Search Results (1,641)

Floods rank among the most frequent and destructive natural hazards, threatening ecosystems, human settlements, and national economies. This study delineates flood-prone areas across Kosovo by developing a national-scale Flood Risk Database (FRDB) and a comprehensive mapping framework integrating Geographic Information Systems (GIS), Multi-Criteria Decision-Making (MCDM), and the Analytical Hierarchy Process (AHP). Eight hydrological and topographic conditioning factors—slope, elevation, flow accumulation, distance to rivers, land use/land cover, soil type, precipitation, and drainage density—were analyzed. AHP was employed to assign factor weights based on their relative influence on flood susceptibility, while MCDM aggregated these weighted spatial layers to generate a national flood risk map. Model validation, based on historical flood points, achieved an AUC of 0.909, confirming its high predictive accuracy. The resulting flood risk map classifies Kosovo’s territory into five risk levels: very high (0.56%), high (14.44%), moderate (36.68%), low (46.46%), and very low (1.88%). This research provides the first systematic national-scale FRDB for Kosovo, offering a reliable scientific basis for flood management, spatial planning, and climate resilience policy. Full article

This study advances the reconceptualization of Open municipal markets (OMMs) as networked ecosystems that connect food producers, vendors and citizenship across rural and urban contexts, sustaining short food supply chains and reinforcing territorial resilience through the interplay of mobility and embeddedness. Aimed at understanding OMMs as components of a broader, networked and adaptable food ecosystem, the research introduces a new methodology that builds on existing scholarship framing markets as relational and mobile spaces. It contributes to the literature by integrating these perspectives into an ecosystemic lens. By applying a mobility-based approach, the research shifts attention from static views of markets to their dynamic and circulatory nature, highlighting their role in fostering more sustainable and socially rooted food systems. Focusing on 105 OMMs in the Province of Girona (Spain), the research combines spatial analysis and data analysis of 300 surveys completed by 300 stallholders to examine how mobility practices shape market dynamics. The paper provides a new methodology of market stallholders and types of markets as well as four key indicators (recurrence, variety, closeness and rootedness) to assess stallholder activity and territorial embeddedness. These findings reveal that stallholders, particularly producers, connect rural production with urban consumption through flexible and multi-scalar circuits. The paper advocates for ecosystem-based urban food planning that harnesses stallholder mobility to strengthen territorial cohesion and food sovereignty, positioning OMMs as strategic public facilities for resilient and socially responsible food systems. Full article

Predicting the sustainability of national educational systems presents a complex, multifaceted issue due to the intricate connections between education and wider societal, economic, healthcare, and technological sectors. Current educational models tend to be rigid, narrow in focus, and insufficiently responsive to these changing external factors. This research seeks to fill this void by framing education as an ecosystem and creating a methodological framework that merges systems thinking with sophisticated data-driven methods. The study’s aim is to outline, quantify, and analyze the relationships among education-related subsystems to guide the creation of an adaptive, sustainability-focused educational ecosystem. A mixed-methods approach was utilized, incorporating qualitative coding, system mapping, and natural language processing techniques (specifically Word2Vec) to uncover relational patterns within a structured literature set. These findings were integrated with quantitative metrics to assess subsystem efficacy and pinpoint leverage points. The investigation centers on five primary systems in the education ecosystem: Business, Economic, Government, Healthcare, and Sustainability. The Word2Vec analysis identified significant conceptual relationships between these systems, while the quantitative evaluation indicated strong performance across curriculum, policy, and healthcare metrics. Conversely, inclusivity and accreditation displayed weaker outcomes, indicating areas that need focused improvement. The results highlight the benefits of merging systems thinking with NLP-driven relational analysis as a methodological innovation in education research. The study offers evidence-based recommendations for prioritizing factors that can boost system efficacy and create beneficial cross-system ripple effects, aiding in the advancement of adaptive and sustainable educational ecosystems. Full article

