As a vital ecological barrier and water source for the Beijing-Tianjin-Hebei (BTH) region, the Luan River Basin (LRB) plays a crucial role in maintaining regional ecological balance. However, comprehensive research on Ecological Vulnerability (EV) evaluation in the LRB remains scarce, making EV evaluation and forecasting particularly significant. This study evaluated EV dynamics (2002–2022) via the SRP model with 17 indicators, with driving factors analyzed via Geodetector and 2032 scenarios forecasted by CA-Markov. Key findings revealed: (1) EVI increased from 0.397 to 0.428 (peak at 0.445 in 2017), with Microscopic vulnerability dominating (46.47% average area). (2) Spatially, EV exhibited a “low-medium–high” gradient (lower in northwestern high-altitude areas and higher in southeastern plains), confirmed by significant clustering (Global Moran’s I = 0.889–0.938, p value < 0.001). (3) Geodetector identified elevation (q = 0.855), biological abundance (q = 0.812), annual temperature (q = 0.800), and cultivated land proportion (q = 0.783) as primary driving factors. (4) CA-Markov forecasts for 2032 indicate declines in Potential vulnerability (− 5.83%), Microscopic vulnerability (− 2.01%), and Severe vulnerability (− 2.30%), but increases in Mild vulnerability (+ 6.57%) and Moderate vulnerability (+ 3.57%). These findings provide a scientific basis for evidence-based ecological policies in the LRB, contributing to the promotion of regional sustainable development and the balance between ecological conservation and economic growth.

The study of vegetation net primary productivity (NPP) is essential in the Yanshan–Taihang Mountain Ecological Conservation Zone (YTECZ). Serving as an ecological security barrier for the Beijing–Tianjin–Hebei region, understanding the spatiotemporal dynamics and drivers of NPP in the YTECZ is fundamental for supporting effective sustainable development policies. Utilizing MODIS NPP, climatic data (temperature and precipitation), and the Human Footprint Index (HFP, a comprehensive metric of anthropogenic pressure), this study employed univariate linear regression, ArcGIS spatial analysis, and the Geographical Detector to investigate the spatiotemporal patterns and drivers of vegetation NPP in the YTECZ from 2004 to 2023 and to project its future trends through time series analysis. Our findings reveal a significant fluctuating upward trend in vegetation NPP over the 21-year period (mean annual increase: 4.58 g C·m⁻²), displaying a distinct spatial gradient characterized by higher values in western and northern sectors relative to eastern and southern areas. The interannual variability of vegetation NPP was primarily dominated by precipitation fluctuation, while its spatial heterogeneity was jointly driven by vapor pressure deficit (VPD) and temperature. Notably, human activities exhibited significant explanatory power on NPP’s spatial pattern, and their interaction with climatic factors (e.g., VPD) resulted in non-linear enhancements. Future projections suggest that the current increasing trend is unlikely to be sustained in the long term, indicating substantial uncertainty in vegetation carbon sequestration patterns. This study provides critical insights into vegetation response mechanisms to global change drivers, offering a scientific foundation for ecological management strategies toward sustainable development in the YTECZ.

The net primary productivity (NPP) of vegetation—a critical component of ecosystem carbon cycling and a key indicator of the quality and functionality of ecosystems—is jointly influenced by natural and anthropogenic factors. As NPP is a vital agricultural and ecological region in China, understanding the spatiotemporal dynamics and driving mechanisms of vegetation NPP in the North China Plain (NCP) has significant implications for regional sustainable development. Utilizing MODIS NPP, temperature, precipitation, and human activity data from 2003 to 2023, this study employs univariate linear regression, ArcGIS spatial analysis, and the Hurst index to investigate the spatiotemporal characteristics, driving factors, and future trends in vegetation NPP. The results indicate that vegetation NPP exhibited a fluctuating upward trend over the 21-year period, with an annual increase of 2.60 g C/m². Spatially, NPP displayed a “high in the south, low in the north” pattern. There is significant spatial heterogeneity between temperature, precipitation, and vegetation NPP in the study area, with natural factors generally exerting a greater influence than human activities; however, the coupling of human activities with other factors significantly amplify their impact. The Hurst index (mean: 0.43) revealed an anti-persistent future trend in vegetation NPP, suggesting substantial uncertainties regarding its long-term dynamics. These findings enhance our understanding of the responses of vegetation to global change and provide a scientific basis for balancing food security and ecological conservation in the NCP.

