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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/3380
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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/restrictedAccess2025-09-29T13:44:26Zoai: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:l2025-09-29 13:44:26.771INTA 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 |
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1844619126048292864 |
score |
12.559606 |