A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach
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Negative correlation caused by competition among individuals and positive spatial correlation due to environmental heterogeneity may lead to biases in estimating genetic parameters and predicting breeding values (BVs) from forest genetic trials. Former models dealing with competition and environmental heterogeneity did not account for the additive relationships among trees or for the full spatial covariance. This paper extends an individual-tree mixed model with direct additive genetic, genetic, and environmental competition effects, by incorporating a two-dimensional smoothing surface to account for complex patterns of environmental heterogeneity (competition + spatial model (CSM)). We illustrate the proposed model using simulated and real data from a loblolly pine progeny trial. The CSM was compared with three reduced individual-tree mixed models using a real dataset, while simulations comprised only CSM versus true-parameters comparisons. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. Simulation results showed that the CSM yielded posterior mean estimates of variance components with slight or negligible biases in the studied scenarios, except for the permanent environment variance. The worst performance of the simulated CSM was under a scenario with weak competition effects and small-scale environmental heterogeneity. When analyzing real data, the CSM yielded a lower value of the deviance information criterion than the reduced models. Moreover, although correlations between predicted BVs calculated from CSM and from a standard model with block effects and direct genetic effects only were high, the ranking among the top 5 % ranked individuals showed differences which indicated that the two models will have quite different genotype selections for the next cycle of breeding.
KeywordsIndividual-tree mixed model Genetic and environmental competition effects Environmental heterogeneity Two-dimensional smoothing surface Gibbs sampling
This research was supported by grants of Agencia Nacional de Ciencia y Tecnología (FONCyT PICT 00321) of Argentina, under the Programa de Modernización Tecnológica III, Contrato de Préstamo BID 1728/OC-AR. The authors would like to thank to Forestry Research and Experimentation Centre (CIEF, Buenos Aires, Argentina) for kindly providing the P. taeda L. dataset used in this study. FM and LS received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement no. 284181 (“Trees4Future”).
Data archiving statement
We followed standard Tree Genetics and Genomes policy. Simulated dataset used in this manuscript is available in the Zenodo repository, http://dx.doi.org/10.5281/zenodo.32036. Supplementary information of the P. taeda L. trial and family numbers is also available in the Zenodo repository, http://dx.doi.org/10.5281/zenodo.32040. In addition, diameter at breast height of the P. taeda L. dataset will be available upon request.
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