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  Drivers of geographical patterns of North American language diversity

Coelho, M. T. P., Pereira, E. B., Haynie, H. J., Rangel, T. F., Kavanagh, P., Kirby, K., et al. (2019). Drivers of geographical patterns of North American language diversity. Proceedings of the Royal Society B: Biological Sciences, 286(1899): 20190242. doi:10.1098/rspb.2019.0242.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-4ACE-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-D354-0
Genre: Journal Article

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 Creators:
Coelho, Marco Túlio Pacheco, Author
Pereira, Elisa Barreto, Author
Haynie, Hannah J., Author
Rangel, Thiago F., Author
Kavanagh, Patrick, Author
Kirby, Kathryn1, Author              
Greenhill, Simon J.1, Author              
Bowern, Claire, Author
Gray, Russell D.1, Author              
Colwell, Robert K., Author
Evans, Nicholas, Author
Gavin, Michael C.1, Author              
Affiliations:
1Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2074311              

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Free keywords: geographically weighted regression, language diversity, path analysis
 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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Language(s): eng - English
 Dates: 2019-03-272019-03
 Publication Status: Published in print
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1098/rspb.2019.0242
Other: shh1201
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Title: Proceedings of the Royal Society B: Biological Sciences
  Abbreviation : Proc. R. Soc. B
Source Genre: Journal
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Publ. Info: London : Royal Society
Pages: 20190242 Volume / Issue: 286 (1899) Sequence Number: 20190242 Start / End Page: - Identifier: ISSN: 0962-8452
CoNE: https://pure.mpg.de/cone/journals/resource/110975500577295_2