Autocorrelação espacial e variação craniométrica em populações humanas modernas
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/8846 |
Resumo: | 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. |
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Diniz Filho, José Alexandre Felizolahttp://lattes.cnpq.br/0706396442417351Diniz Filho, José Alexandre FelizolaSilva, Daniela de Melo eRodrigues, Flávia Melohttp://lattes.cnpq.br/6235391869852999Prado, Juliana Silva2018-09-04T11:37:20Z2018-02-14PRADO, J. S. Autocorrelação espacial e variação craniométrica em populações humanas modernas. 2018. 77 f. Dissertação (Mestrado em Genética e Biologia Molecular) - Universidade Federal de Goiás, Goiânia, 2018.http://repositorio.bc.ufg.br/tede/handle/tede/8846Understanding 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.Compreender quais são os fatores que estão por trás da variação morfológica humana tem sido há muitos anos um dos principais objetivos de diversas áreas de pesquisa, destacando a biologia evolutiva, genética e antropologia. A diversidade morfológica do crânio humano desperta grande interesse científico, onde o uso de dados quantitativos (devido à complexidade genética que o influencia) demostrando a atuação de processos microevolutivos, é útil para analisar e buscar compreender questões relativas a história evolutiva das populações, como eventos de dispersão genética, fluxo gênico, isolamento por distância e expansão em grande escala, dentre outros. Para tal, o uso das técnicas multivariadas, como a Análise de Componentes Principais (PCA), tem sido defendido para se avaliar a variação genética humana em regiões continentais. Nesse contexto, o objetivo central deste trabalho foi caracterizar a variação craniana humana, utilizando a PCA e a técnica de Correlação Espacial Multivariada (MSC), a fim de avaliar e identificar possíveis processos evolutivos que contribuíram para a variação observada. Para tal propósito, foram utilizadas as características métricas cranianas disponíveis no banco de dados obtido por W. Howells (57 variáveis) proveniente de 1248 espécimes adultos do sexo masculino distribuídos em 30 localidades (populações) pelo mundo. Os resultados demonstraram que houve estruturação espacial dos dados, indicado pelas estatísticas de autocorrelação espacial (Teste de Mantel 0.4077, P = 0.001; 59,64% dos índices I de Moran significativos a 0.05, e correlograma médio com valores positivos nas primeiras classes de distância e negativos nas seguintes). O uso da PCA e do MSC demonstraram que a técnica do MSC capturou melhor o padrão espacial dos dados, aumentando os valores da percentagem de variação passando de 54,74% para 69,33% nos 2 primeiros componentes principais, onde as técnicas demonstraram que 26 variáveis correspondentes a tamanho neurocraniano, possuíam correlações positivas nos dois primeiros componentes principais. Os mapas sintéticos e as análises de regressão multivariada utilizando dados ambientais e idade média de dispersão demonstraram que a variação do tamanho do crânio nas populações seguiu um padrão de aumento do tamanho craniano correlacionado a temperaturas baixas e idade de colonização recente. Os resultados obtidos são condizentes com a Regra Ecogeográfica de Bergmann, que pode então ser aplicada a humanos modernos.Submitted by Liliane Ferreira (ljuvencia30@gmail.com) on 2018-09-03T12:14:16Z No. of bitstreams: 2 Dissertação - Juliana Silva Prado - 2018.pdf: 3426266 bytes, checksum: 8677cc417b12183a4ded6032cee7d911 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-09-04T11:37:20Z (GMT) No. of bitstreams: 2 Dissertação - Juliana Silva Prado - 2018.pdf: 3426266 bytes, checksum: 8677cc417b12183a4ded6032cee7d911 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2018-09-04T11:37:20Z (GMT). No. of bitstreams: 2 Dissertação - Juliana Silva Prado - 2018.pdf: 3426266 bytes, checksum: 8677cc417b12183a4ded6032cee7d911 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-14Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfporUniversidade Federal de GoiásPrograma de Pós-graduação em Genética e Biologia Molecular (ICB)UFGBrasilInstituto de Ciências Biológicas - ICB (RG)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAutocorrelação espacialVariação craniométricaMapas sintéticosRegra de BergmannSpatial autocorrelationCraniometric