Global-scale phylogenetic linguistic inference from lexical resources

There are more than 7,000 languages spoken in the world today 1. It has been argued that the natural and social environment of languages drives this diversity 2-13. However, a fundamental question is how strong are environmental pressures, and does neutral drift suffice as a mechanism to explain diversification? We estimate the phylogenetic signals of geographic dimensions, distance to water, climate and population size on more than 6,000 phylogenetic trees of 46 language families. Phylogenetic signals of environmental factors are generally stronger than expected under the null hypothesis of no relationship with the shape of family trees. Importantly, they are also-in most cases-not compatible with neutral drift models of constant-rate change across the family tree branches. Our results suggest that language diversification is driven by further adaptive and non-adaptive pressures. Language diversity cannot be understood without modelling the pressures that physical, ecological and social factors exert on language users in different environments across the globe. Present-day linguistic diversity is non-randomly distributed across the globe, forming patterns at multiple levels. For example, more than 7,000 languages are currently spoken, and these can be classified into a few hundred language families 1. Each family contains (ideally) all-and only-descendants of a single ancestral protolanguage. Given that languages evolve through time in a manner similar to the evolution of biological species-through splits, extinctions and horizontal exchange-a language family can be approximated by a structured family tree (or phylogeny) that comprises a set of languages spoken by actual human groups occupying geographical space. An intriguing observation is that not only individual languages are non-randomly distributed across the globe; language families are too: some families are huge, spanning vast areas, while others are much more circumscribed. It has been proposed that this patterning reflects ancestral historical events and processes, such as demographic migrations and spreads, or language shift through elite dominance 14. Additionally, there is an emerging view that language diversification cannot be fully understood except in the wider context of physical, cultural and biological variation 15-17. A fundamental question, then, is why and how do language family trees unfold? Is linguistic diversification a self-contained process, or do pressures related to geographic and demographic dimensions drive diversification and shape language family trees? The classic view holds that explanations of diversity have to be sought 'first on the basis of recognized processes of internal change' 18. Here, 'internal' changes are either seen as a 'rather directionless pursuit of individual forms down the branches of the family tree' 19 or as regular phenomena such as sound change and analogy 19. Internal changes are often associated with the term 'linguistic drift' 20 , which

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“…There were a total of 29 family trees for each. Details on data availability are provided online 40 .…”

Section: Methodsmentioning

confidence: 99%

The evolution of language families is shaped by the environment beyond neutral drift

et al. 2018

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“…As in most previous Bayesian phylogenetic studies we employ topological constraints, but unlike our predecessors we introduce as many constraints as possible since we are not concerned with language classification but only with the dating of recognized subgroups. A recent study [46] using the maximum likelihood phylogenetic software RAxML [56] showed that trees inferred from word lists in the ASJP database largely agree with the Glottolog [19] classification in terms of quartet distances [57]. We therefore find it justifiable to not take the extra step of inferring all nodes in the phylogenies but rather to fix most of the nodes of the tree based on the Glottolog classification.…”

Section: Plos Onementioning

confidence: 87%

“…Using both cognate and sound class data. Bayesian phylogenetic studies typically use cognate classes to infer phylogenies, but it has been shown [46] that using both cognates and sound classes give better results for phylogenetic inference when drawing upon (an earlier version of) the ASJP database. We employ the automated cognate identification system described above [42] to assign cognate judgments to word lists.…”

Section: Methodsmentioning

confidence: 99%

A test of Generalized Bayesian dating: A new linguistic dating method

Rama

Wichmann

2020

PLoS ONE

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In current practice, when dating the root of a Bayesian language phylogeny the researcher is required to supply some of the information beforehand, including a distribution of root ages and dates for some nodes serving as calibration points. In addition to the potential subjectivity that this leaves room for, the problem arises that for many of the language families of the world there are no available internal calibration points. Here we address the following questions: Can a new Bayesian framework which overcomes these problems be introduced and how well does it perform? The new framework that we present is generalized in the sense that no family-specific priors or calibration points are needed. We moreover introduce a way to overcome another potential source of subjectivity in Bayesian tree inference as commonly practiced, namely that of manual cognate identification; instead, we apply an automated approach. Dates are obtained by fitting a Gamma regression model to tree lengths and known time depths for 30 phylogenetically independent calibration points. This model is used to predict the time depths of both the root and the internal nodes for 116 language families, producing a total of 1,287 dates for families and subgroups. It turns out that results are similar to those of published Bayesian studies of individual language families. The performance of the method is compared to automated glottochronology, which is an update of the classical method of Swadesh drawing upon automated cognate recognition and a new formula for deriving a time depth from percentages of shared cognates. It is also compared to a third dating method, that of the Automated Similarity Judgment Program (ASJP). In terms of errors and correlations with known dates, ASJP works better than the new method and both work better than automated glottochronology.

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“…We choose WALS [32], Glottolog [24] and Etnologue [33] as expert based reference classifications and Gerhard Jäger [34]'s world tree as the -to our knowledge -best computer inferred global classification available so far. Reference classifications are evolving overtime.…”

Section: Choosing and Handling Reference Classification To Benchmark mentioning

confidence: 99%

Stochastic approach to worldwide language classification: the signals and the noise towards long-range exploration

Beaufils¹,

Tomin²

2020

Preprint

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The exploration of distant language relationships reaching back to the Neolithic age remains very demanding and is often perceived as controversial. Inputs from data-driven systems have the potential to contribute substantially to the debate. We present a quantitative approach, using a very limited, but highly targeted set of language features as signals to identify and quantify language relatedness. The system processes language data and calculates distance values between languages completely automatically. The features, which are the basis of the algorithm, are themselves inferred through computer methods. The system ”learns" how to best classify languages by inferring the word list, sound change rules and other parameters that deliver the best classification on a limited amount of well-documented languages: we use undisputed classifications as a reference to “train" our system. The basis is a quantification of relatedness signals in the format of distances between pairs of languages and their statistical context, without judgement about relatedness. The enormous output of pairwise distances between thousands of languages allows a clustering process, which leads to an automatic classification. The possibility to apply statistical methods on the output data gives us the opportunity to explore and assess language relationships in a mere mathematical way and to challenge the results against chance. The scope of the project is global: the system processes 1962 languages from all families, the limit for inclusion of more languages being the availability of the material needed by our system. After exploring the methodology, we show that it leads to a reliable language classification, matching the reference listings to a high proportion. We then analyze long-range relationships and branch subgroupings the system unveils, which are not yet part of generally accepted classifications, but already subject of discussion by various scholars, so that our results add quantitative facts to existing debates. A further contribution is a series of new approaches in the field of measurement of the result accuracy as well as in the way we challenge them against chance.

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Global-scale phylogenetic linguistic inference from lexical resources

Cited by 60 publications

References 50 publications

The evolution of language families is shaped by the environment beyond neutral drift

The evolution of language families is shaped by the environment beyond neutral drift

A test of Generalized Bayesian dating: A new linguistic dating method

Stochastic approach to worldwide language classification: the signals and the noise towards long-range exploration

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