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.
Instituto de Recursos Biológicos
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muñoz, Facundo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; Francia
Fil: Sanchez, Leopoldo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; Francia
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fuente
Tree genetics and genomes 11 : 120. (December 2015)
Materia
Competencia Biológica
Biological Competition
Environmental Heterogeneity
Gibbs Sampling
Individual-tree Mixed Model
Heterogeneidad Ambiental
Muestreo de Gibbs
Modelo Mixto Arbol Individual
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/3380
Acceder
id INTADig_680c2e20babcf92a8f502322f7f18244
oai_identifier_str oai:localhost:20.500.12123/3380
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approachCappa, Eduardo PabloMuñoz, FacundoSanchez, LeopoldoCantet, Rodolfo Juan CarlosCompetencia BiológicaBiological CompetitionEnvironmental HeterogeneityGibbs SamplingIndividual-tree Mixed ModelHeterogeneidad AmbientalMuestreo de GibbsModelo Mixto Arbol IndividualNegative 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.Instituto de Recursos BiológicosFil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muñoz, Facundo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; FranciaFil: Sanchez, Leopoldo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; FranciaFil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaSpringer2018-09-17T18:39:48Z2018-09-17T18:39:48Z2015-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/3380https://link.springer.com/article/10.1007/s11295-015-0917-3#citeas1614-29421614-2950 (Online)https://doi.org/10.1007/s11295-015-0917-3Tree genetics and genomes 11 : 120. (December 2015)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-10-23T11:16:40Zoai:localhost:20.500.12123/3380instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:16:40.57INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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
Competencia Biológica
Biological Competition
Environmental Heterogeneity
Gibbs Sampling
Individual-tree Mixed Model
Heterogeneidad Ambiental
Muestreo de Gibbs
Modelo Mixto Arbol Individual
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 Competencia Biológica
Biological Competition
Environmental Heterogeneity
Gibbs Sampling
Individual-tree Mixed Model
Heterogeneidad Ambiental
Muestreo de Gibbs
Modelo Mixto Arbol Individual
topic Competencia Biológica
Biological Competition
Environmental Heterogeneity
Gibbs Sampling
Individual-tree Mixed Model
Heterogeneidad Ambiental
Muestreo de Gibbs
Modelo Mixto Arbol Individual
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.
Instituto de Recursos Biológicos
Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muñoz, Facundo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; Francia
Fil: Sanchez, Leopoldo. Institut National de la Recherche Agronomique. Unité Amélioration, Génétique et Physiologie Forestières; Francia
Fil: Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; 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
2018-09-17T18:39:48Z
2018-09-17T18:39:48Z
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/20.500.12123/3380
https://link.springer.com/article/10.1007/s11295-015-0917-3#citeas
1614-2942
1614-2950 (Online)
https://doi.org/10.1007/s11295-015-0917-3
url http://hdl.handle.net/20.500.12123/3380
https://link.springer.com/article/10.1007/s11295-015-0917-3#citeas
https://doi.org/10.1007/s11295-015-0917-3
identifier_str_mv 1614-2942
1614-2950 (Online)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Tree genetics and genomes 11 : 120. (December 2015)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1846787511786930176
score 12.982451

Publicaciones similares