CAREER: Quantitative assessment of models for phylogenetic data

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Understanding the evolutionary relationships among species and the timing of speciation events on the tree of life is critical to many fields of biology, medicine, and biochemistry. Accurately modeling biological evolution is an increasingly complex process, involving modeling how molecular and fossil features of organisms change over geological time. This project will provide insights about how to best model data to obtain an accurate picture of deep-time evolutionary dynamics. Data- and mathematically-intensive work is increasingly common in the biological sciences. To prepare a future workforce for a data-intensive future, undergraduate training must be reformed to include more quantitative and computational learning. This project will increase computational and quantitative skills in a diverse student body and analyze outcomes to determine how quantitative thinking can be most effectively integrated into early undergraduate learning.Recent advances in methods for inferring dated phylogenetic trees, such as the fossilized birth-death process (FBD), model the extant and extinct data together as part of the same process of diversification. The FBD is typically implemented as a hierarchical Bayesian model involving a model of molecular and/or morphological character evolution, a model describing how rates of evolution are distributed across the tree, and a model of how diversification has proceeded in the focal taxa. These methods offer many advantages over older methods, such as being able to place specimens known from only morphological data on the tree. Despite their mathematical elegance, these models are also complex, which can make it difficult for researchers to apply them to their datasets and evaluate their performance. This project will develop methods to assess if a complex phylogenetic model is adequately capturing the vagaries of empirical data. The project will produce software to enable researchers to evaluate and understand the performance of models in real time with empirical data. This project is jointly funded by the Systematics and Biodiversity Science Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.