between Italo‐Celtic and Tocharian.

The concept of a temporal phylogenetic network is a mathematical model of evolution of a family of natural languages. It takes into account the fact that languages can trade their characteristics with each other when linguistic communities are in contact, and also that a contact is only possible when the languages are spoken at the same time. We show how computational methods of answer set programming and constraint logic programming can be used to generate plausible conjectures about contacts between prehistoric linguistic communities, and illustrate our approach by applying it to the evolutionary history of Indo-European languages.