Search Results (4,804)

Global warming is driving large-scale shifts in the climatically suitable habitats of many species. However, climate-only species distribution assessments may overestimate the spatial availability of future suitable habitats when dynamic land-use change is not considered. To assess potential spatial overlaps between climate-driven habitat suitability shifts and human land-use patterns, this study focuses on Quercus L. as a widely distributed keystone forest taxon in China. The genus-level assessment was designed to identify broad-scale habitat–land-use conflict patterns under multiple climate pathways and territorial spatial planning scenarios, rather than to predict species-specific distribution responses. We developed a soft-coupled framework integrating the Maximum Entropy (MaxEnt) model and the Patch-generating Land-Use Simulation (PLUS) model, and applied the Habitat–Land-Use Conflict Index (HLCI) as a categorical spatial overlay framework to classify potential overlaps between projected suitable habitats and future land-use categories across 16 exploratory scenario combinations integrating Shared Socioeconomic Pathway (SSP)-based climate projections and land-use/land-cover (LULC) scenarios for the 2040s at the grid scale. The results indicate that: (1) climate warming may reshape Quercus habitat suitability, characterized by northward/westward expansion and southward contraction in some low-latitude regions; (2) future land-use patterns may reduce the spatial availability of projected suitable habitats by increasing their overlap with built-up land and cultivated land. Under high-emission scenarios, potential newly suitable habitats overlapped with built-up land by up to 5.90 × 10⁴ km² and with cultivated land by up to 36.42 × 10⁴ km²; and (3) the Ecological Protection scenario showed lower overlap with non-ecological land-use categories and a larger area of potentially realizable habitat expansion. This study provides a scenario-based spatial assessment of where future Quercus habitat suitability may overlap with human land-use patterns, offering broad-scale support for adaptive forest conservation and territorial spatial planning. Full article

Land-use change poses a growing threat to ecological security, yet existing regional assessments often rely on a single ecological indicator and lack direct linkage to territorial spatial planning. This study develops an integrated ESV–ERI framework coupled with quadrant zoning to provide spatially explicit, planning-compatible guidance for ecological protection in tropical island regions. Taking Hainan, China, as a case study, this research draws on land-use data from 1994 to 2024, applying ESV and ERI assessments coupled with Z-score standardization to examine their spatiotemporal evolution characteristics. The results indicate the following: (1) forest land persistently dominated land use (>63%), while construction land expanded by 197.68% and forest land and grassland decreased by 9.36% and 93.78%, respectively. (2) ESV showed a downward trend, decreasing by 14.683 billion yuan, with forest land accounting for over 83% of total ESV. Spatially, ESV exhibited a “high inland, low coast” pattern, with high-value zones across inland water bodies and central nature reserves and low-value zones in coastal urban agglomerations. (3) ERI increased from 0.0371 to 0.0539, with low-risk zones in the middle mountains and high-risk zones around the island. (4) Based on the dual dimensions of ESV and ERI, the entire island was delineated into four ecological zones. These findings provide scientific decision support for territorial spatial planning and differentiated ecological protection in tropical island regions. Full article

Urbanization is increasingly limiting daily human exposure to natural forest environments, highlighting the growing importance of urban green infrastructure and nature-based solutions in supporting human health and well-being. Among the mechanisms underlying the beneficial effects of forests, biogenic volatile organic compounds (BVOCs), particularly terpenes, are recognized as key contributors due to their bioactive properties and role in cultural ecosystem services related to human well-being. This study explores the potential of urban and natural trees of Pinus nigra J. F. Arnold to serve as sources of health-relevant BVOCs by examining seasonal and spatial variability in needle terpene profiles. Needle samples were collected from trees growing in an urban park and a protected natural area across three seasons (spring, summer, and autumn), and analyzed using headspace GC/MS. The study was designed as an exploratory assessment aimed at identifying general patterns of terpene variability across contrasting environments. Across all seasons and locations, α- and β-pinene consistently dominated the terpene profile, together accounting for the majority of detected compounds, and showed no significant variation in relation to site or season. In contrast, secondary monoterpenes and sesquiterpenes exhibited greater variability, contributing to context-dependent differences between environments. Despite these variations, the overall terpene composition remained relatively stable, particularly with respect to compounds previously associated with health-related effects. These preliminary findings provide insights into the potential role of Pinus nigra within urban and natural green infrastructure associated with nature-based health-oriented practices. The observed stability of health-related terpenes suggests that urban Austrian pine trees can represent a consistent source of compounds previously associated with health-related effects, although their relevance requires further investigation involving total and individual BVOC emissions measurements and human exposure assessments. Full article

