Cryptomeria japonica D. Don is a coniferous tree species widely grown in southern China for its high ornamental value. Recently, during disease surveys in China, a symptom of dieback occurred on C. japonica in Nanjing, Jiangsu Province, China. A total of 130 trees were surveyed and more than 90% showed the same symptom. The crowns of affected trees were brown when viewing from a distance, and the bark showed no difference from the healthy ones. In this study, 157 isolates were isolated from the 3 affected plants of C. japonica, and based on the living culture on PDA, the fungal isolates were preliminarily divided into 6 groups. Thirteen representative isolates were selected for the pathogenicity test, and seven of them showed obvious pathogenicity on C. japonica, causing stem basal canker. These isolates were identified based on comparisons of the DNA sequences of the internal transcribed spacer regions (ITS), partial translation elongation factor 1-alpha (tef1), β-tubulin (tub2), and DNA-directed RNA polymerase II subunit (rpb2) and combined with their morphological characteristics. Results showed that these seven isolates belong to two taxa in Neofusicoccum, including a species new to science. The new species, Neofusicoccum cryptomeriae, was hereby described and illustrated. The other species was N. parvum. Both species were pathogens of stem basal canker of Cryptomeria japonica.

Rubber trees in southern China are often impacted by natural disturbances, and accurate rubber tree crown segmentation and property retrieval are of great significance for forest cultivation treatments and silvicultural risk management. Here, three plots of different rubber tree clones, PR107, CATAS 7-20-59 and CATAS 8-7-9, that were recently impacted by hurricanes and chilling injury were taken as the study targets. Through data collection using ground-based mobile light detection and ranging (LiDAR) technology, a weighted Rayleigh entropy method based on the scanned branch data obtained from the region growing algorithm was proposed to calculate the trunk inclination angle and crown centre of each tree. A watershed algorithm based on the extracted crown centres was then adopted for tree crown segmentation, and a variety of tree properties were successfully extracted to evaluate the susceptibility of different rubber tree clones facing natural disturbances. The results show that the angles between the first-order branches and trunk ranged from 35.1–67.7° for rubber tree clone PR107, which is larger than the angles for clone CATAS 7-20-59, which ranged from 20.2–43.2°. Clone PR107 had the maximum number of scanned leaf points, lowest tree height and a crown volume that was larger than that of CATAS 7-20-59, which generates more frontal leaf area to oppose wind flow and reduces the gaps among tree crowns, inducing strong wind loading on the tree body. These factors result in more severe hurricane damage, resulting in trunk inclination angles that are larger for PR107 than CATAS 7-20-59. In addition, the rubber tree clone CATAS 8-7-9 had the minimal number of scanned leaf points and the smallest tree crown volume, reflecting its vulnerability to both hurricanes and chilling injury. The results are verified by field measurements. The work quantitatively assesses the susceptibility of different rubber tree clones under the impacts of natural disturbances using ground-based mobile LiDAR.

This study aimed to investigate the association between the plant community structure, leaf surface microstructure, nutrient element content, and the dust-retention capacity of garden plants in urban road green spaces. The plant community located along Ying Tian Street in Nanjing City was selected as the focal point of the investigation. Random sampling was performed on the urban road green spaces, determining the amount of dust trapped in plant leaves. Subsequently, the microstructure of the leaf surface was observed, and the content of nutrient elements in the plant leaves was determined. The study also entailed an analysis of the interrelationships between the leaf surface microstructure, plant nutrient element content, and the dust-retention ability of the plants. The findings of this study revealed notable variations in the dust-retention capacity of garden plants and the community structure observed along Ying Tian Street. Among the tree species, Cedrus deodara and Ginkgo biloba exhibited a remarkable dust-retention ability per unit leaf area. Among the shrub species, Abelia × grandiflora and Loropetalum chinense displayed a strong dust-retention capacity per unit leaf area. Similarly, Ophiopogon japonicus and Cynodon dactylon exhibited a robust dust-retention ability per unit leaf area among the herbaceous plants. Furthermore, the dust-retention ability of the plants exhibited a strong positive correlation with the dimensions of leaf stomata, specifically the length and width, while displaying a moderate positive correlation with the width of grooves on the upper and lower surfaces of the leaves. Conversely, the thickness of the leaves did not exhibit a significant correlation. Additionally, the nitrogen content of the leaves exerted a significant influence on the dust-retention ability of the plants (p < 0.05), although the phosphorus and potassium content factors did not exhibit a significant influence (p > 0.05). Based on the findings, it is recommended to prioritize the utilization of plants with robust dust-retention abilities, such as C. deodara, A. grandiflora, O. japonicus, and C. dactylon, and implement a mixed planting approach encompassing a combination of trees, shrubs, and herbaceous plants within urban road green spaces.