Ecological control studies of the Asian longhorned beetle (Anoplophora glabripennis) distinguish susceptible, resistant, and dead-end trap trees as functionally different plant categories. We used shotgun metagenomics to examine bacterial and archaeal profiles detected in adult gut samples after 72 h exposure to three dietary plants or prolonged water-only starvation. The study included 24 metagenomes, with three biological replicates per DietGroup × SexGroup combination. No time-zero gut samples were available, so the observed patterns remain superimposed on the beetles' field history. The retained catalogue contained 152,895 bacterial genes and 9 archaeal genes. The original observed-richness difference was strongly correlated with host-depleted read depth and was not supported after common-depth rarefaction. Genus-level Bray-Curtis analysis detected a DietGroup × SexGroup interaction that persisted after depth adjustment and exclusion of low-yield samples. This interaction was exploratory because of the small within-cell sample size. Raw Bray-Curtis analysis of KEGG Orthology profiles showed a DietGroup association, but this association was not robust to direct-depth adjustment or Aitchison analysis. CAZy profiles were descriptive and showed no significant DietGroup effect. These results indicate short-term, depth-sensitive associations between dietary treatment and gut-sample bacterial and archaeal profiles. They do not establish resident status, microbial activity, or a physiological mechanism.

Pine wilt disease (PWD), driven by the highly destructive pine wood nematode (PWN; Bursaphelenchus xylophilus), is recognized globally as one of the most catastrophic threats to pine ecosystems. Characterized by its explosive spread and high mortality rate in susceptible hosts, PWD has inflicted monumental economic damage and generated profound ecological instability worldwide. Early detection and intervention before trees exhibit visible symptoms is crucial for halting disease spread and enabling effective management. Remote sensing (RS) technology, with its multiscale, nondestructive, and spatiotemporally continuous observation, has become an essential tool for early detection of PWD. This review systematically presents the pathogenic process of PWD and the corresponding principles of RS response. We analyze how the complex interplay between critical environmental and biological variables, RS-specific factors, and detection algorithms collectively influences the performance of early-stage PWD detection. This study concludes by identifying and discussing the major limitations and key challenges facing current RS technologies in achieving robust and reliable PWD early detection, thereby charting a path for future study in forest health monitoring. Our analysis reveals that current RS efforts predominantly target the slight discoloration stage of PWD, representing a significant gap in detecting the crucial previsual early stage. Autonomous aerial vehicle (AAV)-based hyperspectral imaging remains the dominant technical approach. Red-edge regions and vegetation indices (VIs) derived from them are widely used in early detection studies. At the algorithmic level, shallow machine learning (ML) methods are generally more suitable for tasks involving small sample sizes and low-dimensional data, whereas deep learning (DL) approaches are better suited for end-to-end detection using large-scale datasets. Integrating RS data with prior knowledge of plant physiological mechanisms and developing interpretable detection models represent a key and promising direction for advancing early PWD detection. Future studies should place greater emphasis on the standardization of biological information and urgently promote open data sharing; both are essential for ensuring the reproducibility and generalizability of research findings across different ecological environments. Simultaneously, substantial efforts are required to address several critical challenges. These include the susceptibility of early RS signals to noise and mixed-pixel effects, the limited availability of early-stage samples, particularly during the previsual stage, and the effective discrimination of PWD from other co-occurring pest and disease disturbances. This review aims to provide comprehensive technical guidance and methodological recommendations for early PWD detection, thereby supporting the scientific monitoring and management of this destructive forest disease.

