Institute of Biosciences and Bioresources

National Research Council of Italy

IBBR Publication #620

Molecular proxies for climate maladaptation in a long-lived tree (Pinus pinaster Aiton, Pinaceae)

Jaramillo-Correa J-P, Rodríguez-quilón I, Grivet D, Lepoittevin C, Sebastiani F, Heuertz M, Garnier-Géré PH, Alía R, Plomion C, Vendramin GG, González-Martínez SC

Genetics (Genetics Society of America USA - ISSN: 1943-2631). (2014)


Understanding adaptive genetic responses to climate change is a main challenge for preserving biological diversity. Successful predictive models for climate-driven range shifts of species depend on the integration of information on adaptation, including that derived from genomic studies. Long-lived forest trees can experience substantial environmental change across generations, which results in a much more prominent adaptation lag than in annual species. Here, we show that candidate-gene SNPs (Single Nucleotide Polymorphisms) can be used as predictors of maladaptation to climate in maritime pine (Pinus pinaster Aiton), an outcrossing long-lived keystone tree. A set of 18 SNPs potentially associated with climate, five of them involving amino acid-changing variants, were retained after performing logistic regression, latent factor mixed models and Bayesian analyses of SNP-climate correlations. These relationships identified temperature as an important adaptive driver in maritime pine and highlighted that selective forces are operating differentially in geographically discrete gene pools. The frequency of the locally advantageous alleles at these selected loci was strongly correlated with survival in a common garden under extreme (hot and dry) climate conditions, which suggests that candidate-gene SNPs can be used to forecast the likely destiny of natural forest ecosystems under climate change scenarios. Differential levels of forest decline are anticipated for distinct maritime pine gene pools. Geographically-defined molecular proxies for climate adaptation will thus critically enhance the predictive power of range-shift models and help establishing mitigation measures for long-lived keystone forest trees in the face of impending climate change.


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