ArticleProceedings. Biological sciences2019
Drivers of geographical patterns of North American language diversity.
Article in Proceedings. Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An inverse correlation between structural linguistic and human genetic diversity.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Projected speaker numbers and dormancy risks of Canada's Indigenous languages.Royal Society open science · 2025Article
- Global language geography and language history: challenges and opportunities.Open research Europe · 2024Article
- The social lives of isolates (and small language families): the case of the Northwest Amazon.Interface focus · 2023Article
- Article
- The geography and development of language isolates.Royal Society open science · 2021Article
- Can Bayesian phylogeography reconstruct migrations and expansions in linguistic evolution?Royal Society open science · 2021Article
- Diversity begets diversity in mammal species and human cultures.Scientific reports · 2020Article
- Article
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Authors and funding
12 authors.
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Abstract
Although many hypotheses have been proposed to explain why humans speak so many languages and why languages are unevenly distributed across the globe, the factors that shape geographical patterns of cultural and linguistic diversity remain poorly understood. Prior research has tended to focus on identifying universal predictors of language diversity, without accounting for how local factors and multiple predictors interact. Here, we use a unique combination of path analysis, mechanistic simulation modelling, and geographically weighted regression to investigate the broadly described, but poorly understood, spatial pattern of language diversity in North America. We show that the ecological drivers of language diversity are not universal or entirely direct. The strongest associations imply a role for previously developed hypothesized drivers such as population density, resource diversity, and carrying capacity with group size limits. The predictive power of this web of factors varies over space from regions where our model predicts approximately 86% of the variation in diversity, to areas where less than 40% is explained.
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Registered trials
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