Developing tree ring genomics to understand the genetic basis of and predict forest response to climate change

Projektinformationen

TREE-RING GENOMICS

ID Finanzhilfevereinbarung: 101078208

DOI

10.3030/101078208

EK-Unterschriftsdatum 1 Dezember 2022

Startdatum 1 Januar 2023

Enddatum 31 Dezember 2027

Finanziert unter

• European Research Council (ERC)

Gesamtkosten

€ 1 498 596,00

EU-Beitrag

€ 1 498 596,00

Koordiniert durch

SVERIGES LANTBRUKSUNIVERSITET
Sweden

Periodic Reporting for period 1 - TREE-RING GENOMICS (Developing tree ring genomics to understand the genetic basis of and predict forest response to climate change)

Berichtszeitraum: 2023-01-01 bis 2025-06-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Conifers are ecologically dominant and economically important, but are succumbing to drought, disease, early-budding and other challenges globally because the climate has changed so that mature trees are no longer adapted to their environment. If we could predict how individual tree genotypes would respond to different environments, we could — given environmental predictions — plant the right tree in the right space to ensure healthy forest under changing climate. While over the past century people there has been extensive work in developing experimental plots that allow researchers to understand how trees grow outside of their home environment, these trials are expensive, limiting the genetic diversity that can be evaluated, and take decades to mature. In this project, we are developing a method for quickly estimating adaptive responses to different environments for any forest tree. The key to our proposal is a unique aspect of tree physiology, tree-rings, which result from the cessation of growth at the end of the grouping season, and provides a record of tree growth across its lifespan. We sample this annual growth with tree increment core samples, minimally invasive 5mm biopsies from a tree trunk, which simultaneously provide DNA for genotyping and annual growth measurements, estimated from growth rings. Because there is substantial variation in environment between years, this allows us to partition the sources of 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 can then use these estimates to understand the genetics underlying adaptation to environment, and to predict the most suitable individuals for a given location. As environments shift under climate change, this approach will provide a powerful tool to maintain healthy, resilient forests.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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. So far we have focused primarily on the first aim, developing tools and approaches for sampling, isolating genetics, growth and putting it together using statistical modeling approaches. We developed a system for accurate and replicable measurement and validation of annual growth rings using a deep learning (CNN) approach to predict ring boundaries, and integrating these predictions into a comprehensive online tool for measuring rings and assigning then to the correct growth year, written in an R-shiny environment. We find that our cheap and easy genotyping approach based on sampling only the parts of the genome that are active -- which is important for a genome 10X larger than humans -- indeed samples regions that are important for adaptive variation. Using a multiscalar sampling approach we developed in the group, we are able to show that we can isolate genetic, GxE and environmental contributions to annual growth across trees growing from France to Czechia and that, critically, these estimates can be used for mapping the genetic basis of adaptive response and also show promise for genomic prediction of responses, although more work is needed (a manuscript is currently in preparation). Going forward, we will continue to focus on refining modeling and aims two and three, using the tools developed so far to understand and leverage the biology of adaptive response.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

This project has tremendous promise for agronomic forestry and for conservation ecology. Ecologically, as climate changes, ecosystems are in flux, and if we can maintain the same species of trees, which are ecosystem dominants, in a location then it will give ecosystems more time to adapt to changing environments. For forestry, we can enable the evaluation of much more genetic variation to better identify valuate individuals. This will require making connections with industry and with governmental bodies, as well as scaling up beyond the capabilities of a single academic lab.

Overview of the approach