Competition effects can mislead selection in eucalypt breeding trials

选择(遗传算法) 竞赛(生物学) 生物 生态学 计算机科学 人工智能
作者
Filipe Manoel Ferreira,Saulo Fabrício da Silva Chaves,Osmarino Pires dos Santos,Andrei Caíque Pires Nunes,Evandro Vagner Tambarussi,Guilherme da Silva Pereira,Glêison Augusto dos Santos,Leonardo Lopes Bhering,Kaio Olímpio das Graças Dias
出处
期刊:Forest Ecology and Management [Elsevier BV]
卷期号:561: 121892-121892 被引量:3
标识
DOI:10.1016/j.foreco.2024.121892
摘要

The assessment of plant performance and the accuracy of genetic selection can be significantly affected by genetic competition among individuals. In addition to genetic causes, competition is influenced by external factors such as environment and age. This research uses multi-location multi-age Eucalyptus dunii trials to answer four questions: i) Are there major changes when competition effects (both genetic and residual) are estimated in single-age and multi-age models? ii) What are the implications of considering competition effects on the early selection of eucalypt clones? iii) What are the impacts of considering the reliability of direct (DGE) and indirect genotypic effects (IGE) as a weight in a selection index?, and iv) Which clones hold the potential to form highly productive clonal plantations in Southern Brazil when deployed together as clonal composites? The dataset contained three trials established in different locations in Southern Brazil, where growth traits were measured at 3.5 and 7 years. We fitted single-age and multi-age spatial competition models for each trial. The multi-age spatial competition model was a valuable tool for selecting superior eucalypt clones. Neglecting the IGEs led to changes in clone ranking, reducing the effectiveness of early selection. Additionally, a selection index where DGEs and IGEs are weighted by their reliability was proposed. Therefore, our study contributes to understanding the magnitude of IGE's impact on the daily practice of eucalypt breeding, such as early selection and multi-age analyses, and proposes selection strategies that consider the quantity and quality of information provided by the models for each individual.
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