We have presented a formal model for the quantitative analysis of phylogenetic and specific effects on the distribution of trait values among species. Total trait values are divided into phylogenetic values, inherited from an ancestral species, and specific values, the result of independent evolution. This allows a quantitative assessment of the strength of the phylogenetic inertia, or burden, displayed by a character in a lineage, so that questions concerning the relative importance of phylogenetic constraints in evolution can be answered. The separation of phylogenetic from specific effects proposed here also allows phylogenetic factors to be explicitly included in cross-species comparative analyses of adaptation. This solves a long-standing problem in evolutionary comparative studies. Only species' specific values can provide information concerning the independent evolution of characters in a set of related species. Therefore, only correlations among specific values for traits may be used as evidence for adaptation in cross-species comparative analyses. The phylogenetic autocorrelation model was applied to a comparative analysis of the determinants of sexual dimorphism in weight among 44 primate species. In addition to sexual dimorphism in weight, mating system, habitat, diet, and size (weight itself) were included in the analysis. All of the traits, except diet, were substantially influenced by phylogenetic inertia. The comparative analysis of the determinants of sexual dimorphism in weight indicates that 50% of the variation among primate species is due to phylogeny. Size, or scaling, could account for a total of 36% of the variance, making it almost as important as phylogeny in determining the level of dimorphism displayed by a species. Habitat, mating system, and diet follow, accounting for minor amounts of variation. Thus, in attempting to explain why a particular modern primate species is very dimorphic compared to other primates, we would say first because its ancestor was more dimorphic than average, second because it is a relatively large species, and third because it is terrestrial, polygynous, and folivorous.

No abstract

We have presented a formal model for the quantitative analysis of phylogenetic and specific effects on the distribution of trait values among species. Total trait values are divided into phylogenetic values, inherited from an ancestral species, and specific values, the result of independent evolution. This allows a quantitative assessment of the strength of the phylogenetic inertia, or burden, displayed by a character in a lineage, so that questions concerning the relative importance of phylogenetic constraints in evolution can be answered. The separation of phylogenetic from specific effects proposed here also allows phylogenetic factors to be explicitly included in cross-species comparative analyses of adaptation. This solves a long-standing problem in evolutionary comparative studies. Only species' specific values can provide information concerning the independent evolution of characters in a set of related species. Therefore, only correlations among specific values for traits may be used as evidence for adaptation in cross-species comparative analyses. The phylogenetic autocorrelation model was applied to a comparative analysis of the determinants of sexual dimorphism in weight among 44 primate species. In addition to sexual dimorphism in weight, mating system, habitat, diet, and size (weight itself) were included in the analysis. All of the traits, except diet, were substantially influenced by phylogenetic inertia. The comparative analysis of the determinants of sexual dimorphism in weight indicates that 50% of the variation among primate species is due to phylogeny. Size, or scaling, could account for a total of 36% of the variance, making it almost as important as phylogeny in determining the level of dimorphism displayed by a species. Habitat, mating system, and diet follow, accounting for minor amounts of variation. Thus, in attempting to explain why a particular modern primate species is very dimorphic compared to other primates, we would say first because its ancestor was more dimorphic than average, second because it is a relatively large species, and third because it is terrestrial, polygynous, and folivorous.

Boas argued that anthropologists should make historical comparisons within well-defined regional contexts. A century later, we have many improvements in the statistical methodologies for comparative research, yet most of our regional constructs remain without a valid empirical basis. We present a new method for developing and testing regions. The method takes into account older anthropological concerns with relationships between culture history and the environment, embodied in the culture-area concept, as well as contemporary concerns with historical linkages of societies into world systems. We develop nine new regions based on social structural data and test them using data on 35 I societies. We compare the new regions with Murdock's regional constructs and find that our regional classification is a strong improvement over Murdock's. In so doiig we obtain evidence for the cross-cultural importance of gender and descent systems, for the importance of constraint relationships upon sociocultural systems, for the historical importance of two precapitalist world systems, and for strikingly different geographical alignments of cultural systems in the Old World and the Americas.

Questions concerning the relative effects of various evolutionary forces in molding the genetic variability exhibited by groups of human populations have typically been investigated by comparing a variety of genetic and cultural/historical "distance" matrices. A major methodological difficulty has been the lack of formal testing procedures with which to assess the degree of confirmation or disconfirmation of an estimated measure of relationship between such matrices. In this paper, we examine a very flexible matrix combinatorial procedure which generates statistical significance levels for correlational measures of pattern similarity between distance matrices. A recent generalization of the basic procedure to the three-matrix case allows questions concerning which of two matrices best fits a third matrix to be formally tested. Applications of these hypothesis testing and inference procedures to two separate sets of genetic, geographic, and cultural distance matrices illustrates their potential for finally solving a long-standing problem in anthropological genetics.