Search Results (1,825)

China’s coastal fisheries face challenges to their sustainability due to climate and human-induced pressures on key habitat drivers. This study provides an 18-year (2003–2020) assessment of six key ecological and data-available environmental factors (sea-surface temperature (SST), salinity, transparency, currents (eastward velocity, EV; northward velocity, NV), and net primary productivity (NPP), selected for their ecological relevance and data availability, across the Bohai, Yellow, and East China Seas at a spatial resolution of 0.083°. Non-parametric trend tests and seasonal climatologies were applied using MODIS-Aqua and CMEMS data with a refined quasi-analytical algorithm (QAA-v6). The results show distinct gradients: SST ranging from 9 to 13 °C (Bohai Sea) to >20 °C (East China Sea); transparency ranging from <5 m (turbid coasts) to 29.20 m (offshore). Seasonal peaks occurred for SST (summer: 18.92 °C), transparency (summer: 12.54 m), and primary productivity (spring: 1289 mg/m²). Long-term trends reveal regional SST warming in the northern Yellow Sea (9.78% of the area), but cooling in the central East China Sea. Widespread increases in transparency were observed (65.14% of the area), though productivity declined significantly (27.3%). The drivers showed spatial coupling (e.g., SST–salinity r = 0.95), but the long-term trends were decoupled. This study provides a comprehensive and long-term assessment of multiple key habitat drivers across China’s coastal seas. The results provide an unprecedented empirical baseline and dynamic management tools for China’s changing coastal ecosystems. Full article

Snowmelt runoff is a major soil erosion trigger in mid-to-high latitude and altitude regions. Through runoff plot observations and simulations in the northeastern black soil region, this study reveals the key regulatory mechanism of water migration on snowmelt erosion. Results demonstrate that the interaction between thawed upper and frozen lower soil layers creates a significant hydraulic gradient during snowmelt. Impermeability of the frozen layer causes meltwater accumulation and moisture supersaturation (>47%, exceeding field capacity) in the upper layer. Freeze–thaw action accelerates vertical moisture migration and redistributes shallow moisture by increasing porosity. This process causes soils with high initial moisture to reach supersaturation faster, triggering earlier and more frequent erosion. Gray correlation analysis shows that soil moisture migration’s contribution to erosion intensity is layered: migration in shallow soil (0–10 cm) correlates most strongly with surface erosion; migration in deep soil (10–15 cm) exhibits a U-shaped contribution due to freeze–thaw front boundary effects. A regression model identified key controlling factors (VIP > 1.0): changes in bulk density, porosity, and permeability of deep soil significantly regulate erosion intensity. The nonlinear relationship between erosion intensity and moisture content (R² = 0.82) confirms supersaturation dominance. Physical structure and mechanical properties of unfrozen layers regulate erosion dynamics via moisture migration. These findings clarify the key mechanism of moisture migration governing snowmelt erosion, providing a critical scientific foundation for developing targeted soil conservation strategies and advancing regional prediction models essential for sustainable land management under changing winter climates. Full article

This study explores the convergence in greenhouse gas emissions (GHGs) and its determinants across 38 OECD countries during the period 1996–2022, employing the novel approach which combined club convergence method with supervised machine learning algorithm Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) method. The findings reveal the presence of three distinct convergence clubs shaped by structural economic and institutional characteristics. Club 1 exhibits low energy efficiency, high fossil fuel dependence, and weak governance structures; Club 2 features strong institutional quality, advanced human capital, and effective environmental taxation; and Club 3 displays heterogeneous energy profiles but converges through socio-economic foundations. While traditional growth-related drivers such as technological innovation, foreign direct investments, and GDP growth play a limited role in explaining emission convergence, energy structures, institutional and policy-related factors emerge as key determinants. These findings highlight the limitations of one-size-fits-all climate policy frameworks and call for a more nuanced, club-specific approach to emission mitigation strategies. By combining convergence theory with interpretable machine learning, this study contributes a novel empirical framework to assess the differentiated effectiveness of environmental policies across heterogeneous country groups, offering actionable insights for international climate governance and targeted policy design. Full article

