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.

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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.

Abstract. The naming of natural features, such as hills, lakes, springs, meadows etc., provides a wealth of linguistic information; the study of the names and naming systems is called onomastics. We consider a data set containing all names and locations of about 58,000 lakes in Finland. Using computational techniques, we address two major onomastic themes. First, we address the existence of local dependencies or repulsion between occurrences of names. For this, we derive a simple form of spatial association rules. The results partially validate and partially contradict results obtained by traditional onomastic techniques. Second, we consider the existence of relatively homogeneous spatial regions with respect to the distributions of place names. Using mixture modeling, we conduct a global analysis of the data set. The clusterings of regions are spatially connected, and correspond quite well with the results obtained by other techniques; there are, however, interesting differences with previous hypotheses.