Search Results (1,124)

Soil erosion (SE) is a constant, complex land degradation process, a common natural disaster that occurs all over the world and severely impacts soil fertility, food security, and environmental balance. Soil erosion depends on many factors, including soil properties, slope, vegetation, rainfall amount and intensity, and anthropogenic activities. There are two main natural erosive forces by which soil is eroded and transported—water and wind. Water erosion refers to the detachment, transportation, and deposition of soil particles (solid runoff) into river networks. These particles, varying in size and composition, are the main products of soil erosion and most strongly affect river ecosystems. Solid runoff, or sediment-laden runoff, affects water quality, destroying habitats, carrying pollutants, reducing reservoir storage, and causing flooding. Erosion control activities also influence river ecosystems in different ways. Hydrotechnical facilities, a major erosion control practice, can alter the composition of aquatic biota by disrupting longitudinal connectivity and isolating populations. Reforestation and afforestation are other erosion control practices that have a strong impact on ecosystems. Stormwater retention systems in urban and forest areas are also important measures addressed in this review. This review examines complex environmental interactions and the roles of erosion and erosion control activities in river ecosystems. During the research, several key points were established: erosion and erosion control activities significantly affect river ecosystems. There is a lack of quantitative analysis of erosion intensity and its influence on ecosystems. This is probably due to the exceptional complexity and diversity of river ecosystems, but such a study would provide important information about complex relationships in nature. Full article

Sandy coastlines are dynamic geomorphological units supporting dense human populations and intensive economic activities. However, their evolution is increasingly dominated by anthropogenic modification rather than natural processes. This study investigates shoreline evolution along the western Liaodong Bay coast, China, where extensive anthropogenic engineering has potentially altered natural dynamics. A 30-year satellite-derived shoreline (SDS) analysis of 23 sandy beaches (Xingcheng–Suizhong, 1995–2024) was conducted using the CoastSeg framework and DSAS statistical methods across three sub-periods (1995–2005, 2005–2015, 2015–2024). Shoreline change rates ranged from −1.35 to +2.12 m/yr; 11 beaches (47.8%) exhibited net erosion and 12 (52.2%) net accretion or stability, with marked spatial heterogeneity within individual beaches. This complex spatio-temporal pattern shows the strongest spatial correspondence with the non-uniform distribution of anthropogenic structures—including ports, breakwaters, and land reclamation—which generate an “engineering proximity effect” that may fragment natural beach continuity and contribute to a regional alternating erosion–accretion mosaic pattern, though direct mechanistic verification awaits future hydrodynamic modeling. Shoreline evolution along the western Liaodong Bay coast has entered a stage of “multi-layered anthropogenic control,” requiring frameworks that integrate multi-scale, multi-process coupling mechanisms and transcend traditional regional-averaging approaches. These findings provide critical insights for spatially differentiated management of engineering-intensive sandy coasts. Full article

This study evaluates the multi-year taxonomic diversity and functional structure of beetle assemblages (Coleoptera) within Sub Arini Park, a historic urban green space in Sibiu, Romania. Following a preliminary baseline and methodological calibration phase in 2023, systematic monitoring was conducted during the 2024 and 2025 seasonal cycles utilizing standardized pitfall trapping across diverse park zones. We explicitly tested two hypotheses: (H1) that long-standing historic park management preserves a resilient and functional insect community structure, and (H2) that local spatial heterogeneity and microhabitat variations significantly drive species distribution. A total of 14,843 individuals belonging to 39 species were analyzed. While total abundance exhibited a slight decrease from 2024 (N = 7112) to 2025 (N = 6551), true diversity metrics (Hill numbers) revealed a significant increase in raw species richness (q = 0) from 30 to 39 species, alongside an enhanced equity of frequent species (Shannon diversity, q = 1, increased from 4.26 to 5.12). Functional guild analysis and multivariate PCA demonstrated a highly structured biocenotic distribution; specialist and hygrophilous species (e.g., Carabus variolosus Fabricius, 1787) were strictly constrained to high-humidity riparian corridors, whereas thermophilous generalists dominated open lawns under high anthropogenic stress. Our spatial analysis identified critical degradation within these heavily managed zones, specifically driven by intensive mowing, soil compaction, and organic debris removal. These findings confirm both hypotheses, revealing that the park operates as a heterogeneous mosaic of ecological refugia rather than a uniform habitat block. Crucially, this study provides a concrete, quantitative basis—derived from empirical thresholds of species richness, abundance shifts, and mapped microhabitat preferences—for implementing nature-based management strategies (such as establishing buffer zones with reduced mowing frequencies, limiting trampling, and retaining coarse woody debris) aimed at mitigating urban biodiversity loss and maintaining vital biological pest control services in Central–Eastern Europe. Full article

