Impact of stand- and landscape-level variables on pine wilt disease-caused tree mortality in pine forests

Research on Pine Wilt Disease Spread Prediction Based on an Improved Light Gradient Boosting Machine Model

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.

Integrated behavior and transcriptomic analysis provide valuable insights into the response mechanisms of Dastarcus helophoroides Fairmaire to light exposure

Light traps have been widely used to monitor and manage pest populations, but natural enemies are also influenced. The Dastarcus helophoroides Fairmaire is an important species of natural enemy for longhorn beetles. However, the molecular mechanism of D. helophoroides in response to light exposure is still scarce. Here, integrated behavioral, comparative transcriptome and weighted gene co-expression network analyses were applied to investigate gene expression profiles in the head of D. helophoroides at different light exposure time. The results showed that the phototactic response rates of adults were 1.67%-22.5% and females and males displayed a negative phototaxis under different light exposure [6.31 × 1018 (photos/m2/s)]; the trapping rates of female and male were influenced significantly by light exposure time, diel rhythm, and light wavelength in the behavioral data. Furthermore, transcriptome data showed that a total of 1,052 significantly differentially expressed genes (DEGs) were identified under different light exposure times relative to dark adaptation. Bioinformatics analyses revealed that the "ECM-receptor interaction," "focal adhesion," "PI3K-Akt signaling," and "lysosome" pathways were significantly downregulated with increasing light exposure time. Furthermore, nine DEGs were identified as hub genes using WGCNA analysis. The results revealed molecular mechanism in negative phototactic behavior response of D. helophoroides under the light exposure with relative high intensity, and provided valuable insights into the underlying molecular response mechanism of nocturnal beetles to light stress.