The Zhangcheng District is critically responsible for protecting water resources, preserving sand sources, and improving the ecological environment in Beijing. Quantitative evaluation and research on the ecosystem water supply services in this area are beneficial for developing conservation planning and establishing ecological compensation mechanisms in water conservation areas. In this paper, based on the land use, meteorological, soil, and field observation data of the research area, the InVEST water yield model is used to estimate the water supply of the ecosystem in the Zhangcheng District. The model quantitatively analyzes the spatiotemporal distribution characteristics of water supply services in the basin and the influence of different topographic factors. The results show that the average supply of ecosystem water in the Zhangcheng District is approximately 45 mm, and there is a degree of spatial heterogeneity. The total water supply in the Zhangcheng District is relatively small. The water resource supply in the southwest is relatively small, the rainfall in mountainous forest areas in the southeast is high, its water supply is higher, and the supply of forest land water is relatively high. The high-value areas are mainly distributed at 1500 to 3500 m and 15°~40°; the water supply on the sunny slope is greater than that on the shady slope. With the increase in altitude and slope, the water supply in the basin tends to increase first and then decrease.

Land-use transition has diverse influences on habitat quality. At present, land-use patterns and habitat quality in the ecologically fragile Yellow River Basin are undergoing significant change. However, the relationship between land-use transition and habitat quality and the driving factors of habitat quality dynamics across the whole basin remain unclear. In this study, we utilized a land-use transition matrix and an InVEST model to analyze the dynamics of land use, habitat quality, and the relationship between the two in the Yellow River Basin from 2005 to 2020. The driving factors of habitat quality dynamics were explored with a spatial econometric model. The results showed the following: (1) The areas of farmland and grassland accounted for more than 70%, but decreased by 14,600 km² and 2500 km², respectively. The areas of forest and construction land increased by 1800 km² and 16,900 km², respectively. (2) The habitat quality showed a trend of decrease-then-increase. The areas of low value (0–0.2) were the largest, accounting for about 50% of the total area; the regions of relatively high (0.6–0.8) and high value (0.8–1) were small and scattered in the mountainous forest area, accounting for about 10%. (3) The habitat quality was the lowest in the land categorized as transitioning to construction, and highest in unchanged forest and in the land characterized as transitioning to forest. The coupling coordination degree of land-use degree and habitat quality showed a steady upward trend. (4) The growth rate in the value added by secondary industries, GDP per capita, population density, ecological-protection policy score, average annual temperature, and average annual precipitation were the primary factors affecting habitat quality. This study fills the gap in the analysis of the relationship between land-use transition and habitat quality across the whole Yellow River Basin; the work assists in the understanding of the ecological effects of land-use transition in the region and provides suggestions for the development of other densely populated and ecologically fragile areas.

Sugarcane is an important economic crop in China, and its yield is significantly affected by climate change. With climate change leading to significant shifts in environmental conditions, the suitable cultivation zones for the crop are expected to change, impacting China’s sugarcane production and industry layout. This study aims to analyze potential distribution areas for sugarcane under different climate change scenarios, providing scientific guidance for optimizing future cultivation zones and resource allocation. Data on sugarcane distribution in China and 38 related environmental factors were collected. After excluding variables with high correlations, the MaxEnt model and ArcGIS 10.2 software were used to analyze the main environmental factors affecting crop survival based on contribution rates and the Jackknife method. The study simulated the suitable areas in China during the historical period and predicted future suitable areas under three greenhouse gas emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results showed that the Area Under the Receiver Operating Characteristic Curve (AUC) was 0.921, indicating high accuracy in the model’s analysis of suitability zones. The three dominant environmental variables influencing sugarcane distribution in China were identified as annual precipitation, min temperature of the coldest month and elevation. The primary suitable zones are concentrated in southern China, forming a “V” shape, including regions such as Guangxi, Sichuan, Guizhou, Yunnan, Hainan, and Fujian. In the future, the area of unsuitable zones is expected to decrease. The overall suitable zones for sugarcane are projected to shift towards the central and northern parts of China. This research can assist China’s sugarcane industry in addressing the challenges of climate change and provide references for its cultivation, industry layout optimization, and the selection of new planting sites.