As a core component of terrestrial ecosystems, forests play an irreplaceable ecological role in carbon sequestration, biodiversity conservation, and global climate regulation. Home to key global forest belts including the Congo Basin, the African continent’s forest changes directly shape regional ecological balance and sustainable development while profoundly affecting global ecological security and climate dynamics. Based on the Shared Socioeconomic Pathways (SSPs), a unified narrative framework for global socioeconomic and environmental change scenarios, this study couples techniques such as the Future Land Use Simulation (FLUS) model, dynamic degree analysis, transition matrix, K-means clustering analysis, and patch fragmentation analysis. This work aims to answer two key questions: (1) What are the spatiotemporal characteristics and dominant drivers of African woodland changes under different SSPs? (2) How do spatial clustering and fragmentation patterns vary across scenarios? It systematically predicts and analyzes the spatiotemporal characteristics, driving mechanisms, and fragmentation change patterns of African woodlands in 2030, 2050, and 2070 under five scenarios (SSP1-SSP5) with 2020 as the baseline. These five official IPCC SSP frameworks represent five distinctly divergent socioeconomic development trajectories ranging from sustainable to fossil-fuel-driven development, which are the core differentiated scenarios recommended by IPCC; full inclusion facilitates systematic comparison of varied forest feedback features across Africa’s diversified national development backgrounds. The research results show that understory forests in the SSP5 (Fossil Fuel-dominated Development) scenario exhibit a stable growth trend, with the total area transferred in significantly exceeding the area transferred out from 2020 to 2070, resulting in a net increase of 143,513 km². This growth occurs because high-income economies under this scenario invest heavily in ecological restoration and forest protection, offsetting carbon-intensive development impacts. The core forest density continues to increase and is distributed in contiguous areas; the SSP4 (uneven development) scenario regarding forest degradation is the most severe, with the dynamic rate expected to drop to −0.05% between 2050 and 2070, and a net transfer of −265,581 km². Forest fragmentation is highest, and the core density area is gradually shrinking. Cluster analysis shows that forest area remains relatively stable in most African countries, with stable countries accounting for as much as 95.49% under scenario SSP5. Regions with woodland expansion are mainly distributed in North Africa and localized parts of Southern Africa. After refinement using independent tree-density evidence, woodland expansion in South Africa is shown to be more limited and spatially heterogeneous; these newly expanded woodlands are mostly artificial plantations and alien invasive tree stands rather than native natural woodlands, mainly occurring in eastern and southeastern areas rather than in arid western regions. The spatiotemporal transfer process exhibits significant periodic differentiation, with 2030–2050 being a critical transitional period for forest change, and the differentiation effect between scenarios intensifying. Fragmentation analysis indicates that scenario SSP3 (regional rivalry, with moderate population growth and weak policy constraints) has the best forest integration and the lowest degree of fragmentation, while scenario SSP4 is most strongly affected by human activities and has the highest risk of patch fragmentation. These findings can provide a scientific basis for African countries to formulate differentiated forest protection policies and optimize ecological restoration plans, while also offering theoretical insights for continental-scale forest ecological management. Full article