Under the combined influence of global climate change and intensified human activities, ecosystem services (ESs) are undergoing substantial transformations. Identifying their nonlinear driving mechanisms is crucial for promoting regional sustainable development. Taking Anhui Province as a case study, this research evaluates the spatial patterns and temporal dynamics of six key ecosystem services from 2000 to 2020—namely, biodiversity maintenance (BM), carbon fixation (CF), crop production (CP), net primary productivity (NPP), soil retention (SR), and water yield (WY). The InVEST and CASA models were employed to quantify service values, and the XGBoost–SHAP framework was used to reveal the nonlinear response paths and threshold effects of dominant drivers. Results show a distinct “high in the south, low in the north” spatial gradient of ES across Anhui. Regulatory services such as BM, NPP, and WY are concentrated in the southern mountainous areas (high-value zones > 0.7), while CP is prominent in the northern and central agricultural zones (>0.8), indicating a clear spatial complementarity of service types. Over the two-decade period, areas with significant increases in NPP and CP accounted for 50% and 64%, respectively, suggesting notable achievements in ecological restoration and agricultural modernization. CF remained stable across 98.3% of the region, while SR and WY exhibited strong sensitivity to topography and precipitation. Temporal trend analysis indicated that NPP rose from 395.83 in 2000 to 537.59 in 2020; SR increased from 150.02 to 243.28; and CP rose from 203.18 to 283.78, reflecting an overall enhancement in ecosystem productivity and regulatory functions. Driver analysis identified precipitation (PRE) as the most influential factor for most services, while elevation (DEM) was particularly important for CF and NPP. Temperature (TEM) and potential evapotranspiration (PET) affected biomass formation and hydrothermal balance. SHAP analysis revealed key threshold effects, such as the peak positive contribution of PRE to NPP occurring near 1247 mm, and the optimal temperature for BM at approximately 15.5 °C. The human footprint index (HFI) exerted negative impacts on both BM and NPP, highlighting the suppressive effect of intensive anthropogenic disturbances on ecosystem functioning. Anhui’s ES exhibit a trend of multifunctional synergy, governed by the nonlinear coupling of climatic, hydrological, topographic, and anthropogenic drivers. This study provides both a modeling toolkit and quantitative evidence to support ecosystem restoration and service optimization in similar transitional regions. Full article

The Okapi Wildlife Reserve (OWR) in northeastern Democratic Republic of the Congo represents both a biodiversity hotspot and the ancestral homeland of the Indigenous Mbuti and Efe peoples, whose livelihoods and knowledge systems are closely tied to forest resources. This study investigates how Indigenous knowledge and practices contribute to sustainable resource management under conditions of rapid socio-cultural transformation. A mixed-methods approach was applied, combining socio-demographic surveys (n = 80), focus group discussions, floristic inventories, and statistical analyses (ANOVA, logistic regressions, chi-square, MCA). Results show that hunting, fishing, gathering, and honey harvesting remain central livelihood activities, governed by customary taboos and restrictions that act as de facto ecological regulations. Agriculture, recently introduced through intercultural exchange with neighboring Bantu populations, complements rather than replaces traditional practices and demonstrates emerging agroecological hybridization. Nevertheless, evidence of biodiversity decline (including local disappearance of species such as Dioscorea spp.), erosion of intergenerational knowledge transmission, and increased reliance on monetary income indicate vulnerabilities. Multiple Correspondence Analysis revealed a highly structured socio-ecological gradient (98.5% variance explained; Cronbach’s α = 0.977), indicating that perceptions of environmental change are strongly coupled with demographic identity and livelihood strategies. Floristic inventories confirmed significant differences in species abundance across camps (ANOVA, p < 0.001), highlighting site-specific pressures and the protective effect of persistent customary norms. The findings underscore the resilience and adaptability of Indigenous Peoples but also their exposure to ecological and cultural disruptions. We conclude that formal recognition of Indigenous institutions and integration of their knowledge systems into co-management frameworks are essential to strengthen ecological resilience, secure Indigenous rights, and align conservation policies with global biodiversity and climate agendas. Full article

Lakes account for approximately 87% of the Earth’s surface water resources and serve as sensitive indicators of climate and environmental change. Understanding how lake areas respond to climate change across different elevation gradients is crucial for guiding sustainable water resource management in Xinjiang. We utilized Landsat series remote sensing imagery (1990–2023) on the Google Earth Engine (GEE) platform to extract the temporal dynamics of natural lakes larger than 10 km² in Xinjiang, China (excluding reservoirs). We analyzed the relationships between lake area dynamics, climatic factors, and human activities to assess the sensitivity of lakes at different altitudinal zones to environmental change. The results showed that (1) the total area of Xinjiang lakes increased by 1188.36 km² over the past 34 years, with an average annual area of 5998.54 km²; (2) plain lakes experienced fluctuations, reaching their maximum in 2000 and their minimum in 2015, alpine lakes peaked in 2016, and plateau lakes continued to expand, with the maximum recorded in 2020 and the minimum in 1995; and (3) human activities such as urban and agricultural water use were the primary causes of shrinking plain lakes, while an increased PET accelerates evaporation, alpine lakes were influenced by both climate variability and human disturbance, and plateau lakes were highly sensitive to climate change, with rising temperatures increasing snowmelt and glacial runoff into lakes, which were the main drivers of their expansion. These findings highlight the importance of incorporating elevation-specific lake responses into climate adaptation strategies and sustainable water management policies in arid regions. 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

