Pont David, Dungey Heidi S, Suontama Mari, Stovold Grahame T
Forest Informatics, Scion, Rotorua, New Zealand.
Forest Genetics, Scion, Rotorua, New Zealand.
Front Plant Sci. 2021 Jan 7;11:596315. doi: 10.3389/fpls.2020.596315. eCollection 2020.
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 () to -21.03% for wood stiffness (), and improvements in narrow sense heritabilities from 38.64% for to 14.01% for . Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.
对单株树木进行表型分析以量化基因型、环境和管理实践之间的相互作用，对于精准林业的发展以及最大限度地提高改良树种的机会至关重要。在本研究中，我们利用机载激光扫描（ALS）数据来检测和表征单株树木，以生成树木水平的表型和树间竞争指标。为了检验我们考虑环境变异及其对单株树木性状相对重要性的能力，我们研究了使用基于ALS衍生竞争指标的空间模型和传统自回归空间技术。与标准模型相比，发现利用竞争协变量项的模型能够量化先前无法解释的表型变异，大幅降低残差方差，并改善一组操作相关性状的遗传力估计。包括空间自相关和竞争项的模型表现最佳，被标记为ACE（自相关-竞争-误差）模型。最佳的ACE模型在残差方面实现了统计学上的显著降低，树高（）的残差降低了-65.48%，木材硬度（）的残差降低了-21.03%，狭义遗传力从（）的38.64%提高到（）的14.01%。因此，建议使用ACE方法对单株树木进行表型分析，以分析性状易受空间效应影响的研究试验。