Insider threats continue to be the most persistent and most destructive threat to cybersecurity; malicious or negligent users work only in the real-time restricted area of the organization and are gradually breaking the boundaries of company norms. Conventional rule-based and statistical detection methods have difficulty detecting inconspicuous, context-dependent, and ever-changing behavior, leading to detection delays and high false-positive rates. Our paper introduces an explainable AI-based Insider-Threat Detection (AIB-ITD) model that integrates enterprise telemetry—including email, web, logon/VPN, and file events—into a unified behavioral framework. The effectiveness of combining heterogeneous behavioral indicators observed in AIB-ITD is consistent with recent behavioral analytics implementations that have demonstrated the value of multimodal user-behavior profiling for insider-threat identification in enterprise environments. The proposed AIB-ITD framework is based on anomaly-driven processing, unsupervised models (Isolation Forest, PCA reconstruction, and Autoencoder) are combined with sequential modeling (with an LSTM Autoencoder) to model both static and temporal deviations in behavior. An ensemble strategy is applied to combine the outputs of these models to yield a probabilistic insider risk score. To improve transparent analysis and to help the analyst gain trust, SHapley Additive Explanations (SHAP) is used to keep every detection outcome transparent and interpretable using the features. It also integrates feature correlation analysis, static vs sequential-model comparisons, and SHAP stability assessment to validate methodological robustness and reproducibility. An experimental review of the hybrid ensemble using the SEI/CMU CERT Insider Threat Dataset reveals that it performs better than single models for anomaly detection and stability, especially with the inclusion of temporal patterns. The assessment prioritizes anomaly score consistency and reliable risk ranking, rather than classification accuracy, to better reflect real deployment scenarios. In addition, an Automated Response and Privilege Containment (ARPC) feature automatically converts risk scores to multilevel mitigation actions that serve to protect the privacy of the user as the least privileged policies are enforced promptly. The proposed model showed superior robustness, stability, and operational effectiveness to classical methods, especially in the presence of scarce labeled data. Through hybrid anomaly recognition, explainable AI and automated response, AIB-ITD is a practical and scalable solution for next-generation insider-threat detection in enterprise systems. Full article

Understanding the driving mechanisms of land-use structure change is fundamental for exploring co-evolutionary dynamics of coupled human-land systems. This study focuses on traditional villages in Enshi Tujia and Miao Autonomous Prefecture, integrating spatial analysis, landscape pattern indices, and structural equation modeling (SEM) with field surveys and multi-source data. It analyzes spatial distribution, spatiotemporal evolution, and the direct and indirect pathways of topographic heterogeneity, human activities, economic development, and social level on land-use structure change. Results show: (1) Villages concentrate in mountainous junctions at 800–1200 m (52.2%), forming a multi-core, west-dense east-sparse pattern with significant spatial dependence. (2) During 1990–2020, Dong villages exhibited a development-oriented evolution, with slightly expanded cultivated and forest land, intensified fragmentation (patch density increased by up to 8.55%), increased landscape diversity, and slightly decreased grassland and water, reflecting an adaptive balance between economic development and ecological constraints. (3) SEM reveals that topographic heterogeneity exerts significant negative direct effects on human activities (−0.694) and economic development (−0.686), and indirectly constrains social level through multiple mediating pathways; human activities (0.829) and economic development (0.837) strongly drive social level, with economic development also synergistically promoting human activities. This study reveals cascading transmission mechanisms of land-use structure change, providing an empirically grounded theoretical foundation and decision-making reference for ecological protection, cultural inheritance, and sustainable development in mountainous ethnic areas. Full article

As metropolitan areas (MAs) become increasingly complex, reconciling land development with ecological protection has become a major challenge in spatial governance. Although ecological security patterns (ESPs) are widely used to assess ecological networks, they are often treated as diagnostic outputs after simulation rather than directly incorporated into land use/land cover (LULC) simulation processes. In addition, conventional ecosystem health assessments commonly assign uniform values to broad LULC classes, thereby overlooking variations among patches within the same class. This study proposes a spatially explicit framework that integrates forest-centered ESPs into LULC simulation as scenario-specific conversion constraints. It also applies a modified Pressure–Vitality–Organization (P–V–O) model that incorporates explicit socioeconomic pressures instead of relying on uniform, class-based resilience values and assesses ecosystem health separately for each LULC type. The framework was applied to the Tokyo, Chubu, and Kinki MAs in Japan. From 2000 to 2020, forest-corridor configurations evolved differently among the three MAs. Declines in forest connectivity were more pronounced in Tokyo and Chubu, whereas Kinki remained comparatively stable. Patch-scale ecosystem health showed marked spatial heterogeneity within cultivated land, grassland, and shrubland, and its temporal trends varied among MAs and LULC types. Simulations for 2050 under the Urban Priority, Business-as-Usual, and Ecological Priority scenarios showed that increasing levels of ecological protection imposed progressively broader constraints on land conversion, resulting in region-specific patterns of urban expansion, cultivated land change, and forest retention. The proposed framework shows how ESPs and patch-level ecosystem health information can be operationalized as spatial planning constraints, providing a practical basis for comparing development and conservation priorities and supporting differentiated LULC planning across MAs. Full article