Wood-feeding insects often rely on microbial symbionts to thrive on nutrient-poor xylem. Anoplophora glabripennis is a wood-boring pest that inhabits a wide range of healthy deciduous hosts. The fungus Fusarium solani is associated with A. glabripennis. This study investigated their relationship in the native range of A. glabripennis, and evaluated how F. solani is carried and transmitted, as well as the phylogenetic relationship of F. solani species complex (FSSC) populations from different countries. Fungal communities differed among eggs carried by adult, oviposition secretions, healthy phloem adjacent to the oviposition pit, and soft rot phloem consumed by newly hatched larvae; but were similar in eggs and secretions. F. solani was highly enriched in eggs (93.07%), oviposition secretions (86.39%), and soft rot phloem (63.44%), but was absent in healthy phloem. The F. solani isolation rate from oviposition pits was 100% across different hosts and locations, and it was found in larval guts and frass at all life stages. In addition, GFP-labeled F. solani was only detected in larval guts (10, 40, 60 days post-feeding), but not in the fat body or epidermal tissue. Newly hatched larvae had the highest FSSC-specific copy numbers in their guts than those at other life stages. FSSC isolated from the gut of A. glabripennis in China forms a separate clade, with a relatively distant genetic relationship to the United States larval isolates. These results support the symbiotic relationship between A. glabripennis and F. solani, and demonstrate that F. solani is transmitted via female adult oviposition and carried in the guts by larval feeding.

Effective forest management depends on accurately monitoring changes in forest health. The Asian longhorned beetle (ALB) has caused extensive mortality in broadleaf trees worldwide. ALB typically manifests as a distinctive treetop-dieback phenotype that progresses downward in damaged trees. Although LiDAR is widely used for plant-stress detection, two challenges persist: (1) non-specific structural responses that hinder the identification of damage-specific phenotypes and (2) limited transferability of structural metrics across age- and sizeheterogeneous stands. We address these challenges with a within-tree ratio-based framework that targets the treetop-dieback phenotype with internal normalization. Two poplar plots (young, old) were selected to represent age- and size-related heterogeneity. Based on field evidence (oviposition pits, frass holes, exit holes), each tree was labeled as healthy, lightly damaged, or severely damaged. For each tree, we vertically segmented the point cloud at 50% and 80% of total height and computed crown volume (V), point density (PD), and leaf area index (LAI) of each segment. We then derived upper-to-lower ratio metrics, using the lower crown as an internal normalizer, to capture top-down dieback while normalizing size- and age-related heterogeneity. We also defined a family of combination ratios that aggregate ratios at 50% and 80% heights. Using Linear Discriminant Analysis (LDA), we evaluated separability of each metric under two strategies: (1) a 70/30 random split by trees and (2) a plot-transfer test (train: Standyoung; test: Standold). The results indicated that the proposed metrics, Summary Ratio of Volume (SRV = Vupper50% Vlower50% + Vupper20%Vlower80%), achieved the highest overall accuracy (OA): 76.23% in random split, and 70.58% in plot-transfer, significantly outperforming other proposed metrics and existing LiDAR indices. By coupling phenotype-focused features with internal normalization, the approach enables precise detection of treetop dieback and improves transferability across age and size heterogeneity.

In the Western Palaearctic, the congeneric bark beetles Hylurgus ligniperda and Hylurgus micklitzi are developing on pine trees. Although H. ligniperda has a larger native range in Europe, both species often occur in sympatry in the Mediterranean Basin. They share rather similar life histories, thus providing an excellent model for comparative studies on species differentiation and ecological traits. Moreover, along with increasing human activities and international trade, H. ligniperda has invaded all continents, whereas H. micklitzi remains confined to the Mediterranean Basin. Our research utilised COI markers to assess the genetic diversity and structure, alongside the demographic history, of both species and revealed marked differences. H. micklitzi exhibited low genetic diversity and shallow population structures with restricted expansion. In contrast, H. ligniperda displayed a longer and more complex post-glacial evolutionary route with four distinct clades originating from separate glacial refugia, coupled with considerable genetic variability across Europe. Comparative analyses about the two species suggest that species traits, such as larger body size, increased voltinism, broader ecological niches and frequent introduction events may have helped the fast global invasion and spread of H. ligniperda over the past century.

