NSF Postdoctoral Fellowship in Biology FY 2016

Stull Gregory W, Ann Arbor MI

Investigators

Abstract

Postdoctoral Fellow: Gregory Stull Proposal Number: 1612032 This award funds an NSF Postdoctoral Research Fellowship in Biology for FY 2017, Research Using Biological Collections. The fellowship supports a research and training plan for the Fellow to take transformative approaches to grand challenges in biology that employ biological collections in highly innovative ways. The title of the research plan for this fellowship to Gregory W. Stull is "Integrating diverse collections data for deep-time distribution modeling in a tropical flowering plant family (Icacinaceae) with an extensive fossil record." The host institution for this fellowship is University of Michigan, and the sponsoring scientists are Stephen A. Smith and Christopher W. Dick. The goal of this research is to explore using geographic data from modern species to generate species distribution models of extinct species, thereby allowing for the reconstruction of species distribution patterns across broad time scales. The research uses the tropical plant family Icacinaceae, which has an extensive fossil record, as a model group of organisms, and investigates the climatic suitability of major land bridges/rafts for the migration of tropical plants throughout the Cenozoic Era (65 million years ago to the present). The research is important because understanding how species have responded to previous climate change can help predict the impact of future climate change on the distribution of current biological diversity. Because the fossil record is incomplete, additional tools are necessary to reconstruct historical distributions at different points in the past (e.g., during the early Eocene, when the earth?s climate was considerably warmer than the present). The Fellow is using species distribution modeling (SDM), which has emerged as a powerful computational tool for modeling the ecological requirements of species, incorporating the wealth of locality/geographic data available for modern species in natural history collections. SDM has been used extensively to predict possible future distributions as shaped by climate change. However, the application of SDM for reconstructing distributions in deep time has been underexplored, despite its potential for understanding how climate change has shaped the diversity and distribution of organisms through time. The Fellow's research constitutes one of the first applications of SDM across such a broad time scale, using the fossil record to validate the results. This research therefore serves as an important proof of concept for this approach, and the novel methodological tools generated will advance future research related to biogeography and climate change. In terms of training, the Fellow is acquiring skills in bioinformatics and computational biology, particularly related to methods of species distribution modeling and biogeographic modeling. These skills (e.g., fluency in scripting/programming languages such as Python) are becoming increasingly important in biological research, and this research will therefore prepare the Fellow well for a career focused on research using biological collections. The products of this work are being distributed to the research community through a variety of outlets including peer-reviewed publications, international conferences, a workshop, and web tutorials. The Fellow is also mentoring and training two undergraduates per year, seeking out individuals from minority groups underrepresented in the sciences.