Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine (Pinus elliottii) and loblolly pine (Pinus taeda), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established via the combined treatment of detrended variable–significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient (R²) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.

Slash pine (Pinus elliottii Engelm.) is an important timber and resin species in the United States, China, Brazil and other countries. Understanding the genetic basis of these traits will accelerate its breeding progress. We carried out a genome-wide association study (GWAS), transcriptome-wide association study (TWAS) and weighted gene co-expression network analysis (WGCNA) for growth, wood quality, and oleoresin traits using 240 unrelated individuals from a Chinese slash pine breeding population. We developed high quality 53,229 single nucleotide polymorphisms (SNPs). Our analysis reveals three main results: (1) the Chinese breeding population can be divided into three genetic groups with a mean inbreeding coefficient of 0.137; (2) 32 SNPs significantly were associated with growth and oleoresin traits, accounting for the phenotypic variance ranging from 12.3% to 21.8% and from 10.6% to 16.7%, respectively; and (3) six genes encoding PeTLP, PeAP2/ERF, PePUP9, PeSLP, PeHSP, and PeOCT1 proteins were identified and validated by quantitative real time polymerase chain reaction for their association with growth and oleoresin traits. These results could be useful for tree breeding and functional studies in advanced slash pine breeding program.

This study aims to understand the genetic basis of key industrial traits in Slash pine (Pinus elliottii Engelm. var. elliottii) to enhance improvement efficiency. Detailed analyses were conducted on inter-family differences, genetic parameters, correlations, and breeding values (BVs) for growth, wood properties, and resin traits of Slash pine planted in Changle Forest Farm of Hangzhou, leading to the identification of elite families. It indicates that growth traits are primarily influenced by environmental effects, while wood properties exhibit a significant impact of genetic effects. The variation in resin traits arises from both genetic and environmental effects. Notably, Beta-pinene exhibits the highest variability and genetic gains among the traits analyzed. The family heritability ranges for growth, wood properties, and resin traits are 0.543−0.794, 0.870−0.885, and 0.285−0.695, respectively. Significant positive correlations are evident between growth and resin traits, while a negative correlation is observed between growth and wood properties. Elite families identified through single-trait and multi-trait combined selection are 8−126 for growth traits, 2−325 and 0−373 for wood properties, and 8−131 for resin traits. The average genetic gains for these elite families are 7.44%, 7.17%, and 8.84%, respectively. These findings provide valuable insights for high-generation breeding of Slash pine and lay a genetic foundation for formulating effective breeding strategies for conifers.

This is the first comprehensive study of the genetic analysis of the majority of oleoresin components of slash pine (Pinus elliottii). Pine oleoresin, the resin secreted from the pine tree, is a raw material widely used in industrial products. The objective of this study was to explore the genetic variation and correlation between the major oleoresin components of 50 open pollinated families of slash pine. The individual narrow-sense heritability of the 23 oleoresin components and genetic correlations between them were estimated using the residual maximum likelihood in the flexible mixed modeling program, ASReml-R. A high heritability of 0.424 was observed for β-pinene. Moderate levels of heritability were estimated for β-phellandrene, methyl abietate, estragole, 15-hydroxy-dehydroabietic acid, and isopimaric acid methyl ester at 0.303, 0.294, 0.27, 0.258, and 0.2, respectively. The heritabilities for pimaric acid methyl ester, abieta-8, 13-diene-18-oic acid methyl ester, sandaracopimaric acid, methyl ester, and camphene were relatively low and ranged from 0.11 to 0.17. Many negative genetic correlations were observed as unfavorable while the corresponding phenotypic correlations presented no significant relationships or positive phenotypic correlations. However, the heritabilities and genetic correlations showed that single or multiple component selections and improvement, directly or indirectly, were effective. We postulate that genetic parameters estimated in this study will work as a reference in breeding programs of oleoresin components, especially in slash pine.