BACKGROUND:The pine wood nematode (PWN) has caused tremendous damage to pine forests in China. Accurately predicting the infestation stage of PWN is crucial for implementing appropriate management, such as chemically controlling early-infested trees and felling and removing trees in the severe stages of infestation. Unmanned aerial vehicle (UAV)-based hyperspectral technology can capture images with high spatial and spectral resolutions, facilitating more extensive coverage and enhanced detection efficiency. To date, few studies have used the correlation coefficient between full spectra and physiological traits to screen dual-band vegetation indices (VIs). Moreover, there is a lack of comprehensive comparison between the screened VIs, feature wavelengths, and full spectra using various machine learning methods to predict the infection stage of PWN. RESULTS:We evaluated the abilities of screened VIs, feature wavelengths selected by successive projections algorithm (SPA), and full spectra in estimating PWN infection levels. Random forest (RF), artificial neural network (ANN), support vector machine (SVM), and three convolutional neural networks (CNN) were applied. Screened VIs performed the best (OA%: 76.03-80.99; Kappa: 0.68-0.74), and RF approach obtained highest classification accuracies (OA%: 72.73-80.99; Kappa: 0.63-0.74). In discriminating between healthy trees and PWN-infected trees at an early stage, RF using screened VIs outperformed other approaches (healthy trees: PA% = 76.92, UA% = 76.92; early-infested trees: PA% = 66.67, UA% = 72.00), and normalized difference spectral index (NDSI) selected by chlorophyll content was the most sensitive feature. CONCLUSION:We propose the integration of RF with the screened VIs as a recommended approach for the early detection of PWN infections in Chinese Pine, which give reference to the management of PWN infections. © 2025 Society of Chemical Industry.

Acoustic detection technology has emerged as a promising, non-destructive and continuous monitoring method for pest early detection at the single tree level. However, field application still encounters problems, especially under complex infestation scenarios, i.e., co-infestations by multiple pest species. This study aims to develop a novel acoustic-based recognition model for detecting forest wood-boring pests, specially designed to enhance monitoring accuracy under complex infestation scenarios. We collected feeding vibration signals from four wood-boring pests: Semanotus bifasciatus, Phloeosinus aubei, Agrilus planipennis, and Streltzoviella insularis. Three infestation scenarios were designed: single-species, co-infestation without mixed signals, and co-infestation with mixed signals. Three machine learning (ML) models (Random Forest, Support Vector Machine, and Artificial Neural Network) based on seven acoustic feature variables, and three deep learning (DL) models (AlexNet, ResNet, and VGG) using spectrograms were employed to classify the signals. Results showed that ML models achieved perfect accuracy (OA: 100%, Kappa: 1) in single-species scenarios but declined significantly under co-infestation scenarios with mixed signals. In contrast, DL models, particularly ResNet, maintained high accuracy (OA: 85.0–88.75%) and effectively discriminated mixed signals. In conclusion, this study demonstrates the superiority of spectrogram-based DL models for acoustic detection under complex infestation scenarios and provides a foundation for developing a general, real-time detection model for integrated pest management in forest ecosystems.

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.

Areca catechu L. (Arecaceae) is an important cash crop in Taiwan(China), Hainan (China), and several South Asian countries. Areca palm yellow leaf disease (YLD) poses a severe threat, leading to reduced yields and eventual plant mortality. The current study differentiates areca palm damage solely based on spectral features. We are the first to integrate LiDAR point clouds with multispectral imagery to distinguish between different damage levels. We standardized the geographic coordinate systems of the LiDAR data and multispectral images, then aligned them using the control point method. During YLD infestation, areca palm leaves turn yellow and eventually fall off. We surveyed over 1000 trees, counting the number of leaves and calculating the proportion of the canopy area covered by yellowing foliage. Based on crown color changes and leaf count, we classified areca palm damage into five levels: healthy, slightly damaged, moderately damaged, severely damaged, and dying/dead. Meanwhile, this study optimized the individual tree segmentation process for areca palm. Using trunk point clouds, we generated seed points and applied region-growing cluster segmentation to achieve a more accurate individual tree profile compared to the traditional watershed algorithm. Based on the segmentation results and crown contours, we extracted the structural and spectral characteristics of individual trees. Multiple algorithms were then applied to classify areca palms into four damage levels: healthy, slightly damaged, moderately damaged, and severely damaged. The classification achieved an overall accuracy of 86.46% and a kappa value of 0.819. The inclusion of LiDAR data improved the overall accuracy by 23.94% compared to using only spectral features. Comparatively, past studies have relied only on spectral differences to determine the area of leaf yellowing and thus further determine the level of damage. In this study, we further noted the structural changes in the canopy caused by leaf abscission, provided a more realistic description of the different damage levels, and constructed more accurate models. The proposed method demonstrates great potential in YLD damage classification and provides an important basis for precise management of plantations.

