Abstract
Predicting the evolutionary potential of natural tree populations requires the estimation of heritability and genetic correlations among traits on which selection acts, as differences in evolutionary success between species may rely on differences for these genetic parameters. In situ estimates are expected to be more accurate than measures done under controlled conditions which do not reflect the natural environmental variance. The aim of the current study was to estimate three genetic parameters (i.e., heritability, evolvability, and genetic correlations) in a natural mixed oak stand composed of Quercus petraea and Quercus robur about 100 years old, for 58 traits of ecological, and functional relevance (growth, reproduction, phenology, physiology, resilience, structure, morphology, and defense). First, we estimated genetic parameters directly in situ using realized genomic relatedness of adult trees and parentage relationships over two generations to estimate the traits’ additive variance. Secondly, we benefited from existing ex situ experiments (progeny tests and conservation collection) installed with the same populations, thus allowing comparisons of in situ heritability estimates with more traditional methods. Heritability and evolvability estimates obtained with different methods varied substantially and showed large confidence intervals; however, we found that in situ were less precise than ex situ estimates, and assessments over two generations (with deeper relatedness) improved estimates of heritability while large sampling sizes are needed for accurate estimations. At the biological level, heritability values varied moderately across different ecological and functional categories of traits, and genetic correlations among traits were conserved over the two species. We identified limits for using realized genomic relatedness in natural stands to estimate the genetic variance, given the overall low variance of genetic relatedness and the rather low sampling sizes of currently used long-term genetic plots in forestry. These limits can be overcome if larger sample sizes are considered, or if the approach is extended over the next generation.
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Acknowledgments
We thank the Office National des Forêts and its staff in charge of the Petite Charnie Forest for their constant support and technical assistance during the long term research activities conducted at La Petite Charnie. We are grateful to the staff of the State Nursery of Guéméné Penfao for the raising of the seedlings of the progeny tests and grafting of the trees for the conservation collection. We acknowledge the contribution of Roberto Bacilieri, Maria Evangelista Bertocchi, Gaëlle Capdeville, Benjamin Dencausse, François Ehrenmann, Frédéric Expert, Edith Guilley, Marilyn Harroué, Thibault Leroy, Yannick Mellerin, Nastasia Merceron, André Perrin, Andrei Prida, Jean-Louis Puech, Cyrille Rathgeber, Patrick Reynet, Daniel Rittié, Guy Roussel, Armel Thöni, as well as the staff of the Experimental Units of INRA Pierroton (UE 0570, INRA, 33612 Cestas, France) for the collection of material, the plantation of the progeny tests, and for the monitoring of the numerous traits at various periods during the last 30 years. Sequencing and genoty** for the parentage analysis and relatedness estimation was performed by Christophe Boury, Emilie Chancerel, Erwan Guichoux, Lélia Lagache at the Genome-Transcriptome facility at the Functional Genomic Center of Bordeaux. Bioinformatic analysis for SNP calling was done by Isabelle Lesur (Helix Venture, 33700 Merignac, France).
Funding
This research was supported by the European Research Council through the Advanced Grant Project TREEPEACE (no. FP7-339728). UMR BIOGECO is supported by a grant overseen by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” programme Labex COTE (ANR-10-LABEX45).
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AK and HA conceived the study. HA conducted the data analysis with the contribution of CF and AK. AD, JML, LT and BM planned the experimental operations, organized collection of material and data, participated to the data management. GN, JMTR, LT, SD, FL contributed to the collection of physiological, growth, resilience and wood density data and their pre analysis. HA and AK wrote the manuscript and all other authors revised the manuscript.
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Communicated by: J. Wright
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Data are stored at the Treepop database of the Quercus Portal. https://treepop.pierroton.inra.fr/
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Alexandre, H., Truffaut, L., Ducousso, A. et al. In situ estimation of genetic variation of functional and ecological traits in Quercus petraea and Q. robur. Tree Genetics & Genomes 16, 32 (2020). https://doi.org/10.1007/s11295-019-1407-9
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DOI: https://doi.org/10.1007/s11295-019-1407-9