Search Results (8,408)

Diabrotica virgifera virgifera Le Conte, 1868 (Coleoptera: Chrysomelidae), known as the western corn rootworm, is one of the most important alien insect pests affecting maize crops globally. It causes significant economic losses by feeding on the roots, which affects plant stability and nutrient absorption, as well as by attacking essential aerial organs (leaves, silk, pollen). Since its accidental introduction into Europe, the species has expanded its range across maize-growing regions, raising concerns about future distribution under climate change. This study aimed to estimate the risk of pest establishment across Europe over three future time frames (2034, 2054, 2074) based on geographic coordinates, climate data, and maize distribution. Spatial simulations were performed in QGIS using national centroid datasets, risk classification criteria, and temperature anomaly maps derived from Copernicus and ECA&D databases for 1992–2024. The results indicate consistently high risk in southern and southeastern regions, with projected expansion toward central and western areas by 2074. Risk zones showed clear spatial aggregation and directional spread correlated with warming trends and maize availability. The pest’s high reproductive potential, thermal tolerance, and capacity for human-assisted dispersal further support these predictions. The model emphasizes the need for expanded surveillance in at-risk zones and targeted policies in areas where D. v. virgifera has not yet established. Future work should refine spatial predictions using field validation, genetic monitoring, and dispersal modeling. The results contribute to anticipatory pest management planning and can support sustainable maize production across changing agroclimatic zones in Europe. Full article

The Source Region of the Yangtze and Yellow Rivers (SRYY), situated on the Qinghai-Tibet Plateau, serves as a vital ecological barrier and a critical component of the global carbon cycle. However, this region faces severe ecosystem degradation driven by climate change and human activities. This study establishes an integrated ecological security assessment framework that couples ecological risk, ecosystem health, and ecosystem services to evaluate ecological dynamics in the SRYY from 2000 to 2020. Leveraging multi-source data (vegetation, hydrological, meteorological) and advanced modeling techniques (spatial statistics, geographically weighted regression), we demonstrate that: (1) The Ecological Security Index (ESI) exhibited an initial increase followed by a significant decline after 2010, falling below its 2000 level by 2020. (2) The rising Ecological Risk Index (ERI) directly weakened both the ESI and Ecosystem Service Index (ESsI), with this negative effect intensifying markedly post-2010. (3) A distinct spatial gradient pattern emerged, shifting from high-security core areas in the east to low-security zones in the west, closely aligned with terrain and elevation; conversely, areas exhibiting abrupt ESI changes showed little correlation with permafrost degradation zones. (4) Vegetation coverage emerged as the key driver of ESI spatial heterogeneity, acting as the central hub in the synergistic regulation of ecological security by climate and topographic factors. Full article

Bahía de La Paz is the largest bay in the southern Gulf of California. This bay is an important area with a variety of commercial fish species and other natural resources and recreational activities. Epibenthic dinoflagellates are common inhabitants of harbors, inlets and semi-enclosed coastal lagoons; they produce potent toxins that may negatively affect human health and marine biota. The purpose of the present study was to identify potentially harmful epibenthic dinoflagellates growing on macroalgae from different coastal sites of the bay to determine their species composition, abundances, seasonal distributions, interannual and spatial variations. A total of 153 quantitative samples were collected in 2015–2019 (at 10 sites during four samplings in May, June and December) mainly from macroalgae. About 23 dinoflagellate species from the genera Prorocentrum, Ostreopsis, Sinophysis, Gambierdiscus, Fukuyoa, Amphidinium, Blixaea, Bysmatrum, Cabra, Coolia, Durinskia and Plagiodinium were found as epiphytes on at least 58 macroalgal species of 42 genera. Toxigenic genera, such as Gambierdiscus, Ostreopsis, Coolia and Prorocentrum, were widespread throughout the study area. Playa El Tecolote and Playa Costa Baja were the best habitats for dinoflagellates; therefore, the two locations can be considered the beaches with the greatest risk to human health. Full article

