Solanum rostratum is a globally regulated invasive species, known for its detrimental impacts on local biodiversity, human and livestock health, and agricultural productivity. This study employed the Biomod2 ensemble modeling framework to analyze the geographic distribution of S. rostratum in China, identify key environmental factors limiting its spread, and provide a scientific basis for its management and control. By integrating species distribution data with multiple environmental variables, we predicted the potential geographic distribution of this species. Pearson correlation analysis and variance inflation factor (VIF) testing were applied to identify significant environmental variables constraining its spread, including precipitation seasonality (bio15), mean temperature of the wettest quarter (bio8), precipitation of the warmest quarter (bio18), isothermality (bio3), precipitation of the driest month (bio14), and human footprint. Three Biomod2-based ensemble models (EMmean, EMca and EMwmean) were based on the receiver operating characteristic curve (ROC), true skill statistic (TSS), and Kappa coefficient. Of these, EMca demonstrated the highest predictive accuracy. The model identified highly suitable habitats for S. rostratum primarily in semi-arid and semi-humid regions with high human activity, including the Northeast Plain, bounded by the Greater Khingan, Lesser Khingan, and Changbai Mountains; the northern North China Plain extending to the Shandong Hills and Yellow River basin; and the Junggar Basin extending to the Altai Mountains. These regions should be prioritized for future monitoring and control efforts. This study provides both empirical data and theoretical insights to accurately delineate potential invasion zones of S. rostratum, enhancing surveillance and guiding effective prevention and control strategies.

Ecosystem restoration is central to achieving sustainability goals, yet reconciling trade-offs between carbon sequestration, water security, and soil erosion regulation remains a critical challenge. Here, we leverage China's large-scale Ecological Restoration Programs (ERPs) by combining 1-km fine-scale remote sensing data with biophysical modeling from 2000 to 2020. Our analysis demonstrates that ERPs substantially enhanced carbon sequestration, with gains exceeding 80% in some regions, but triggered spatially divergent water-soil trade-offs. While grid-scale analyses showed predominant widespread synergies of 64-75% coverage, county-scale assessments revealed trade-offs in 32% of regions, linked to climatic gradients and land-use pressures. We identified three strategic intervention points that successfully transformed trade-offs into synergies. Collectively, we provide a transferable 'Spatial-Temporal-Policy Priority' framework for optimizing restoration outcomes, offering a blueprint for aligning climate and sustainable development goals in global restoration initiatives.

IntroductionUrban greening is widely recognized as an important factor in human health. However, existing studies have yielded inconsistent conclusions regarding its health benefits, partly due to divergent greening metrics and the prevalent assumption of linear relationships.MethodsThis study investigated the associations between three types of urban greening indicators -green cover (GC), general green space (GS), and active public green space (PGS) --and the self-rated physical and mental health of urban residents across China. We matched individual-level health data from the 2020 China Family Panel Studies (CFPS) with county-level greening indicators derived from national statistical yearbooks. To account for potential nonlinearities and to evaluate feature importance, we employed explainable machine learning models (XGBoost) combined with SHapley Additive exPlanations (SHAP).ResultsThe results indicated that GC and GS had no significant associations with physical health, and their associations with mental health were inconsistent. In contrast, PGS and the ratio of PGS to GS (PGSRatio) demonstrated robust, significantly positive associations with both physical and mental health, with slightly stronger effects observed for physical health. SHAP-based analyses further revealed nonlinear threshold effects: PGS and PGSRatio offered limited health benefits at lower levels, but their impacts increased sharply once baseline thresholds of 12.4 and 36.3% were exceeded. Ideal health-promoting thresholds were identified at 18% for PGS and 45% for PGSRatio.DiscussionThese findings emphasize that not all green space yields equivalent health benefits; rather, the provision of sufficient, accessible, and active public green space is critical for maximizing the dual health benefits of urban greening.

