Autocorrelação espacial e variação craniométrica em populações humanas modernas

• Ano de defesa: 2018
• Autor(a) principal: Prado, Juliana Silva
• Orientador(a): Diniz Filho, José Alexandre Felizola
• Banca de defesa: Diniz Filho, José Alexandre Felizola, Silva, Daniela de Melo e, Rodrigues, Flávia Melo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Genética e Biologia Molecular (ICB)
• Departamento: Instituto de Ciências Biológicas - ICB (RG)
• País: Brasil
• Palavras-chave em Português: Autocorrelação espacial; Variação craniométrica; Mapas sintéticos; Regra de Bergmann
• Palavras-chave em Inglês: Spatial autocorrelation; Craniometric variation; Synthetic maps; Bergmann’s rule
• Área do conhecimento CNPq: CIENCIAS BIOLOGICAS::BIOLOGIA GERAL
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/8846

Understanding what factors are behind human morphological variation has for many years been one of the key objectives of various research fields, namely evolutionary, genetic and anthropological biology. The morphological diversity of the human skull sparks great scientific interest, seeing as though quantitative data (due to the genetic complexity in play) showing the patterns of microevolution is useful for analyzing and understanding matters concerning the evolutionary history of populations, such as dispersal, gene flow, isolation by distance, large-scale expansion, among others. For this purpose, the use of multivariate techniques, such as Principal Component Analysis (PCA), has been supported to assess the human genetic variation on continents. Within this context, the key objective of this article was to characterize human cranial variation, utilizing PCA and Multivariate Spatial Correlation (MSC), so as to assess and identify possible evolutionary processes that contributed to the variation observed. To this end, cranial measurements available on the database obtained by W. Howells (57 variables), sourced from 1248 adult male specimens distributed throughout 30 locations (populations) in the world, were utilized. The results show that there has been spatial structuration of data, as indicated by the spatial autocorrelation statistics (Mantel Test 0.4077, P = 0.001; 59,64% of Moran's Index value with 0.05 significance and average correlogram with positive values in the first few distance bands and negative values in the subsequent bands). The use of PCA and MSC demonstrated that MSC was able to best capture the spatial pattern of data, increasing variation percentages from 54,74% to 69,33% in the first two principal components, where the techniques showed that 26 variables relative to cranial size had positive correlations in these components. The mapping and multivariate regression analyses utilizing environmental data and average dispersion age showed that the variation in the cranial size of populations followed a pattern of increase in cranial size correlated with low temperatures and recent colonization. The results obtained are consistent with Bergmann's Rule, which may thus be applied to modern humans.