Cloud-based platforms form the backbone of smart city ecosystems, powering essential services such as transportation, energy management, and public safety. However, their operational complexity generates vast volumes of system logs, making manual anomaly detection infeasible and raising reliability concerns. This study addresses the challenge of data scarcity in log anomaly detection by leveraging Large Language Models (LLMs) to enhance domain-specific classification tasks. We empirically validate that domain-adapted classifiers preserve strong natural language understanding, and introduce a Proximal Policy Optimization (PPO)-based approach to align semantic patterns between LLM outputs and classifier preferences. Experiments were conducted using three Transformer-based baselines under few-shot conditions across four public datasets. Results indicate that integrating natural language analyses improves anomaly detection F1-Scores by 5–86% over the baselines, while iterative PPO refinement boosts classifier’s “confidence” in label prediction. This research pioneers a novel framework for few-shot log anomaly detection, establishing an innovative paradigm in resource-constrained diagnostic systems in smart city infrastructures. Full article

Woody debris decomposition is a key process in forest ecosystem material cycles, with invertebrate communities playing a vital role. Distinct physicochemical properties of woody debris types lead to varying effects on these communities. Taking woody debris in Saihanba’s Larix principis-rupprechtii plantations, Betula platyphylla natural secondary forests, and larch–birch mixed forests (northern China) as objects, we collected woody debris-inhabiting invertebrates via hand-sorting. We studied how tree species (larch/birch), forest types (pure/mixed), and decay stages (I–V) collectively regulate invertebrate community assembly. Results showed significant differences in woody debris physicochemical properties across these factors. Phytophagous groups dominated early decay stages (I–III) and decreased significantly (p < 0.05) with reduced wood density. In contrast, saprophagous and predatory groups increased with decay, correlated with higher TN and were more abundant in mixed than pure forests. NMDS indicated significant community differences among tree species/forest types in early decay, converging later. PLS-PM further confirmed functional groups’ response pathways to woody debris characteristics. Thus, preserving woody debris integrity and diversity in plantations is crucial for maintaining invertebrate diversity, promoting nutrient cycling, and enhancing forest ecosystem functions. Full article

Forest carbon sinks (FCS)—referring specifically to ecosystem-based carbon sequestration provided by forest ecosystems—are being increasingly recognized as a strategic form of natural capital under China’s “dual carbon” goals. While the ecological value of FCS is being translated into economic benefits through carbon markets, eco-compensation, and green finance, the extent to which ecosystem carbon sinks can continuously drive regional economic growth—and how such effects differ across regions—remains insufficiently understood. Using panel data for 294 Chinese prefecture-level cities from 2010 to 2022, this study employs dynamic panel methods to examine the dynamic, nonlinear, and heterogeneous impacts of ecosystem-based FCS on economic growth. The results show that (1) FCS significantly promote economic growth but follow an inverted U-shaped pattern, indicating diminishing marginal returns; (2) notable regional heterogeneity exists, with the strongest effects in central and western regions, while eastern cities exhibit weaker responses due to structural and spatial constraints; and (3) clear threshold effects are present, suggesting that industrial upgrading, urbanization, and moderate government intervention can amplify the economic contribution of FCS. These findings clarify the mechanism through which FCS transitions from ecological assets to economic capital, providing theoretical and empirical support for sustainable forest management, ecological-industrial integration, and carbon market optimization in the pursuit of carbon neutrality. Full article

Monitoring vegetation biomass dynamics is essential for assessing ecosystem functioning and biodiversity pressures in protected mountain areas, where reduced accessibility limits in situ data collection. This study investigates the multitemporal variation in vegetation biomass within the Cioclovina–Șura Mare–Piatra Roșie strictly protected area of the Grădiștea Muncelului–Cioclovina Natural Park (Southern Carpathians, Romania), using vegetation indices derived from Sentinel-2 imagery for the 2018–2022 period. Four complementary indices (NDVI, SAVI, MSAVI, and LAI) were computed and normalized, then integrated into an original synthetic indicator (BCIS—Biomass Change Integrated Score) for quantifying biomass changes. The results indicate an overall reduction in vegetation biomass, with 89.49% of the area classified under degradation trends, while 4.53% shows regeneration processes. Grasslands and mixed agricultural–natural lands are the most affected habitats, where degradation is linked to anthropogenic pressures and ecotonal vulnerability, whereas broadleaf forests display a high degree of resilience, maintaining substantial proportions of stable or regenerating surfaces. The multispectral integration through the BCIS indicator enabled a more robust detection of critical zones, supporting sustainable vegetation management and biodiversity monitoring in protected mountain ecosystems. Full article

