Quantitative phylogenetic methods have been used to study the evolutionary relationships and divergence times of biological species, and recently, these have also been applied to linguistic data to elucidate the evolutionary history of language families. In biology, the factors driving macroevolutionary processes are assumed to be either mainly biotic (the Red Queen model) or mainly abiotic (the Court Jester model) or a combination of both. The applicability of these models is assumed to depend on the temporal and spatial scale observed as biotic factors act on species divergence faster and in smaller spatial scale than the abiotic factors. Here, we used the Uralic language family to investigate whether both 'biotic' interactions (i.e. cultural interactions) and abiotic changes (i.e. climatic fluctuations) are also connected to language diversification. We estimated the times of divergence using Bayesian phylogenetics with a relaxed-clock method and related our results to climatic, historical and archaeological information. Our timing results paralleled the previous linguistic studies but suggested a later divergence of Finno-Ugric, Finnic and Saami languages. Some of the divergences co-occurred with climatic fluctuation and some with cultural interaction and migrations of populations. Thus, we suggest that both 'biotic' and abiotic factors contribute either directly or indirectly to the diversification of languages and that both models can be applied when studying language evolution.
The adoption of evolutionary approaches to study language change as a type of non-biological evolution has gained increasing interest and introduced a variety of quantitative tools to linguistics. The focus has thus far mainly been on language families, or ‘linguistic macroevolution,’ and taken the shape of linguistic phylogenetics. Here we explore whether evolutionary methods could be applicable for studying intra-lingual variation (‘linguistic microevolution’) by testing a population genetic clustering method for analyzing the ‘population structure’ of Finnish dialects. We compare the results with traditional dialect divisions established in the literature and with K-medoids clustering, which is free from biological assumptions. The results are encouragingly similar to each other and agree with traditional views, suggesting that population genetic tools could be a useful addition to the dialectological toolkit. We also show how the results of the model-based clustering could serve as a basis for further study.
Encouraged by ongoing discussion of the classification of the Uralic languages, we investigate the family quantitatively using Bayesian phylogenetics and basic vocabulary from seventeen languages. To estimate the heterogeneity within this family and the robustness of its subgroupings, we analyse ten divergent sets of basic vocabulary, including basic vocabulary lists from the literature, lists that exclude borrowing-susceptible meanings, lists with varying degrees of borrowing-susceptible meanings and a list combining all of the examined items. The results show that the Uralic phylogeny has a fairly robust shape from the perspective of basic vocabulary, and is not dramatically altered by borrowing-susceptible meanings. The results differ to some extent from the ‘standard paradigm’ classification of these languages, such as the lack of firm evidence for Finno-Permian.
BackgroundThe processes leading to the diversity of over 7000 present-day languages have been the subject of scholarly interest for centuries. Several factors have been suggested to contribute to the spatial segregation of speaker populations and the subsequent linguistic divergence. However, their formal testing and the quantification of their relative roles is still missing. We focussed here on the early stages of the linguistic divergence process, that is, the divergence of dialects, with a special focus on the ecological settings of the speaker populations. We adopted conceptual and statistical approaches from biological microevolution and parallelled intra-lingual variation with genetic variation within a species. We modelled the roles of geographical distance, differences in environmental and cultural conditions and in administrative history on linguistic divergence at two different levels: between municipal dialects (cf. in biology, between individuals) and between dialect groups (cf. in biology, between populations).ResultsWe found that geographical distance and administrative history were important in separating municipal dialects. However, environmental and cultural differences contributed markedly to the divergence of dialect groups. In biology, increase in genetic differences between populations together with environmental differences may suggest genetic differentiation of populations through adaptation to the local environment. However, our interpretation of this result is not that language itself adapts to the environment. Instead, it is based on Homo sapiens being affected by its environment, and its capability to adapt culturally to various environmental conditions. The differences in cultural adaptations arising from environmental heterogeneity could have acted as nonphysical barriers and limited the contacts and communication between groups. As a result, linguistic differentiation may emerge over time in those speaker populations which are, at least partially, separated.ConclusionsGiven that the dialects of isolated speaker populations may eventually evolve into different languages, our result suggests that cultural adaptation to local environment and the associated isolation of speaker populations have contributed to the emergence of the global patterns of linguistic diversity.Electronic supplementary materialThe online version of this article (10.1186/s12862-018-1238-6) contains supplementary material, which is available to authorized users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.