Plant diversity plays an important role in maintaining the stability of ecosystem functioning. Based on field surveys and indoor analyses, this study investigated the relationship between species diversity and community stability at different stages of bare patch succession in degraded alpine meadow ecosystems. Results show that: (1) Using the ICV (the Inverse of the Coefficient of Variation) method to analyze changes in plant community stability, community stability was generally ranked as follows: Long-term recovered patches > Healthy alpine meadow > Degraded alpine meadow > Short-term recovered patch > Bare Patches. (2) Using factor analysis to construct an evaluation system, the stability ranking based on species diversity was as follows: Healthy alpine meadow > Long-term recovered patches > Degraded alpine meadow > Short-term recovered patches > Bare Patches. (3) The community stability index was significantly positively correlated with vegetation coverage, height, biomass, species richness, Shannon–Wiener diversity index, species evenness, and Simpson’s diversity index (p < 0.05). Therefore, a positive correlation exists between plant diversity and community stability, such that plant communities with a higher species diversity tend to be more stable. To maintain the plant diversity and community stability of alpine meadow ecosystems, it is necessary to consider the characteristics of grassland plant composition and community structure, as well as their influencing factors, and promote the positive succession process of grasslands.

Clarifying the response of wetland changes to climate change can improve the scientific conservation and utilization capabilities of wetland ecosystems, which is vital for their sustainable development. In this study, the spatial distribution and area changes of the different types of wetlands in the Yellow-River-Source National Park (YRSNP) were obtained using the object-based classification method for the years 2000, 2010, and 2020. The relationship between wetland change and climate factors was investigated by combining grey relation analysis and correlation analysis. The response of wetland change to different climatic factors was consequently clarified. The results showed that the river wetlands and lake wetlands increased significantly from the year 2000 to 2010 (4.04% and 4.21%, respectively). However, the total wetland area demonstrated a decreasing trend (7.08%), primarily due to the significant decrease in the marsh wetlands (6.81%). The total wetland area demonstrated a slightly increasing trend from the year 2010 to 2020 (0.14%), in which river wetlands and lake wetlands increased by 3.25% and 2.09%, respectively, while the marsh wetlands demonstrated a tendency to be stable. From the year 2000 to 2010, 75.53% of precipitation and 27.68% of temperature demonstrated a significant increase and an obvious warm–humid climate trend. However, from the year 2010 to 2020, the trend of increasing precipitation weakened, the temperature decreased slightly, and the warm–humid climate trend was not significant. From the year 2000 to 2020, the YRSNP river wetlands and lake wetlands were significantly and positively correlated with temperature and precipitation, while the marsh wetlands were most affected by climate warming, especially the warm-season temperatures. The spatial–temporal difference was not obvious in the correlation coefficient between marsh wetlands area change and the precipitation and temperature. The results of the study can provide a theoretical basis and technical support for the conservation of wetland ecosystems in the Three-River-Source National Park.

The Yellow River Source National Park (YRSNP), one of largest alpine wetlands in China which serves as the origin of the Yellow River, is situated in the heart of the Qinghai–Tibetan Plateau. The alpine wetland ecosystem, which is its primary ecological system, is crucial for maintaining ecological balance, preserving biodiversity, and facilitating the sustainable development of the Yellow River Basin. In this study, based on the Google Earth Engine (GEE) platform combined with Landsat 5 7 8 remote sensing images, we used a random forest classification model to identify and classify the alpine wetland from 2000 to 2020 and analyze its pattern of dynamic changes. The main driving forces that drive the change of the alpine wetland area in the YRSNP from 2000 to 2020 are identified using a random forest regression analysis in combination with data on precipitation, temperature, potential evapotranspiration, soil moisture, and population density. The results show that: (1) From 2000 to 2020, the average overall accuracy of remote sensing classification and extraction of the YRSNP alpine wetlands is 0.8492 and the Kappa coefficient is 0.8051. (2) From 2000 to 2020, the shrinking trend of the YRSNP alpine wetland area is restrained. However, the lake wetland, marsh wetland, and marsh meadow all increase by 0.58%, 0.06%, and 3.34%, respectively, whereas the river wetland shows a declining trend. (3) The results of the identification of driving forces indicate that soil moisture is the main factor influencing the dynamic changes of the alpine wetland, although the decline in population density has a favorable impact on the alpine wetland. The results can provide scientific basis for maintaining the stability, diversity, and sustainability of the alpine wetland ecosystem in the Yellow River Source National Park.

Understanding the spatiotemporal variations and driving mechanisms of coastlines is crucial for their adequate protection, utilization, and sustainable development. In this study, the changes in various coastline types in Zhuhai from 1987 to 2022 were analyzed by using long-term Landsat and GaoFen satellite imagery. The Index of Coastline Type Diversity (ICTD), Index of Coastline Utilization Degree (ICUD) and the Digital Shoreline Analysis System (DSAS) analysis indicators were employed to investigate coastline change. Both quantitative and qualitative analyses were integrated to comprehensively elucidate the impacts of various driving factors. The results indicate that the total length of Zhuhai coastline increased from 761.50 km in 1987 to 798.91 km in 2022, with natural coastlines decreasing by 89.82 km and artificial coastlines increasing by 153.40 km. The rapid expansion of artificial coastlines since 2007 led to a marked decline in the ICTD indicator, while the ICUD indicator increased from 146.42 in 1987 to 216.37 in 2022, reflecting the intensified and continuous influence of anthropogenic activities. Additionally, the end point rate (EPR) and Weighted Linear Regression Rate (WLR) changed by 8.09 m/yr and 6.62 m/yr, respectively. The Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) exhibited average changes of 331.42 m and 224.32 m, respectively. Gray correlation and regression analyses further revealed that climate factors exhibited the strongest association with natural coastline changes, while economic development indicators showed the strongest correlation with artificial coastline dynamics. The relationship of Number of Berths in Main Ports (Nb) with coastline changes strongly suggests that human activities are the primary driver of these changes. These findings provide a robust scientific basis for coastal zone management in Zhuhai.