Eyespots are functionally complex and highly variable elements of butterfly wing patterns. The Meadow Brown, Maniola jurtina, is a classic model species studied for variation in eyespots as an index of evolutionary divergence and adaptation. However, the role of fine-scale ecogeographic conditions on eyespot variation remains poorly understood. In this study, we examined hindwing eyespot number, distribution, and combination patterns in male M. jurtina across climatically and topographically diverse north-western Balkans. Compared to the species average, males in this region displayed greater spottiness and phenotypic diversity. While the typical two-spot phenotype was dominant and stable, in some populations, three-spotted and even four-spotted males occurred at similar frequencies. Rare six-spotted individuals were recorded only at mountain localities above 1200 m. Geographic and climatic factors together influenced this variation: higher altitudes and cooler, thermally stable environments promoted increased eyespot number and greater phenotypic plasticity than warmer, more variable environments. This pattern contrasts with large-scale latitudinal trends previously described for the species, emphasizing the importance of local climatic heterogeneity. Our findings suggest the north-western Balkans as a possible transitional zone where environmental complexity promotes elevated eyespot variability, contributing to the understanding of adaptive morphological plasticity in M. jurtina. Full article

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. 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

Accurate forecasting of waste is essential for effective management and allocation of resources. As urban populations grow, the demand for municipal waste systems increases, creating the need for reliable forecasting methods to support planning and decision making. This study compares statistical models Error Trend Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) with advanced machine learning approaches, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks. Five waste categories were analyzed: dead animal, building, commercial, domestic, and liquid waste. Historical datasets were used for model training and validation, with accuracy assessed through mean absolute error and root mean squared error. Results indicate that ARIMA generally outperforms ETS in forecasting building, commercial, and domestic waste streams, especially in capturing long-term domestic waste patterns. Both statistical models, however, show limitations in predicting liquid waste due to its irregular and highly variable nature, where even baseline models sometimes perform competitively. In contrast, machine learning methods consistently achieve the lowest forecasting errors across all categories. Their capacity to capture nonlinear relationships and adapt to complex datasets highlights their reliability for real-world waste management. The findings underline the importance of selecting forecasting techniques tailored to the characteristics of each waste type rather than applying a uniform method. By improving forecasting accuracy, municipalities and policymakers can design more effective waste management strategies that align with Sustainable Development Goal 11 on sustainable cities and communities, Sustainable Development Goal 12 on responsible consumption and production, and Sustainable Development Goal 13 on climate action. 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

Background/Objectives: Climate change is intensifying vector-borne disease (VBD) transmission globally, causing over 700,000 annual deaths, yet systematic evidence comparing climate–health pathways across urban and rural settlements remains fragmented. This scoping review aimed to synthesize evidence on the differential impacts of extreme weather events on vector-borne disease transmission between urban and rural environments and identify settlement-specific prevention and healthcare preparedness strategies. Methods: A scoping review following PRISMA-ScR guidelines searched PubMed, EMBASE, Web of Science, and Scopus databases for studies examining climate–vector-borne disease relationships across settlement types. Sixteen empirical studies were analyzed using narrative synthesis, with urban–rural comparisons largely inferential given limited direct comparative studies. Results: From 6493 records identified, 4875 were screened after duplicate removal, yielding 16 studies for analysis. Studies covered multiple vector-borne diseases, including malaria, dengue, leishmaniasis, chikungunya, and Zika, across diverse geographic regions. Urban environments demonstrated infrastructure-mediated transmission dynamics characterized by heat island amplification exceeding vector survival thresholds, drainage system vulnerabilities creating breeding habitats, and density-driven epidemic spread affecting healthcare surge capacity. Rural settings exhibited ecosystem-mediated pathways involving diverse vector communities, agricultural breeding sites, and seasonal spillover from wildlife reservoirs, with healthcare accessibility challenges during extreme weather events. Critical research gaps included a limited number of longitudinal comparative studies and geographic variations in evidence generation. Conclusions: Extreme weather events create fundamentally distinct vector-borne disease transmission pathways across urban–rural gradients, necessitating settlement-specific prevention strategies and healthcare preparedness approaches. Evidence-based recommendations include urban infrastructure improvements, rural early warning systems, and cross-sectoral coordination frameworks to enhance the adaptive capacity for climate-resilient vector-borne disease prevention. Full article

