Combining Genomics, Remote Sensing, and Geospatial Modeling to Understand Adaptation to Growing Season Length in Balsam Poplar

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PI: Stephen Keller (University of Vermont & State Agricultural College)CoPIs: Andrew Elmore, Matthew Fitzpatrick, David Nelson, and Cathlyn Stylinski (University of Maryland Center for Environmental Science, Frostburg, MD)Key Collaborator: Raju Soolanayakanahally (Agriculture and Agri-Food, Canada)Translating genomic information into knowledge of environmental adaptation and prediction of performance under field conditions are core challenges facing plant biologists. The goal of this project is to associate genome-wide diversity to functional plant phenotypes using high-throughput phenotyping under field conditions and new analytical tools in balsam poplar, Populus balsamifera, a keystone tree species. This project will provide cross-disciplinary training in the latest techniques in ecological genomics, remote sensing, and spatial modeling to undergraduate and graduate students. Minority and first-generation undergraduate students will be recruited through partnerships with Frostburg State University's McNair Program and other organizations. Public outreach to rural communities will be conducted through a multi-faceted science program centered on engaging the public in the science of genomics, plant phenology, and climate change, in collaboration with the National Phenology Network (www.usanpn.org/). This project will develop and integrate tools from genomics, remote sensing, and geospatial modeling to study the genetic basis of climate adaptation in Populus balsamifera, balsam poplar. Sampling will be focused on balsam poplar's southern range edge in order to study the physiological adaptations of populations to the warmest, earliest onset growing seasons within its geographic range. Genome-wide single nucleotide polymorphism (SNP) data will be generated for 600 poplar genotypes and used to perform genome scans for local adaptation and association mapping for phenology, growth, and water use efficiency traits. Regions of the genome associated with climate adaptation will be used to predict field performance using an independent sample of genotypes and an innovative remote sensing approach to measure phenology. New spatial analytical methods will be developed to characterize the associations between genomic variation and environmental gradients of climate and growing season length, and to visualize the landscape surface of adaptive variation under both current and projected climates. Genomic sequence data will be publically available through NCBI's sequence read archive and DOE's Knowledgebase. SNP genotypes, phenotypic traits, and remotely sensed phenology data will be publically accessible through Data DRYAD (www.datadryad.org). A software package in landscape genomics will be developed for the R project for statistical computing, and publically accessible through the Comprehensive R Archive Network (CRAN: http://cran.r-project.org/). Finally, new germplasm and associated genomic and phenotypic results will be available upon request.