Amino acid metabolism has been increasingly recognized as a central determinant of obesity and insulin resistance, yet the specific contributions of individual amino acids require further clarification. The aim of the study was to detect relationships between serum amino acid concentrations and metabolic parameters in overweight and obese individuals. Amino acid concentrations were measured in 50 individuals classified as normal weight, overweight, or obese, and were analyzed using principal component analysis (PCA), K-means clustering, multiple linear regression, and Random Forest models. Obese individuals exhibited markedly elevated levels of branched-chain amino acids (BCAAs: valine, isoleucine, leucine) and glutamic acid, accompanied by reduced concentrations of serine, glycine, and glutamine, compared with normal weight participants. PCA revealed that the first component, which explained 35.5% of the total variance, was driven primarily by BCAAs, serine, and glutamine, while the second component, accounting for 9.5% of variance, was influenced by threonine, tryptophan, and asparagine. The multiple linear regression model explained 89.6% of the variance in HOMA-IR (R² = 0.896, p < 0.001), with isoleucine emerging as the strongest positive predictor (p < 0.001), valine and leucine showing additional significant associations (p = 0.035), and tyrosine demonstrating a significant negative association (p = 0.039), while proline was not significant. The Random Forest model predicting insulin resistance achieved robust cross-validated performance (R² = 0.86 ± 0.06), with valine, isoleucine, and leucine accounting for the majority of predictive importance, followed by tyrosine and glutamine. Together, these findings demonstrate that amino acid profiling provides powerful discriminatory and predictive capacity for insulin resistance and obesity. BCAAs consistently emerged as the most important predictors across complementary analytical frameworks, confirming their central role in metabolic dysregulation, while glycine appeared to exert a potential protective effect. The identification of a metabolically overweight subgroup underscores the heterogeneity of the overweight state and highlights the utility of amino acid profiling for early risk stratification and the development of targeted interventions. Full article

Understanding the spatiotemporal dynamics and driving mechanisms of ecosystem services in response to land use change is critical for regional ecological security and sustainable development, especially under the combined pressure of intensive human activities and future climate change in tropical regions. Existing studies often lack an integrated framework for multi-scenario simulation, multi-dimensional ecosystem service quantification, and spatial driving factor identification. To support sustainable management, this study focused on Hainan Island and utilized land use data from 2000 to 2025. The Markov-Patch-generating Land Use Simulation (PLUS) model was employed to simulate land use patterns for 2050 under historical trend, SSP1-1.9, and SSP5-8.5 scenarios, incorporating future climate data. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to quantify habitat quality, carbon storage, water yield, and soil conservation. The Multi-weighted Entropy Ecosystem Service Index (MEESI) was established to evaluate ecosystem service performance. Furthermore, the GeoDetector model was applied to assess the explanatory power of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Bare Soil Index (BSI) on ecosystem service dynamics. The results indicated that: (1) during 2000–2025, land use change in Hainan Island was dominated by forest-to-cropland conversion and impervious surface expansion, while future suggestions included stronger ecological protection under SSP1-1.9 and greater ecological pressure under SSP5-8.5; (2) during 2000–2025, habitat quality and carbon storage generally declined, whereas water yield and soil conservation increased, and SSP1-1.9 maintained higher overall ecosystem service performance (habitat quality = 0.6207, carbon storage = 327.89 × 10⁶ t, and MEESI = 0.3509) than the historical trend and SSP5-8.5 scenarios in 2050; and (3) NDVI exhibited the strongest explanatory power for ecosystem service variation, whereas NDBI showed the weakest. These findings suggest that ecosystem management should consider the trade-offs and synergies among multiple ecosystem services rather than focusing on a single service. This study provides a systematic and spatially explicit framework for ecosystem service assessment under future scenarios. The study can also support scientific land use optimization, ecological conservation, and sustainable management decisions in tropical island regions. Full article