Alpine wetlands, key carbon sinks and biodiversity hubs, remain understudied, especially under climate change pressures. Hence, the present study was conducted to assess the variability in soil enzyme activity (SEA) and the carbon management index (CMI) and to utilize principal component analysis (PCA) to explore the variation and correlation between SEA and CMI as influenced by altitudinal gradients in alpine wetlands. This information is essential for exploring the impacts of soil degradation and guiding restoration efforts. The study was designed in blocks (catchments) with six altitudinal variations (from 2500 to 3155 m a.s.l), equivalent to alpine wetlands from three catchments (Senqunyane, Khubelu and Sani) as follows: Khorong and Tenesolo in Senqunyane; Khamoqana and Khalong-la-Lichelete in Sani; and Lets’eng-la-Likhama and Koting-Sa-ha Ramosetsana in Khubelu. The soil samples were collected in February 2025 (autumn season, i.e., wet season) at depths of 0–15 and 15–30 cm and analyzed for bulk density, texture, pH, electrical conductivity (EC), soil organic carbon (SOC), SEA, and carbon pools, and the CMI was computed following standard procedures. The results demonstrated that the soil was loam to sandy loam and was slightly acidic and non-saline in nature in the 0–15 cm layer across the wetlands. The significant decreases in SEA were 45.33%, 32.20% and 15.11% (p < 0.05) for dehydrogenase, fluorescein di-acetate and β-Galactosidase activities, respectively, in KSHM compared with those in Khorong (lower elevated site). The passive carbon pool (C_PSV) was dominant over the active carbon pool (C_ACT) and contributed 76–79% of the SOC to the total organic carbon, with a higher C_PSV (79%) observed at KSHM. The CMI was also greater (91.05 and 75.88) under KSHM at the 0–15 cm and 15–30 cm soil depths, respectively, than in all the other alpine wetlands, suggesting better carbon management at higher altitudinal gradients and less enzymatic activity. These trends shape climate change outcomes by affecting soil carbon storage, with high-altitude regions serving as significant, though relatively less active, carbon reservoirs. The PCA-Biplot graph revealed a negative correlation between the CMI and SEA, and these variables drove more variation across sites, highlighting a complex interaction influenced by higher altitude with its multiple ecological drivers, such as temperature variation, nutrient dynamics, and shifts in microbial communities. Further studies on metagenomics in alpine soils are needed to uncover altitude-driven microbial adaptations and their role in carbon dynamics. Full article

Soil organic carbon (SOC) constitutes the largest terrestrial carbon pool and plays a crucial role in climate regulation, soil fertility, and ecosystem functioning. Understanding its spatiotemporal dynamics is particularly important in semi-arid regions, where fragile environments and extensive ecological restoration may alter carbon cycling. The Loess Plateau, the world’s largest loess accumulation area with a history of severe erosion and large-scale vegetation restoration, provides a natural laboratory for examining how environmental gradients influence SOC storage over time. This study used a random forest model with multi-source environmental data to quantify soil organic carbon density (SOCD) dynamics in the 0–100 cm soil layer of the Loess Plateau from 2005 to 2020. SOCD showed strong spatial heterogeneity, decreasing from the humid southeast to the arid northwest. Over the 15-year period, total SOC storage increased from 4.84 to 5.23 Pg C (a 7.9% rise), while the annual sequestration rate declined from 0.046 to 0.020 kg·m⁻²·yr⁻¹, indicating that the regional carbon sink may be approaching saturation after two decades of restoration. Among soil types, Cambisols were the largest carbon pool, accounting for over 44% of total SOC storage. Vegetation productivity emerged as the dominant driver of SOC variability, with clay content as a secondary factor. These results indicate that although ecological restoration has substantially enhanced SOC storage, its marginal benefits are diminishing. Understanding the spatial and temporal patterns of SOC and their environmental drivers provides essential insights for evaluating long-term carbon sequestration potential and informing future land management strategies. Broader generalization requires multi-regional comparisons, long-term monitoring, and deeper soil investigations to capture ecosystem-scale carbon dynamics fully. Full article