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

Potentially toxic element (PTE) contamination in rice poses significant food safety risks, particularly in regions with intensive agriculture, industry, and traffic. This study provides a systematic assessment of the accumulation, translocation, sources, and health risks of PTEs (As, Cd, Cr, Cu, Ni, Pb, Zn) in the atmospheric deposition–soil–rice system across four distinct anthropogenic source areas (industrial, peri-urban, rural, and roadside areas) in Hunan Province, China. The rural area was categorized as clean. Industrial areas had the highest soil pollution index, while roadside areas recorded the highest atmospheric deposition flux of Pb (19.95 μg/m²/day) and As (1.93 μg/m²/day). Correspondingly, industrial areas exhibited the highest Cd (0.38 mg/kg) and Pb (0.94 mg/kg) in rice grains, whereas roadside areas showed the highest Pb (1.40 mg/kg) and As (2.99 mg/kg) in leaves. The findings indicated that rice in roadside areas primarily accumulate PTEs through foliar absorption of atmospheric deposition, whereas in industrial and peri-urban areas it was primarily through root uptake and translocation of PTEs to rice grains, particularly for Cd and Pb. Source apportionment identified natural, industrial, and traffic as the three primary sources. The Bayesian mixing model revealed that the natural source contributed the highest proportion to rice grains (48.3–70.6%) across all four source areas. Except for natural sources, industrial sources dominated in industrial areas (29.1%), traffic emissions prevailed in roadside areas (19.4%), while mixed sources had the highest proportion in peri-urban areas (28.4%). Health risk assessment revealed that the total hazard index followed the order of peri-urban > industrial > roadside > rural areas, with rice ingestion being the dominant exposure pathway, accounting for over 90% of the total risk. The primary contributors to health risks were identified as As, Cd, and Pb, particularly in industrial and peri-urban areas. These findings provide a scientific basis for developing region-specific mitigation strategies tailored to the dominant contamination pathways in each area. Full article

Arsenic represents a ubiquitous element in the environment, characterized by high mobility, complex chemical speciation and a strong sensitivity to redox conditions and biological activity, with microbial processes play a central role in its biogeochemical cycling. The present review provides a comprehensive and integrative synthesis of arsenic biogeochemical cycling across terrestrial, freshwater and marine environments, in which chemical speciation is explicitly treated as the central unifying concept controlling arsenic mobility, transformation and bioavailability, linking geological, chemical and biological processes across environmental compartments. Natural processes regulating arsenic distribution are examined from mineralogical sources and soil–water interactions to biologically mediated transformations in aquatic and marine biotic compartments, largely driven by microbial activity, highlighting the contrast between inorganic arsenic dominance in abiotic reservoirs and the prevalence of organoarsenicals in tissues of living organisms. The review further explores arsenic behaviour under natural environmental alterations and in extreme or unconventional ecosystems, where redox constraints, sulphide chemistry or intense fluid–sediment exchanges lead to deviations from the baseline speciation patterns. Against this framework, anthropogenic perturbations are discussed through several documented case studies, illustrating how industrial releases, the long-term effects of mining activities, agricultural practices and the use of synthetic arsenical compounds may change arsenic pathways primarily by altering geochemical and biological controls rather than through a generalized increase in total arsenic content. Overall, the topics covered provide an integrated framework for interpreting arsenic dynamics across environmental systems, emphasizing the complex biogeochemical processes governing arsenic cycling. Full article

