As a prominent forest pest on international quarantine lists, pine wilt disease (PWD) is characterized by its ease of transmission, rapid onset, high mortality rate, and the complexity of its prevention and control. The disease inflicts devastating damage on pine forest ecosystems and biodiversity in affected regions, resulting in substantial losses of ecological and economic value. This study uses 40 years of county-level data on PWD occurrences in China to investigate the historical spatiotemporal distribution patterns, the spreading process, and the impact of PWD on forest ecosystems. We divided the spread of PWD in China into three stages based on the changes in the number of affected areas. We used SaTScan spatial scanning to analyze the spatiotemporal distribution patterns and regional characteristics of the disease in each stage. Based on the spatial relationships of the affected areas, we identified two types of spread, namely continuous spread and leapfrogging spread, and conducted ecological models of the two spreading processes to describe the spread of PWD over the past 40 years. The results indicate that PWD has two major expansion periods in China. They show a diffusion pattern spreading from points to areas, ultimately forming four clusters with regional characteristics. Driving factors were selected for model construction based on the biological characteristics and spatiotemporal distribution patterns of PWD. The Susceptible (SIS) model and Random Forest (RF) model achieve good results in simulating continuous and leapfrog spread. By integrating the models of the two spreading processes, we can clearly quantify the 40-year spread of PWD in China. The long-term dynamic ecological modeling of PWD, based on historical dissemination characteristics, facilitates the development of disaster prediction models and the maintenance of forest ecosystems while also providing case studies for the invasion and spread of forest pests and pathogens.

The long-term spatial–temporal variation in shoot blight of larch in China has not yet been clearly defined, and the mechanisms behind its long-distance spread remain unknown. This study, based on the historical occurrence dataset of shoot blight of larch in China, used spatial statistical analysis to describe the spatial changes in the disease across five stages since 1973. Subsequently, the study utilized Geo Detector and Random Forest models to investigate the relationship between the spread and occurrence of shoot blight of larch and seven influencing factors. The results revealed the following: (1) The spread of shoot blight of larch in China exhibits significant directionality, with the affected regions distributed along a northeast–southwest axis, and the epicenter of the spread is shifting southwestward; (2) Shandong and Jilin provinces served as the initial introduction points for shoot blight of larch, with most infected counties in other provinces experiencing outbreaks between 1989 and 1996, accompanied by a noticeable spread to neighboring provinces; (3) the occurrence of shoot blight of larch demonstrates a significant positive spatial clustering effect, forming a monocentric “core–periphery” structure centered in Liaoning Province, where kernel density values decrease gradually outward from the core. Geo Detector identified “seedling planting area” as a potential spatial driving factor for the disease. These findings underscore the critical influence of the combined effects of human activities and natural factors in shaping the spatiotemporal distribution patterns of shoot blight of larch.

Pine wilt disease has caused significant damage to China's ecological and financial resources. To prevent its further spread across the country, proactive control measures are necessary. Given the low accuracy of traditional models, we have employed an enhanced light gradient boosting machine (LGBM) model to predict the development trend of pine wilt disease in China, providing a theoretical basis for its monitoring and prevention. We collected and organized data on the occurrence points of pine wilt disease at the county level in China. By incorporating anthropogenic factors such as the volume of pine wood imports from 2017 to 2022, the density of graded roads, the number of adjacent counties, and the presence of wood processing factories, as well as natural factors such as temperature, humidity, and wind speed, we employed Pearson correlation and LGBM model's feature importance analysis to select the 17 most significant influencing factors. Spatial analysis was conducted on the epidemic subcompartments (a divisional unit smaller than a township) of pine wilt disease for 2022 and 2023, revealing the distribution patterns of epidemic subcompartments within 2 km of roads and the spatial relationships between new and old epidemic subcompartments. We improved the LGBM model using a Bayesian algorithm, sparrow search algorithm, and hunter-prey optimization algorithm. By comparison, the enhanced model was validated to outperform in terms of accuracy, precision, recall, sensitivity, and specificity. Based on the results of correlation analysis and spatial analysis, an enhanced model was used to predict the emergence of pine wilt disease in new counties and districts in the future. Currently, pine wilt disease is primarily concentrated in the central-southern and northeastern provinces of China. Predictions indicate that the disease will further spread to the northeastern and southern regions of the country in the future.

Larch shoot blight, caused by the fungus Neofusicoccum laricinum, threatens larch (Larix spp.) forests across northeastern China, jeopardizing both timber productivity and ecological stability. This study aimed to investigate the genomic diversity, population structure, and potential adaptive mechanisms of N. laricinum across contrasting climatic regions. To achieve this, we conducted whole-genome resequencing of 23 N. laricinum isolates collected from three major provinces—Heilongjiang, Inner Mongolia, and Jilin—that represent distinct climatic zones ranging from cold-temperate to relatively warmer regions. We identified ~219.1 K genetic variants, offering a detailed portrait of the pathogen’s genomic diversity. Population structure analyses, including principal component analysis and phylogenetic tree, revealed clear genetic differentiation aligning with geographic origin and climate. Functional annotation (GO and KEGG) highlighted enrichment in metabolic, stress-response, and membrane transport pathways, suggesting potential adaptation to varied temperature regimes and environmental pressures. Moreover, region-specific variants—particularly missense and stop-gain mutations—were linked to genes involved in ATP binding, oxidoreductase activity, and cell division, underscoring the fungus’s capacity for rapid adaptation. Collectively, these findings fill a critical gap in the population genetics of N. laricinum and lay a foundation for future disease management strategies to larch shoot blight under changing climatic conditions.