The planting of Pinus elliottii Engelm. has now reached close to three million ha in China. Molecular breeding as part of the improvement program for P. elliottii in southern China has been carried out in recent years. Third-generation sequencing (Pacbio sequencing technology, TGS) was used to obtain the exome of P. elliottii for molecular breeding. A total of 35.8 Gb clean reads were generated using TGS. After removing the redundant reads, we obtained 80,339 high-accuracy transcripts. Significantly, a total of 76,411 transcripts (95.1%) were blasted to public annotation databases. We predicted 65,062 intact coding sequences (CDSs), 8916 alternative splicing events, 1937 long non-coding RNAs, and 22,109 simple sequence repeats (SSRs) based on these obtained transcripts. Using the public databases and the data obtained above, 23 orthologous single-copy genes were identified to analyze the phylogenetic relationships for Pinus firstly including P. elliottii. Many positive selection genes involved in important biological processes and metabolism pathways were identified between P. elliottii and other pines. These positive selection genes could be candidate genes to be researched on the genetic basis of superior performance. Our study is the first to reveal the full-length and well-annotated transcripts of P. elliottii, which could provide reference for short transcriptome sequences in the research of genetics, phylogenetics, and genetic improvement for the non-reference genome species.

Pine resin is one of the best known and most exploited non-wood products. Resin is a complex mixture of terpenes produced by specialized cells that are dedicated to tree defense. Chemical defenses are plastic properties, and concentrations of chemical defenses can be adjusted based on environmental factors, such as resource availability. The slope orientation (south/sunny or north/shady) and the altitude of the plantation site have significant effects on the soil nutrient and the plant performance, whereas little is known about how the slope affects the pine resin yield and its components. In total, 1180 slash pines in 18 plots at different slope positions were established to determine the effects on the α- and β-pinene content and resin production of the slash pine. The near-infrared spectroscopy (NIR) technique was developed to rapidly and economically predict the turpentine content for each sample. The results showed that the best partial least squares regression (PLS) models for α- and β-pinene content prediction were established via the combined treatment of multiplicative scatter correction–significant multivariate correlation (MSC–sMC). The prediction models based on sMC spectra for α- and β-pinene content have an R² of 0.82 and 0.85 and an RMSE of 0.96 and 0.82, respectively, and they were successfully implemented in turpentine prediction in this research. The results also showed that a barren slope position (especially mid-slope) could improve the α-pinene and β-pinene content and resin productivity of slash pine, and the β-pinene content in the resin had more variances in this research.

It is not easy to distinguish hybrid pines using their morphological characteristics. Traditional methods of identification, such as chemical analysis or molecular marker technology, are complicated, time-consuming and costly and are not very accurate. They are not, therefore, an ideal means of identification and the use of near-infrared technology, which is comparatively inexpensive and simple to use, is preferable. For the future development of Pinus taedax P. caribaea var. hondurensis (PTC) hybrid trials and breeding programmes, and to provide a more comprehensive understanding of their physiological and biochemical characteristics, it is necessary to identify PTC among the parents ( P. elliottii, P. taeda and P. caribaea var. hondurensis) and other possible hybrids ( P. elliottiix P. caribaea var. hondurensis) in order to be able to distinguish them from each other. In this study, partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) regression modelling are used, The results are as follows: PLS-DA has a low accuracy rate, at 89.09%, but using the PLS-DA scores as the input data into the LDA resulted in LDA distinction models, with accuracy rates reaching 99%, allowing a reliable identification of pure species and hybrids. It is clear from the results that near infrared technology can be used to identify hybrid and purebreds in pine and that the accuracy rate is higher than that derived when using standard molecular techniques.