A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach
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.
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Subjects: | U10 - Informatique, mathématiques et statistiques, K01 - Foresterie - Considérations générales, théorie Bayésienne, compétition végétale, phytogénétique, amélioration des plantes, amélioration génétique, Pinus taeda, http://aims.fao.org/aos/agrovoc/c_28840, http://aims.fao.org/aos/agrovoc/c_35264, http://aims.fao.org/aos/agrovoc/c_49985, http://aims.fao.org/aos/agrovoc/c_5956, http://aims.fao.org/aos/agrovoc/c_49902, http://aims.fao.org/aos/agrovoc/c_5913, |
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dig-cirad-fr-5968572024-01-29T03:08:31Z http://agritrop.cirad.fr/596857/ http://agritrop.cirad.fr/596857/ A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach. Cappa Eduardo Pablo, Munoz Facundo, Sanchez Leopoldo, Cantet Rodolfo J.C.. 2015. Tree Genetics and Genomes, 11:120, 15 p.https://doi.org/10.1007/s11295-015-0917-3 <https://doi.org/10.1007/s11295-015-0917-3> A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach Cappa, Eduardo Pablo Munoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo J.C. eng 2015 Tree Genetics and Genomes U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales théorie Bayésienne compétition végétale phytogénétique amélioration des plantes amélioration génétique Pinus taeda http://aims.fao.org/aos/agrovoc/c_28840 http://aims.fao.org/aos/agrovoc/c_35264 http://aims.fao.org/aos/agrovoc/c_49985 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_49902 http://aims.fao.org/aos/agrovoc/c_5913 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/596857/1/journal.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/s11295-015-0917-3 10.1007/s11295-015-0917-3 info:eu-repo/semantics/altIdentifier/doi/10.1007/s11295-015-0917-3 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/s11295-015-0917-3 info:eu-repo/semantics/reference/purl/https://rdcu.be/b9HFl |
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U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales théorie Bayésienne compétition végétale phytogénétique amélioration des plantes amélioration génétique Pinus taeda http://aims.fao.org/aos/agrovoc/c_28840 http://aims.fao.org/aos/agrovoc/c_35264 http://aims.fao.org/aos/agrovoc/c_49985 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_49902 http://aims.fao.org/aos/agrovoc/c_5913 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales théorie Bayésienne compétition végétale phytogénétique amélioration des plantes amélioration génétique Pinus taeda http://aims.fao.org/aos/agrovoc/c_28840 http://aims.fao.org/aos/agrovoc/c_35264 http://aims.fao.org/aos/agrovoc/c_49985 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_49902 http://aims.fao.org/aos/agrovoc/c_5913 |
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U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales théorie Bayésienne compétition végétale phytogénétique amélioration des plantes amélioration génétique Pinus taeda http://aims.fao.org/aos/agrovoc/c_28840 http://aims.fao.org/aos/agrovoc/c_35264 http://aims.fao.org/aos/agrovoc/c_49985 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_49902 http://aims.fao.org/aos/agrovoc/c_5913 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales théorie Bayésienne compétition végétale phytogénétique amélioration des plantes amélioration génétique Pinus taeda http://aims.fao.org/aos/agrovoc/c_28840 http://aims.fao.org/aos/agrovoc/c_35264 http://aims.fao.org/aos/agrovoc/c_49985 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_49902 http://aims.fao.org/aos/agrovoc/c_5913 Cappa, Eduardo Pablo Munoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo J.C. A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach |
description |
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. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales théorie Bayésienne compétition végétale phytogénétique amélioration des plantes amélioration génétique Pinus taeda http://aims.fao.org/aos/agrovoc/c_28840 http://aims.fao.org/aos/agrovoc/c_35264 http://aims.fao.org/aos/agrovoc/c_49985 http://aims.fao.org/aos/agrovoc/c_5956 http://aims.fao.org/aos/agrovoc/c_49902 http://aims.fao.org/aos/agrovoc/c_5913 |
author |
Cappa, Eduardo Pablo Munoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo J.C. |
author_facet |
Cappa, Eduardo Pablo Munoz, Facundo Sanchez, Leopoldo Cantet, Rodolfo J.C. |
author_sort |
Cappa, Eduardo Pablo |
title |
A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach |
title_short |
A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach |
title_full |
A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach |
title_fullStr |
A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach |
title_full_unstemmed |
A novel individual-tree mixed model to account for competition and environmental heterogeneity: A Bayesian approach |
title_sort |
novel individual-tree mixed model to account for competition and environmental heterogeneity: a bayesian approach |
url |
http://agritrop.cirad.fr/596857/ http://agritrop.cirad.fr/596857/1/journal.pdf |
work_keys_str_mv |
AT cappaeduardopablo anovelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach AT munozfacundo anovelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach AT sanchezleopoldo anovelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach AT cantetrodolfojc anovelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach AT cappaeduardopablo novelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach AT munozfacundo novelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach AT sanchezleopoldo novelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach AT cantetrodolfojc novelindividualtreemixedmodeltoaccountforcompetitionandenvironmentalheterogeneityabayesianapproach |
_version_ |
1792500051060195328 |