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Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.

Regarding rubber tree plantations, researchers lack a basic understanding of soil microbial communities; specifically, little is known about whether or not soil microbial variation is correlated with succession in these plantations. In this paper, we used high-throughput sequencing of the 16S rRNA gene to investigate the diversity and composition of the soil bacterial communities in a chronosequence of rubber tree plantations that were 5, 10, 13, 18, 25, and 30 years old. We determined that: (1) Soil bacterial diversity and composition show changes over the succession stages of rubber tree plantations. The diversity of soil bacteria were highest in 10, 13, and 18 year-old rubber tree plantations, followed by 30 year-old rubber tree plantations, whereas 5 and 25 year-old rubber tree plantations had the lowest values for diversity. A total of 438,870 16S rDNA sequences were detected in 18 soil samples from six rubber tree plantations, found in 28 phyla, 66 classes, 139 orders, 245 families, 355 genera, and 645 species, with 1.01% sequences from unclassified bacteria. The dominant phyla were , and (relative abundance large than 3%). There were differences in soil bacterial communities among different succession stages of rubber tree plantation. (2) Soil bacteria diversity and composition in the different stages was closely related to pH, vegetation, soil nutrient, and altitude, of which pH, and vegetation were the main drivers.

Background Tapping panel dryness (TPD) in rubber tree has become the most severe restricting factor of natural rubber production. To date, there is no effective measures to prevent and control TPD. Previous studies primarily focused on analyzing the molecular mechanism underlying TPD occurrence. However, there is no research on the molecular mechanism of TPD recovery. Results In this study, the TPD trees were recovered by treatment with TPD rehabilitation nutrient agents that could promote the recovery of latex flow on the tapping panel of TPD trees. The genes and proteins involved in TPD recovery were first identified by employing integrated transcriptomics and proteomics analyses. In total, 2029 differentially expressed genes (DEGs) and 951 differentially expressed proteins (DEPs) were detected in the bark of recovery trees compared to that of TPD trees. Among them, 19 DEPs and 11 DEGs were found to be involved in the starch and sucrose metabolism pathway, suggesting their important roles in regulating the syntheses of sucrose and D-glucose, which were the key precursors of natural rubber biosynthesis. Furthermore, 16 DEPs and 15 DEGs were identified in the rubber biosynthesis pathway. Interestingly, almost all the DEPs and DEGs related to rubber biosynthesis exhibited significantly up-regulated expressions in the recovery trees, indicating that latex biosynthesis were probably markedly enhanced during TPD recovery. Conclusions These results provide new insights into the molecular mechanisms underlying TPD recovery, as well as excellent supplements to the mechanisms of TPD occurrence, which will contribute to the development of more effective agents for the prevention and treatment of TPD in the future.

Background In rubber tree, bark is one of important agricultural and biological organs. However, the molecular mechanism involved in the bark formation and development in rubber tree remains largely unknown, which is at least partially due to lack of bark transcriptomic and genomic information. Therefore, it is necessary to carried out high-throughput transcriptome sequencing of rubber tree bark to generate enormous transcript sequences for the functional characterization and molecular marker development. Results In this study, more than 30 million sequencing reads were generated using Illumina paired-end sequencing technology. In total, 22,756 unigenes with an average length of 485 bp were obtained with de novo assembly. The similarity search indicated that 16,520 and 12,558 unigenes showed significant similarities to known proteins from NCBI non-redundant and Swissprot protein databases, respectively. Among these annotated unigenes, 6,867 and 5,559 unigenes were separately assigned to Gene Ontology (GO) and Clusters of Orthologous Group (COG). When 22,756 unigenes searched against the Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) database, 12,097 unigenes were assigned to 5 main categories including 123 KEGG pathways. Among the main KEGG categories, metabolism was the biggest category (9,043, 74.75%), suggesting the active metabolic processes in rubber tree bark. In addition, a total of 39,257 EST-SSRs were identified from 22,756 unigenes, and the characterizations of EST-SSRs were further analyzed in rubber tree. 110 potential marker sites were randomly selected to validate the assembly quality and develop EST-SSR markers. Among 13 Hevea germplasms, PCR success rate and polymorphism rate of 110 markers were separately 96.36% and 55.45% in this study. Conclusion By assembling and analyzing de novo transcriptome sequencing data, we reported the comprehensive functional characterization of rubber tree bark. This research generated a substantial fraction of rubber tree transcriptome sequences, which were very useful resources for gene annotation and discovery, molecular markers development, genome assembly and annotation, and microarrays development in rubber tree. The EST-SSR markers identified and developed in this study will facilitate marker-assisted selection breeding in rubber tree. Moreover, this study also supported that transcriptome analysis based on Illumina paired-end sequencing is a powerful tool for transcriptome characterization and molecular marker development in non-model species, especially those with large and complex genomes.

