Tracy Heath Presents at the BCB Faculty Seminar

Wednesday, October 7, 2015 - 4:10pm
Event Type: 


Tracy Heath
BCB Faculty Member in the Ecology, Evolution and Organismal Biology Department


Bayesian Phylogenetic Methods for Synthesizing Paleontological and Neontological Data


From her website, Tracy writes:


I develop methods that broaden our understanding of the evolutionary processes responsible for generating the patterns we observe in the tree of life. Specifically, I develop novel computational and statistical methods for estimating evolutionary parameters in a phylogenetic context. I evaluate the properties and performance of phylogenetic methods using biological data sets and through the development of new simulation tools. Additionally, my collaborators and I apply phylogenetic techniques to empirical data sets to understand patterns of biodiversity, morphological evolution, and historical biogeography.


Combining Paleontological and Neontological Data for Phylogenetic Inference

Historical observations in the form of fossil occurrence times or other geological data are fundamental components necessary for inferring the absolute timing of speciation events. The common approach to time calibration taken by most neontologists is to assign minimum age estimates, based on fossil specimens, to nodes within their group of interest. However, we can rarely assign fossils to nodes of a tree without error, and much of the information associated with fossil taxa is lost when we try to represent the fossil record as a single time estimate applied to a single internal node.

The Fossilized Birth-Death Process

In collaboration with Tanja Stadler (ETH Zürich) and John Huelsenbeck (UC–Berkeley), I developed a unified extant and extinct- species diversification model that eliminates the need for the ad hoc prior densities that are common practice in Bayesian phylogenetics. The ‘fossilized birth-death’ (FBD) process is a model for calibrating divergence-time estimates in a Bayesian framework and explicitly acknowledges that extant species and fossils are representatives of the same macroevolutionary process. Under this model, we can estimate internal node ages conditional on a tree of extant taxa and set of fossil occurrence times. We developed novel reversible-jump MCMC methods to marginalize over realizations of this process and implemented them in a recent version of DPPDiv. This model improves upon standard approaches for calibrating phylogenies with fossil data by allowing for inclusion of all available fossils and providing coherent measures of statistical uncertainty. (See Heath et al., PNAS 2014)

Fossil Phylogenetics with Trilobites as a Model System

For some taxonomic groups, the fossil record provides a wealth of information about the evolution of morphological characters and lineage diversification. Because of this, the development of methods for fully integrating information from fossil specimens into phylogenetic analyses is a critical area of research. Toward this end, I am collaborating with Drs. Mark Holder and Bruce Lieberman (U. Kansas) to develop improved methods for likelihood-based phylogenetic analysis of morphological data with an emphasis on fossil taxa. As part of this project, I will integrate complex models of morphological evolution and stratigraphic information into Bayesian methods for phylogenetic inference. This work will build on the FBD model and estimate the explicit phylogenetic relationships of fossil organisms. Furthermore, we will extend the diversification models to account for our current understanding of the rock record and stratigraphy. We will harness the rich fossil record of the Trilobita, specifically the well-studied groups Olenellina and Cheiruridae, to evaluate and test new phylogenetic methods. Ultimately, this work will expand our understanding of the patterns of diversification and morphological evolution in this group. Furthermore, our methods are naturally applicable to data sets combining both modern and fossil species, thus contributing to current research in molecular phylogenetics and macroevolution. This project is funded by NSF grant DEB-1256993.