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To address land space issues in the West Qinling Mountains—including habitat degradation, ecosystem damage, spatial pattern imbalance and unsustainable resource use—this study employed the InVEST habitat quality model and spatial autocorrelation analysis. Based on land use remote sensing data from 1990 to 2020, we simulated and evaluated habitat quality and degradation over this 30-year period to propose scientific recommendations and optimization strategies. The results showed that: (1) The area of grassland and farmland in the West Qinling Mountains decreased significantly, the area of construction land, bare land and forest land increased mainly; (2) The habitat quality of the West Qinling Mountains was generally high, and the average of the habitat quality showed an overall decreasing trend in the period of 1990–2020. The proportion of worst habitat increased from 4.11% to 5.21%. The habitat quality is in the process of polarization, the spatial distribution of habitat quality in West Qinling shows a pattern of “high in the west, low in the north and southeast”; (3) The hot and cold spots of habitat quality in West Qinling are spatially manifested as “hotter in the west and the south; colder in the center and the east”; (4) The spatial clustering of habitat quality in the West Qinling Mountains is obvious, with the area of the high–high area and the low–low area increasing with time, the high–low area decreasing, and the low–high area slightly increasing. (5) The degree of habitat degradation in the West Qinling Mountains is generally low, the average value of degradation from 1990 to 2020 showed an upward trend, habitat degradation is in the process of converging to medium risk. The area of medium habitat degradation expanded by nearly 1.5 times between 1990 and 2020. The spatial distribution of habitat degradation in the West Qinling Mountains generally shows a pattern of low in the west and high in the north and high in the southeast. In future planning and management, the west Qinling Mountains should formulate and carry out scientific ecological restoration plans and projects in terms of improving the quality of habitats, curbing habitat degradation, optimizing the direction of regional land use and reasonably protecting land resources, in an effort to balance urban development and ecological protection, curbing ecological degradation, guaranteeing the sustainable development of the habitats in a benign direction. Full article

Background/Objectives: Rabies is a fatal zoonotic disease caused by the rabies virus (RABV), and effective therapeutic treatments are currently lacking. Vaccination remains the primary strategy for rabies control. The Semliki Forest virus-rabies virus glycoprotein (SFV-RVG), a virus-like vesicle rabies vaccine combining Semliki Forest virus replicase and rabies glycoprotein, has shown potential as a promising vaccine candidate. This study aimed to optimize the production of SFV-RVG and evaluate adjuvant formulations to improve its immunogenicity in both mice and dogs. Methods: SFV-RVG production was optimized by determining the optimal multiplicity of infection (MOI) at 0.03 and cell density at 1 × 10⁶–1.3 × 10⁶ cells/mL, followed by scaling up the process in bioreactors. Eleven adjuvant formulations were tested in mice and dogs to assess their effects on immunogenicity. Cytokine analysis and antibody responses were measured, including IFN-γ, IL-4, IgG2a/IgG1 ratios, and neutralizing antibody titers. Results: The optimized SFV-RVG production was successfully scaled up, and M103 adjuvant induced rapid early antibody titers in mice. In dogs, GEL02 led to the highest neutralizing antibody levels, exceeding 40 IU/mL by 28 days post-immunization. Cytokine analysis indicated that both M103 and GEL02 significantly enhanced IFN-γ and IL-4 expression, balancing the Th1/Th2 immune response. SFV-RVG with GEL02 demonstrated stronger immunogenicity than a commercial vaccine, and challenge studies confirmed robust protection against lethal RABV in mice. Conclusions: This study establishes GEL02 as a superior adjuvant for rabies vaccines and provides a scalable SFV-RVG production process. These findings highlight SFV-RVG with GEL02 as a promising rabies vaccine candidate for dogs, offering significant potential for rabies control. Full article

