Impact of character correlation and variable groupings on modern human population tree resolution

Datos Bibliográficos

ID8314217
AutoresSheela Athreya (0000-0002-8498-8313, Washington University in St. Louis, autor de correspondencia), Michelle M Glantz (Colorado State University)
Año2003
Volumen122
Número2
Páginas134-146
Fecha de publicación2003-10-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaAmerican Journal of Physical Anthropology (JOURNAL)
Identificadores de la revistaISSN: 0002-9483 • E-ISSN: 1096-8644
EditorialWiley (PUBLISHER • GB)
DOI10.1002/ajpa.10220
PMID12949834
OpenAlexW2045094077
IdiomaEN
Citas recibidas3
Referencias citadas57

Relationships among modern human populations are often explored through the use of linear measurements taken on the cranium and expressed in the form of dendrograms. However, craniometric variables are strongly correlated and thereby violate the assumption of independence that most statistical analyses require. This study explores the relationship between differing methods of variable treatment and the statistical robustness of the outcomes they yield, as depicted in interpopulational trees of relatedness among modern humans. Three methods of grouping variables are examined. The first method leaves them ungrouped, the second groups variables on the basis of the developmental and/or functional complex of the cranium to which they are thought to belong, and the last method reduces variables by using principal components analysis. The strength of each of these methods is tested through the use of the Continuous Character Maximum Likelihood (CONTML) program in the PHYLIP phylogeny inference package. This program produces output in the form of trees, and the resolution of the branching topology is given as a log‐likelihood value, with statistical confidence intervals supporting each branch placement on the tree. The results indicate that leaving variables ungrouped provides misleadingly strong results by failing to account for character correlation. Of the alternative two grouping methods, the covarying components method yields the best‐resolved tree with stronger statistical support for its topology than the approach of grouping variables on the basis of their location on the cranium. Finally, the implications for interpreting population histories based on such methods are discussed. Am J Phys Anthropol 121:000–000, 2003. © 2003 Wiley‐Liss, Inc

Combinatorics · Inference · Population · Principal component analysis · Statistics · Tree (set theory) · Artificial Intelligence · Computer Science · Demography · Evolution and Paleontology Studies · Genetic diversity and population structure · Mathematics · Wildlife Ecology and Conservation