The long-lasting outbreak of the pine shoot beetle (PSB, Tomicus spp.) threatens forest ecological security. Effective monitoring is urgently needed for the Integrated Pest Management (IPM) of this pest. UAV-based hyperspectral remote sensing (HRS) offers opportunities for the early and accurate detection of PSB attacks. However, the insufficient exploration of spectral and structural information from early-attacked crowns and the lack of suitable detection models limit UAV applications. This study developed a UAV-based framework for detecting early-stage PSB attacks by integrating hyperspectral images (HSIs), LiDAR point clouds, and structure from motion (SfM) photogrammetry data. Individual tree segmentation algorithms were utilized to extract both spectral and structural variables of damaged tree crowns. Random forest (RF) was employed to determine the optimal detection model as well as to clarify the contributions of the candidate variables. The results are as follows: (1) Point cloud segmentation using the Canopy Height Model (CHM) yielded the highest crown segmentation accuracy (F-score: 87.80%). (2) Near-infrared reflectance exhibited the greatest decrease for early-attacked crowns, while the structural variable intensity percentile (int_P50-int_P95) showed significant differences (p < 0.05). (3) In the RF model, spectral variables were predominant, with LiDAR structural variables serving as a supplement. The anthocyanin reflectance index and int_kurtosis were identified as the best indicators for early detection. (4) Combining HSI with LiDAR data obtained the best RF model accuracy (classification accuracy: 87.31%; Kappa: 0.8275; SDR estimation accuracy: R2 = 0.8485; RMSEcv = 3.728%). RF integrating HSI and SfM data exhibited similar performance. In conclusion, this study identified optimal spectral and structural variables for UAV monitoring and improved HRS model accuracy and thereby provided technical support for the IPM of PSB outbreaks.

Bursaphelenchus xylophilus (pine wood nematode, PWN) has been present in China for over 40 years and has spread to northeast China, where native pine species are key components of the local top community. Pinus thunbergii is known to be susceptible to PWN among local conifer species, whereas research on PWN’s pathogenicity in Larix remains limited. Furthermore, there are no research reports on PWN infestation in Picea and Abies species within China. This study conducted a detailed analysis of phenotypic changes and temporal spectral reflectance variations in four conifer species in northeast China—P. thunbergii, Larix kaempferi, Picea koraiensis, and Abies holophylla—following artificial inoculation with PWN. The aim of this study is to establish a theoretical basis for identifying the potential hosts and threats of PWN. The study incorporated a 60-day post-inoculation observation period to systematically monitor and compare temporal changes in external morphology, disease susceptibility (incidence and mortality rates), spectral reflectance, and the normalized wilt index (NWI) in 2–3-year-old seedlings of P. thunbergii, L. kaempferi, P. koraiensis, and A. holophylla after inoculation with PWN. The results showed that P. thunbergii displayed the earliest infection symptoms, followed by L. kaempferi, A. holophylla, and finally P. koraiensis. After inoculation, P. thunbergii was the first to experience mortality, followed by L. kaempferi, P. koraiensis, and A. holophylla. Following inoculation, P. thunbergii exhibited the earliest significant increase in NWI (p < 0.001), followed by L. kaempferi and A. holophylla; P. koraiensis showed the latest increase (p < 0.001). In conclusion, the experiment identified P. koraiensis as having the strongest resistance to PWN among the four species, followed by A. holophylla. P. thunbergii showed the weakest resistance, while L. kaempferi exhibited moderate resistance. The ranking of PWN susceptibility for the four conifer species, from highest to lowest, is as follows: P. thunbergii, L. kaempferi, A. holophylla, and P. koraiensis.