Water scarcity and increased human pressures are crucial issues facing Jordan. Chemical pollutants significantly influence groundwater quality in Jordan due to increased pollution risks, ease of contamination, and various human activities that release harmful compounds into the groundwater. The Yarmouk River Groundwater Basin (YRB) is one of the main basins in northern Jordan. It is exploited for domestic, drinking, agricultural, and industrial uses. This study assessed the groundwater vulnerability for the YRB through the implementation of a dual-method approach, employing the SINTACS intrinsic groundwater vulnerability model and the Random Forest (RF) machine learning method. The results revealed similarities and differences between the two models. The delineation of low-vulnerability zones was similar, suggesting that the intrinsic hydrogeological characteristics of these areas provide robust natural protection against contamination. In addition, both models suggest that the eastern, northern, and southern parts are areas of ‘high’ and ‘very high’ vulnerability. Subtle differences can be observed, particularly in the precise delineation of boundaries and the fragmentation of vulnerability zones. Generally, the results show that over (47%) and (43%) of the basin area falls into the high- and very high-vulnerability classes, while the very low and low classes make up about (14%) and (15%), based on the SINTACS and RF models, respectively. Using the SINTACS and RF groundwater vulnerability assessments in the YRB provides valuable insights into groundwater susceptibility in this critical area of Jordan. The identified high- and very high-vulnerability areas within YRB highlight the urgent need for protective measures to safeguard this vital groundwater resource for both present and future generations. The SINTACS model proves to be a reliable tool for intrinsic vulnerability assessment in the study area, consistent with its application in other parts of Jordan and similar dry regions. Full article

Increasing macroeconomic uncertainty necessitates that firms optimize their R&D investment and commercialization strategies. Patents, as crucial outcomes of R&D with legal protection, impose significant costs due to progressively increasing maintenance fees. Predicting patent life accurately thus becomes critical for effective patent management. Previous studies have often and primarily employed classification models for patent life prediction, while limiting practical utility due to coarse granularity. This study proposes a robust ensemble regression model combining multiple machine learning techniques, such as Random Forest and deep neural networks, to directly predict patent life. The proposed model achieved superior performance, surpassing individual baseline models, and recorded a Mean Absolute Error (MAE) of approximately 852.81. Additional validation with active patents further demonstrated the model’s practical feasibility, showing its potential to support sustainable intellectual property management by accurately predicting longer life for high-quality patents currently maintained. Consequently, the proposed model provides ongoing firms and brand-new startups with a decision support tool for strategic patent maintenance and commercialization decisions. By promoting efficient allocation of R&D resources and reducing unnecessary maintenance of low-value patents, the approach fosters sustainable management of innovation assets, enhancing predictive accuracy and long-term applicability. Full article

Protected areas play a fundamental role in the implementation of international environmental strategies in order to ensure effective management systems that support the conservation of biodiversity and the provision of ecosystem services. However, the actual capacity of national parks to generate a specific “park effect” remains an open question. This study aims to assess whether the transformations observed in Italian national parks between 1960 and 2018 can be attributed to a specific park effect or are instead the result of other territorial dynamics. We analyzed long-term changes in land use and land cover (LUMCs) and variations in ecosystem services (ES), both inside and outside park boundaries, taking into account the SNAI classification. The results show a significant expansion of forest areas (+52%) and sparse vegetation (+56%), alongside a marked decline in arable land (−60%) and permanent crops (−26%). At the same time, the overall value of ES remains stable at around EUR 4 billion per year, with regulating services—accounting for 80% of the total—increasing by 20% between 1960 and 2018 and provisioning services declining by 41%. Italy’s national parks represent strategic socioecological laboratories capable of generating benefits both locally and globally. To fully realize this potential, more integrated management is needed, enabling their transformation from mere conservation areas to drivers of territorial resilience and social cohesion. Full article

Forests’ dynamics have become increasingly complex under climate change and human activities. Mainland Southeast Asia (MSEA), with extensive forest cover and a mosaic of protected and non-protected areas, is an ideal region for examining forest responses to climate and anthropogenic activities. To ensure robust long-term monitoring, we retrieved EVI2 from daily AVHRR and MODIS surface reflectance data and performed pixel-wise assimilation of the two datasets, substantially reducing systematic bias and constructing a consistent 1982–2024 annual EVI2 dataset. Using this harmonized dataset, along with land use, climate, and protected area data, we analyzed over four decades of forest greenness trends, variability, and drivers in protected and non-protected zones. Results show that forests in MSEA maintained high coverage (mean EVI2 = 0.6253) and exhibited a clear greening trend (+0.014 per decade). Temperature was the dominant driver, contributing over 50% of the variation, followed by human activities (>30%), while precipitation played a smaller and mixed role. Forests in protected areas were 1.3 times more stable than those outside (CV = 4.1% vs. 5.2%), highlighting the buffering role of protection. These findings provide a reliable long-term benchmark for forest monitoring and inform targeted conservation and sustainable management strategies in MSEA. The constructed assimilated long-term EVI2 dataset is available to support future research on vegetation dynamics, climate impacts, and ecosystem sustainability. Full article