Forest fires pose significant threats to ecological security, human settlements, and sustainable regional development, particularly in mountainous regions with complex environmental and anthropogenic conditions. Previous studies have yet to construct a forest fire risk assessment framework that integrates multi-source data, which limits the comprehensiveness and accuracy of existing assessments. To address this gap, taking Liangshan Prefecture in China as a case study, this research selected eight risk factors to characterize vegetation conditions, topographic features, climatic conditions, and human activities. A combined weighting approach integrating the mandatory determination method and the coefficient of variation method was employed to determine the weights of different indicators. Forest fire risk probability was calculated using a weighted comprehensive evaluation model, and spatial autocorrelation analysis based on global Moran’s I and local indicators of spatial association (LISA) was further conducted to investigate spatial clustering characteristics. The results indicate that high-risk and very high-risk areas are mainly concentrated in southeastern Liangshan, particularly in Xichang, Jinyang, Ningnan, Huili, and Huidong, where warmer climatic conditions, dense vegetation coverage, mountainous terrain, and intensive human activities jointly contribute to elevated forest fire risk. The global Moran’s I value of 0.219175 indicates significant positive spatial autocorrelation in forest fire risk distribution. Validation using historical fire-scar data from 2010 to 2020 showed that 83.66% of the fire scars were distributed within medium-, high-, and very high-risk areas, suggesting that the proposed assessment framework provides a reasonable representation of forest fire risk patterns in Liangshan. The findings of this study can support regional forest fire prevention planning, targeted resource allocation, and risk management in mountainous areas. Full article

Estuarine ecosystems face intense anthropogenic pressures, yet systematic research on how domestic wastewater influences the dissolved organic carbon (DOC) pool via microbial community regulation remains limited. In this study, we conducted a microcosm experiment simulating wastewater input into the Qiantang River and integrated multi-omics (16S rRNA sequencing, metagenomics, metatranscriptomics, and FT-ICR MS) to elucidate the mechanism. Results showed that: (1) Wastewater input increased initial DOC and changed its degradation pattern: slower decay but higher removal. (2) Compared to the control, the wastewater-amended group exhibited a decreased fluorescence intensity contribution of carboxyl-rich alicyclic molecule (CRAM)-like compounds, indicating reduced chemical stability of recalcitrant DOC (RDOC). (3) Wastewater drove directional microbial succession from catabolic-dominant taxa (e.g., Comamonas, Citrobacter) to anabolic-dominant taxa (e.g., Reyranella), shifting metabolism from pollutant degradation to endogenous synthesis, thereby lowering the system’s efficiency in forming stable RDOC. (4) Multi-omics revealed a “stimulation-balance” functional response: early activation of xenobiotic degradation and signal transduction (day 2), followed by a shift to anabolic metabolism (day 28). This functional transition, driven by microbial succession, ultimately reduced RDOC stability. Our findings reveal that wastewater reshapes the microbial carbon pump, providing a theoretical basis for assessing estuarine carbon sink responses to pollution control measures. Full article

