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Plants evolved the capacity to synthesize highly diverse sets of secondary metabolites which are important for plant adaptation and health. In forest trees, many classes of compounds, particularly ones related to defense against insects, fungi, and bacteria accumulate to levels that enable their recovery and commercial use. One of the oldest examples is conifer terpenes, but terpenes are important secondary products from other tree species including eucalypts. Because terpenes, latex, and natural gums are synthesized and stored in specialized secretory glands, ducts, and laticifers in mostly pure forms they can be collected from live trees in addition to being extracted during industrial processing of wood. This minireview describes the potential of breeding and genetic engineering approaches to increase the quantities of terpene secondary metabolites to increase the amount of secondary products and thereby increasing the value of planted and managed forest trees. I advance the view that breeding and genetic engineering of metabolic pathways and specialized cell secretory structures can dramatically increase tissue terpene content.

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

Southern pine forests play a key role in the ecological function and economic vitality of the southeastern United States. High-resolution terrestrial laser scanning (TLS) has become an indispensable tool for advancing tree structural research and monitoring. A critical challenge in this field is the accurate segmentation of leaf and wood components, which directly impacts the reliability of Quantitative Structure Models (QSMs). Segmentation techniques have progressed, but most existing methods are tailored for broadleaf species, with limited exploration for coniferous species such as southern pines. Addressing this gap, our study evaluates the performance of multiple segmentation algorithms on TLS data from southern pines, providing valuable insights into improving structural analysis and supporting more precise and efficient forest research and monitoring methodologies. We collected TLS data from longleaf pine ( Pinus palustris Mill.) and loblolly pine ( Pinus taeda L.) trees in Florida, USA, and compared the performance of four segmentation algorithms: TLSep, Graph, DBSCAN, and KPConv to separate leaf and wood. We found that KPConv was the most accurate method of segmenting wood and leaf points, with an overall accuracy (OA) of 98% and F1 score of 98% for loblolly pine and 95% and 94%, respectively, for longleaf pine. Although KPConv requires a substantial initial investment for training, its inference time is fast, making it a strong candidate for high-accuracy large-scale applications. These results led to highly reliable QSMs across trunk, branch, and total volume estimates. In contrast, DBSCAN, while slightly less accurate (OA of 92% for loblolly pine and 90% for longleaf pine), does not require training data and offers a favorable trade-off between performance and efficiency. These findings highlight the importance of selecting segmentation algorithms based on specific research goals, balancing accuracy and computational feasibility in forest structural modeling.

Genetically improving constitutive resin canal development in Pinus stems may enhance the capacity to synthesize terpenes for bark beetle resistance, chemical feedstocks, and biofuels. To discover genes that potentially regulate axial resin canal number (RCN), single nucleotide polymorphisms (SNPs) in 4027 genes were tested for association with RCN in two growth rings and three environments in a complex pedigree of 520 Pinus taeda individuals (CCLONES). The map locations of associated genes were compared with RCN quantitative trait loci (QTLs) in a (P. taeda × Pinus elliottii) × P. elliottii pseudo‐backcross of 345 full‐sibs (BC1). Resin canal number was heritable (h² ˜ 0.12–0.21) and positively genetically correlated with xylem growth (rg ˜ 0.32–0.72) and oleoresin flow (rg ˜ 0.15–0.51). Sixteen well‐supported candidate regulators of RCN were discovered in CCLONES, including genes associated across sites and ages, unidirectionally associated with oleoresin flow and xylem growth, and mapped to RCN QTLs in BC1. Breeding is predicted to increase RCN 11% in one generation and could be accelerated with genomic selection at accuracies of 0.45–0.52 across environments. There is significant genetic variation for RCN in loblolly pine, which can be exploited in breeding for elevated terpene content.

Rapidly enhancing oleoresin production in conifer stems through genomic selection and genetic engineering may increase resistance to bark beetles and terpenoid yield for liquid biofuels. We integrated association genetic and genomic prediction analyses of oleoresin flow (g 24 h−1) using 4854 single nucleotide polymorphisms (SNPs) in expressed genes within a pedigreed population of loblolly pine (Pinus taeda) that was clonally replicated at three sites in the southeastern United States. Additive genetic variation in oleoresin flow (h 2 ≈ 0.12–0.30) was strongly correlated between years in which precipitation varied (r a ≈ 0.95), while the genetic correlation between sites declined from 0.8 to 0.37 with increasing differences in soil and climate among sites. A total of 231 SNPs were significantly associated with oleoresin flow, of which 81% were specific to individual sites. SNPs in sequences similar to ethylene signaling proteins, ABC transporters, and diterpenoid hydroxylases were associated with oleoresin flow across sites. Despite this complex genetic architecture, we developed a genomic prediction model to accelerate breeding for enhanced oleoresin flow that is robust to environmental variation. Results imply that breeding could increase oleoresin flow 1.5- to 2.4-fold in one generation.