variationSynthetic mapsBergmann’s ruleCIENCIAS BIOLOGICAS::BIOLOGIA GERALAutocorrelação espacial e variação craniométrica em populações humanas modernasSpace autocorrelation and craniometric variation in modern human populationsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis-3983316729959641468600600600600-3872772117827373404-16345593859312446972075167498588264571reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGORIGINALDissertação - Juliana Silva Prado - 2018.pdfDissertação - Juliana Silva Prado - 2018.pdfapplication/pdf3426266http://repositorio.bc.ufg.br/tede/bitstreams/58b9d453-0917-4841-945a-58791d9dd728/download8677cc417b12183a4ded6032cee7d911MD55LICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv |
Autocorrelação espacial e variação craniométrica em populações humanas modernas |
dc.title.alternative.eng.fl_str_mv |
Space autocorrelation and craniometric variation in modern human populations |
title |
Autocorrelação espacial e variação craniométrica em populações humanas modernas |
spellingShingle |
Autocorrelação espacial e variação craniométrica em populações humanas modernas Prado, Juliana Silva Autocorrelação espacial Variação craniométrica Mapas sintéticos Regra de Bergmann Spatial autocorrelation Craniometric variation Synthetic maps Bergmann’s rule CIENCIAS BIOLOGICAS::BIOLOGIA GERAL |
title_short |
Autocorrelação espacial e variação craniométrica em populações humanas modernas |
title_full |
Autocorrelação espacial e variação craniométrica em populações humanas modernas |
title_fullStr |
Autocorrelação espacial e variação craniométrica em populações humanas modernas |
title_full_unstemmed |
Autocorrelação espacial e variação craniométrica em populações humanas modernas |
title_sort |
Autocorrelação espacial e variação craniométrica em populações humanas modernas |
author |
Prado, Juliana Silva |
author_facet |
Prado, Juliana Silva |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Diniz Filho, José Alexandre Felizola |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/0706396442417351 |
dc.contributor.referee1.fl_str_mv |
Diniz Filho, José Alexandre Felizola |
dc.contributor.referee2.fl_str_mv |
Silva, Daniela de Melo e |
dc.contributor.referee3.fl_str_mv |
Rodrigues, Flávia Melo |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6235391869852999 |
dc.contributor.author.fl_str_mv |
Prado, Juliana Silva |
contributor_str_mv |
Diniz Filho, José Alexandre Felizola Diniz Filho, José Alexandre Felizola Silva, Daniela de Melo e Rodrigues, Flávia Melo |
dc.subject.por.fl_str_mv |
Autocorrelação espacial Variação craniométrica Mapas sintéticos Regra de Bergmann |
topic |
Autocorrelação espacial Variação craniométrica Mapas sintéticos Regra de Bergmann Spatial autocorrelation Craniometric variation Synthetic maps Bergmann’s rule CIENCIAS BIOLOGICAS::BIOLOGIA GERAL |
dc.subject.eng.fl_str_mv |
Spatial autocorrelation Craniometric variation Synthetic maps Bergmann’s rule |
dc.subject.cnpq.fl_str_mv |
CIENCIAS BIOLOGICAS::BIOLOGIA GERAL |
description |
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. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-09-04T11:37:20Z |
dc.date.issued.fl_str_mv |
2018-02-14 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
PRADO, J. S. Autocorrelação espacial e variação craniométrica em populações humanas modernas. 2018. 77 f. Dissertação (Mestrado em Genética e Biologia Molecular) - Universidade Federal de Goiás, Goiânia, 2018. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/8846 |
identifier_str_mv |
PRADO, J. S. Autocorrelação espacial e variação craniométrica em populações humanas modernas. 2018. 77 f. Dissertação (Mestrado em Genética e Biologia Molecular) - Universidade Federal de Goiás, Goiânia, 2018. |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/8846 |
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por |
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por |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Universidade Federal de Goiás |
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Programa de Pós-graduação em Genética e Biologia Molecular (ICB) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Instituto de Ciências Biológicas - ICB (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
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