Combining Climatic and Genomic Data Improves Range-Wide Tree Height Growth Prediction in a Forest Tree.

AbstractPopulation response functions based on climatic and phenotypic data from common gardens have long been the gold standard for predicting quantitative trait variation in new environments. However, prediction accuracy might be enhanced by incorporating genomic information that captures the neutral and adaptive processes behind intrapopulation genetic variation. We used five clonal common gardens containing 34 provenances (523 genotypes) of maritime pine ( Aiton) to determine whether models combining climatic and genomic data capture the underlying drivers of height growth variation and thus improve predictions at large geographical scales. The plastic component explained most of the height growth variation, probably resulting from population responses to multiple environmental factors. The genetic component stemmed mainly from climate adaptation and the distinct demographic and selective histories of the different maritime pine gene pools. Models combining climate of origin and gene pool of the provenances as well as height-associated positive-effect alleles (PEAs) captured most of the genetic component of height growth and better predicted new provenances compared with the climate-based population response functions. Regionally selected PEAs were better predictors than globally selected PEAs, showing high predictive ability in some environments even when included alone in the models. These results are therefore promising for the future use of genome-based prediction of quantitative traits.