The Blue Schools initiative integrates the ocean into classroom learning through project-based approaches, cultivating environmental awareness and a deeper sense of responsibility toward marine ecosystems and human–ocean interactions. Although the European Blue School initiative has grown steadily since its launch in 2020, its uneven uptake raises important questions about the territorial factors that influence certification. This study examines the spatial determinants of Blue School certification in Mallorca, Spain, where a bottom-up pilot initiative successfully certified 100 schools. Using Maximum Entropy (MaxEnt) modeling, we estimated the spatial probability of certification based on 16 geospatial variables, including proximity to Blue Economy actors, hydrological networks, transport accessibility, and socio-economic indicators. The model achieved strong predictive performance (AUC = 0.84) and revealed that features such as freshwater ecosystems, traditional economic structures, and sustainable public transport play a greater role in school engagement than coastal proximity alone. The resulting suitability map identifies over 30 high-potential, non-certified schools, offering actionable insights for targeted outreach and educational policy. This research highlights the potential of presence-only modeling to guide the strategic expansion of Blue Schools networks. Full article

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models. Full article

This study examined the spatiotemporal dynamics and driving mechanisms of ecosystem service value (ESV) in Wuhan from 1985 to 2020. Using multi-temporal land-use data, remotely sensed vegetation indices, and socioeconomic statistics, we estimated the ESV with an improved equivalent-factor method and analyzed its drivers using a Geodetector and geographically weighted regression (GWR). Over the 35-year period, total ESV for Wuhan showed a mildly declining trend, decreasing from CNY 37.464 billion in 1985 to CNY 36.439 billion in 2020. Waterbodies contributed the largest share of ESV, followed by croplands and forests. In the urban core, ESV declined significantly, with low-value zones expanding outward from the city center. Spatial autocorrelation analysis revealed significant “high–high” and “low–low” clustering. Geodetector results indicated slope, elevation, and normalized difference vegetation index (NDVI) as the primary natural drivers, with human footprint, gross domestic product (GDP), and population density acting as important socioeconomic auxiliaries. Interactions between natural and socioeconomic factors substantially increased the explanatory power. Furthermore, GWR revealed pronounced spatial heterogeneity in the sign and magnitude of the factor effects across the study area, underscoring the complexity of ESV drivers. These findings provide quantitative evidence to support spatially differentiated ecological planning and conservation strategies during urbanization in Wuhan and the broader mid-Yangtze region. Full article

In recent years, under the dual pressures of climate change and human activities, wetlands in Liaoning Province, China, are increasingly threatened, raising concerns about regional ecological security. To better understand these changes, we developed a vulnerability assessment framework integrating a 30 m wetland dataset (2000–2020) with multi-source environmental and socio-economic data. Using the XGBoost–SHAP model, we analyzed wetland spatiotemporal evolution, driving mechanisms, and ecological vulnerability. Results show the following: (1) ecosystem service functions exhibited significant spatiotemporal differentiation; carbon storage has generally increased, water conservation capacity has significantly improved in the northern region, while wind erosion control and soil retention functions have declined due to urban expansion and agricultural development; (2) driving factors had evolved dynamically, shifting from population density in the early period to increasing influences of precipitation, vegetation index, GDP, and wetland area in later years; (3) ecologically vulnerable areas demonstrated a pattern of fragmented patches coexisting with zonal distribution, forming a three-level spatial gradient of ecological vulnerability—high in the north, moderate in the central region, and low in the southeast. These findings demonstrate the cascading effects of natural and human drivers on wetland ecosystems, and provide a sound scientific basis for targeted conservation, ecological restoration, and adaptive management in Liaoning Province. Full article

In recent years, most conventional emotion recognition approaches have concentrated primarily on facial cues, often overlooking complementary sources of information such as body posture and contextual background. This limitation reduces their effectiveness in complex, real-world environments. In this work, we present a multi-branch emotion recognition framework that separately processes facial, bodily, and contextual information using three dedicated neural networks. To better capture contextual cues, we intentionally mask the face and body of the main subject within the scene, prompting the model to explore alternative visual elements that may convey emotional states. To further enhance the quality of the extracted features, we integrate both channel and spatial attention mechanisms into the network architecture. Evaluated on the challenging NCAER-S dataset, our model achieves an accuracy of 56.42%, surpassing the state-of-the-art GLAMOUR-Net. These results highlight the effectiveness of combining multi-cue representation and attention-guided feature extraction for robust emotion recognition in unconstrained settings. The findings also highlight the importance of accurate emotion recognition for human–computer interaction, where affect detection enables systems to adapt to users and deliver more effective experiences. Full article