Rice (Oryza sativa L.) production in the Philippines remains highly vulnerable to recurrent outbreaks of the Rice Black Bug (RBB; Scotinophara coarctata F.) and White Stemborer (WSB; Scirpophaga innotata W.), two of the most destructive pests in Southeast Asian rice ecosystems. Classical economic threshold levels (ETLs) are difficult to estimate in smallholder settings due to the lack of cost–loss data, often leading to either delayed or excessive pesticide application. To address this, the present study developed an adaptive outbreak-forecasting framework that integrates the Number–Size (N–S) fractal model with machine learning (ML) classifiers to define and predict pest regime transitions. Seven years (2018–2024) of light-trap surveillance data from the Philippine Rice Research Institute–Midsayap Experimental Station were combined with daily climate variables from the NASA POWER database, including air temperature, humidity, precipitation, wind, soil moisture, and lunar phase. The N–S fractal model identified natural breakpoints in the log–log cumulative frequency of pest counts, yielding early-warning and severe-outbreak thresholds of 134 and 250 individuals for WSB and 575 and 11,383 individuals for RBB, respectively. Eight ML algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Balanced Bagging, LightGBM, XGBoost, and CatBoost were trained on variance-inflation-filtered climatic and temporal predictors. Among these, CatBoost achieved the highest predictive performance for WSB at the 94.3rd percentile (accuracy = 0.932, F1 = 0.545, ROC–AUC = 0.957), while Logistic Regression performed best for RBB at the 75.1st percentile (F1 = 0.520, ROC–AUC = 0.716). SHAP (SHapley Additive exPlanations) analysis revealed that outbreak probability increases under warm nighttime temperatures, high surface soil moisture, moderate humidity, and calm wind conditions, with lunar phase exerting additional modulation of nocturnal pest activity. The integrated fractal–ML approach thus provides a statistically defensible and ecologically interpretable basis for adaptive pest surveillance. It offers an early-warning system that supports data-driven integrated pest management (IPM), reduces unnecessary pesticide use, and strengthens climate resilience in Philippine rice ecosystems. Full article

The Xiaohai Lagoon is a vital coastal ecosystem that has faced decades of significant natural and anthropogenic pressures. This study investigated the spatio-temporal dynamics its phytoplankton communities through quarterly sampling from 2024 to 2025. Significant spatial and seasonal variations (p < 0.05) in physicochemical parameters were observed. The concentrations of various physicochemical parameters were highest at the lagoon mouth and decreased inwards. In contrast, sites inside the lagoon experienced elevated nutrient and organic matter indicators. Seasonally, the highest temperatures were recorded in Summer. However, Autumn recorded the highest NH₃-N and NO₂-N levels, while Winter recorded the highest NO₃-N levels. The findings generally suggest minimal pollution, as key physicochemical parameters, met the China water quality standard for environmental protection (GB 3838–2002). Overall, 109 phytoplankton species belonging to 38 genera and 5 phyla, including Cyanophyta, Bacillariophyta, Chlorophyta, Cryptophyta, and Dinophyta, were identified. The phytoplankton average density was 1.65 × 10³ Ind L⁻¹ with insignificant differences both spatially and seasonally (p > 0.05). One-way ANOSIM indicated significant seasonal dissimilarity in phytoplankton community composition (R = 0.828, p < 0.001), with SIMPER results revealing that Ceratocorys sp., Chaetoceros sp., Coscinodiscus subtilis, Oscillatoria princes, and Thalassionema nitzschioides contributed to the seasonal difference. CCA indicated phytoplankton composition and abundance were influenced by COD, TN, TDS, salinity, oxidation-reduction potential, EC, water temperature, NH₃-N, and NO₃-N. This study highlights the critical need for effective management strategies to protect and preserve the ecological integrity of Xiaohai Lagoon. Full article

