Asymmetric responses of aboveground net primary productivity (ANPP) to precipitation were identified as a signal to predict ecosystem state shifts at temperate grassland zones in Inner Mongolia, China. However, mechanism studies were still lacking. This study hypothesized that the enhanced growth and newly emerged herbaceous after increased precipitation resulted in the highest asymmetry at the transition zone between desert and typical steppe. We monitored the responses of the normalized difference vegetation index (NDVI) of different species to precipitation events using un-manned aerial vehicle technology to test this hypothesis. NDVI and species richness were measured twice at fixed points in July and August with a time interval of 15 days. Results showed that: (1) From July to August, NDVI in the transition zone increased significantly after precipitation (P < 0.05), but NDVI in both the desert and typical steppe showed a non-significant change (P > 0.05). (2) In the transition zone, NDVI increases from the shrub and herbaceous contributed to 37 and 63% increases of the site NDVI, respectively. (3) There was a significant difference in species richness between July and August in the transition zone (P < 0.05), mainly caused by the herbaceous (Chenopodiaceae, Composite, Convolvulaceae, Gramineae, Leguminosae, and Liliaceae), which either emerged from soil or tillers growth from surviving plants. This study demonstrated that herbaceous dominant the changes of NDVI in the transition zone, which provides a scientific basis for the mechanism studies of ANPP asymmetric response to precipitation and warrants long-term measurements.

Kobresia pygmaea is the endemic and one of the most important species in the alpine meadow in the Qinghai-Tibet Plateau. It is the key stage in the management of degraded grassland, and irreversible degradation will take place after the degradation succession phases of the Kobresia pygmaea community. However, knowledge about the spatial distribution and driving factors were still unknown. In this study, the potential distribution of the Kobresia pygmaea community was determined using the BIOMOD niche model. Combine with the reality distribution based on remote sensing classification, the driving factors of climate and human activities were identified. The findings revealed that: (1) among all environmental factors, the maximum radiation, monthly temperature difference, driest period precipitation were the main climate influencing factors for the Kobresia pygmaea community distribution, and random forest model achieved the highest prediction accuracy and best stability of any niche model. (2) The potential distribution area of Kobresia pygmaea community was 653.25 km² (account for 3.28% of the study area), and mostly located in northern and central of Zeku County, northeast of Henan County, and northeast, central, and eastern parts of Maqu County. (3) Climate factors driven 21.12% of Kobresia pygmaea community reality distribution, while human activities driven for 79.98%. Our results revealed that human activities dominant the reality distribution of Kobresia pygmaea community in alpine meadow grassland in the east source region of Yellow River, China.

Alpine grassland is one of the most critical grassland types in the world, and it is vulnerable and sensitive to external disturbances. The development and outbreak of locust might result in the irreversible degradation. However, most locust studies have been on the tropical, temperate, and desert areas. Our knowledge of inhabitable areas in alpine grassland still needs to be explored. This study was carried out in the alpine grassland in the Qilian Mountain National Park. Environmental factors (remote sensing vegetation index, meteorology, soil, topography, and grassland types) and their impact on locust density were investigated. Finally, the inhabitable areas of locust in the study area were mapped. The results showed that: (1) six out of 26 factors [including precipitation, solar radiation (average and maximum value), normalized vegetation index (NDVI), soil, and temperature] had great influence on locust density, with a relative contribution (RC) more than 10%. (2) Among all locust density estimation models, those based on average and maximum solar radiation, maximum precipitation, maximum NDVI, average temperature, and clay content in deep soil performed better than others, with R ranging from 0.58 to 0.73 and root mean square error ranging from 21.70 to 25.82 head/m². (3) The areas most suited for locust growth, development, and frequent outbreak were found in the south of Tianjun County, middle and northwest of Qilian County (account for 27% of the study area), while the inhabitability was weak in south of Gangcha County, northwest of Tianjun County, and most of Delingha City. Thus our study clarified the distribution region and occurrence variation of the locust and provided a scientific basis for locust prevention and control in alpine grassland in the Qilian Mountain National Park.

Gynaephora alpherakii (G. alpherakii) is one of the dominant pests in the alpine meadow; its outbreak has substantial impacts on grass vegetation production and carrying capacity. The increasing frequency of G. alpherakii outbreaks will take place with global warming and human activities. Therefore, it is crucial to identify inhabitable areas to apply prevention measures efficiently and facilitate sustainable grassland management. In this study, field measurements of G. alpherakii (from 2010 to 2021) were used to explore the relationships between its density and environmental factors in the National Park of Qilian Mountain, China. Then, the inhabitability area was mapped. The results showed that (1) eight of the twenty-five factors studied (average and maximum value of solar radiation; minimum, standard deviation, and average value of normalized difference vegetation index (NDVI); maximum precipitation; and digital elevation model) had a greater influence on G. alpherakii density. (2) Among all of the estimation models, models based on the average of solar radiation, the minimum of NDVI, and the maximum precipitation were better than other factors, with a higher determination coefficient (R²) of 0.53–0.66 and a lower root mean square error (RMSE) of 40.54–47.32 head/m². Models based on rest factors had a lower accuracy, with R² within 0.38–0.49 and an RMSE of 50.91–58.68 head/m². (3) The inhabitable area which was most suited for G. alpherakii growth, development, and frequent outbreaks was located in the northeast of the research area (24.72%), with inhabitability decreasing from southeast to northwest. This method is helpful for clarifying the distribution regions and occurrence dynamics of the G. alpherakii in the alpine meadow. The spatial distribution of G. alpherakii in the National Park of Qilian Mountain can also be clearly defined by using this method, which can provide data support for its prevention and control.