Healthcare access is a fundamental human right, yet barriers often negatively impact health, particularly in developing countries like Pakistan, where maternal mortality remains a crisis. This study aimed to identify factors influencing healthcare access barriers among married women aged 15–49 years using spatial analysis. Methods: Data were drawn from the 2017-18 Pakistan Demographic and Health Survey (PDHS), which included an unweighted sample of 8127 women. Healthcare access barriers were identified as the outcome variable. Results: A spatial analysis using ArcGIS 10.7.1 and SaTScan identified clustered distributions, with concentration areas identified in Gilgit Baltistan, Khyber Pakhtunkhwa (KPK), Federally Administered Tribal Areas (FATA), Punjab, and Balochistan. SaTScan highlighted primary clusters in FATA, Southern KPK, Northern Balochistan, and Eastern Punjab. Geographically Weighted Regression identified women who had five or more living children, respondents who did not have four or more antenatal care (ANC) visits, respondents who experienced a lower income (wealth index), respondents who did not participate in decision-making, respondents with a primary education, and respondents who accepted domestic violence as the significant predictors of healthcare access barriers. Conclusions: To improve women’s healthcare access, integrated policy interventions are needed, addressing socioeconomic disparities, strengthening national health policies, empowering women, and expanding healthcare accessibility. Strengthening health insurance and economic empowerment is crucial for achieving Sustainable Development Goals. Full article

Landslides are a persistent and destructive hazard in Angra dos Reis, located in the highlands of Rio de Janeiro State, southeastern Brazil, where steep slopes, intense orographic rainfall, and unregulated urban expansion converge to trigger recurrent mass movements. In this study, we applied Multiscale Geographically Weighted Regression (MGWR) to examine the spatially varying relationships between landslide occurrence and topographic, hydrological, geological, and anthropogenic factors. A detailed inventory of 319 landslides was compiled using high-resolution PlanetScope imagery after the December 2023 rainfall event. Following multicollinearity testing and variable selection, thirteen predictors were retained, including slope, rainfall, lithology, NDVI, forest loss, and distance to roads. The MGWR achieved strong performance (R² = 0.94; AICc = 134.99; AUC = 0.99) and demonstrated that each factor operates at a distinct spatial scale. Slope, rainfall, and lithology exerted broad-scale controls, while road proximity had a consistent global effect. In contrast, forest loss and land use showed localized significance. These findings indicate that landslide susceptibility in Angra dos Reis is primarily driven by the interaction of orographic rainfall, steep terrain, and geological substrate, intensified by human disturbances such as road infrastructure and vegetation removal. The study underscores the need for targeted adaptation strategies, including slope stabilization, restrictions on road expansion, and vegetation conservation in steep, rainfall-prone sectors. Full article

Background: Psoriasis is a chronic immune-mediated disease affecting approximately 43 million individuals worldwide. While previous studies provide certain insights, there remains different conclusions and a lack of a comprehensive analysis regarding the burden of psoriasis. In response to ongoing therapeutic advances and a growing patient population, this study utilizes the Global Burden of Disease (GBD) 2021 estimates to characterize the spatiotemporal evolution of the psoriasis burden from 1990 through 2021. By integrating these biological, geographic, and socioeconomic determinants, this study aims to inform more targeted and effective health policy planning. Methods: To track changes over time, the Estimated Annual Percentage Change (EAPC) was determined using a linear regression model. In addition, a frontier analysis was utilized to investigate the link between psoriasis burden and socio-demographic progress. Furthermore, geographically weighted regression was used for the spatial econometric assessment of EAPC, age-standardized rates (ASRs), and Human Development Index (HDI) covariance structures across nation-states. Results: Between 1990 and 2021, the global burden of psoriasis increased consistently, with ASRs exhibiting a positive correlation with the Socio-demographic Index (SDI). High-SDI regions reported the highest burden, while high–middle-SDI regions experienced the steepest rise. Conclusions: This study reveals an increasing global psoriasis burden (1990–2021) through systematic analyses, indicating distinct regional progression patterns. These findings advocate for geographically tailored strategies to alleviate healthcare system pressures. Full article

Sustainable tourism development has emerged as a strategic priority across ASEAN countries, yet the role of green innovation and environmentally responsible investment in shaping tourism outcomes remains underexplored. Existing studies often overlook the spatial interdependencies that characterize regional integration and cross-border environmental dynamics. This study investigates how green patents and green foreign direct investment (FDI) influence sustainable tourism development, both within and across ASEAN nations. Drawing on endogenous growth theory, ecological modernization, and FDI spillover frameworks, the analysis employs a Spatial Durbin Model (SDM) using panel data from 2000 to 2023. The findings reveal that green innovation and green FDI significantly enhance tourism development, with notable spatial spillover effects that benefit neighboring countries. These effects are most pronounced in leading ASEAN economies, where institutional capacity and absorptive readiness amplify the impact of green practices. The relationship is further shaped by economic growth, human capital, and political stability, while environmental degradation and inflation pose constraints. The study underscores the nonlinear and regionally heterogeneous nature of green tourism development, offering policy insights for fostering inclusive, resilient, and environmentally sustainable tourism strategies across ASEAN. Full article