Vegetation is an important component of an ecosystem, fulfilling various ecological functions in areas such as soil and water conservation, climate regulation, and water source maintenance. This study focuses on the Yongding River Basin as a research area. This study used vegetation indices with long time series as a data source in combination with Landsat land use data. This study applied linear trend estimation to analyze the interannual variation trend in vegetation greenness from 2002 to 2022 in the Yongding River Basin and quantitatively analyzed the impact of land use changes on vegetation greenness. The results show that, from 2002 to 2022, the vegetation greenness in the Yongding River Basin has shown an overall increasing trend. The average growth season and the maximum annual normalized difference vegetation index (NDVI) growth rates were 0.006/10a and 0.008/10a, respectively, and the area of increased vegetation greenness accounted for 90% of the total area. During the main growth season (April to October) in the Yongding River Basin, the NDVI generally showed a spatial pattern of being higher in mountainous areas and lower in water areas, with the largest coefficient of variation in vegetation in the river water areas, and the most stable vegetation in forest land. In terms of the changes in vegetation greenness, the contribution rate of arable land was between 36.73% and 38.63%, followed by grassland and forest land, with contribution rates of 26.86% to 27.11% and 23.94% to 26.43%, respectively. The total contribution rate of water areas, construction land, and unused land was around 10.18%. This study can provide a theoretical basis for environmental protection and rational land use in the Yongding River Basin.

Soil microbial communities face the combined pressures of climate change and biodiversity loss, yet how these stressors interact to shape ecosystem function remains a critical uncertainty. To investigate this, we established a constructed grassland plant community and conducted a fully factorial experiment manipulating plant diversity (1, 3, and 6 species), temperature (ambient, +2 °C), and precipitation (ambient, +50%). High-throughput 16S rRNA gene sequencing revealed that plant diversity exerted a stronger influence on soil bacterial community structure than did warming or precipitation changes. Beta diversity analysis revealed a distinct clustering of bacterial communities corresponding to the plant diversity gradient. This shift was characterized by a consistent enrichment of the metabolically versatile genus Sphingomonas in medium-diversity plots that experienced elevated precipitation, suggesting a predicted potential for enhanced organic matter decomposition. Despite overall stability in alpha diversity, the interaction between plant diversity and warming significantly modulated bacterial diversity and dominance patterns. Our findings highlight that plant diversity plays a key role in mediating soil bacterial responses to simulated climate factors in the short term. Incorporating these plant–soil feedback mechanisms into ecological models appears crucial for advancing predictions of ecosystem dynamics under future climate conditions.

Understanding spatiotemporal changes in landscape ecological risk is essential for regional ecological security and sustainable land management, particularly in environmentally sensitive areas. Using land-use data from five time points (2000, 2005, 2010, 2015, and 2020) together with topographic, climatic, vegetation, and socioeconomic data, we assessed landscape ecological risk dynamics in Xinzhou City, a typical ecologically sensitive region in the middle Yellow River Basin. We integrated a landscape ecological risk assessment model, spatial autocorrelation analysis, and an optimal-parameter geographical detector (OPGD) to identify the spatiotemporal patterns and key drivers of ecological risk. Built-up land expanded by 131.47% over the study period, while cropland and grassland both contracted. Land-use change was dominated by bidirectional conversion between cropland and grassland, alongside a net shift of both to built-up land. Overall, ecological risk declined: high- and relatively high-risk areas decreased by 1354.15 km². Spatially, the pattern remained stable, with higher risk in basins and lower risk in mountainous regions, although the trend varied across phases. Risk exhibited significant spatial clustering: low–low clusters remained stable in mountainous ecological source areas, whereas high–high clusters persisted in basin agricultural areas. Elevation was the dominant factor in spatial risk differentiation. Combined natural and anthropogenic factors shaped the overall risk pattern. Despite the regional decline, urbanization-induced disturbance remains the principal driver of local risk increases. Our findings demonstrate the value of coupling long-term land-use data with OPGD to evaluate the relative and interactive contributions of terrain constraints and anthropogenic drivers in complex mountain–basin systems, with implications for spatially targeted ecological restoration and sustainable land management in ecologically sensitive transition zones.