Against the background of the rapid transformation of traditional economies and societies and continuous global climate change, how to ensure the long-term stability of the coastal ecological environment has become a key issue to be studied. In this paper, we take the 20 km buffer zone extending inland from the South African coastal zone as the study area. By constructing a vegetation vulnerability evaluation system, the current and future scenarios are compared in depth based on the base period (2010–2020), the near term (2030–2059), and the long term (2070–2099) with the help of GIS spatial analysis, the Moran index, and other methods. The results show that there are obvious spatial differences in vegetation vulnerability in the South African coastal zone. The extremely vulnerable areas of vegetation are mostly distributed on the west coast of South Africa, and some areas have obvious high–high aggregation patterns. The transfer of SSP1-2.6 scenarios in the near term is relatively stable, and the vegetation vulnerability level rebounds significantly in the long term; the vulnerability level of SSP2-4.5 scenarios has increased in both the near term and the long term, indicating that the risk of vegetation vulnerability has increased; while the SSP5-8.5 scenario has a significant deterioration trend in the long term, and the risk of vegetation vulnerability shifting to a high vulnerability level has increased significantly. Land use type has a significant impact on the response of vegetation vulnerability to SSP prediction. In the process of transformation from the base period to the long term, the proportion of vegetation vulnerability shifting to extremely vulnerable and severely vulnerable levels is notably high for both cultivated land and forest land—particularly under high-emission scenarios, driven by agricultural intensification for cultivated land and climate stress for forest land. This paper deeply explores the spatiotemporal evolution law and driving mechanism of vegetation vulnerability in the South African coastal zone under different shared socioeconomic pathway (SSP) scenarios, providing decision support for better development and protection of the South African coastal zone in the future. Full article

The conservation of ecosystems is essential for ensuring natural resources like water, a key ecosystem service sustaining human activities. However, its effect on human well-being remains uncertain. This study explores links between water provision, local activities, and Sustainable Development Goals (SDG) targets in El Carmen de Jadán, a rural Andean community in Azuay, Ecuador. Located downstream of the Aguarongo Protected Forest (APF), residents rely on headwaters for agriculture, livestock, and domestic use. Conservation efforts since the 1990s have played a key role in maintaining this supply. We developed a storyline to create a conceptual framework connecting water provision, respondents’ activities, and personal and community goals and challenges. We surveyed 55 water users and evaluated relationships through two indices: Level of Support for Contribution (LSC) and Importance of Contribution (IC). A network analysis of highest values revealed that human consumption activities (44–52 responses) were most linked to water, followed by livestock (29–37), agriculture (24–29), and ancestral health practices (28). Human consumption was associated with SDG targets 2.4, 6.1, 6.4, 6.6, 12.8, 13.3, 15.2, and 15.4 (346–416 links), while other activities were linked to all 11 targets analyzed, including 1.1, 1.2, and 2.1 (220–427). The results show strong connections between conservation, livelihoods, and sustainability goals. Full article