Street-level concentrations of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) pose serious public health risks in European cities, yet accurate multi-year prediction at traffic-dominated sites remains challenging. This study applies XGBoost (XGB) and Random Forest (RF) to predict hourly NO₂ and daily PM_2.5 at two street monitoring sites in Copenhagen, Denmark, trained on 17 years of observational data and evaluated on two independent years. Three-dimensional variational assimilation (3D-Var) and the Extended Kalman Filter (EKF) are then applied as post-processing corrections to the ML predictions using co-located observations. XGB achieved RMSE values of 9.5 and 7.4 µg/m³ for HCAB and JGTV NO₂, respectively, in the 2018 test year. Both DA methods improved substantially on the ML baseline, with 3D-Var reducing NO₂ RMSE by up to 57% and spike event RMSE by up to 51%. EKF achieved near-complete elimination of systematic bias across all configurations. The framework is computationally lightweight and can be applied to any deterministic model prediction at a monitoring station, including outputs from physics- and chemistry-based dispersion models. Overall, the findings show a practical way to improve street-level air quality prediction, with direct relevance for operational forecasting and public health protection. Full article

Trillium camschatcense, a plant renowned for its ecological and medicinal value, is predominantly found in the temperate forests of East Asia. However, its habitat is increasingly threatened from climate change, habitat fragmentation, and intensified human activities. In this study, the Maxent (Maximum Entropy) model was used to assess the current ecological suitability of T. camschatcense based on historical climate data (1970–2000), and further simulate its potential distribution shifts under multiple future climate change scenarios to predict long-term habitat variation trends across northeast China.All modeling and spatial mapping analyses were performed using MaxEnt and ArcGIS 10.8.1 software. Drawing upon 93 known distribution points and 26 pertinent environmental variables covering climate, soil, and elevation, we built species distribution models for both present and future periods to pinpoint the crucial environmental factors influencing its distribution. Our findings revealed that elevation, soil nitrogen content, seasonal temperatures, annual precipitation, mean temperature during the coldest quarter, and mean diurnal temperature range were the primary factors influencing the distribution of T. camschatcense. Notably, highly suitable habitats were predominantly concentrated in Baishan City and the southwestern region of Yanbian Korean Autonomous Prefecture in Jilin Province. This insight offers valuable scientific guidance for the conservation planning, sustainable utilization, and potential introduction and cultivation of T. camschatcense. Furthermore, targeted conservation strategies can help identify climate refugia and protect climatically stable habitats for the long-term persistence and resilience of the species under continuous global warming. Full article

Mangrove forests deliver globally significant climate-mitigation and coastal-protection benefits, yet their resilience to climate extremes remains poorly quantified—a key uncertainty for sustainable coastal management. We reassess the unprecedented 2015–2016 mangrove dieback along ~1000 km of the Gulf of Carpentaria, northern Australia, to determine its driver and whether the collapse was structurally abrupt. Combining a mangrove-extent mask, an 11-year radar backscatter series, satellite precipitation, the modeled sea level, the reanalysis temperature and atmospheric dryness, and an El Niño index, we show that an apparent abrupt radar decline during the event was an artifact of non-vegetated tidal-flat and open-water pixels: once analysis was restricted to mangrove pixels, the signal remained stable throughout. Independent spaceborne lidar confirmed that canopy structure concentrates within the mapped mangrove zones, validating the mask. The dieback coincided with a strong sea-level fall, with anomalies reaching about −15 cm, under near-to-above-average rainfall and low atmospheric dryness, indicating that sea-level fall, not rainfall deficit, was the proximate stressor. These findings advance sustainable, mask-applied satellite monitoring of blue-carbon ecosystems and provide an evidence base for climate-adaptive coastal-resilience planning under intensifying climate variability. Full article