Background White root rot disease in rubber trees, caused by the pathogenic fungi Rigidoporus microporus , is currently considered a major problem in rubber tree plantations worldwide. Only a few reports have mentioned the response of rubber trees occurring at the non-infection sites, which is crucial for the disease understanding and protecting the yield losses. Results Through a comparative proteomic study using the two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) technique, the present study reveals some distal-responsive proteins in rubber tree leaves during the plant-fungal pathogen interaction. From a total of 12 selected differentially expressed protein spots, several defense-related proteins such as molecular chaperones and ROS-detoxifying enzymes were identified. The expression of 6 candidate proteins was investigated at the transcript level by Reverse Transcription Quantitative PCR (RT-qPCR). In silico, a highly-expressed uncharacterized protein LOC110648447 found in rubber trees was predicted to be a protein in the pathogenesis-related protein 10 (PR-10) class. In silico promoter analysis and structural-related characterization of this novel PR-10 protein suggest that it plays a potential role in defending rubber trees against R. microporus infection. The promoter contains WRKY-, MYB-, and other defense-related cis -acting elements. The structural model of the novel PR-10 protein predicted by I-TASSER showed a topology of the Bet v 1 protein family, including a conserved active site and a ligand-binding hydrophobic cavity. Conclusions A novel protein in the PR-10 group increased sharply in rubber tree leaves during interaction with the white root rot pathogen, potentially contributing to host defense. The results of this study provide information useful for white root rot disease management of rubber trees in the future.

The rubber tree is the primary source of natural rubber and is mainly cultivated in Southeast Asian countries. Low temperature is the major abiotic stress affecting the yield of the rubber tree. Therefore, uncovering the cold resistance mechanism in the rubber tree is necessary. The present study used RNA-sequencing technology and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to analyze the transcriptomic and metabolomic changes in two rubber tree clones with different cold resistance capacities (temperature-sensitive Reyan 8-79 and cold-resistant Yunyan 77-4) at 0 h, 2 h, 6 h, and 20 h of exposure to 4°C. Independent analysis of the transcriptome and metabolitome showed that under prolonged low-temperature treatment, Yunyan 77-4 expressed more genes involved in regulating enzyme activity, changing cell permeability, and synthesizing significant metabolites, such as flavonoids and amino acids, than Reyan 8-79. The KEGG annotation and enrichment analysis identified arginine metabolism and biosynthesis of flavonoids as the major pathway associated with cold resistance. Integrated transcriptome and metabolome analysis showed that the increase in the expression of genes modulated flavonoid biosynthesis, arginine biosynthesis, and anthocyanins biosynthesis, resulting in higher levels of metabolites, such as naringenin chalcone, apigenin, dihydroquercetin, cyanidin 3-glucoside, L-arginosuccinate, N-acetyl-ornithine, ornithine, and N-acetyl-glutamate, in Yunyan 77-4 than in Reyan 8-79 after prolonged low-temperature treatment. Phylogenetic analysis identified the genes, such as CHS ( gene356 ) and F3H ( gene33147 ) of flavonoid biosynthesis and NAGS ( gene16028, gene33765 ), ArgC ( gene2487 ), and ASS ( gene6161 ) of arginine biosynthesis were the key genes involved in the cold resistant of rubber tree. Thus, the present study provides novel insights into how rubber clones resist cold and is a valuable reference for cold-resistance breeding.