To enhance the monitoring and conservation efforts for China’s Class II endangered species, specifically the wild giant salamander and its ecosystems, this study addresses the urgent need to counteract the rapid decline of its wild population caused by habitat loss and insufficient surveillance. We present an innovative localization system based on passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) technology, employing a Double-Transform (D-Tr) methodology that integrates an enhanced 3D LANDMARC algorithm with GAIN generative adversarial networks. This system effectively reconstructs missing Received Signal Strength Indicator (RSSI) data due to environmental barriers by applying a log-distance path loss model. The D-Tr framework simultaneously generates RSSI sequences alongside their first-order differential characteristics, allowing for a comprehensive analysis of spatiotemporal signal relationships. Field tests conducted in the Hubei Xianfeng Zhongjian River Giant Salamander National Nature Reserve reveal that the positioning error consistently remains within 10 cm, with average accuracy improvements of 20.075%, 15.331%, and 12.925% along the X, Y, and Z axes, respectively, compared to traditional time-series models such as long short-term memory (LSTM) and gated recurrent unit (GRU). This system, designed to investigate the behavioral patterns and movement paths of farmed giant salamanders, achieves centimeter-level tracking of their cave-dwelling activities. It provides essential technical support for quantitatively assessing their daily activity patterns, habitat choices, and population trends, thereby promoting a shift from passive oversight to proactive monitoring in the conservation of endangered species. Full article

This study is one of the first comprehensive assessments of soil carbon dynamics on the Black Sea coast of Russia, focusing on the role of soils in the terrestrial carbon cycle and the greenhouse gas balance of sub-Mediterranean ecosystems. Our integrated approach combined soil classification with the analysis of the distribution of organic and inorganic carbon, as well as the measurement of microbial biomass and respiration. Soil respiration components, including substrate-induced respiration (SIR) and basal respiration (BR), as well as greenhouse gas (carbon dioxide (CO₂) and methane (CH₄)) dynamics, were evaluated using a combination of laboratory and field measurements. Our results revealed significant differences between natural Rendzic Leptosols and terraced Skeletic Rendzic Leptosols (Technic and Transportic types). The latter contained higher organic carbon stocks (up to 25 kg m⁻²) associated with buried humus horizons, whereas the former were dominated by inorganic carbon accumulation. Microbial biomass carbon (MBC) ranged from 113 to 1119 µg C g⁻¹ of soil and decreased with depth. Basal respiration averaged 0.39 ± 0.30 µg C–CO₂ g⁻¹ h⁻¹. CO₂ emissions were strongly correlated with soil temperature (r = 0.65, p < 0.05) and negatively correlated with soil moisture, reflecting the predominant influence of abiotic factors. Seasonal chamber observations confirmed that these soils consistently function as CH₄ sinks, with negative CH₄ fluxes recorded across all seasons. Thus, Rendzic Leptosols on the Black Sea coast serve as significant CO₂ sources and stable CH₄ sinks simultaneously, and anthropogenic terracing enhances their potential for organic carbon sequestration. These findings refine our understanding of the carbon balance in sub-Mediterranean forest soils and highlight their dual role in greenhouse gas dynamics under changing climate conditions. Full article

Landscapes in semiarid regions are highly sensitive to climate change and anthropogenic activities, and their evolution directly influences ecosystem services and regional ecological security. Although previous research has examined land use changes, systematic quantitative analyses of long-term evolutionary trends and driving mechanisms, particularly the comprehensive relationships between key hydrological elements and landscape pattern evolution in water-scarce, semiarid watersheds, remain limited. To address the research gap in long-term, multifactor, and hydro–landscape integrated analysis, China’s Tuwei River watershed was selected as the study area in this study, and methods such as landscape pattern indices and gray relational analysis were employed to quantitatively reveal the spatiotemporal evolution of watershed landscape fragmentation from 1980 to 2020 and identify its dominant driving forces. The results revealed that (1) over the 40-year period, the land use structure of the watershed underwent significant restructuring, with developed land expanding by 1282%, cropland and bare land areas decreasing by 14.2% and 32.01%, respectively, and grassland and forestland areas increasing by 24.5% and 14.9%, respectively; (2) land-scape fragmentation continued to intensify, with the landscape fragmentation composite index (FCI) increasing by 37.6%, patch density (PD) continuously increasing, edge density (ED) and landscape shape index (LSI) increasing significantly, and landscape connectivity weakening; (3) natural and socioeconomic factors jointly drove landscape evolution, with temperature and mean annual flow contributing the most among natural factors and the urbanization rate and secondary industry output value serving as the core drivers among socioeconomic factors; and (4) the trend of landscape fragmentation was synchronized with changes in annual rainfall and runoff and exhibited a significant negative correlation with the groundwater level. In summary, through long-term, multifactor comprehensive analysis, the evolution characteristics and driving mechanisms of landscape patterns in the Tuwei River watershed were systematically revealed in this study. These findings not only deepen the understanding of landscape fragmentation processes under the dual pressures of climate change and anthropogenic activities but also provide scientific evidence for the sustainable management of landscapes and associated ecosystems in semiarid watersheds. Full article

