A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach

Autores
Cappa, Eduardo Pablo; Muñoz, Facundo; Sanchez, Leopoldo; Cantet, Rodolfo Juan Carlos
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muñoz, Facundo. Institut National de la Recherche Agronomique; Francia
Fil: Sanchez, Leopoldo. Institut National de la Recherche Agronomique; Francia
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Individual-Tree Mixed Model
Genetic And Environmental Competition Effects
Environmental Heterogeneity
Two-Dimensional Smoothing Surface
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/19005
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/19005
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approachCappa, Eduardo PabloMuñoz, FacundoSanchez, LeopoldoCantet, Rodolfo Juan CarlosIndividual-Tree Mixed ModelGenetic And Environmental Competition EffectsEnvironmental HeterogeneityTwo-Dimensional Smoothing Surfacehttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Negative 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.Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muñoz, Facundo. Institut National de la Recherche Agronomique; FranciaFil: Sanchez, Leopoldo. Institut National de la Recherche Agronomique; FranciaFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaSpringer Heidelberg2015-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/19005Cappa, Eduardo Pablo; Muñoz, Facundo; Sanchez, Leopoldo; Cantet, Rodolfo Juan Carlos; A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach; Springer Heidelberg; Tree Genetics & Genomes; 11; 12-2015; 120-1351614-29421614-2950CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11295-015-0917-3info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11295-015-0917-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:06:25Zoai:ri.conicet.gov.ar:11336/19005instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:06:25.554CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach
title A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach
spellingShingle A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach
Cappa, Eduardo Pablo
Individual-Tree Mixed Model
Genetic And Environmental Competition Effects
Environmental Heterogeneity
Two-Dimensional Smoothing Surface
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 A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach
dc.creator.none.fl_str_mv Cappa, Eduardo Pablo
Muñoz, Facundo
Sanchez, Leopoldo
Cantet, Rodolfo Juan Carlos
author Cappa, Eduardo Pablo
author_facet Cappa, Eduardo Pablo
Muñoz, Facundo
Sanchez, Leopoldo
Cantet, Rodolfo Juan Carlos
author_role author
author2 Muñoz, Facundo
Sanchez, Leopoldo
Cantet, Rodolfo Juan Carlos
author2_role author
author
author
dc.subject.none.fl_str_mv Individual-Tree Mixed Model
Genetic And Environmental Competition Effects
Environmental Heterogeneity
Two-Dimensional Smoothing Surface
topic Individual-Tree Mixed Model
Genetic And Environmental Competition Effects
Environmental Heterogeneity
Two-Dimensional Smoothing Surface
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv 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.
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muñoz, Facundo. Institut National de la Recherche Agronomique; Francia
Fil: Sanchez, Leopoldo. Institut National de la Recherche Agronomique; Francia
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
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.
publishDate 2015
dc.date.none.fl_str_mv 2015-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/19005
Cappa, Eduardo Pablo; Muñoz, Facundo; Sanchez, Leopoldo; Cantet, Rodolfo Juan Carlos; A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach; Springer Heidelberg; Tree Genetics & Genomes; 11; 12-2015; 120-135
1614-2942
1614-2950
CONICET Digital
CONICET
url http://hdl.handle.net/11336/19005
identifier_str_mv Cappa, Eduardo Pablo; Muñoz, Facundo; Sanchez, Leopoldo; Cantet, Rodolfo Juan Carlos; A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach; Springer Heidelberg; Tree Genetics & Genomes; 11; 12-2015; 120-135
1614-2942
1614-2950
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s11295-015-0917-3
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11295-015-0917-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Heidelberg
publisher.none.fl_str_mv Springer Heidelberg
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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