The effects of diaspore traits and vegetation structure (VS) on the wind-dispersal processes of an invasive alien tumbleweed Solanum rostratum are not well understood. In this study, we used a wind tunnel to determine the wind velocity required for stem breakage of the plant. VSs of bare ground, desert steppe, typical steppe, and meadow steppe were simulated to determine the threshold wind velocity (TWV) of the plant (diaspore) and to measure diaspore velocity (DV) at wind velocities of 2, 4, 6, 8, 10, 12, and 14 m s(-1). The results showed that the stem of the plant did not break, even at a wind velocity of 14 m s(-1). The TWV is so high (> 6 m s(-1) on bare ground) that the diaspore could not move on the meadow steppe at a wind velocity of 14 m s(-1). Diaspore morphological traits and VS were key factors affecting the TWV, contributing 40.6% and 40.5%, respectively. Wind velocity, VS, and diaspore traits were the significant factors affecting DV, contributing 70.6%, 13.5%, and 6.6%, respectively. The DV significantly decreased from bare ground, desert steppe to typical steppe, and even remained still at a wind velocity of 14 m s-1 on the meadow steppe. Therefore, the diaspore adopts an anti-long-distance wind-dispersal strategy when the plant stem does not break, or the diaspore adopts a non-long-distance wind-dispersal strategy once the plant stem breaks. Understanding the wind-dispersal mechanism of S. rostratum is of practical significance for predicting its distribution, controlling its spread range, and promoting the restoration of degraded vegetation.

The ecological impacts of roads in permafrost regions remain poorly understood, particularly at small and medium scale, limiting effective conservation and road management strategies. We propose a multi-scale remote sensing-based paradigm to quantify these impacts throughout road construction and operation. Using spatial deviation (D), change rate (I), and Road Ecological Impact Index (REII), we assessed the Gongyu Expressway, the first expressway on the Tibetan Plateau. Results show that surface water and vegetation impacts extended up to 100 m, with seasonal variations: rainy season effects were 4-6 times stronger, and construction impacts exceeded operation impacts by 1.1-1.4 times. Monthly impact amplitudes surpassed annual values, highlighting intensified short-term variations. Our paradigm enables rapid, high-precision monitoring of road-induced ecosystem stresses, reducing time lags in ecological assessment. Given the sensitivity of permafrost environments, this approach is crucial for predicting and mitigating the long-term ecological effects of expanding expressway networks.

Accurate attribution of vegetation dynamics is essential to ensure the conservation, restoration and sustainability of terrestrial ecosystems. However, due to the time-lag and accumulation effects of vegetation responding climate change and anthropogenic activities, traditional statistical methods often fail to capture the nonlinear impacts, and leading to ongoing debates about the relative contributions. In this paper, we explored the spatiotemporal dynamics of various vegetation types across China from 1982 to 2022 employing the Normalized Difference Vegetation Index (NDVI). Then we integrated machine learning methods with an improved residual trend approach to precisely quantify the contributions of anthropogenic activities and climate change on vegetation dynamics. Our results indicated that (i) the annual increase in NDVI has amounted to 0.012 per decade across all vegetated areas in China, and 57.0 % of the vegetated areas underwent notable greening trends in the past four decades. (ii) Over 92 % of vegetation areas exhibited climatic temporal effects in China, mainly with 1 to 2-month accumulation in response to temperature and precipitation, and 1 to 3-month lag in responses to sunshine duration. Furthermore, forest exhibited the longest time-lag (1.36 months) and accumulation months (0.93 months) to precipitation, whereas grassland responded the shortest time-lag (0.13 months) and accumulation months (0.59 months) to temperature. (iii) Anthropogenic activities predominantly influence 69.7 % of vegetation dynamics in China during 2000-2022. Our improved approaches can diminish quantization uncertainty and show higher accuracy (R2 = 0.97 and RMSE = 0.02). Our study provides valuable insights for understanding vegetation dynamics and informs ecological protection and restoration efforts in China.