Large-scale deforestation in the tropics, triggered by logging and subsequent agricultural monoculture has a significant adverse impact on biodiversity due to habitat degradation. Here, we measured the diversity of butterfly species in three agricultural landscapes, agroforestry orchards, oil palm, and rubber tree plantations. Butterfly species were counted at 127 sampling points over the course of a year using the point count method. We found that agroforestry orchards supported a greater number of butterfly species (74 species) compared to rubber tree (61 species) and oil palm plantations (54 species) which were dominated by generalist (73%) followed by forest specialists (27%). We found no significant difference of butterfly species composition between agroforestry orchards and rubber tree plantation, with both habitats associated with more butterfly species compared to oil palm plantations. This indicates butterflies were able to persist better in certain agricultural landscapes. GLMMs suggested that tree height, undergrowth coverage and height, and elevation determined butterfly diversity. Butterfly species richness was also influenced by season and landscape-level variables such as proximity to forest, mean NDVI, and habitat. Understanding the factors that contributed to butterfly species richness in an agroecosystem, stakeholders should consider management practices to improve biodiversity conservation such as ground vegetation management and retaining adjacent forest areas to enhance butterfly species richness. Furthermore, our findings suggest that agroforestry system should be considered to enhance biodiversity in agricultural landscapes.

Background MicroRNAs (miRNAs) are key regulators of gene expression as they play crucial roles at the post-transcriptional level. In particular, the miR319 -GRF module is an important gene regulatory network in plants, extensively involved in processes such as plant growth and development. Although miR396 is one of the most conserved miRNA families, its role in rubber trees remains poorly understood. In this study, bioinformatics analysis, including target prediction, was performed to reveal the evolutionary and expression patterns of the Hbr-miR396 family members. Results A total of six Hbr-miR396 members were identified, distributed across four chromosomes. Secondary structure analysis revealed that the precursor sequences of the six Hbr-miR396 members could form a typical stem-loop (hairpin) structure. Sequence analysis show that the members of the Hbr-miR396 family form three mature sequences. Furthermore, phylogenetic analysis demonstrated that the Hbr-miR396 family members are closely related to those from cassava. Eight members of the growth regulatory factor (GRF) family were predicted as potential targets of Hbr-miR396 . The dual-luciferase assays also confirmed that Hbr-miR396b strongly inhibited the expression of HbrGRF3. Expression analysis of the HbrGRF targets in different tissues revealed that HbrGRFs are mainly expressed in the cambium and flowers. Therefore, Hbr-miR396 may potentially regulate growth and floral organ development in rubber trees by targeting HbrGRFs. Conclusions The data presented in this study offer valuable insights into the functional and molecular regulatory mechanisms of the miR396 -GRFs module in rubber tree growth and development, laying a foundation for further investigation into its biological roles in enhancing both rubber production and timber quality.

Background Natural rubber ( cis -1,4-polyioprene, NR) is an indispensable industrial raw material obtained from the Pará rubber tree ( H. brasiliensis ). Natural rubber cannot be replaced by synthetic rubber compounds because of the superior resilience, elasticity, abrasion resistance, efficient heat dispersion, and impact resistance of NR. In NR production, latex is harvested by periodical tapping of the trunk bark. Ethylene enhances and prolongs latex flow and latex regeneration. Ethephon, which is an ethylene-releasing compound, applied to the trunk before tapping usually results in a 1.5- to 2-fold increase in latex yield. However, intense mechanical damage to bark tissues by excessive tapping and/or over-stimulation with ethephon induces severe oxidative stress in laticifer cells, which often causes tapping panel dryness (TPD) syndrome. To enhance NR production without causing TPD, an improved understanding of the molecular mechanism of the ethylene response in the Pará rubber tree is required. Therefore, we investigated gene expression in response to ethephon treatment using Pará rubber tree seedlings as a model system. Results After ethephon treatment, 3270 genes showed significant differences in expression compared with the mock treatment. Genes associated with carotenoids, flavonoids, and abscisic acid biosynthesis were significantly upregulated by ethephon treatment, which might contribute to an increase in latex flow. Genes associated with secondary cell wall formation were downregulated, which might be because of the reduced sugar supply. Given that sucrose is an important molecule for NR production, a trade-off may arise between NR production and cell wall formation for plant growth and for wound healing at the tapping panel. Conclusions Dynamic changes in gene expression occur specifically in response to ethephon treatment. Certain genes identified may potentially contribute to latex production or TPD suppression. These data provide valuable information to understand the mechanism of ethylene stimulation, and will contribute to improved management practices and/or molecular breeding to attain higher yields of latex from Pará rubber trees.

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R2 values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management.