Xylophagous insects, like Monochamus saltuarius significantly affect tree-stem-associated microbial communities and pose major threats to forest ecosystems. As a vector for the pinewood nematode (Bursaphelenchus xylophilus) and a wood-boring pest, M. saltuarius infests various Pinus species. Yet, the interactions between M. saltuarius-associated fungi and the endophytic fungi of its host tree, Pinus koraiensis, remain uncharacterized. In this study, high-throughput sequencing was used to characterize fungal communities within infested and uninfested host trees. Endophytic fungi with lignocellulose-depolymerizing potential were identified, and their enzymatic activity was experimentally assessed. A significant reduction in fungal diversity was detected in infested samples, indicating that M. saltuarius infestation disrupts the native fungal community, favoring plant pathogens and diminishing the abundance of lignocellulosic depolymerizing fungi. To explore ecological shifts and diagnostic taxa, an integrated biomarker discovery framework combining Random Forest and LEfSe analysis was applied. This model revealed fungal biomarkers enriched in uninfested tissues and potentially involved in lignocellulose degradation based on functional annotation and in vitro assays. These taxa may serve as indicators of infestation status and provide insight into host-microbe-insect interactions. These findings contribute to understanding how xylophagous insect infestations restructure fungal communities and offer a model-based approach for detecting ecological signatures of pest impact in forest ecosystems.

Dendrolimus species are the major defoliating forest pests in China, causing severe damage to pine forests. Establishing an effective early monitoring system was crucial for timely implementation of control measures to prevent further infestation, significantly reducing economic losses and ecological damage. While previous studies have demonstrated the limited effectiveness of spectral data alone in early detection of Dendrolimus spp. infestations, our research reveals that needle loss is the primary damage symptom, whereas canopy structural characteristics remain underexplored in early monitoring. To address this knowledge gap, this study innovatively integrates unmanned aerial vehicle-based hyperspectral imaging (HSI) with Light Detection and Ranging (LiDAR) data. This study employed SPA, ISIC, and ISIC-SPA algorithms in combination with Random Forest (RF) to select sensitive hyperspectral imaging (HSI) bands. Subsequently, vegetation indices (VIs) were developed from these optimal wavelengths and integrated with LiDAR metrics. Finally, the performance of RF models trained on individual data sources (HSI VIs or LiDAR metrics) and on the combined data (HSI+LiDAR) was evaluated for detecting Dendrolimus spp. damage at the individual tree level. For HSI band selection, compared to the 10 bands selected by SPA-RF (OA = 71.05, Kappa=0.57) and the 21 bands selected by ISIC-RF (OA = 75.44, Kappa=0.63), ISIC-SPA-RF (OA = 70.18, Kappa=0.55) selected only 3 bands and achieved good classification results on the validation set, which substantially reduced data redundancy and improved VI construction. For individual tree-level detection of Dendrolimus spp. damage, four VIS and seven LiDAR-derived metrics were utilized. The results showed that the HSI method (OA = 72.81%, Kappa=0.59) outperformed the LiDAR method (OA = 71.05%, Kappa=0.56). The combined data approach achieved the highest overall accuracy (OA = 83.33%, Kappa=0.75), with an early detection accuracy of 82.93%, which was significantly better than using HSI or LiDAR data alone. Our study demonstrates that LiDAR can effectively capture the spatial distribution changes of needles caused by defoliation, while also revealing spectral reflectance characteristics in the near-infrared (NIR) band. The integration of HSI and LiDAR data significantly enhances the early detection accuracy for Dendrolimus spp. infestations. This approach not only provides critical technical support for Dendrolimus spp. control, but also establishes a novel remote sensing methodology for monitoring other defoliation pests.

