Summary: Genetically improving constitutive resin canal development inPinusstems 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 withRCNin two growth rings and three environments in a complex pedigree of 520Pinus taedaindividuals (CCLONES). The map locations of associated genes were compared withRCNquantitative trait loci (QTLs) in a (P. taeda × Pinus elliottii) × P. elliottiipseudo‐backcross of 345 full‐sibs (BC1). Resin canal number was heritable (h² ˜ 0.12–0.21) and positively genetically correlated with xylem growth (r_g ˜ 0.32–0.72) and oleoresin flow (r_g ˜ 0.15–0.51). Sixteen well‐supported candidate regulators ofRCNwere discovered inCCLONES, including genes associated across sites and ages, unidirectionally associated with oleoresin flow and xylem growth, and mapped toRCN QTLs inBC1. Breeding is predicted to increaseRCN11% in one generation and could be accelerated with genomic selection at accuracies of 0.45–0.52 across environments. There is significant genetic variation forRCNin loblolly pine, which can be exploited in breeding for elevated 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.

Fusiform rust resistance can involve gene-for-gene interactions where resistance (Fr) genes in the host interact with corresponding avirulence genes in the pathogen, Cronartium quercuum f.sp. fusiforme (Cqf). Here, we identify trees with Fr genes in a loblolly pine population derived from a complex mating design challenged with two Cqf inocula (one gall and 10 gall mixtures). We used single nucleotide polymorphism (SNP) genotypes at sufficient density to ensure linkage between segregating markers and Fr genes identifying SNPs that explained high proportions of variance in disease incidence using BayesCp, that also were significant using Bayesian Association with Missing Data (BAMD) software. Two SNPs mapped near Fr1 and generated significant LOD scores in single marker regression analyses for Fr1/fr1 parent 17 as well as four other parents. One SNP mapped near Fr8 and was significant for parent 28. Two SNPs mapped to linkage groups not previously shown to contain Fr genes and were significant for three parents. Parent 2 showed evidence of Fr gene stacking. Our results suggest that it is feasible to identify trees segregating for Fr genes, and to map Fr genes, based on parental analysis of SNPs that cosegregate with disease incidence in designed resistance screening trials.