NSF Postdoctoral Fellowship in Biology FY 2020: Untangling the Environmental and Genetic Drivers of Phenological Timing in Red Oak (Quercus rubra) to Improve Predictions

Blumstein, Meghan, Cambridge MA

Investigators

Abstract

This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2020. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Meghan Blumstein is "Untangling the Environmental and Genetic Drivers of Phenological Timing in Red Oak (Quercus rubra) to Improve Climate Predictions". The host institutions for the fellowship are the Center for Functional Ecology and Evolution (Montpellier, FR) and the Massachusetts Institute of Technology (MIT) and the sponsoring scientists are Drs. David Des Marais and Isabelle Chuine. Forests absorb a large proportion of carbon emissions each year via photosynthesis, making them a key player in mitigating global change. Thus, knowing when trees start and end photosynthesis each year is essential to predicting future warming. Extensive research has shown that the timing of leaf-out in trees is governed by both genetics and environment. However, no study to date has combined our understanding of these two drivers and their interaction into one framework. This study will address this need by elucidating how environment and genetics interact to alter leaf-out timing in the North American species red oak (Quercus rubra). Broader impacts include broadening participation of students from underrepresented groups and the generation of new genomic and phenotypic resources that will improve the ability to predict leaf-out timing and provide guidance for setting up assisted migration conservation programs aimed to speed the movement of locally adapted individuals northward in pace with environmental change. Training objectives for the Fellow include genomics, gene expression profiling, ecophysiology, and process-based modeling. This project will leverage a wide-spread system of phenological cameras (phenocams) in a novel way to quantify the genetic and environmental drivers of leaf-out variation in red oak and integrate this into a process-based model. Specific objectives include: 1) genetically sequencing red oak individuals growing in phenocam "viewsheds". These sequences will be used to derive population structure and develop a set of candidate loci associated with phenological timing; 2) experimentally manipulating twigs collected after budset from a subset of individuals to parse out the environmental versus genetic drivers of variation; 3) collecting gene expression data via transcriptomics to further refine candidate loci; and, 4) modifying existing process-based model structures to test hypotheses regarding how the inclusion of population structure, genetic variation, and local adaptation can improve model performance and alter our predictions. This project will provide a broad array of mentoring and research training for graduate and undergraduate students via summer and term-time programs aimed at increasing diversity in STEM fields. In addition, the Fellow will participate in public outreach via the U.S. and French National Phenological Networks' citizen science programs, civic engagement via the MIT Policy Lab, and mentoring All genomic data will be made publicly available on the Hardwood Genomics Project open-source database (https://www.hardwoodgenomics.org/). Keywords: Rules of Life, predictive modeling, genomics, genotype x environment interactions (GxE), image acquisition using phenological cameras, red oak (Quercus rubra) 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.