The pine wood nematode (Bursaphelenchus xylophilus, PWN) is a globally significant quarantine pest that causes severe economic and ecological damage to coniferous forests worldwide. Additionally, PWNs continue to expand into higher latitudes. However, studies on their cold tolerance remain limited. This study investigated the overwintering environment of PWNs in epidemic areas of Liaoning Province, China. It established a protocol to induce anhydrobiosis in PWNs, evaluated their low-temperature resistance, observed morphological changes during anhydrobiosis, and explored potentially involved key genes. The results showed that (1) there was no significant difference in thermal insulation between infected and healthy wood in Liaoning Province; both effectively reduced temperature fluctuation rates, providing a protective function for PWN overwintering. (2) PWNs significantly enabled their cold tolerance through anhydrobiosis, accompanied by significant morphological changes and substantial lipid droplet depletion. (3) Eleven anhydrobiosis-related genes were identified. Among these, the collagen gene family showed consistent expression patterns throughout dehydration and rehydration. This suggests a potential role in cuticle structural changes and osmoregulation during anhydrobiosis. These findings provide a theoretical basis for understanding how PWNs survive winter conditions in high-latitude regions. Additionally, they offer valuable insights for future research into PWN anhydrobiosis and the development of effective control strategies.

The mutualistic symbiosis relationship between the gut microbiome and their insect hosts has attracted much scientific attention. The native woodwasp, Sirex nitobei, and the invasive European woodwasp, Sirex noctilio, are two pests that infest pines in northeastern China. Following its encounter with the native species, however, there is a lack of research on whether the gut microbiome of S. noctilio changed, what causes contributed to these alterations, and whether these changes were more conducive to invasive colonization. We used high-throughput and metatranscriptomic sequencing to investigate S. noctilio larval gut and frass from four sites where only S. noctilio and both two Sirex species and investigated the effects of environmental factors, biological interactions, and ecological processes on S. noctilio gut microbial community assembly. Amplicon sequencing of two Sirex species revealed differential patterns of bacterial and fungal composition and functional prediction. S. noctilio larval gut bacterial and fungal diversity was essentially higher in coexistence sites than in separate existence sites, and most of the larval gut bacterial and fungal community functional predictions were significantly different as well. Moreover, temperature and precipitation positively correlate with most of the highly abundant bacterial and fungal genera. Source-tracking analysis showed that S. noctilio larvae at coexistence sites remain dependent on adult gut transmission (vertical transmission) or recruitment to frass (horizontal transmission). Meanwhile, stochastic processes of drift and dispersal limitation also have important impacts on the assembly of S. noctilio larval gut microbiome, especially at coexistence sites. In summary, our results reveal the potential role of changes in S. noctilio larval gut microbiome in the successful colonization and better adaptation of the environment.

In northeast China, the invasive woodwasp., Sirex noctilio, attacks Pinus sylvestris var. mongolica Litv and often shares habitat with native Sirex nitobei. Previous research showed that S. noctilio can utilize the volatiles from its symbiotic fungus (A. areolatum IGS-BD) to locate host trees. Consequently, symbiotic fungi (A. areolatum IGS-D and A. chailletii) carried by S. nitobei may influence the behavioral selection of S. noctilio. This study aimed to investigate the impact of fungal odor sources on S. noctilio’s behavior in laboratory and field experiments. Our observations revealed that female woodwasps exhibited greater attraction toward the fungal volatiles of 14-day-old Amylostereum IGS-D in a “Y”-tube olfactometer and wind tunnel. When woodwasps were released into bolts inoculated separately with three strains in the field, females of S. noctilio exhibited a preference for those bolts pre-inoculated with A. areolatum IGS-BD. Gas chromatography–mass spectrometry (GC–MS) analysis revealed that the volatiles emitted by the two genotypes of A. areolatum were similar yet significantly distinct from those of Ampelopsis chailletii. Hence, we postulate that the existence of native A. areolatum IGS-D could potentially facilitate the colonization of S. noctilio in scenarios with minimal or no A. areolatum IGS-BD present in the host.

