Predicting genetic response to future climate change in Eucalyptus grandis by combining genomic data with climate models

Abstract

Exploring the genetic response of forest trees to climate change can provide rapid and economical scientific guidance for forest management. A viable genomic approach, environmental association analysis (EAA) can help forest managers make informed decisions about conservation and translocation of wild populations, selection in breeding populations, or other management interventions. To accomplish this, EAA associates environmental drivers with genomic information to identify the underlying genes responsible for adaptation. In this study, high-quality single nucleotide polymorphisms (SNPs) were assayed in 137 individuals of Eucalyptus grandis from 16 populations using double-digest genotyping-by-sequencing (ddGBS), leading to the identification of 50 putative adaptive loci and 11 adaptive genes. The predicted precipitation of the coldest quarter was identified as an important driver of genetic variation using generalized dissimilarity modelling and risk of non-adaptedness (RONA) analyses. Sixteen populations showed low overall risk of non-adaptedness under different future climatic scenarios. However, three populations at the extremities of the species’ natural range showed higher risk of non-adaptedness with RONA scores > 0.1 in the late-century period (2081–2100) of a high-emission scenario. The results of this study suggest that several populations could be suitable sources of adaptive germplasm for future management interventions, such as translocations. Overall, this study demonstrates a practicable and effective strategy to assess the local adaptation of forest trees, and potentially other organisms using E. grandis as an example. The benefits and potential areas for future improvement of EAA in the context of forest management under projected future climate scenarios are discussed.