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Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree

hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorARCHAMBEAU, Juliette
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorBENITO-GARZÓN, Marta
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBARRAQUAND, Frédéric
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorDE MIGUEL VEGA, Marina
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorPLOMION, Christophe
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorGONZALEZ-MARTINEZ, Santiago C.
dc.date.created2021-08-16
dc.date.issued2020-11-16
dc.description.abstractEnPopulation 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 intra-population genetic variation. We used five clonal common gardens containing 34 provenances (523 genotypes) of maritime pine ( Pinus pinaster 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, and positive-effect height-associated alleles (PEAs) captured most of the genetic component of height-growth and better predicted new provenances compared to 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.
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enClimate change
dc.subject.enlocal adaptation
dc.subject.enphenotypic plasticity
dc.subject.enpopulation response functions
dc.subject.enpositive-e ect alleles
dc.subject.enrange-wide predictive models
dc.subject.enmaritime pine
dc.title.enCombining climatic and genomic data improves range-wide tree height growth prediction in a forest tree
dc.typeDocument de travail - Pré-publication
dc.typePrepublication/Preprint
dc.identifier.doi10.1101/2020.11.13.382515
dc.subject.halSciences de l'environnement
dc.subject.halSciences du Vivant [q-bio]
hal.identifierhal-03372230
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03372230v1
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