The Russian olive (Elaeagnus angustifolia), which functions as a “dead-end trap tree” for the Asian long-horned beetle (Anoplophora glabripennis) in mixed plantations, can successfully attract Asian long-horned beetles for oviposition and subsequently kill the eggs by gum. This study aimed to investigate gum secretion differences by comparing molecular and metabolic features across three conditions—an oviposition scar, a mechanical scar, and a healthy branch—using high-performance liquid chromatography and high-throughput RNA sequencing methods. Our findings indicated that the gum mass secreted by an oviposition scar was 1.65 times greater than that secreted by a mechanical scar. Significant differences in gene expression and metabolism were observed among the three comparison groups. A Kyoto Encyclopedia of Genes and Genomes annotation and enrichment analysis showed that an oviposition scar significantly affected starch and sucrose metabolism, leading to the discovery of 52 differentially expressed genes and 7 differentially accumulated metabolites. A network interaction analysis of differentially expressed metabolites and genes showed that EaSUS1, EaYfcE1, and EaPGM1 regulate sucrose, uridine diphosphate glucose, α-D-glucose-1P, and D-glucose-6P. Although the polysaccharide content in the OSs was 2.22 times higher than that in the MSs, the sucrose content was lower. The results indicated that the Asian long-horned beetle causes Russian olive sucrose degradation and D-glucose-6P formation. Therefore, we hypothesized that damage caused by the Asian long-horned beetle could enhance tree gum secretions through hydrolyzed sucrose and stimulate the Russian olive’s specific immune response. Our study focused on the first pair of a dead-end trap tree and an invasive borer pest in forestry, potentially offering valuable insights into the ecological self-regulation of Asian long-horned beetle outbreaks.

Elaeagnus angustifolia L. can attract adult Asian longhorned beetle (ALB), Anoplophora glabripennis (Motschulsky), and kill their offspring by gum secretion in oviposition scars. This plant has the potential to be used as a dead-end trap tree for ALB management. However, there is a limited understanding of the attraction ability and biochemical defense response of E. angustifolia to ALB. In this study, we conducted host selection experiments with ALB and then performed physiological and biochemical assays on twigs from different tree species before and after ALB feeding. We analyzed the differential metabolites using the liquid chromatograph–mass spectrometer method. The results showed that ALB’s feeding preference was E. angustifolia > P.× xiaohei var. gansuensis > P. alba var. pyramidalis. After ALB feeding, the content of soluble sugars, soluble proteins, flavonoids, and tannins decreased significantly in all species. In three comparison groups, a total of 492 differential metabolites were identified (E. angustifolia:195, P.× xiaohei var. gansuensis:255, P. alba var. pyramidalis:244). Differential metabolites were divided into overlapping and specific metabolites for analysis. The overlapping differential metabolites 7-isojasmonic acid, zerumbone, and salicin in the twigs of three tree species showed upregulation after ALB feeding. The specific metabolites silibinin, catechin, and geniposide, in E. angustifolia, significantly increased after being damaged. Differential metabolites enriched in KEGG pathways indicated that ALB feeding activated tyrosine metabolism and the biosynthesis of phenylpropanoids in three tree species, with a particularly high enrichment of differential metabolites in the flavonoid biosynthesis pathway in E. angustifolia. This study provides the metabolic defense strategies of different tree species against ALB feeding and proposes candidate metabolites that can serve as metabolic biomarkers, potentially offering valuable insights into using E. angustifolia as a control measure against ALB.