The Qinling Mountains ecosystem serves as a vital ecological barrier and geographic demarcation line in China. Monitoring long-term land cover changes in the Qinling Mountains is essential for ecosystem pattern evaluation, environmental protection, and sustainable development. Focusing on the Qinling Mountains in Shaanxi Province, this study aimed to quantify the land cover changes from 1986 to 2020 using remote sensing and GIS technologies. An optimized Support Vector Machine (SVM) classification method was developed using Landsat satellite images and historical field samples. The method was employed to conduct land cover classification across eight discrete time periods: 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The average overall accuracy (OA) of the classification results for the eight time periods was 96.42%, with a Kappa coefficient (K) of 0.9230, thus confirming the reliability of the mapping results. We subsequently developed a spatiotemporal Geo-information Tupu that facilitated a detailed analysis of land cover changes in the study area across different periods. The results show the following: (1) Forest was the dominant land cover type, followed by cropland. From 1986 to 2020, the forest, impervious surface, and water body areas showed overall increasing trends, although fluctuations were observed over time, and the increase was estimated at 6677.30 km², 557.57 km², and 135.71 km², respectively. In contrast, the areas of cropland, grassland, and bare soil showed a fluctuating decreasing trend, with a decrease in areal coverage of 2790.57 km², 1528.76 km², and 3042.66 km², respectively. During the study period, the forest area experienced the greatest increase but maintained the lowest dynamic degree. In contrast, bare soil showed the largest decrease and the highest dynamic degree. (2) A total of 30.74% of the area underwent dynamic changes during the study period, with the most active transformation occurring after 2010; these changes were mainly manifested in the outflow of cropland (4997.27 km²), the transfer of forest (8557.43 km²), and the expansion of impervious surfaces (771.33 km²). In conclusion, the overall ecological environment is improving. The results demonstrate a land cover reconstruction process that enables the management department to rationally utilize natural resources in the Qinling Mountains. Full article

Misiones province covers < 1% of Argentina’s land area yet harbors > 50% of the country’s biodiversity, with a significant remnant of Atlantic Forest, a global biodiversity hotspot. Approximately 540,000 ha of this native forest is protected, with the remaining areas facing threats from ongoing land conversion, an expanding road network, and a growing rural population. A prior study incorporated noninvasive data on five carnivores into a multifaceted cost analysis to define the optimal location for a multispecies biological corridor, with the goal of enhancing landscape connectivity among protected areas. Subsequent analyses, with an updated framework, emphasized management strategies that balanced human–wildlife coexistence and habitat needs. Building on these efforts, our study applied ecological niche modeling to data located by conservation detection dogs, with genetics used to confirm species identity, and two land-use scenarios, to predict potential distributions of three game species—lowland tapir (Tapirus terrestris), white-lipped peccary (Tayassu pecari), and collared peccary (Pecari tajacu)—that are not only threatened by poaching, road mortality, and habitat loss but also serve as essential prey for carnivores. We assessed the suitability of unique and overlapping vegetation types, within and outside of protected areas, as well as within this multispecies corridor, identifying zones of high conservation concern that underscore the need for integrated planning of predators and prey. These results highlight that ensuring the long-term viability of wildlife across the heterogeneous land-use matrices of Misiones requires going beyond protected areas to promote functional connectivity, restore degraded habitats, and balance human–wildlife needs. Full article

The increasing emphasis on environmental protection, climate change mitigation, and the transition to a circular economy requires industries, including the wood-processing sector, to integrate sustainability into strategic and operational management. Green growth indicators represent essential tools for evaluating the environmental, economic, and social impacts of business activities, while also contributing to the sustainable economics and responsible management of forest resources and products. This study applies a qualitative research design using structured interviews with 10 executives from medium and large woodworking enterprises in Slovakia. The interviews examined company strategies, practices, and challenges in sustainable development and forest resource utilization. The findings reveal that while many companies actively manage waste, invest in green technologies, and conduct internal audits, the broader implementation of environmental management systems and the uptake of public sustainability funding remain limited. Notably, 90% of respondents emphasized waste volume and recovery rates as critical indicators. Based on the results, a set of green growth indicators was developed and categorized across key thematic areas including waste management, energy efficiency, stakeholder communication, certification, and strategic planning. These indicators not only support the assessment of corporate sustainability but also strengthen efficient forest resource management, responsible use of raw materials, and the long-term economic viability of the sector. The study highlights the importance of systematically designed and practically applicable indicators for guiding companies toward sustainable competitiveness and emphasizes the need for stronger institutional support, improved access to reliable data, and integration of sustainability metrics into core business decision-making. Full article