The large-scale and cross-regional payment for ecosystem services (PES) contributes positively to ecology-economy balance and thus helps prevent environmental challenges such as "sand storm". However, existing PES programs often overlook the connection between service-providing areas (SPAs) and service-benefiting areas (SBAs). Here, we developed an interregional PES framework based on the theory of ecosystem services flow and applied it to the largest Chinese grassland nature reserve, Xilingol Prairie, to quantitatively identify SPAs, SBAs, and flow paths of the ecosystem wind erosion prevention service (WEPS). We showed that, from 2000 to 2020, the grassland ecosystem of Xilingol Prairie had brought an annual WEPS benefit of 1.21 × 108 t/a and economic value of 12.44 × 108 CNY/a, accounting for approximately 107.71% of the GDP in the same area and year and with a slightly increasing trend in most areas. We reveal obvious seasonal (over half in the spring) and interannual variations in the benefit and that Inner Mongolia, Hebei, and Northeast China are the most important beneficiaries of WEPS, rather than Beijing and Tianjin as traditionally thought. Our results warn that the WEPS supply capacity will not last long and call for finer spatial (e.g., among cities) and temporal (e.g., focus on the spring) resolution for PES policy design.

Alien invasive plants have been found in the semi-arid region of Northeast China for a long time,but the overall invasion situation is rarely reported.In this study,we established a database of alien invasive plants in the semi-arid area of Northeast China through field investigation,specimen collection,research of specimen online in-formation platform and literature.The results showed that there were 34 species of alien invasive plants belonging to 26 genera and 10 families in the semi-arid area of Northeast China,among which the Composite family had the lar-gest number of richness,with 9 genera(34.6％)and 11 species(32.4％).There were 15 species(44.1％)in 11 genera(42.3％)of Legumes,Solanaceae and Gramineae.In all the alien invasive plants,33 species were herba-ceous plants,being overwhelmingly dominant(97.1％).There were both 7 species of countrywide invasive plants with invasive grade 1 and 2,each accounting for 20.6％of the total.The number of species with invasive grade 4 was the largest,17 species,accounting for 50％of the total.The invasive plants originated in North America and Europe was the most,accounting for 64.7％,while those from South America,Asia and Africa accounted for 35.3％.Totally,44.1％of all the invasive alien plants were intentionally introduced,while 55.9％were uninten-tionally introduced.In the semi-arid area of Northeast China,81.3％of the counties(cities)had the distribution of alien invasive plants,and the invasion situation was very serious.

Afforestation and reforestation are deemed promising strategies for mitigating greenhouse gas emissions and global warming. Here, we estimated the impact of China's Grain for Green Program (GFGP) on carbon storage and carbon sink capacity over the past 20 years. We then projected the carbon potential of the GFGP under expansion and management scenarios for 2030 and 2060. Our results showed that the GFGP contributed to 1482.62 Tg of a carbon sink from 2000 to 2020. By 2030 and 2060, the carbon sink induced by the GFGP reached 47.59-75.85 Tg per year, and 38.67-73.67 Tg per year, respectively. This sink offset 0.74-1.19 % and 0.98-1.87 % of the CO2 emissions from all of China in 2030 and 2060, respectively. Our results indicate that national ecological restoration programs can mitigate China's extensive carbon emissions in China, and highlight new solutions for removing CO2 from the atmosphere in other countries.

As global demand for ecological resources continues to surpass ecosystem capacity, predicting future changes in ecological supply, consumption, and carrying capacity is crucial for informed decision-making regarding sustainable land use and ecological security. This study forecasts ecological carrying states in Nepal, a representative region in the southern Himalayan foothills, from 2020 to 2030. We employ three alternative scenarios from the Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP): SSP2-RCP4.5 (BAU), SSP1-RCP2.6 (TSS) and SSP5-RCP8.5 (SSS). Our findings indicate a continuous rise in ecological consumption, particularly in densely populated tropical areas such as the hilly region (HR) and Terai region (TR). Under the BAU and SSS scenarios, ecological supply decreases rapidly in these regions. Alarmingly, over one-third of Nepal's districts, mainly in central and eastern HR and TR, are projected to experience overloaded ecological carrying states by 2030. Climate change sensitivity and escalating consumption due to improved living standards and population growth pose significant challenges for these regions to reverse ecological deficits. This study offers a scientific foundation for reconciling the conflict between ecological protection and societal demands, facilitating the preservation, restoration, and sustainable use of terrestrial ecosystems while striving to achieve the Sustainable Development Goals by 2030.

