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Mantle xenoliths from the Western Qinling, central China, are dominated by lherzolites, which can be divided into four subgroups-namely, garnet-facies, coexisting spinel-garnet, spinel-facies, and carbonate-bearing ones. All these rocks display light rare earth element enrichment, positive Sr and Ba anomalies, carbonatite-like trace element patterns, and Sr-Nd-Pb isotopic mixing between depleted mantle and enriched mantle type II end members, consistent with the geochemical features resulting from carbonatite metasomatism. The garnet-facies lherzolites show high trace element concentrations but low LaN/YbN ratios, and they show high Sr and Pb isotopic ratios that are similar to those of carbonatites, suggesting that they were highly metasomatized. The spinel-facies group has the lowest trace element concentrations but higher LaN/YbN ratios than the garnet-facies group; their lowest Sr and Pb isotopic ratios are closer to those of the depleted mantle end member, implying low-degree metasomatism. Geochemical variation of the coexisting spinel-garnet sample lies between that of the garnet and spinel groups. The elevated and highly variable trace element concentrations and Sr-Pb isotopic values of the carbonate-bearing lherzolite group are most likely related to the modal content of carbonate minerals. Collectively, these geochemical features indicate a rising front of carbonatite metasomatism in the lithospheric mantle beneath the Western Qinling. Combining experimental and empirical data, the positive Pb, Y, and high-field strength element anomalies in the peridotites might be ascribed to the involvement of a subduction component in the carbonatite melts. On the basis of the data presented in this article, we propose a general model for carbonatite metasomatism in the lithospheric mantle to interpret the different signatures recorded in the garnet-facies peridotites (chemical imprint) and spinel-facies peridotites (occurrence of carbonate minerals), which has potential application to other regions that have undergone carbonatite metasomatism.

Human activities profoundly impact the Earth system such as climate change, biodiversity, disease transmission. Accurately acquiring and assessing the human activity intensity (HAI) is crucial to exploring human‐nature relationships. However, the mismatch of geospatial data products between humans and natural environmental factors is a data bottleneck that restricts the innovation and development of regional human‐Earth systems. Nowadays, some HAI data products exist, such as the global human footprint map and the cumulative human modification map, but their spatial resolution is still too coarse (1 km) for regional research. Importantly, there are limitations to the method of mapping HAI: an incomplete indicator system that ignores the natural dimension makes the assessment of HAI less accurate and comprehensive; ignoring correlations among indicators, subjective weighting method and overlapping indicators lead to potential overestimation of HAI. Here, a new approach to improve the quantification of HAI at the regional scale was presented and the HAI of the Qinling‐Daba Mountains (QinBa) was mapped and analysed as a case study. First. an improved indicator system was constructed from two dimensions: natural environment and resources (including topography and river density), social and economics (including population density, degree of land modification, remoteness from roads/railways, remoteness from settlements and road density). The models for scoring the indicators were then improved. Additionally, principal component analysis was adopted to transform seven indicators into four independent principal components (PCs). The four PCs were combined based on their variance contribution to generate the HAI map, effectively eliminating redundancy and correlation among the indicators. The results showed that the improved method solved the problem of overestimation in previous studies and objectively mapped the HAI of QinBa. We found that although QinBa's HAI was moderate (MHAI = 0.48), places with low HAI were isolated as ‘islands’ by places with high HAI, indicating that the scope of human activities in this area is extensive. This study not only provides novel insights into quantifying HAI but also provides high‐resolution HAI data (100 m) and priority attention zones for human‐nature interaction studies in QinBa, which can help guide policy‐making for management and conservation efforts. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog.

To investigate the mutual influence and interaction mechanisms between the Tibetan Plateau and the West Qinling Mountains, this study selects Zhuoni County, Lintan County, and Tanchang County as representative areas of the combined zone between the two geographic units. Additionally, Maqu County, characterized by the geomorphological features of the Tibetan Plateau, and Cheng County in the West Qinling Mountains are chosen as control areas. Based on the quantitative construction of water and soil erosion sensitivity, ecological risk sensitivity, land desertification sensitivity, and biodiversity sensitivity, this study comprehensively evaluated the ecological sensitivity of five typical counties using the equal weight superposition method. Using spatial autocorrelation and geographic detector analysis, this study reveals the influence pattern and key influencing factors of the Tibetan Plateau on the ecological sensitivity of the West Qinling Mountains. The results show that: (1) As the distance between the counties and the Tibetan Plateau increases, the mean value of the comprehensive ecological sensitivity index shows an upward trend. The ecological sensitivity in the western side of the West Qinling Mountains, which is closer to the Tibetan Plateau, is relatively low, while the ecological sensitivity on the eastern side, which is farther away, is relatively higher. (2) The Tibetan Plateau landform, the combined zone of the Tibetan Plateau and the West Qinling Mountains, and the West Qinling Mountains landform represent counties with a relatively small percentage of extremely high sensitivity and high sensitivity areas, resulting in low ecological sensitivity and good ecological environmental conditions. (3) As the distance between the counties and the Tibetan Plateau increases, the global Moran’s I for ecological sensitivity gradually rises, indicating a stronger spatial correlation of ecological sensitivity on the eastern side of the West Qinling Mountains. (4) The biodiversity of the Tibetan Plateau has a significant impact on the ecological sensitivity of the West Qinling Mountains, while the interaction between vegetation coverage and land use patterns serves as a core driving factor influencing the ecological environment of the West Qinling Mountains. Future research should continue to focus on the potential effects of vegetation coverage and land use changes on the ecological sensitivity of the West Qinling Mountains. This study provides an important foundation for analyzing the interactions between the two geographic units.