This study evaluates the influence of land use and water stress on ecosystem resilience, using panel data for thirty-three European countries from 2007 to 2024, following the identification of a research gap in the literature on this topic. The dependent variable is the bioclimatic ecosystem resilience index (BER), and the explanatory variables are Agricultural Land Share (ALS), Forest Land Share (FLS), and the Level of Water Stress (WS). The estimated models are a fixed-effects panel regression with Driscoll-Kraay standard errors, robust to autocorrelation, heteroscedasticity, and spatial dependence, and a kernel-based regularized least squares model, which offers a new, nonlinear, heterogeneous, and sensitive to local contexts perspective on ecosystem resilience. The results indicate a significant positive effect of FLS on ecosystem resilience, ALS has a mixed influence, while WS has a negative impact. Robustness checks using cluster-robust standard errors and alternative model specifications confirmed the stability and direction of the estimated coefficients. The conclusions support the promotion of forest conservation policies, sustainable water resource management, and ecosystem-friendly agriculture practices as main directions for enhancing the capacity of ecosystems to respond to human and climate pressures. Full article

The 4th and 5th centuries marked a pivotal phase in the development of the Goguryeo regime. Its tomb murals epitomize the visual strategies of state-building, serving to establish a “sacred theater” of power. Taking Tomb No. 4 of the Wukui complex as a case in point, the murals reveal localized adaptations of the Fuxi–Nüwa imagery, blending the Central Plains’ sun-deity worship with Goguryeo’s ancestral mythology through the symbol of the sun-centered Three-Legged Crow, thereby legitimizing the sacred lineage of royal authority. The function of the Four Symbols (Sishen) imagery evolved from mere directional markers into guardians of sovereignty, reflecting deeper cultural transformations. The diachronic evolution of mural themes traces the trajectory of political change: in the 4th century, murals centered on wrestling and banqueting scenes, reinforcing ethnic identity and consolidating tribal alliances through ritualized displays of strength and hierarchical banquet etiquette. By the 5th century, the themes shifted to hunting, processions, and Buddhist rituals, where military metaphors and ceremonial norms underscored the rise of a centralized bureaucratic system and the imperatives of territorial expansion. Through three interlocking mechanisms—symbolic reconfiguration, spatial narrative, and sensory manipulation—Goguryeo tomb murals constructed a closed value system linking worldly authority to posthumous order, serving as material testimony to the enduring “covenant between humans and deities.” Full article

Urban heat islands are intensifying under the dual pressures of global climate change and rapid urbanization, posing serious challenges to ecological sustainability and human well-being. Among the factors influencing urban thermal environments, vegetation and green spaces play a critical role in mitigating heat accumulation through canopy cover, evapotranspiration, and ecological connectivity. In this study, a comparative analysis of Seoul and Busan—two representative metropolitan areas in South Korea—was conducted using land surface temperature (LST) data derived from Landsat 8 and a set of multi-source spatial indicators. The nonlinear effects and interactions among built environment, socio-economic, and ecological variables were quantified using the Extreme Gradient Boosting (XGBoost) model in conjunction with Shapley Additive Explanations (SHAP). Results demonstrate that vegetation, as indicated by the Normalized Difference Vegetation Index (NDVI), consistently exerts significant cooling effects, with a pronounced threshold effect observed when NDVI values exceed 0.6. Furthermore, synergistic interactions between NDVI and surface water availability, measured by the Normalized Difference Water Index (NDWI), substantially enhance ecological cooling capacity. In contrast, areas with high building and population densities, particularly those at lower elevations, are associated with increased LST. These findings underscore the essential role of green infrastructure in regulating urban thermal environments and provide empirical support for ecological conservation, urban greening strategies, and climate-resilient urban planning. Strengthening vegetation cover, enhancing ecological corridors, and integrating greening policies across spatial scales are vital for mitigating urban heat and improving climate resilience in rapidly urbanizing regions. Full article