Climate change has caused significant impacts on water resource redistribution around the world and posed a great threat in the last several decades due to intensive human activities. The impacts of human water use and management on regional water resources remain unclear as they are intertwined with the impacts of climate change. In this study, we disentangled the impact of climate-induced human activities on groundwater resources in a typical region of the semi-arid North China Plain based on a process-oriented groundwater modelling approach accounting for climate-human-groundwater interactions. We found that the climate-induced human effect is amplified in water resources management ('amplifying effect') for our study region under future climate scenarios. We specifically derived a tipping point for annual precipitation of 350 mm, below which the climate-induced human activities on groundwater withdrawal will cause significant 'amplifying effect' on groundwater depletion. Furthermore, we explored the different pumping scenarios under various climate conditions and investigated the pumping thresholds, which the pumping amount should not exceed (4 x 10(7) m(3)) in order to control future groundwater level depletion. Our results highlight that it is critical to implement adaptive water use practices, such as water-saving irrigation technologies in the semi-arid regions, in order to mitigate the negative impacts of groundwater overexploitation, particularly when annual precipitation is anomalously low.

Accurate and timely landslide mapping plays a critical role in emergency response and long-term land use planning. Deep learning–based methods represented by convolutional neural networks have been widely exploited in automatic landslide detection for their outstanding capability of feature representation and end-to-end learning mode. Most of the recent deep learning–based studies used toll-access high-resolution imagery for landslide detection. Considering demands for the future large-scale landslide mapping, this study aims to develop a new deep learning–based method to detect landslides using medium-resolution imagery and digital elevation model (DEM) data which are free-access and covered globally. Firstly, a workflow for constructing the landslide dataset is developed. Then, we design a semantic segmentation model to learn deep features and generate per-pixel landslide predictions. Specifically, the proposed network has a dual-encoder architecture with feature fusion to hierarchically represent deep features from the optical bands and DEM data. We also employ a self-attention module in the decoder of the proposed network to improve the performance. Experiments on two regions demonstrate that our method achieves the best F1 score of 79.24

Large-scale ecological restoration programs have been initiated globally with the aim of combating desertification and improving ecosystem services, especially for sand fixation service (SF) in arid and semi-arid regions. However, the effectiveness of ecological restoration in the radiation benefit of SF, such as improving air quality, remains not well known. In this study, we selected Xilingol as the study area, investigated the dynamics of SF, and quantified the radiation benefit of SF in downwind areas by employing PM10 concentration as the proxy. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was applied to assess the response of radiation benefit to ecological restoration by designing land use scenarios. Results showed that the SF in Xilingol increased with fluctuation at an average rate of 0.27%/year from 2000 to 2018. Under the effect of ecological restoration, the radiation benefit in the downwind regions was substantially improved, as manifested by a 104.22 μg/m3 reduction in PM10 concentration. The changes in radiation benefit varied greatly across space, and northern and southern Xilingol were hot spots for increased radiation benefit. Based on regional disparity in benefit level, this work could provide a reference to make differentiated cross-regional ecological compensation schemes at the national level.

The selection and planning of the location of emergency shelters have a crucial impact on the safety of residents and cities. In this paper, based on multivariate open geographic data, the Gaussian two-step floating catchment area method, K-means clustering, and particle swarm optimization algorithm methods are utilized to carry out a spatial accessibility analysis and location optimization of emergency shelters in Deyang City, Sichuan Province, China. The study shows that: (1) Deyang City's emergency shelters are higher than the government's relevant standard requirements in terms of major indicators such as single building area, total area and per capita shelter area. (2) The spatial distribution of emergency shelters in the study area is uneven and unreasonable, with accessibility from the urban center outwards, exhibiting a "high-low" distribution pattern. (3) The study suggests that 10 new emergency shelters can reduce the number of accessible blind areas by 43.31%. The study recommends that the assessment and construction of emergency shelter facilities in rural areas in China and globally should be emphasized. Reliable recommendations for improvement of emergency shelter planning in Deyang city are provided in the study results.