The widespread and complex formation of saline soils in China significantly affects the sustainable development of regional ecosystems. Intense climate changes and regional land use further exacerbate the uncertainties faced by ecosystems in saline areas. Therefore, studying the distribution characteristics of typical halophytic vegetation under the influence of climate change and human activities, and exploring their potential distribution areas, is crucial for maintaining ecological security in saline regions. This study focuses on Tamarix chinensis, Tamarix austromongolica, and Tamarix leptostachya, integrating geographic information systems, remote sensing, species distribution models, and landscape ecological risk (LER) theories and technologies. An optimized MaxEnt model was established using the ENMeval package, incorporating 143, 173, and 213 distribution records and 13 selected environmental variables to simulate the potential suitable habitats of these three Tamarix species. A quantitative assessment of the spatial characteristics and the area of their potential geographical distribution was conducted. Additionally, a landscape ecological risk assessment (LERA) of the highly suitable habitats of these three Tamarix species was performed using land use data from 1980 to 2020, and the results of the LERA were quantified using the Landscape Risk Index (LERI). The results showed that the suitable areas of Tamarix chinensis, Tamarix austromongolica, and Tamarix leptostachya were 9.09 × 10⁵ km², 6.03 × 10⁵ km², and 5.20 × 10⁵ km², respectively, and that the highly suitable habitats for the three species were concentrated in flat areas such as plains and basins. Tamarix austromongolica faced increasing ecological risk in 27.22% of its highly suitable habitat, concentrated in the northern region, followed by Tamarix chinensis in 16.70% of its area with increasing ecological risk, concentrated in the western and northern highly suitable habitats; Tamarix chinensis was the least affected, with an increase in ecological risk in only 1.38% of its area. This study provides valuable insights for the protection of halophytic vegetation, represented by Tamarix, in the context of China’s national land development.

Studying the temporal and spatial variation characteristics and driving factors of carbon reserves in the middle reaches of the Yangtze River urban agglomeration is crucial for achieving sustainable development and regional ecological conservation against the backdrop of the “double carbon” plan. Based on three periods of land use data from 2000 to 2020, combined with the InVEST model(Version 3.14.2), the spatiotemporal changes in carbon storage in the urban agglomeration in the middle reaches of the Yangtze River were analyzed. The PLUS model (Version 1.3.5) was used to predict three scenarios of natural development, urban development, and eco-development in the urban agglomeration in the middle reaches of the Yangtze River in 2035 and estimate the carbon storage of the ecosystems under different scenarios, and it used optimal parameter GeoDetectors (Version 4.4.2) to reveal the driving factors affecting the spatiotemporal differentiation of carbon storage. The results show that farmland and construction land area increased and forestland area continued to decrease from 2000 to 2020. Carbon storage decreased by 1 × 10⁶ t, with forestland conversion to farmland and construction land being the main decreasing drivers. The carbon storage of natural and urban developments decreased by 0.26 × 10⁶ t and 0.32 × 10⁶ t, while it increased by 0.16 × 10⁶ under ecological development. The results of the factor detector showed that the NDVI (Normalized Difference Vegetation Index) had the highest explanatory power on the spatiotemporal variation in carbon storage (q = 0.588), followed by the slope (q = 0.454) and elevation (q = 0.391), and the explanatory power of natural environmental factors on the spatiotemporal variation in of carbon storage was dominant. The interaction detector results showed that the spatiotemporal variation in carbon storage was affected by multiple factors, the interaction intensity between each driving factor was stronger than that of a single factor, and the synergy between the NDVI and slope was the strongest, at q = 0.646.

Ginkgo biloba L. was named by Carl Linnaeus in 1771; a “living fossil” with immense medicinal and conservation value, it is a nationally first-class protected wild plant. However, many Ginkgo populations are under threat from habitat destruction, human exploitation, and over-harvesting, which have limited their numbers and range. Using an optimized MaxEnt model in R, this study analyzed Ginkgo distribution points and 22 ecological factors in China to explore the key environmental factors affecting its geographical distribution. The study also predicted the spatial distribution patterns and centroid changes of potential suitable areas under three different carbon emission pathways: current conditions, 2021–2040 (2030s), 2041–2060 (2050s), and 2061–2080 (2070s). The findings are as follows: (1) The optimal combination of model parameters (RM = 3.2, FC = LPH) reduced model complexity and overfitting and achieved very high prediction accuracy with an optimized AUC value of 0.928. (2) The key environmental factors influencing Ginkgo growth include precipitation in the driest month (20–175 mm), minimum temperature in the coldest month (−4 to 3 °C), precipitation in the hottest quarter (450–2500 mm), and a temperature seasonal variation deviation greater than 580. (3) Under the three future climate scenarios (SSP126, SSP245, and SSP585), the potential suitable habitat area for Ginkgo in China was increased, with the distribution range migrating to higher latitudes, Under the three different development models, the total suitable area followed this order: SSP126 > SSP245 > SSP585. Highly and moderately suitable areas are concentrated in the Yangtze River Basin. This study is highly significant for the ecological protection of Ginkgo, aiding in the rational planning of potential suitable areas, enhancing the monitoring of key conservation areas, and developing effective protection strategies in a timely manner.