The Cellulose Synthase-Like D (CslD) genes have important, although still poorly defined, roles in cell wall formation. Here, we show an unexpected involvement of CslD1 from maize (Zea mays) in cell division. Both division and expansion were altered in the narrow-organ and warty phenotypes of the csld1 mutants. Leaf width was reduced by 35%, due mainly to a 47% drop in the number of cell files across the blade. Width of other organs was also proportionally reduced. In leaf epidermis, the deficiency in lateral divisions was only partially compensated by a modest, uniform increase in cell width. Localized clusters of misdivided epidermal cells also led to the formation of warty lesions, with cell clusters bulging from the epidermal layer, and some cells expanding to volumes 75-fold greater than normal. The decreased cell divisions and localized epidermal expansions were not associated with detectable changes in the cell wall composition of csldl leaf blades or epidermal peels, yet a greater abundance of thin, dense walls was indicated by high-resolution x-ray tomography of stems. Cell-level defects leading to wart formation were traced to sites of active cell division and expansion at the bases of leaf blades, where cytokinesis and cross-wall formation were disrupted. Flow cytometry confirmed a greater frequency of polyploid cells in basal zones of leaf blades, consistent with the disruption of cytokinesis and /or the cell cycle in csld1 mutants. Collectively, these data indicate a previously unrecognized role for CSLD activity in plant cell division, especially during early phases of cross-wall formation.

Research toward renewable and sustainable energy has identified specific terpenes capable of supplementing or replacing current petroleum-derived fuels. Despite being naturally produced and stored by many plants, there are few examples of commercial recovery of terpenes from plants because of low yields. Plant terpene biosynthesis is regulated at multiple levels, leading to wide variability in terpene content and chemistry. Advances in the plant molecular toolkit, including annotated genomes, high-throughput omics profiling, and genome editing, have begun to elucidate plant terpene metabolism, and such information is useful for bioengineering metabolic pathways for specific terpenes. We review here the status of terpenes as a specialty biofuel and discuss the potential of plants as a viable agronomic solution for future terpene-derived biofuels. Options for alternative non-petroleum-based transportation fuels are limited. Specific terpenes have the physical and chemical properties required for specialty biofuels capable of blending with or replacing petroleum-derived jet, missile, diesel, and gasoline fuels. Many plants naturally produce and store these specific terpenes. The biosynthesis of plant-derived terpenes is under strong genetic control. Strategies to increase terpene content are presented based on modern genetics and genomics approaches.

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.

The use of calibrated near infrared (NIR) spectroscopy for predicting the chemical composition of Pinus taeda L. (loblolly pine) wood samples is investigated. Seventeen P. taeda radial strips, representing seven different sites were selected and NIR spectra were obtained from the radial longitudinal face of each strip. The spectra were obtained in 12.5 mm sections from pre-determined positions that represented juvenile wood (close to pith), transition wood (zone between juvenile and mature wood), and mature wood (close to bark). For these sections, cellulose, hemicellulose, lignin (acid soluble and insoluble), arabinan, galactan, glucan, mannan, and xylan contents were determined by standard analytical chemistry methods. Calibrations were developed for each chemical constituent using the NIR spectra, wood chemistry data and partial least squares (PLS) regression. Relationships were variable with the best results being obtained for cellulose, glucan, xylan, mannan, and lignin. Prediction errors were high and may be a consequence of the diverse origins of the samples in the test set. Further research with a larger number of samples is required to determine if prediction errors can be reduced.

The aim of this study was to generate and evaluate the efficiency of improved field experiments while simultaneously accounting for spatial correlations and different levels of genetic relatedness using a mixed models framework for orthogonal and non-orthogonal designs. Optimality criteria and a search algorithm were implemented to generate randomized complete block (RCB), incomplete block (IB), augmented block (AB) and unequally replicated (UR) designs. Several conditions were evaluated including size of the experiment, levels of heritability, and optimality criteria. For RCB designs with half-sib or full-sib families, the optimization procedure yielded important improvements under the presence of mild to strong spatial correlation levels and relatively low heritability values. Also, for these designs, improvements in terms of overall design efficiency (ODE%) reached values of up to 8.7%, but these gains varied depending on the evaluated conditions. In general, for all evaluated designs, higher ODE% values were achieved from genetically unrelated individuals compared to experiments with half-sib and full-sib families. As expected, accuracy of prediction of genetic values improved as levels of heritability and spatial correlations increased. This study has demonstrated that important improvements in design efficiency and prediction accuracies can be achieved by optimizing how the levels of a treatment are assigned to the experimental units.