Grazing intensity is one of the crucial anthropogenic activities on alpine grasslands. However, how grazing intensity affects soil microorganism diversities and their co-occurrence networks in alpine steppe remains uncertain. We carried out a controlled grazing experiment (null grazing, CK; moderate grazing, MG; and heavy grazing, HG) on a typical alpine steppe in the Lhasa River Basin, Central Tibet, China. We used high-throughput sequencing to find the sequences of bacterial 16S rRNA and fungal ITS gene amplicons. Then, we analyzed their alpha and beta diversities and set up co-occurrence networks that show how often they occur together. MG significantly increased the bacterial Shannon index and changed the bacterial community structure. In contrast, HG decreased the fungal ACE and Chao1 indices and also changed the fungal community structure (p < 0.05). Linear mixed-effect model revealed that available phosphorus in soil significantly impacted on soil bacterial Shannon, ACE, and Chao1 indices across grazing intensities, while total carbon in subsoil significantly affected these indicators of soil fungi. Moreover, MG increased the complexity of the co-occurrence network in the bacterial community, while HG simplified it. However, both MG and HG made the co-occurrence networks in the fungal community less complicated. This shows that the intensity of grazing has different impacts on how microbes interact with each other. Therefore, sustainable grazing intensity necessitates a deeper understanding of biodiversity conservation in alpine grasslands.

Unveiling the scale effects on the interactions and driving factors of ecosystem services (ESs) contributes to the differentiated conservation of ESs across multiple scales. Utilizing models such as Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Self-organizing map (SOM), and Geographic Detector, we analyzed the spatiotemporal changes, interactions, and dominant driving factors of water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ) in the Three River Source Basin (TRSB) from 2000 to 2020 at both grid and sub-watershed scales; explored the impact of scale effects; and proposed ecological conservation strategies. The results indicated the following: (1) Over the 20 years, the average values of WY, SC, CS, and HQ in the TRSB were 146.12 mm, 10.95 t, 224.77 gC m⁻², and 0.6942, respectively, which exhibited a distribution with lower values in the northwest and higher values in the southeast. Over the study period, WY and SC experienced declines at rates of −7.26 mm per 5 years and −0.08 t per 5 years, while CS and HQ showed increases at rates of 6.97 gC m⁻² per 5 years and 0.007 per 5 years. (2) The relationships among WY, SC, and CS were primarily synergistic, whereas HQ exhibited more trade-offs with other ESs, and as the scale increased, the trade-off effects diminished, and synergistic effects were enhanced. (3) ESs were influenced by a combination of factors, and the interactions among these factors strengthened the explanatory power regarding ESs. Increasing vegetation cover and reducing irrational human activities were effective approaches to enhance the ESs in the TRSB. This study helps to expand the understanding of the relationships among ESs and the effects of scale, providing support for the protection and sustainable development of the TRSB ecosystem.

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.