Trakošćan Lake is an artificial lake created in the mid-19th century for aesthetic and economic purposes. The area around the lake has been protected as park forest. Recently, the lake has become the most famous example of eutrophication in Croatia, as by 2022, a significant amount of sediment had accumulated in it. Therefore, the lake was drained that same year, followed by mechanical removal of the sediment. The total amount of sediment removed was 204,000 m³. After the removal work, a particularly important question arose of what to do with such a large amount of sediment. The objective of this research was to gain specific insight into the chemical composition of the sediment with the aim of its possible use in agricultural production for increasing the quality of arable land. A comprehensive qualitative geochemical and agrochemical analysis of the sediment composition was carried out for the first time, including indicators of the pH value, amount of organic matter and carbon, total nitrogen, available phosphorus and potassium, amount of carbonates, and the presence of metals, metalloids, and non-metals, of which As, Cd, Hg, and Pb are toxic. Electrochemical, spectrophotometric, and titration methods were used, along with three atomic absorption spectrometry techniques. The results of the analyses were interpreted in comparison with the natural substrate, as well as with the current regulations for agricultural land in the Republic of Croatia. According to this, sediment is not harmful for the environment. Full article

Ecological security barriers safeguard regional ecological security by blocking external risks and supplying internal services. However, existing research has primarily focused on optimizing the connectivity and protection of internal ecological patches within barriers. At a broader scale, there remains insufficient attention on coordinating the “blocking of external ecological risk corridors” and “connecting corridors that supply ecosystem services to internal urban areas”. To address this, this study develops a framework for constructing ecological corridors that integrates both reverse (resistance) and forward (provision) perspectives. Taking the Yanshan–Taihang Mountain Ecological Barrier as a case study, circuit theory is applied to identify risk corridors traversing the barrier area. Service supply corridors directed toward internal urban areas are also established, and key nodes along these corridors are identified. Furthermore, the XGBoost-SHAP method is employed to quantitatively analyze the influencing factors and mechanisms of these key nodes. Finally, strategies are proposed to block risk corridors and connect supply corridors. The main results are as follows: (1) A total of 29 risk corridors, 158 risk pinch points, and 210 risk barriers were identified, along with 250 supply corridors, 158 supply pinch points, and 118 supply barriers, revealing the distinct distribution patterns of both risk transmission and service supply corridors. (2) The dominant factors influencing different types of corridors exhibited significant differences: risk corridors were primarily regulated by natural factors such as mean annual evapotranspiration (EVA) and soil volumetric water content (VWC), whereas supply corridors were mainly influenced by human activities, including the human footprint index (HFP) and land surface temperature (TEM). (3) Even within the same type of corridor, the dominant factors and their operating mechanisms—such as threshold effects and nonlinear interactions—showed considerable heterogeneity across nodes of different characteristics. Based on these findings, differentiated policy recommendations were proposed. This study aims to synergistically enhance the bidirectional functionality of forest-mountain ecological barriers by disrupting external risk corridors and reconstructing internal supply networks. The framework and methodology presented here can provide theoretical and empirical references for the planning and management of other similar barrier regions. Full article

As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics. Full article

This narrative review examines how ecological and economic perspectives can be integrated to support ecosystem services management and biodiversity conservation. We synthesize core valuation approaches (accounting-based exchange values versus welfare-based measures), discuss their appropriate uses and limitations, and illustrate implications through selected cases in watershed protection, protected areas, and forest carbon. We then review design features of Payments for Ecosystem Services (PES) with attention to additionality, leakage, and equity, and distill lessons for policy mixes that combine market-based instruments with regulatory and informational tools. Finally, we outline opportunities and risks in applying artificial intelligence to ecological–economic analysis, emphasizing accuracy–energy trade-offs and responsible data practices. Across topics, we prioritize mechanism-focused interpretation, triangulate findings from representative studies, and highlight decision-relevant takeaways rather than comprehensive coverage. We conclude with practical recommendations for analysts and policymakers: align valuation method with decision context; pair PES with targeting and monitoring; embed price-based instruments in adaptive policy mixes; and adopt transparent, efficiency-aware analytic workflows—especially when using computationally intensive methods. Full article