As the second most important anthropogenic greenhouse gas (GHG), methane (CH₄) has received wide attention in the mitigation of global climate change. China’s Sichuan Basin has been identified as one of the world’s hotspot regions with very high CH₄ emission intensity. Winter-flooded paddies are considered as potential significant sources of CH₄ emissions among various cropping systems in Sichuan. However, current studies are limited to the field scale, and there is a lack of research conducted over a large spatiotemporal scale. Here, we simulated CH₄ emissions from 1980 to 2023 at region scale using the Denitrification–Decomposition (DNDC) model and evaluated the associated climate impact using the radiative forcing-based climate footprint (RFCF) metric. We found that CH₄ emissions have recently decreased, from 0.53 billion tonnes in 2019 to 0.28 billion tonnes in 2023, representing a 47.20% reduction. Moreover, the climate footprint peaked in 2019 at 1.25 mW m⁻² and decreased to 1.08 mW m⁻² in 2023, and the system achieved net zero increase in radiative forcing (RF) in 2020. This means that Sichuan’s winter-flooded paddies no longer contribute to the additional RF in the atmospheric system. Overall, our findings demonstrate that the reduction in CH₄ emissions from winter-flooded paddies has been mainly attributed to a reduction in the cropping area and a decrease in average temperature during the rice growth season. These results provide a scientific basis for region-specific CH₄ mitigation policies and demonstrate how these spatiotemporal changes in CH₄ emissions from winter-flooded paddies in Sichuan can support sustainable agriculture. Full article

Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95, $\kappa$ = 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management. Full article

Intensive mining over recent decades has caused severe ground subsidence in mining regions, threatening safety and long-term sustainability. High-precision, continuous monitoring and prediction of subsidence are therefore urgently needed. Traditional methods—terrestrial surveying and GPS—offer limited coverage, sparse measurement points, high costs, and poor scalability, making them unsuitable for large-scale, long-term surface deformation monitoring. InSAR is widely used for ground deformation monitoring due to its wide-area coverage, long-term sampling, high spatial resolution, and millimeter-scale precision. However, conventional InSAR often fails in vegetated areas and under steep deformation gradients—common in mining zones. To overcome these limitations, this study applied SBAS-InSAR, a method better suited for large-magnitude, continuous subsidence monitoring in mining areas. This study proposed an enhanced hierarchical spatiotemporal dependency graph neural network (HSDGNN) integrated with a Long Short-Term Memory (LSTM) module to improve temporal feature representation. Using this model, this study predicted surface subsidence at the Dexing Copper Mine under environmental drivers. Key findings are as follows: (1) Surface subsidence exhibited pronounced spatial heterogeneity and strong temporal nonlinearity; major subsidence zones were localized in open-pit excavation areas and waste rock dumps, with peak subsidence rates reaching −126.121 mm/yr. (2) Precipitation and soil moisture emerged as the dominant environmental controls on subsidence, displaying distinct seasonal modulation and quantifiable lagged responses—up to several months—relative to subsidence onset. (3) The HSDGNN model achieved high predictive accuracy for both Mine 1 and Mine 2, attaining R² values of up to 0.9950. This work establishes a robust, scalable, and operationally viable framework for high-precision subsidence monitoring and forecasting in geologically and anthropogenically complex mining environments. Full article

Large-scale landscape transformation in mountainous regions during the Late Iron Age remains insufficiently integrated into broader debates on European urbanism. In southwestern Transylvania, extensive slope terracing came to define the spatial core of the Dacian political centre. This study examines the scale, organization, and social implications of this engineered landscape using high-resolution LiDAR data and spatial modelling. Over 4000 anthropogenic terraces were identified, and their spatial patterning was analysed through Kernel Density Estimation (300 m and 800 m radii) in order to evaluate intensity gradients and territorial articulation. The results indicate compact nuclei of high terrace concentration embedded within a broader, yet continuous, system structured along ridge corridors and circulation routes. The spatial correlation between terrace density and elevated architectural features suggests differentiated building practices and hierarchical organization within a territorially extensive settlement pattern. Rather than representing isolated fortified sites, the Dacian mountain core emerges as an integrated and infrastructurally connected landscape. These findings support the interpretation of the area as a form of Late Iron Age low-density urbanism, in which habitation, mobility, and social differentiation were materially embedded in large-scale topographic modification. Full article