Volcanic eruptions can mobilize naturally occurring toxic elements such as arsenic into surrounding ecosystems, contaminating soil, water, and food webs. Despite increasing evidence of arsenic enrichment in volcanic regions, comprehensive exposure assessments that integrate dietary and drinking water pathways remain limited, particularly in post-eruption contexts where baseline data are scarce. Following the 2020 Taal Volcano eruption, this study conducted a probabilistic risk assessment to quantify aggregate exposure to inorganic arsenic (iAs) among residents of Batangas, Philippines. A Monte Carlo simulation framework (10,000 iterations) integrated post-eruption environmental data on total arsenic in soil, lake water, drinking water and clam tissues with modeled bioaccumulation and transfer factors for fish and major terrestrial crops. Two exposure scenarios, lower bound (50% iAs fraction) and upper bound (90% iAs fraction), were applied to capture uncertainty in arsenic speciation and bioavailability. Simulated iAs concentrations followed the order rice > corn > vegetables > root crops. Aggregate daily iAs doses averaged 3.0 ± 1.4 µg/kg bw/day (lower bound) and 4.0 ± 2.0 µg/kg bw/day (upper bound), with females showing slightly higher exposures and pregnant women exhibiting lower doses. Sensitivity analysis identified clam intake, rice intake, and iAs in rice, clams, and drinking water as dominant determinants of total exposure. All simulated individuals exceeded the U.S. EPA non-cancer reference dose (HQ > 1) and cancer risk benchmark (10⁻⁶–10⁻⁴), indicating substantial health concern. These findings highlight the urgent need for sustained environmental monitoring, arsenic speciation analyses, biomonitoring, and public health programs to guide evidence-based management in arsenic-affected volcanic regions. Full article

Ecosystem disservices (EDS)—ecosystem properties and functions that cause discomfort or harm—often shape public attitudes to urban biodiversity more strongly than ecosystem services, yet they remain weakly integrated into inclusive spatial planning. This study develops and tests an EDS classification and a decision-making tree intended to help planners recognise disservices, assess ES–EDS trade-offs, and select proportionate responses without defaulting to ecological simplification. The framework was derived from literature, survey evidence, and expert–stakeholder input from Eastern European cities, and then examined through five contrasting urban action situations in Estonia and Belarus. The cases show that a shared decision logic for EDS is transferable across settings, but that its practical uptake depends on governance conditions. Where communication was proactive and explanatory, participation was meaningful, and long-term management was institutionally secured, disservices were reframed or mitigated while ecological objectives were maintained. Where disservices were framed late, trust was low, or political intervention truncated deliberation, even modest nature-based interventions were stalled or redirected toward grey alternatives. These findings justify treating EDS as a routine planning concern and demonstrate how an EDS-aware approach can strengthen inclusive planning by making both benefits and burdens of urban nature explicit. Full article

Under global environmental change, the health of rivers and lakes on the “Asian Water Tower”—the Qinghai–Tibetan Plateau—is facing mounting pressures. This study examines Qinghai Lake, the Huangshui River, the Golmud River, and the Qinghai reach of the Yangtze River. By integrating the Water Quality Index (WQI) with the AHP–TOPSIS framework, we develop a multidimensional assessment system encompassing water resources, water environment, aquatic ecology, and management functions. The WQI results reveal pronounced spatial heterogeneity in water quality, with conditions ranked as Golmud River > Yangtze River > Huangshui River > Qinghai Lake. Dominant controlling factors also shift from dissolved oxygen in riverine systems to total phosphorus in the lake environment. The comprehensive AHP–TOPSIS evaluation further shows a health ranking of Yangtze River (0.736) > Golmud River (0.602) > Qinghai Lake (0.404) > Huangshui River (0.297), leading to the identification of four distinct management pathways: ecological conservation, natural restoration, nutrient control, and pollution remediation. By moving beyond single-parameter diagnostics, this study provides a robust methodological basis for differentiated river–lake management. The proposed “one river (lake), one strategy” framework, coupled with red-line management recommendations grounded in key indicators, offers direct scientific support for systematic protection and precise governance of aquatic ecosystems on the Qinghai–Tibetan Plateau, contributing to national ecological security and high-level environmental stewardship. Full article