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The identification of promoters is an essential step in the genome annotation process, providing a framework for gene regulatory networks and their role in transcription regulation. Despite considerable advances in the high-throughput determination of transcription start sites (TSSs) and transcription factor binding sites (TFBSs), experimental methods are still time-consuming and expensive. Instead, several computational approaches have been developed to provide fast and reliable means for predicting the location of TSSs and regulatory motifs on a genome-wide scale. Numerous studies have been carried out on the regulatory elements of mammalian genomes, but plant promoters, especially in gymnosperms, have been left out of the limelight and, therefore, have been poorly investigated. The aim of this study was to enhance and expand the existing genome annotations using computational approaches for genome-wide prediction of TSSs in the four conifer species: loblolly pine, white spruce, Norway spruce, and Siberian larch. Our pipeline will be useful for TSS predictions in other genomes, especially for draft assemblies, where reliable TSS predictions are not usually available. We also explored some of the features of the nucleotide composition of the predicted promoters and compared the GC properties of conifer genes with model monocot and dicot plants. Here, we demonstrate that even incomplete genome assemblies and partial annotations can be a reliable starting point for TSS annotation. The results of the TSS prediction in four conifer species have been deposited in the Persephone genome browser, which allows smooth visualization and is optimized for large data sets. This work provides the initial basis for future experimental validation and the study of the regulatory regions to understand gene regulation in gymnosperms. [ABSTRACT FROM AUTHOR]

Simple Summary: Leaves can only toughen after they have finished growing and, as a result, many herbivorous insects specialize in newly developing leaves because softer leaves are easier to chew. The foliage of conifer trees is particularly tough, and so one would expect conifers to invest more defensive chemicals into soft vulnerable growing needles than into tough mature ones. We summarize the literature describing how chemical defenses of foliage change during the growing season in white spruce, an economically important conifer tree. We next report measurements of the toughness of white spruce buds as they swell, burst, and grow into young needles. As expected, buds soften as they swell in spring, but after budburst, needles become tougher until they are similar to previous-year foliage in mid-summer. Leaves grown in the sun are slightly tougher than leaves grown in the shade. However, there was no indication that trees invest more in chemical defense of growing leaves than of mature leaves. Changes during leaf ontogeny affect palatability to herbivores, such that many insects, including the eastern spruce budworm (Choristoneura fumiferana (Clem.)), are specialist feeders on growing conifer leaves and buds. Developmental constraints imply lower toughness in developing foliage, and optimal defense theory predicts higher investment in chemical defense in these vulnerable yet valuable developing leaves. We summarize the literature on the time course of defensive compounds in developing white spruce (Picea glauca (Moench) Voss) needles and report original research findings on the ontogeny of white spruce needle toughness. Our results show the predicted pattern of buds decreasing in toughness followed by leaves increasing in toughness during expansion, accompanied by opposite trends in water content. Toughness of mature foliage decreased slightly during the growing season, with no significant relationship with water content. Toughness of sun-grown leaves was slightly higher than that of shade-grown leaves. However, the literature review did not support the expected pattern of higher defensive compounds in expanding leaves than in mature leaves, suggesting that white spruce might instead exhibit a fast-growth low-defense strategy. [ABSTRACT FROM AUTHOR]

The increasing use of unmanned aerial vehicles (UAV) and high spatial resolution imagery from associated sensors necessitates the continued advancement of efficient means of image processing to ensure these tools are utilized effectively. This is exemplified in the field of forest management, where the extraction of individual tree crown information stands to benefit operational budgets. We explored training a region-based convolutional neural network (Mask R-CNN) to automatically delineate individual tree crown (ITC) polygons in regenerating forests (14 years after harvest) using true colour red-green-blue (RGB) imagery with an average ground sampling distance (GSD) of 3 cm. We predicted ITC polygons to extract height information using canopy height models generated from digital aerial photogrammetric (DAP) point clouds. Our approach yielded an average precision of 0.98, an average recall of 0.85, and an average F1 score of 0.91 for the delineation of ITC. Remote height measurements were strongly correlated with field height measurements (r2 = 0.93, RMSE = 0.34 m). The mean difference between DAP-derived and field-collected height measurements was −0.37 m and −0.24 m for white spruce (Picea glauca) and lodgepole pine (Pinus contorta), respectively. Our results show that accurate ITC delineation in young, regenerating stands is possible with fine-spatial resolution RGB imagery and that predicted ITC can be used in combination with DAP to estimate tree height. [ABSTRACT FROM AUTHOR]