In the fight against climate change, future policy directions in the transition toward a green travel- and tourism-based economy include improving tourism-derived CO2 emission levels and guiding individual low-carbon behavior. In China, people tend to engage in outdoor adventure travel and cultural tourism in natural areas. However, limited information is available on the empirical evaluation of energy use and the CO2 emissions associated with tourism in protected areas. The present study used a life cycle assessment to explore energy use and CO2 emissions due to tourism and identify the factors driving low-carbon behavior. To these ends, survey data for the protected areas of the Qinling Mountains from 2014 to 2019 were used. The results showed that energy use and CO2 emissions in various tourism sectors steadily increased from 2014 to 2019, primarily because of an increase in transportation activity. This study used data derived from the calculation of CO2 emissions per tourist per trip to identify the various factors jointly contributing to the low-carbon behavior of tourists. These included a low-carbon attitude, low-carbon knowledge, environmental education, and policy reward. The broader implications of this study are that several emission reduction policy options are available to address the challenges inherent in sustainable tourism development and that these policies may be selected according to specific conditions. The low-carbon transformation of recreational facilities at travel destinations, policy rewards, and environmental education can regulate tourist behavior, holding the key to sustainable tourism development in protected areas.

To solve the conflict between ecological conservation and humanistic development in the West Qinling Mountains, optimize the land space governance, promote high-quality regional development, this study systematically assessed the ecological baseline and stress risks using remote sensing land use data, the InVEST habitat quality model, land use ecological risk evaluation and spatial overlay analysis. It established an ecological restoration zoning framework based on “natural geographical conditions—ecological stressors—reducing ecological risks”. Key findings include: (1) Grassland and farmland areas in the West Qinling Mountains significantly decreased between 1990 and 2020; (2) The average value of habitat quality shows a persistent decline trend with polarized characteristics; (3) The average value of habitat degradation shows an increasing trend with the characteristic of gathering to the medium risk; (4) The land use ecological risk is predominantly moderate and persistently intensifying, with high-risk zones concentrated in the north, east and southeast. (5) Based on these results, the West Qinling Mountains were divided into five ecological restoration zones, forming a strategic pattern of “west and south protection, east restoration, north prevention”. The framework includes the priority restoration zone for engineering management, the key restoration zone combining industrial transformation with engineering measures, the preventive restoration zone managed through development restrictions and ecological compensation, the general protection zone mandating strict protection alongside scientific research and monitoring, the key protection zone relying on natural restoration supported by monitoring and early warning systems. The zoning framework and differentiated restoration strategies proposed in this study provide scientific basis and practical paradigms for achieving synergistic ecological conservation and humanistic development in the West Qinling Mountains.

Wet deposition significantly impacts the basin ecosystem and water quality of the Qinling Mountains (QMs). However, few research focused on the difference in wet deposition between the northern (QN) and southern (QS) foothills due to the barrier effect of the QMs. This two-year investigation studied nutrient and organic matter deposition in QN and QS during summer and autumn. Results showed higher concentrations of nitrogen (N), phosphorus (P), dissolved organic carbon (DOC), and permanganate index (CODMn) in QN's rainwater, particularly in autumn. The CODMn in QN ranged 0.59–7.67 mg/L, and DOC ranged 0.64–4.45 mg/L. For QS, the CODMn ranged 0.71–3.25 mg/L, and DOC ranged 0.28–2.62 mg/L. Backward trajectory analysis revealed the accumulation of pollutants in QN originating from Northern China, intensified by autumn heating and straw burning. Tyrosine-like and humic-like components in rainwater DOM suggested that they primarily originated from autochthonous sources. Rainwater N:P mass ratios range from 39:1 to 145:1, highlighting a higher N input than P in QMs' wet deposition. The findings underscore the importance of atmospheric wet deposition in nutrient and organic matter input to the QMs and establish a foundation for exploring the ecological effects of wet deposition.

