Lycorma delicatula , a globally invasive pest, has caused considerable economic losses in many countries. Determining the potential distribution range of L. delicatula is crucial for its effective management and control; however, our understanding of this species remains limited. In this study, Maxent model with occurrence records and environmental variables were fit first and then optimized by selecting the best combination of feature classes and regularization multipliers using the lowest score of corrected Akaike information criterion. Subsequently, we predicted global suitable areas for L. delicatula both currently and in the future (2041–2060, 2061–2080, and 2081–2100). The results indicated that the mean temperature of the driest quarter is the most important environmental variable limiting L. delicatula distribution. Currently, the suitable areas are concentrated in East Asia (mainly in China, South Korea, and Japan), central and eastern United States, and southern Europe. Compared with current environmental conditions, in all future climate scenarios, the number of suitable areas for L. delicatula increased. In addition, we revealed that suitable areas are likely to expand northward in the future. Our study results suggest that policymakers and governments should prioritize the development of pest management measures in suitable areas for L. delicatula , especially in high suitable areas, to control this invasive pest and minimize global economic losses.

Climate change is a prominent factor reshaping the distribution of invasive species. Metcalfa pruinosa (Say 1830) (Hemiptera: Flatidae), native to North America, has invaded other continents and poses a serious threat to various agricultural crops and the human residential environment. Understanding the distribution of M. pruinosa based on climatic conditions is a critical first step to prevent its further invasion. Therefore, based on its occurrence records and associated environmental variables, a Maxent model was developed to predict suitable areas for this species in the present and future on a global scale. The model exhibited outstanding performance, with a mean area under the receiver operating characteristic curve and true skill statistic values of 0.9329 and 0.926, respectively. The model also indicated that annual precipitation (Bio12) and max temperature of the warmest month (Bio5) were the key environmental variables limiting the distribution of M. pruinosa. Moreover, the model revealed that the current suitable area is 1.01 × 10⁷ km² worldwide, with southern China, southern Europe, and the eastern United States predicted to be the primary and highly suitable areas in the latter 2 regions. This area is expected to increase under future climate scenarios, mainly in the northern direction. The study’s findings contribute to our understanding of climate change’s impact on M. pruinosa distribution, and they will aid governments in developing appropriate pest management strategies, including global monitoring and strict quarantine measures.

Although many hypotheses have been proposed to understand the mechanisms underlying large-scale richness patterns, the environmental determinants are still poorly understood, particularly in insects. Here, we tested the relative contributions of seven hypotheses previously proposed to explain planthopper richness patterns in China. The richness patterns were visualized at a 1° × 1° grid size, using 14,722 distribution records for 1335 planthoppers. We used ordinary least squares and spatial error simultaneous autoregressive models to examine the relationships between richness and single environmental variables and employed model averaging to assess the environmental variable relative roles. Species richness was unevenly distributed, with high species numbers occurring in the central and southern mountainous areas. The mean annual temperature change since the Last Glacial Maximum was the most important factor for richness patterns, followed by mean annual temperature and net primary productivity. Therefore, historical climate stability, ambient energy, and productivity hypotheses were supported strongly, but orogenic processes and geological isolation may also play a vital role.

One of the main goals in biogeography and ecology is the study of patterns of species diversity and the driving factors in these patterns. However, such studies have not focused on Sternorrhyncha in China, although this region hosts massive species distribution data. Here, based on the 15,450 distribution records of Sternorrhyncha species in China, we analyzed patterns in species richness and endemism at 1° × 1° grid size and determined the effects of environmental variables on these patterns using correlations analysis and the model averaging approach. We found that species richness and endemism of Sternorrhyncha species are unevenly distributed, with high values in the eastern and southeastern coastal regions of mainland China, as well as Taiwan Island. Furthermore, the key factors driving species richness and endemism patterns are inconsistent. Species richness patterns were strongly affected by the normalized difference vegetation index, which is closely related to the feeding habits of Sternorrhyncha, whereas endemism patterns were strongly affected by the elevation range. Therefore, our results indicate that the range size of species should be considered to understand the determinants of species diversity patterns.

China’s bamboo output is closely associated with its national economy; however, it is currently rapidly declining due to damage from the pests Anaka burmensis and Cicadella viridis. Identifying regions that are environmentally suitable for these pests is a critical step in their effective control. Therefore, in this study, we used a Maxent model to predict their current and future potential areas of distribution (2021–2040, 2041–2060, and 2061–2080) and explored changes over time using distribution data and related environmental variables. The model results demonstrates that the current potential areas of distribution of A. burmensis are predominantly concentrated in several provinces of southern and central China, such as Guizhou, Guangxi, and Hubei, whereas the current potential areas of distribution of C. viridis are primarily in many provinces across southern, central, and northeastern China. In the future, the potential distribution of A. burmensis will increase and move minimally, whereas the potential distribution of C. viridis will decrease and move considerably. The results of the present study provide vital information for predicting the spread and outbreaks of C. viridis and A. burmensis and provide a reference framework for developing management strategies to control these two pests, thereby minimizing economic loss in the bamboo industry.