Protected areas (PAs) provide refuges for threatened species and are considered to be the most important approach to biodiversity conservation. Besides climate change, increasing human population is the biggest threat to biodiversity and habitats in PAs. In this paper, the temporal and spatial variations of land cover changes (LCC), vegetation fraction (VFC), and net primary productivity (NPP) were studied to present the ecosystem dynamics of habitats in 6 different types of national nature reserves (NNRs) in 8 climate zones in China. Furthermore, we used Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light datasets and the human disturbance (HD) index estimated from LCC to quantify the living and developing human pressures within the NNRs in the period 2000–2013. The results showed that (1) the living human activities of NNRs increased apparently in the humid warm-temperate zone, Qinghai-Tibet Plateau, mid-temperate semi-arid zone, and mid-temperate humid zone, with the highest increase of nighttime light observed in inland wetlands; (2) the developing human activities in NNRs indicated by the HD index were higher in the humid warm-temperate zone and mid-temperate semi-arid zone as a result of increasing areas of agricultural and built activities, and lower in the sub-tropics due to improved conservation of forest ecosystems; (3) the relationship between HD and VFC suggests that ecosystems in most NNRs of south-subtropics, mid-temperate arid zone and Qinghai-Tibet Plateau were predominantly impacted by climate change. However, HDs were the prevalent factor of ecosystem dynamics in most NNRs of north-subtropics, mid-temperate semi-arid and humid zones.

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.

The preservation and enhancement of ecosystem services are essential for maintaining ecological balance and sustainable growth. The Heihe River Basin (HRB) is important for ecological security in Northwest China, yet is a fragile ecological environment. Understanding the dynamics and evolution of ESs is vital for balancing resource exploitation, socioeconomic development, and ecological protection. Using the InVEST model, we calculated water yield, habitat quality, and carbon stock in the HRB during 2000–2020 and examined shifts in ecosystem services. Trade-offs and synergies among ESs were assessed using GeoDa and key drivers were identified through the geodetector model. The spatial distribution of water yield, habitat quality, and carbon storage varied significantly, with high values concentrated in the upstream Qilian Mountains and low values in the downstream desert areas. High carbon storage clusters were stable, high water yield clusters increased and subsequently decreased, and high habitat quality clusters fluctuated. Carbon storage, water yield, and habitat quality exhibited a synergistic relationship. Climate and topography, particularly vapor emissions and elevation, were the primary factors influencing ESs, while socioeconomic factors had a lesser impact. These findings provide valuable insights for sustainable ecosystem management and conservation in the HRB and other arid inland watershed regions.

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.