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Harvard Forest Research Project 2022

  • Title: Connecting Genotype with Environment to Understand Phenotypes in Quercus rubra
  • Principal investigator: Meghan Blumstein (blumstein@fas.harvard.edu)
  • Institution: Harvard University
  • Primary contact: Meghan Blumstein (blumstein@fas.harvard.edu)
  • Team members: David Basler; Stephen Klosterman
  • Abstract:

    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.

    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.

    Specifically at Harvard Forest, we are sampling red oaks growing in the megaplot that have been annually recensused via drones by Steve Klosterman first, then David Basler.