During the modern synthesis, researchers merged insights from natural history, evolutionary genetics, and paleontology to develop a cohesive theoretical foundation for evolutionary theory. Since then, the rapid emergence of genomic resources has revolutionized our understanding of evolutionary processes. Despite neontological successes, paleobiology has lagged behind, due in part to perceived challenges in collecting and analyzing morphological data. As a result, the earlier synthetic evolutionary view developed between neo- and paleontology has not kept pace with the current data-centric landscape. To address these issues, I aim to integrate morphological data representing fossil and living taxa into the modern evolutionary framework through the development of novel statistical approaches that leverage sources of data previously thought to be unconventional. These developments follow two main threads: 1) development of a statistical framework through which to infer phylogeny among fossil taxa by merging increasingly large and high-throughput quantitative morphological datasets with stratigraphic information, and 2) developing empirical applications of new approaches to comprehensively examine long-hypothesized but under-studied patterns in evolutionary rate throughout time, and mosaic change by integrating morphological, stratigraphic, and developmental data.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149804/1/cfukuchi_1.pd