The Knyszyn Forest is one of the major lowland forest complexes of north-eastern Poland, yet its myxobiota remains insufficiently documented. We surveyed Eumycetozoa in three forest nature reserves—Krzemianka, Jałówka, and Las Cieliczański—to assess local species richness, substrate occurrence, and new distributional records. Field surveys were combined with moist-chamber cultures, and species were identified from mature sporocarps using macro- and microscopic morphology. The inventory yielded 761 occurrence records representing 80 identified taxa. Moist-chamber cultures produced more records and species than field collection, while field surveys detected taxa fruiting naturally during the study period; together, both methods produced a more complete inventory than either method alone. Three species, Paradiacheopsis solitaria, Macbrideola rutilipedata, and Licea operculata, were recorded for the first time in Poland; these national novelties were included among the 14 taxa recorded for the first time in north-eastern Poland. Most records came from dead wood and bark, although other dead organic substrates also contributed to the recorded substrate spectrum. The resulting dataset provides a regional baseline for future biodiversity surveys in the Knyszyn Forest. Full article

Megafires are a growing global phenomenon, with their frequency increasing in recent decades. In 2023, Europe’s largest recorded fire occurred in North-Eastern Greece, burning 94,250 hectares, including the Dadia–Lefkimi–Soufli National Park (DLS-NP). This protected area is of high ecological importance due to its rich biodiversity, hosting 18–20 breeding raptor species. The fire exhibited remarkable spatial heterogeneity, with some patches burning at low to moderate severity while others remained unburned, likely functioning as critical post-fire refugia. In this study, 15 environmental variables were analyzed using Maxent to model pre-fire potential nesting habitat suitability for eight tree-nesting species, including seven raptors and the Black Stork. The models were then used to examine the spatial overlap between historically suitable habitats and unburned, low-, and moderate-severity areas. The results suggest that these patches may represent important spatial remnants of potential nesting habitat for raptor communities after the fire, especially where historically suitable areas coincide with unburned or moderately burned forest structures. Accordingly, post-fire landscape management in Evros should prioritize the protection of unburned refugia and the cautious retention of partially burned forest structures, particularly in areas identified as historically suitable, while avoiding interventions that may unnecessarily simplify habitat structure. Effective management should combine species-specific protection of key habitat islands with landscape-scale strategies that preserve heterogeneity and support prey availability, within an adaptive framework guided by systematic monitoring over the next 3–5 years. Full article

Against the backdrop of coordinated development in the Beijing–Tianjin–Hebei region, Hebei Province serves as an ecological safety barrier for the Beijing–Tianjin–Hebei urban agglomeration. Conducting research on land use and land cover change (LUCC) and ecosystem service value (ESV) holds significant theoretical and practical value for elucidating the mechanisms underlying ESV evolution under the combined effects of rapid urbanization and major ecological engineering projects, and for applying these findings to regional land-use planning and ecological conservation and restoration efforts. This research aligns with the United Nations Decade on Ecosystem Restoration (2020–2030). Based on land-use data from 2000, 2010, and 2020, along with 11 categories of natural and socio-economic drivers, this study systematically analyses regional LUCC and calculates ESV using locally adjusted equivalence factors. It examines the spatiotemporal evolution patterns of ESV through the analysis of local spatial autocorrelation indices (LISAs), centroid, and standard deviation ellipses, and employs a GeoDetector to measure ESV drivers. Three scenarios—a natural evolution scenario (NES), economic development scenario (EDS), and ecological protection scenario (EPS)—were established. The patch-generating Land use simulation (PLUS) model was employed to simulate LUCC for 2030 (Kappa = 0.840) and calculate ESV. Results show that from 2000 to 2020, forest land and impervious surfaces in Hebei Province continued to expand, while cropland and grassland decreased. The cumulative ESV increased by 4.85 billion yuan. Slope was the primary driver of spatial variation in ESV, and the interaction between natural and socioeconomic factors demonstrated significantly stronger explanatory power. In 2030, the total ESV under all three scenarios was lower than in 2020. The EPS reached an ESV of 344.72 billion yuan, representing a relatively suitable model that balances development and conservation. Full article