Bayesian Estimation of Host-Parasite Cospeciation

University Of California-San Diego, La Jolla CA

Investigators

Abstract

DEB-0075406 John P. Huelsenbeck Dr. John P. Huelsenbeck of the University of Rochester, has been awarded a grant to use Bayesian statistacal methods to study coevolutionary interactions between parasites and their host species. He is collaborating with Dr. Bret R. Larget of Duquesne University and Dr. Bruce H. Rannala of the University of Alberta (Canada) on this research. Parasites are organisms that are dependent on another organism (the host) for their survival and reproduction and are typically harmful to the host. Often, the association of a host and parasite is highly specific and ancient. In such cases, it is possible to infer the history of association between hosts and parasites by examining the phylogeny (genealogy) of related hosts and parasites. It is not uncommon for the phylogenies of hosts and parasites to be fully or partially concordant as the two groups have evolved in parallel. For example, if A, B, and C are the host species, and a, b, and c are the respective parasite species (species a parasitizes host A, and so on), a concordant phylogeny of three hosts and parasites might be ((A,B),C) and ((a,b),c). The host phylogeny is consistent with species A and B being each others closest relatives. Similarly, the parasites associated with A and B (namely, a and b) are also each others closest relatives. This pattern is consistent with cospeciation of the hosts and parasites; a speciation event in a host causes the associated parasite to speciate via allopatric speciation. Typically, the phylogenies of hosts and parasites are not completely concordant. Morevoer, the phylogenies of the hosts and parasites are never known without error. We have taken a Bayesian approach to infer the history of host-parasite association. The research funded by NSF proposes to estimate rates of host-switching, parasite speciation, and parasite extinction while accommodating uncertainty in the phylogenies of hosts and parasites. A numerical technique called Markov chain Monte Carlo will be used to perform the Bayesian inference. .