Aims The bacterial diversity in rhizosphere soil is closely associated with its environmental factors, such as, pH, moisture and organic contents. The aims of this study were to understand the bacterial diversity in tea plant Camellia sinensis rhizosphere soils and explore its relationship with major organic elements, water, and pH in tea plant rhizosphere soil. Methods The tea plant rhizosphere soils were randomly collected from five locations at the south side of Qinling Mountains. The bacterial diversity in the soil was determined by high-throughput amplicon sequencing technology. The organic elements, pH and moisture in the soil were measured using traditional analysis methods. The correlations between bacterial diversity and environmental elements were analyzed by using the General Linear Model (GLM), Canonical Correspondence Analysis (CCA) and Spearman’s rank correlation coefficients methods. Results Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Planctomycetes, Proteobacteria and Verrucomicrobia were the dominant phyla in the soil samples. Water content, pH, organic carbon and total nitrogen were the environmental elements that had the greatest correlation with the bacterial diversity in the rhizosphere soil samples. Nitrogen had the greatest effect on the bacterial diversity at genus level. Carbon, potassium, water and pH were positively correlated with Pedobacter and Mucilaginibacter genera. Conclusions The bacterial diversity in tea plant rhizosphere is closely associated with its environmental elements. The soil elements in tea plant rhizosphere could also be adjusted through the change of rhizobacteria diversity besides chemical fertilization.

With the global issues of extreme climate and urbanization, the ecological security patterns (ESPs) in the Qinling Mountains are facing prominent challenges. As a crucial ecological barrier in China, understanding the characteristics of ESPs in the Qinling Mountains is vital for achieving sustainable development. This study focuses on Yangxian and employs methods such as machine learning (ML), remote sensing (RS), geographic information systems (GISs), analytic hierarchy process and principal component analysis (AHP–PCA), and the minimum cumulative resistance (MCR) model to construct an ecological security network based on multi-factor ecological sensitivity (ES) and conduct quantitative spatial analysis. The results demonstrate that the AHP–PCA method based on ML overcomes the limitations of the single-weighting method. The ESPs of Yangxian were established, consisting of 21 main and secondary ecological sources with an area of 592.81 km2 (18.55%), 41 main and secondary ecological corridors with a length of 738.85 km, and 33 ecological nodes. A coupling relationship among three dimensions was observed: comprehensive ecological sensitivity, ESPs, and administrative districts (ADs). Huangjinxia Town (1.43 in C5) and Huayang Town (7.28 in C4) likely have significant areas of ecological vulnerability, while Machang Town and Maoping Town are important in the ESPs. ADs focus on protection and management. The second corridor indicated high-quality construction, necessitating the implementation of strict protection policies in the study area. The innovation lies in the utilization of quantitative analysis methods, such as ML and RS technologies, to construct an ecological spatial pattern planning model and propose a new perspective for the quantitative analysis of ecological space. This study provides a quantitative foundation for urban and rural ecological spatial planning in Yangxian and will help facilitate the sustainable development of ecological planning in the Qinling region.

Rapid urbanization and profound land use changes are driving a decline in ecosystem service value (ESV) and a rise in the Ecological Risk Index (ERI). These trends pose significant challenges to regional sustainable development. This study focuses on the Shaanxi section of the northern foothills of the Qinling Mountains. By integrating remote sensing, statistical yearbooks, and GIS spatial analysis, we applied a coupled “value-risk-simulation” framework using the PLUS model. The findings are as follows. (1) From 1990 to 2020, regional ESV demonstrated a “decline–recovery” trend. Regulatory services contributed the most, and forestland and grassland were the primary contributors to ESV. ERI decreased overall, yet high-risk areas remained in central Xi’an and along the Wei, Ba, and Hei Rivers. (2) Simulations showed that the Ecological Protection Scenario (EPS) and Sustainable Development Scenario (SDS) best balance ecological value with risk management. (3) A significant negative correlation exists between ESV and ERI. High–High clusters are predominantly distributed in water bodies and their surrounding forestlands and grasslands. Mean annual temperature, DEM, GDP, and population density were identified as key factors associated with the spatial patterns of both ESV and ERI. This indicates that while these areas possess high ecosystem service functions, they face risks associated with climatic and environmental changes, landscape fragmentation, and human disturbance due to urban expansion. Consequently, our study identifies them as key areas for ecological restoration. Integrating ecological value assessment with risk prediction can provide a scientific pathway for spatially sustainable governance and policy-making in ecologically sensitive urban-transition zones.