Leptocybe invasa is a globally invasive pest of eucalyptus plantations, and is steadily spread throughout China. Predicting the growth area of L. invasa in China is beneficial to the establishment of early monitoring, forecasting, and prevention of this pest. Based on 194 valid data points and 21 environmental factors of L. invasa in China, this study simulated the potential distribution area of L. invasa in China under three current and future climate scenarios (SSPs1–2.5, SSPs2–3.5, and SSPs5–8.5) via the MaxEnt model. The study used the species distribution model (SDM) toolbox in ArcGIS software to analyze the potential distribution range and change of L. invasa. The importance of crucial climate factors was evaluated by total contribution rate, knife-cut method, and environmental variable response curve, and the area under the receiver operating characteristic (ROC) curve was used to test and evaluate the accuracy of the model. The results showed that the simulation effect of the MaxEnt model is excellent (area under the ROC curve (AUC) = 0.982). The prediction showed that L. invasa is mainly distributed in Guangxi, Guangdong, Hainan, and surrounding provinces, which is consistent with the current actual distribution range. The distribution area of the potential high fitness zone of L. invasa in the next three scenarios increases by between 37.37% and 95.20% compared with the current distribution. Climate change affects the distribution of L. invasa, with the annual average temperature, the lowest temperature of the coldest month, the average temperature of the driest season, the average temperature of the coldest month, and the precipitation in the wettest season the most important. In the future, the core areas of the potential distribution of L. invasa in China will be located in Yunnan, Guangxi, Guangdong, and Hainan. They tend to spread to high latitudes (Hubei, Anhui, Zhejiang, Jiangsu, and other regions).

Background: Climate warming has caused substantial changes in temporal and spatial environmental patterns. The study on hot spots of rare and endangered plants in Northwest China under predicted climate change can provide a scientific reference for the restoration and reconstruction of those degraded habitats, as well as the improvement of the protection system in Northwest China. Results: Based on MaxEnt algorithm, 813 effective distribution records and 11 environmental factor variables of rare and endangered plants in Northwest China, this study identified the changes of biodiversity hotspots of rare and endangered plants in Northwest China under predicted climate change. The results showed that: (1) the prediction accuracy of MaxEnt model is high, the area under the curve (AUC) is 0.876, and the total suitable area for potential geographical distribution of rare and endangered plants in Northwest China is 137.96×104km2, mainly including Western and Southwestern Xinjiang, Southern Gansu, parts of Eastern and Southern Qinghai Province, and Southern Shaanxi Province; (2) altitude, temperature and precipitation are the main environmental factors affecting the hot spots of rare and endangered plants in Northwest China; (3) under four climate change scenarios in the future, with the increase of emission scenarios from low to high forcing, Xinjiang would have the most obvious loss of hot spots of rare and endangered plants in Northwest China, and the most obvious increase of which would occur in Qinghai and Gansu provinces. Conclusion: Under the climate change scenario in the future, with the emission scenario increasing from low forcing to high forcing, the most obvious loss of the hot spots of rare and endangered plants in Northwest China happens in Xinjiang Province, and that of the most obvious increase occurs in Qinghai and Gansu provinces.

Grassland is the important terrestrial ecosystem, aboveground biomass is an important indicator of the productivity of grassland ecosystem, monitoring grassland is very important to the assessment of the current status of grassland growth and the conservation and development of grassland resources to Lanzhou city. In this study, the reflectance was extracted and the 9 vegetation indexes was calculated from Landsat images that combined with field sampling data of the grassland aboveground biomass in Lanzhou city in July-August 2021 to construct the two machine models that Random Forest model (RF) and eExtreme Gradient Boosting model (XGBoost) and chose the best model to invert the grass aboveground biomass in Lanzhou City from 2000 to 2023 and analyze its spatial and temporal dynamics.The results of the study show that: (1) The reflectance of other bands except b5-NIR band and nine vegetation indices were significantly correlated with grassland aboveground biomass by Pearson correlation analysis, so the remaining 14 factors except b5-NIR band were selected as model input variables. (2) Compared with the XGBoost model (R² of 0.78, RMSE of 37.03), the RF (R² of 0.89, RMSE of 23.28) model has a higher accuracy and it was more suitable for the inversion of aboveground biomass of grassland in Lanzhou city. (3) In time, the average value of aboveground biomass of grassland in Lanzhou City from 2000 to 2023 showed an increasing trend as a whole; In space, the aboveground biomass of grassland decreased firstly from southeast to northwest and then increased in Lanzhou City. The area of high value zone of grassland aboveground biomass increased, and the low value zone kept transforming to the high value zone. This study can provide theoretical reference and technical support for the estimation of aboveground biomass of grassland and the protection of grassland ecosystem in Lanzhou City.

Quantitative assessment of water-heat exchanges between forests and grasslands and the atmosphere in the Qilian Mountains contributes to a deeper understanding of the water cycle and energy balance processes in vegetated ecosystems. In this study, half-hourly and daily water heat flux data of forests and grasslands on the northern slopes of the Qilian Mountains in 2023 were obtained based on eddy covariance techniques, and the daily and seasonal characteristics of latent heat flux (LE), sensible heat fluxe (H), and Bowen ratio (β) of forests and grasslands were compared and analyzed, Structural equation modeling (SEM) and Pearson correlation analysis (Pearson) were used to explore the effects of eight environmental factors, namely, net radiation (Rn), air temperature (Ta), wind speed (u), rainfall (P), saturated vapor pressure difference (VPD), relative humidity (RH), soil temperature (Ts), and volumetric soil water content (VWC), on the water-heat flux. The results of the study showed that (1) the daily changes of LE and H in forest and grassland showed a single-peak pattern, with the daily mean value of LE in forest higher than that in grassland, and the daily mean value of H in grassland higher than that in forest; (2) The seasonal changes of forest and grassland LE are single-peaked, the seasonal changes of H are bimodal, and the seasonal changes of β are U-shaped; the monthly mean value of forest LE is higher than that of grassland, the monthly mean values of grassland H and β are higher than that of forest, and the value of β is greater than 1 in summer, and the value of β is less than 1 in the rest of the seasons, and summer Heat exchange between the earth and air was dominated by latent heat form, and the rest of the season was dominated by sensible heat form; (3) Eight environmental factors were correlated with forest and grassland LE, H, and β in the growing season, and P and SWC were not correlated with forest and grassland LE, H, and β in the non-growing season, and the degree of correlation was stronger in the growing season than in the non-growing season, and the degree of correlation in the grassland was stronger than in the forest. Throughout the year, LE, H, and β are mainly affected by the direct positive influence of Rn, u, and VPD, and the direct negative influence of RH, in addition, Rn indirectly affects LE, H, and β by affecting u, VPD, and RH, and VPD indirectly affects LE, H, and β by affecting RH and u. The results of this study can provide a theoretical basis for the management and effective utilization of forest and grassland water resources on the northern slopes of the Qilian Mountains.