This study examines spatio-temporal patterns of dolphin species in a coastal ecosystem located in the Bay of Algeciras–Gibraltar (southern Spain), a highly anthropogenic coastal system influenced by a submarine canyon and exposed to intense anthropogenic pressure. Between 2017 and 2020, spatial and temporal relationships between short-beaked common dolphins (Delphinus delphis), striped dolphins (Stenella coeruleoalba), and bottlenose dolphins (Tursiops truncatus) were analysed. A solitary female bottlenose dolphin (Billie) was considered separately as an individual case due to its distinct behavioural patterns. Georeferenced data showed that distances between sightings of T. truncatus and subsequent observations of the other species ranged from 261 to 12,000 m, with temporal intervals spanning from 33 s to 5 h 38 min. Short temporal overlaps (≤300 s) were infrequent. These results indicate limited spatio-temporal overlap between D. delphis, S. coeruleoalba, and T. truncatus within the study area. While no statistically significant relationships were detected in the applied models, the observed patterns provide a descriptive quantitative characterisation of species distribution and co-occurrence in a highly anthropogenic coastal system. Given that D. delphis is classified as Endangered in the western Mediterranean and that both D. delphis and S. coeruleoalba are frequently observed with calves, these patterns may be relevant for understanding habitat use and potential implications for conservation. Overall, this study provides a detailed empirical characterisation of spatio-temporal patterns among sympatric dolphin species in this coastal system, highlighting the need for further research using targeted analytical approaches to assess interspecific dynamics. Full article

Plant invasions pose a significant threat to plant community integrity at high latitudes and altitudes, particularly under the backdrop of ongoing climate change and anthropogenic disturbance. However, how plant invasion and increasing invasion intensity reshape community functional traits and multidimensional diversity in high-altitude wetland ecosystems remain poorly understood. Here, we conducted a field survey across 284 quadrats in a subalpine wetland of Shennongjia National Nature Reserve, China. Nine invasive plant species were detected and occurred in 51.06% of all sampled quadrats. We compared functional trait composition between invaded and uninvaded communities and assessed species, functional, and phylogenetic diversity along invasion intensity gradients through inclusion and exclusion models of invasive species. Invaded communities showed 9.1% higher chlorophyll content and 30.7% larger specific leaf area but 26.1% lower leaf density than uninvaded communities. In addition, community-weighted traits and diversity indices showed stronger responses when invasive species were included. With increasing invasion intensity, species diversity and phylogenetic diversity declined, whereas functional richness increased. These results demonstrate that plant invasion simultaneously drives species loss and functional reorganization, reshaping both the functional composition and biodiversity of subalpine wetland communities. Our findings highlight how invasive species restructure plant communities in subalpine wetlands, with important implications for biodiversity conservation in high-altitude ecosystems. Full article

This study assessed the spatiotemporal dynamics of eco-environmental quality (EEQ) in Sonid Left Banner from 2000 to 2025, using Landsat imagery and the remote sensing ecological index (RSEI) via Google Earth Engine. Theil–Sen slope, Mann–Kendall test, Hurst exponent, and Pearson correlation analysis were used to analyze trends and their associations with climatic and anthropogenic factors. Results showed that EEQ exhibited an overall improving trend, with a mean RSEI of 0.270 and an annual increase of 0.0022 a⁻¹, though it remained at a fair grade with a spatial pattern of “regionally poor but locally improved.” Hurst exponent analysis has indicated that 75.35% of the study area will sustain improvement, while 17.03% faces continuous degradation risk. Climatic factors showed the strongest associations with RSEI: precipitation (r = 0.329) and humidity (r = 0.313) showed the strongest positive correlations, with a distinct north–south spatial gradient in their association patterns; temperature (r = 0.272) showed bidirectional correlation patterns; and wind speed (r = −0.197) was the primary negative correlated factor. Human activity intensity (HAI) was negatively correlated with RSEI (r = −0.128), with 7.8% of high-intensity development areas showing significant degradation. These findings reveal that moisture availability establishes the ecological baseline in semi-arid grasslands, while human activities modulate ecosystem change, informing targeted ecological restoration. Full article