Economically important and ecologically dominant conifers are succumbing to drought, disease, early-budding and other challenges globally because mature trees are no longer adapted to their current environment under climate change. If we can understand how individual trees respond to different environments, we can match seed sources to an appropriate environment. Existing approaches for understanding adaptive responses to variable environment are effective but agronomic approaches are limited by both long generation times and the genetic diversity they can evaluate and significant variants discovered in environmental GWAS in natural populations are often associated both with adaptation but also demographic structure, confounding inference. We propose a system for quickly estimating adaptive responses for any forest tree. Focusing on annual growth measurements, measured from increment core samples, provides replication of a given genotype across hundreds of experienced year-environments. This allows us to partition growth variation into generalizable environmental responses for years with historical weather or biotic information, using quantitative genomic and ecological approaches to control for correlated responses. We focus on the economically and ecologically important conifer Norway spruce (Picea abies) to 1) develop models and infrastructure to understand the fraction of annual growth that can be attributed to genotype, environment and genotype-by-environment interactions (GxE), 2) map the genetic basis of adaptive response using estimates for GxE as a response in genome-wide association studies (GWAS) and 3) predict genetic responses to novel environments. This approach will enable estimation of the genetic basis of adaptive responses in any population, providing the means to evaluate a tree’s performance in any modelled environment. As environments shift under climate change, this will provide a powerful tool to select parents for healthy, resilient forests.
Fields of science
- HORIZON.1.1 - European Research Council (ERC) Main Programme