Severe threats from ongoing degradation undermine the grasslands to support ecosystem services, biodiversity, and human well-being. Unfortunately, grasslands are often underappreciated and ignored in sustainable development agendas. Despite a series of projects for Grassland Ecosystem Conservation and Restoration (GECR) been implemented in China, the effects and cost-effectiveness of these efforts remain uncertain and untested. Therefore, we developed an integrated assessment framework to evaluate the benefits of GECR, considering ecological value accounting and input-output efficiency estimation. Additionally, we projected potential and risk areas for GECR in the future. The results showed that in 2020, the annual ecological value of China's grassland ecosystem was CNY 246 trillion. The investment in GECR exceeded CNY 7 billion, leading to an ecological benefit of CNY 3478 billion, with an input-output ratio of 1:446. Over the past 20 years, GECR positively impacted nearly 90 % of China's grassland. Furthermore, grasslands in southern provinces with favorable hydrothermal conditions exhibited significantly higher GECR efficiency, boasting an input-output ratio of >1:2000. The arid and semi-arid northern grasslands and the alpine grasslands on the Tibetan Plateau, despite being the main regions for animal husbandry development and GECR, exhibited comparatively lower efficiency and input-output ratio in GECR. Moreover, the central and northwest parts of Tibet showed higher potential and lower risk, indicating their greatest likelihood of benefiting from GECR in the future. Meanwhile, Hulunbeier and Inner Mongolia deserve more special attention to reverse degradation and mitigate climate change due to their lower potential and higher risks. Our study provides an important basis for prioritizing and implementing effective and sustainable GECR treatment methods.

Grasslands deliver essential provisioning and regulatory ecosystem services, concomitantly with indispensable cultural services that merit profound consideration. However, grassland cultural ecosystem services (GCES) face a conspicuous knowledge lacuna due to the lack of a unified research framework and quantitative methodology. This study endeavors to fill this gap by quantifying the potential supply and actual demand of GCES, concurrently scrutinizing spatial congruencies and disparities between GCES supply and demand in Inner Mongolia Autonomous Region (IMAR), China. To achieve this, we integrated social survey data, Point of Interest (POI) data, social media data, the Social Values for Ecosystem Services (SolVES) model and GIS Getis-Ord Gi* statistical analysis. Our analysis unveiled grid-scale spatial patterns of GCES supply and demand, furnishing a nuanced high-low ranking of GCES. It transpired that scenic travel holds the highest potential supply of GCES with a high-value area proportion of 46.0 %, while grassland recuperation ranks the lowest. Notably, road accessibility emerged as the most crucial factor influencing GCES patterns. Furthermore, we observed a substantial misalignment in the GCES supply-demand relationship, with 65.99 % of IMAR experiencing excess supply compared to demand and only 20.66 % achieving equilibrium. At a 95 % significance level, hot spots (excess supply) and cold spots (excess demand) accounted for 26.03 % and 22 %, respectively. We propose targeted suggestions that regions with oversupply of GCES should channel efforts toward augmenting road accessibility, whereas regions grappling with excess demand should prioritize the judicious allocation of resources to avert surpassing the environmental carrying capacity. Our study furnished insights for decision-makers to formulate sustainable development plans pertaining to grassland culture.

The Qinghai-Tibet Plateau (QTP), also known as the Third Pole of the Earth, is a vital ecological security barrier for China. It is a tremendously sensitive region affected by the impacts of global climate change. The escalating intensity of climate change has presented profound challenges to its ecosystem functions and stability. This study first analyzes the spatiotemporal variations of the QTP ecosystem patterns and the key functions of the Plateau including water conservation, soil conservation, and windbreak and sand fixation from 2000 to 2020. It clarifies the regional differences in ecosystem functions and their importance, further evaluates the stability of ecosystem functions, and lays a scientific foundation for an ecological civilization on the Plateau by implementing conservation and restoration projects. The main results show that: (1) From 2000 to 2020, the wetland area in the QTP increased, while the grassland area significantly decreased. There were improvements in water conservation and windbreak and sand fixation capacities, with annual rates of change being 3.57 m3·ha−1·a−1 and 0.23 t·ha−1a−1, respectively. However, the overall soil conservation trend declined during the same period, with an annual change rate of −0.16 t·ha−1a−1. (2) The core areas of water conservation, soil conservation, and windbreak and sand fixation on the QTP accounted for 12.7