Phylogenetic Comparative Methods in Biological Anthropology

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Understanding human evolution requires reconstructing the past and identifying the adaptive basis of primate traits. How can this be achieved for behavior, culture, life history, and other traits that lack a clear fossil record? The development of computer generated tree-based comparative methods has provided new insights to our evolutionary past, and advances in the last five years have greatly improved the statistical rigor of comparative studies. The proposed research will develop training opportunities and analysis tools to make phylogenetic comparative approaches more readily available to biological anthropologists. These resources will include a new evolutionary tree of primates that can be used to control for phylogenetic uncertainty and a freely available database on primate behavioral and ecological traits. The principal investigator also will apply the methods to investigate a fundamental hypothesis in primate ecology, namely that sociality increases parasite risk. In addition to providing new insights to primate social evolution, the goal is to demonstrate the methods, investigate their value relative to previous approaches, and exhibit the data and analysis resources developed in this proposal. The broader impacts will involve two major educational activities. First, a major outcome of this work will be a yearly Workshop on Comparative Methods in Evolutionary Anthropology to provide training for graduate students, postdoctoral researchers and faculty. Each workshop will include three invited instructors from evolutionary biology and biological anthropology. In addition to teaching the methods, the workshop will create a network of researchers interested in applying comparative approaches to questions in biological anthropology. Second, in each year of funding a Harvard graduate student will collaborate with the PI on one of three predictions related to the hypothesis that sociality increases parasite risk. This will provide hands-on training in statistics, phylogenetics and scientific writing for the graduate student.