Cargando…
Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance
Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and charact...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817535/ https://www.ncbi.nlm.nih.gov/pubmed/33488644 http://dx.doi.org/10.3389/fpls.2020.596315 |
_version_ | 1783638659771138048 |
---|---|
author | Pont, David Dungey, Heidi S. Suontama, Mari Stovold, Grahame T. |
author_facet | Pont, David Dungey, Heidi S. Suontama, Mari Stovold, Grahame T. |
author_sort | Pont, David |
collection | PubMed |
description | Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects. |
format | Online Article Text |
id | pubmed-7817535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78175352021-01-22 Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance Pont, David Dungey, Heidi S. Suontama, Mari Stovold, Grahame T. Front Plant Sci Plant Science Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects. Frontiers Media S.A. 2021-01-07 /pmc/articles/PMC7817535/ /pubmed/33488644 http://dx.doi.org/10.3389/fpls.2020.596315 Text en Copyright © 2021 Pont, Dungey, Suontama and Stovold. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Pont, David Dungey, Heidi S. Suontama, Mari Stovold, Grahame T. Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance |
title | Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance |
title_full | Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance |
title_fullStr | Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance |
title_full_unstemmed | Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance |
title_short | Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance |
title_sort | spatial models with inter-tree competition from airborne laser scanning improve estimates of genetic variance |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7817535/ https://www.ncbi.nlm.nih.gov/pubmed/33488644 http://dx.doi.org/10.3389/fpls.2020.596315 |
work_keys_str_mv | AT pontdavid spatialmodelswithintertreecompetitionfromairbornelaserscanningimproveestimatesofgeneticvariance AT dungeyheidis spatialmodelswithintertreecompetitionfromairbornelaserscanningimproveestimatesofgeneticvariance AT suontamamari spatialmodelswithintertreecompetitionfromairbornelaserscanningimproveestimatesofgeneticvariance AT stovoldgrahamet spatialmodelswithintertreecompetitionfromairbornelaserscanningimproveestimatesofgeneticvariance |