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.

Sirex noctilio along with its mutualistic fungal symbiont, Amylostereum areolatum (a white rot fungus), is an invasive pest that causes excessive damage to Pinus plantations in Northeast China. In 2015, S. noctilio were found to attack Pinus sylvestris var. mongolica, and often share larval habitat with the native woodwasp, S. nitobei. The objective of this study was to determine the possible origin(s) of the introduced pest complex in China and analyse the genetic diversity between A. areolatum isolated from invasive S. noctilio, native S. nitobei and other woodwasps collected from Europe (native range) and other countries. Phylogenetic analyses were performed using the intergenic spacer (IGS) dataset and the combined 4-locus dataset (the internal transcribed spacer region (ITS), translation elongation factor alpha 1 (tef1), DNA-directed ribosomal polymerase II (RPB2), and mitochondrial small subunit (mtSSU)) of three Amylostereum taxa. The multilocus genotyping of nuclear ribosomal regions and protein coding genes revealed at least three distinct multilocus genotypes (MLGs) of the fungus associated with invasive S. noctilio populations in Northeast China, which may have come from North America or Europe. The IGS region of A. areolatum carried by S. noctilio from China was designated type B1D2. Our results showed a lack of fidelity (the paradigm of obligate fidelity to a single fungus per wasp species) between woodwasp hosts and A. areolatum. We found that the native S. nitobei predominantly carried A. areolatum IGS-D2, but a low percentage of females instead carried A. areolatum IGS-B1D2 (MLG A13), which was presumably due to horizontal transmission from S. noctilio, during the sequential use of the same wood for larval development. The precise identification of the A. areolatum genotypes provides valuable insight into co-evolution between Siricidae and their symbionts, as well as understanding of the geographical origin and history of both Sirex species and their associated fungi.

Amylostereum areolatum (Chaillet ex Fr.) Boidin (Russulales: Amylostereaceae) is a symbiotic fungus of Sirex noctilio Fabricius that has ecological significance. Terpenoids are key mediators in fungal–insect interactions, yet the biosynthetic mechanisms of terpenoids in this species remain unclear. Under nutritional conditions that mimic natural growth, A. areolatum was sampled during the lag phase (day 7), exponential phase (day 14), and stationary phase (day 21). Metabolome (solid-phase microextraction (SPME) combined with gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS)) and transcriptome (Illumina NovaSeq) profiles were integrated to investigate terpenoid–gene correlations. This analysis identified 103 terpenoids in A. areolatum, substantially expanding the known repertoire of terpenoid compounds in this species. Total terpenoid abundance progressively increased across three developmental stages, with triterpenoids and sesquiterpenoids demonstrating the highest diversity and abundance levels. Transcriptomic profiling (61.66 Gb clean data) revealed 26 terpenoid biosynthesis-associated genes, establishing a comprehensive transcriptional framework for fungal terpenoid metabolism. Among 11 differentially expressed genes (DEGs) (|log2Fold Change| ≥ 1, adjusted p < 0.05), HMGS1, HMGR2, and AaTPS1-3 emerged as key regulators potentially governing terpenoid biosynthesis. These findings provide foundational insights into the molecular mechanisms underlying terpenoid production in A. areolatum and related basidiomycetes.