Pine wilt disease (PWD) caused by the pine wood nematode (PWN) is China’s most serious forest disease in recent decades. Its complex disease system with unclear pathogenesis and differentiated pathogenicity makes it challenging to manage and control. PWD has spread rapidly in Northeast and Northwest China in recent years, exceeding previous predictions and the traditional understanding of its potential distribution. This paper reviewed the comprehensive risk of PWD in Northeast and Northwest China through the occurrence of influencing factors. Regarding PWN’s temperature resistance, all regions of China are suitable for PWD. In terms of altitude, the PWD system is mainly distributed below 1000 m. In China, Pinus and Larix species are confirmed host trees of PWN, while Monochamus alternatus and M. saltuarius are confirmed vector insects of PWN. In this case, PWD may occur in all low-altitude pine forests in China. Northeast China has a higher risk than Northwest China due to its distribution of suitable host trees and vector beetles at low altitudes. To confront the threat, two scientific bottlenecks are required to be demonstrated. The first is the pathogenicity of Chinese PWN strains to Chinese Picea and Abies species. The other is the geographical distribution and highest altitude distribution of other Monochamus species in the Palaearctic region of China and their PWN transmission ability. We also suggest strengthening plant quarantining in Northeast China. In conclusion, this review comprehensively analyzes the risk of PWD in Northeast and Northwest China in terms of temperature, altitude, host trees, and vector insects. It provides a new perspective and reference for the management and research of PWD in China.

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

Monochamus saltuarius (Coleoptera: Cerambycidae) is an important native pest in the pine forests of northeast China and a dispersing vector of an invasive species Bursaphelenchus xylophilus. To investigate the bacterial gut diversity of M. saltuarius larvae in different host species, and infer the role of symbiotic bacteria in host adaptation, we used 16S rRNA gene Illumina sequencing and liquid chromatography-mass spectrometry metabolomics processing to obtain and compare the composition of the bacterial community and metabolites in the midguts of larvae feeding on three host tree species: Pinus koraiensis, Pinus sylvestris var. mongolica, and Pinus tabuliformis. Metabolomics in xylem samples from the three aforementioned hosts were also performed. Proteobacteria and Firmicutes were the predominant bacterial phyla in the larval gut. At the genus level, Klebsiella, unclassified_f__Enterobacteriaceae, Lactococcus, and Burkholderia–Caballeronia–Paraburkholderia were most dominant in P. koraiensis and P. sylvestris var. mongolica feeders, while Burkholderia–Caballeronia–Paraburkholderia, Dyella, Pseudoxanthomonas, and Mycobacterium were most dominant in P. tabuliformis feeders. Bacterial communities were similar in diversity in P. koraiensis and P. sylvestris var. mongolica feeders, while communities were highly diverse in P. tabuliformis feeders. Compared with the other two tree species, P. tabuliformis xylems had more diverse and abundant secondary metabolites, while larvae feeding on these trees had a stronger metabolic capacity for secondary metabolites than the other two host feeders. Correlation analysis of the association of microorganisms with metabolic features showed that dominant bacterial genera in P. tabuliformis feeders were more negatively correlated with plant secondary metabolites than those of other host tree feeders.

The European woodwasp, Sirex noctilio Fabricius, is a major invasive quarantine pest that attacks and kills pine trees outside of its native range. Insect gut structure and gut microbiota play crucial roles in various life activities. Despite a few reports in nutrition and survival, an extensive study on the S. noctilio larval gut microbiome is lacking. We studied the gut structure using a stereo microscope and used high throughput sequencing of the bacterial 16S rRNA genes and fungal internal transcribed spacer 2 (ITS2) regions to investigate gut microbiota in different developmental stages of S. noctilio, including larvae, adults, and larval frass. We used PICRUSt2 to predict the functional profiles. The larval gut was thin and thread-like from the oral cavity to the anus, carrying few xylem particles in the crop. Pseudomonas, Ralstonia, and Burkholderia s.l were the dominant bacteria in the guts of larvae, adults, and frass, respectively. Even though Pseudomonas was the most abundant among all bacteria, Zoogloea, Ruminobacter, and Nitrosospira, which might be involved in degrading organic matter and fixing nitrogen occurred exclusively in the larval gut indicating their possible role in the growth and development of larvae in pine tree xylem. Fungal communities did not change significantly across different developmental stages or the frass. Amylostereum was dominant in the woodwasp’s larval gut. Functional prediction of bacterial and fungal communities revealed that they may encod enzymes involved in degrading lignocellulose and fixing nitrogen. Ours is the first study that compares gut microbial communities present in S. noctilio larvae, adults, and frass. This study could provide an understanding of larval nutrient acquisition in nutrient-deficient host xylem to some extent. Our study may unlock novel strategies for the development of pest management approaches based on interfering with the gut microbiota and restricting their role in larval survival and development.

In recent years, the red turpentine beetle (RTB) (Dendroctonus valens LeConte) has invaded the northern regions of China. Due to the short invasion time, the outbreak of tree mortality corresponded to a low level of damage. Important information about tree mortality, provided by remote sensing at both single-tree and forest stand scale, is needed in forest management at the early stages of outbreak. In order to detect RTB-induced tree mortality at a single-tree scale, we evaluated the classification accuracies of Gaofen-2 (GF2) imagery at different spatial resolutions (1 and 4 m) using a pixel-based method. We also simultaneously applied an object-based method to 1 m pan-sharpened images. We used Sentinel-2 (S2) imagery with different resolutions (10 and 20 m) to detect RTB-induced tree mortality and compared their classification accuracies at a larger scale—the stand scale. Three kinds of machine learning algorithms—the classification and regression tree (CART), the random forest (RF), and the support vector machine (SVM)—were applied and compared in this study. The results showed that 1 m resolution GF2 images had the highest classification accuracy using the pixel-based method and SVM algorithm (overall accuracy = 77.7%). We found that the classification of three degrees of damage percentage within the S2 pixel (0%, <15%, and 15% < x < 50%) was not successful at a forest stand scale. However, 10 m resolution S2 images could acquire effective binary classification (<15%: overall accuracy = 74.9%; 15% < x < 50%: overall accuracy = 81.0%). Our results indicated that identifying tree mortality caused by RTB at a single-tree and forest stand scale was accomplished with the combination of GF2 and S2 images. Our results are very useful for the future exploration of the patterns of spatial and temporal changes in insect pest transmission at different spatial scales.

A new species of the genus Megischus Brullé, 1846, Megischus kuafu Ge & Tan, sp. nov., is described and illustrated from Guizhou Province, China. The key to all four species from China is included. A distribution map of the Chinese species is added.