Plants, as producers in ecosystems, are an integral part of biodiversity in terms of their species diversity. Plant diversity not only enhances the quality of ecosystem services, but also provides habitat for a wide range of plants and animals. The invasion of pine wilt disease (PWD) has posed a significant threat to plant diversity in China, but it is not clear whether this threat would be significantly different in natural and planted forests. In this study, we collected a long time series of refined forest subcompartment data on PWD occurrence and plant diversity sample survey data to analyze the loss and recovery time of plant diversity in China caused by PWD invasion, especially the degree of impact on plant diversity in natural and planted forests. The results showed that after PWD invasion, the plant diversity levels of China’s national, natural, and planted forests reached a minimum in the third year of invasion, with a loss of 9.1%, 6.46%, and 9.82%, respectively, relative to the pre-invasion levels. Starting from the third year of invasion, the plant diversity levels of the three recovered gradually at different rates, among which there was a significant difference in the speed of recovery between natural forests and planted forests, which took two and three years to recover to the original level of plant diversity, respectively. This study revealed the differences in the response of plant diversity to PWD invasion between natural and planted forests and provided a theoretical basis for local governments and managers in preventing and controlling PWD and protecting plant diversity.

The temporal dynamics of forest-associated insects are closely associated with climatic variability, and identifying their temporal patterns and climatic associations is important for understanding insect population dynamics. In this study, automatically recorded Coleoptera and Lepidoptera in Rui’an City, Zhejiang Province, China, were selected as the target insect groups. Based on automatic insect monitoring and meteorological data, the temporal characteristics of recorded insect abundance and their associations with climatic factors were analyzed. Five machine learning models, including Multiple Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Regression, and Generalized Additive Model, were then developed and compared for predicting recorded insect abundance. The results showed that Coleoptera exhibited periodic fluctuations with cycles of approximately 6–20 days, whereas Lepidoptera displayed persistent seasonal variation. Air temperature, specific humidity, and shortwave radiation were significantly and positively correlated with recorded insect abundance, while wind speed showed a weak negative correlation. Among the five models, Random Forest achieved the best prediction performance, with MAE, RMSE, and R² values of 1.37, 4.85, and 0.82 for Coleoptera and 0.57, 1.16, and 0.84 for Lepidoptera, respectively. Independent-year validation using 2023 data showed reduced predictive performance compared with random validation, indicating limited temporal generalization under the available observation period. These findings demonstrate that integrating temporal characteristics with climatic factors can support the prediction of automatically recorded insect abundance, while longer-term monitoring data are needed to further evaluate model robustness and temporal transferability.