Search Results (334)

Spatial heterogeneity of soil nutrients in the dry-hot valleys of Southwest China is strongly shaped by topography, yet quantitative evidence remains limited. In this study, we assessed the effects of slope gradient, slope position, and slope aspect on nine soil nutrient indicators in a representative mango orchard in Yanbian County, Panzhihua City, China. Stratified soil samples were collected from two depths (0–10 cm and 10–20 cm) across contrasting topographic conditions. The results showed that: (1) total nitrogen (TN) and organic matter (OM) declined with increasing slope gradient, while available phosphorus (AP) accumulated in the 10–20 cm layer of gentle slopes (0°, 20°). The N:P ratio peaked at 0° slope (0–10 cm), whereas the C:N ratio peaked at 80° slope (10–20 cm). (2) Soil OM and available potassium (AK) increased with higher slope position, while total phosphorus (TP) decreased. TN and AP reached maximum values on hillslope terraces, and total potassium (TK) was highest on piedmont alluvial fans. Summit platforms exhibited the highest C:N, C:P, and N:P ratios (0–10 cm). (3) Sunny slopes had higher TN, OM, and TP, whereas shady slopes had higher TK and AK. The C:N and C:P ratios (0–10 cm) were greater on sunny slopes, while N:P was higher on shady slopes. Principal component analysis indicated that slope gradient, position, and aspect accounted for 60.6%, 68.2%, and 59.6% of the variance in soil nutrients, respectively. Overall, this study highlights the quantitative influence of topography on soil nutrient distribution, providing a scientific basis for more site specific nutrient management in mango orchards of dry-hot valley 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

China’s rural revitalization strategy has entered a new stage of development, in which optimizing the layout of rural settlements constitutes both a critical component and an urgent task for promoting integrated urban–rural development. Important ecological function areas play a vital role in maintaining ecological security; however, research focusing on the evaluation and optimization of rural settlement suitability within these regions remains limited, thereby constraining their sustainable development. Accordingly, this paper selects Shiyan City, situated within the core water source area of China’s South-to-North Water Diversion Project, as a case study. From an ecological perspective, a suitability evaluation system for rural settlements is developed, specifically tailored to important ecological function areas. This system integrates ecological factors including geological hazards, vegetation coverage, soil and water conservation, and soil erosion. Utilizing GIS spatial analysis and the minimum cumulative resistance model, the study assesses the suitability of rural settlements within these important ecological function areas. Furthermore, it proposes corresponding optimization types and strategies for rural settlements in such areas. The findings indicate the following: (1) The rural settlements in the study area demonstrate a “large dispersed settlements and small clustered settlements” distribution pattern, exhibiting an overall high-density agglomeration, though their internal layout remains fragmented and disordered due to geographical and ecological constraints. (2) The spatial comprehensive resistance values in the study area exhibit significant heterogeneity, with a general pattern of lower values in the north and higher values in the south. The region was categorized into five suitability levels: high yield, highly suitable, generally suitable, less suitable and unsuitable. The highly suitable areas, despite their limited spatial extent, support the highest density of rural settlements. In contrast, unsuitable areas occupy a substantially larger proportion of the territory, reaching 46.83%. These areas are strongly constrained by topographic and ecological factors, limiting their potential for development, and the spatial layout of villages requires further optimization, with emphasis placed on ecological conservation and adaptive sustainability. (3) Rural settlements are categorized into four optimized types: Urban–rural integration settlements, primarily located in high yield areas, are incorporated into urban development plans after optimization. Adjusted and improved settlements, mainly in highly suitable areas, enhance service quality and stimulate economic vitality post-optimization. Relocation and renovation settlements, including those in generally suitable and less suitable areas, achieve concentrated living and improved ecological livability after optimization. Restricted development settlements, predominantly in unsuitable areas, focus on ecological conservation and regional ecological security post-optimization. This study integrates ecological function protection factors with spatial optimization zoning for rural settlements in the study area, providing scientific reference for enhancing residential safety and ecological security for rural residents in important ecological function areas. Full article

Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the simulation of extreme floods. However, the generalization ability of data-driven models remains limited in non-stationary environments. To address this issue, we further propose a hybrid framework that couples the process-based GBHM with the enhanced Transformer via bias correction. This fusion leverages the strengths of both models: the process-based model explicitly captures topographic heterogeneity, the spatial distribution of meteorological forcings, and their temporal variability, while the data-driven model excels at uncovering latent relationships among hydrological variables. The results demonstrate that the coupled model significantly outperforms traditional approaches in peak-flow prediction and exhibits superior robustness and generalizability under changing environmental conditions. Full article

The spatial distribution of plant species is a crucial indicator of the mechanisms driving competition or coexistence both within and between populations and communities. Analyzing these patterns provides essential insights into fundamental ecological processes and aids in evaluating ecological hypotheses. To study the spatial distribution of dominant tree species and their associations both within and among species, we established a 25-hectare forest plot in Lushan Mountain. We employed the g(r) function alongside three null models—complete spatial randomness (CSR), heterogeneous Poisson (HP), and antecedent condition (AC)—to analyze spatial patterns and assess species interactions at various life stages. Additionally, we examined the relationships between spatial distributions and environmental factors such as soil properties and topography using Berman’s test. Our results showed that all 12 dominant tree species exhibited significant aggregation under the CSR model; however, the scales of aggregation were reduced under the HP model. We also found evidence of aggregation among multiple species across different life stages and tree layers under CSR. Notably, this pattern persisted under the AC model but was limited to specific spatial scales. Furthermore, elevation, topographical convexity, and the total content of soil nitrogen (N) and carbon (C) were identified as statistically significant predictors of species distributions. Overall, these findings highlight that both biological and environmental factors play a vital role in shaping plant spatial patterns across different scales. Full article

Soil organic matter (SOM) plays a critical role in maintaining soil fertility, sustaining ecosystem stability, and mitigating climate change impacts, making its conservation essential for agricultural sustainability. However, systematic county-level assessments of SOM spatial heterogeneity and its drivers across Northeast China remain limited, constraining region-specific soil management strategies. Understanding the spatial distribution and drivers of SOM is therefore vital for effective black soil protection in Northeast China. This study investigated the spatial heterogeneity and driving mechanisms of SOM in Northeast China, covering 289 counties across Heilongjiang, Jilin, and Liaoning Provinces. High-resolution (10 m) SOM data combined with 15 natural, climatic, soil, vegetation, and socioeconomic variables were analyzed using spatial autocorrelation (global and local Moran’s I) and the Geodetector model. Results showed that SOM exhibited a clear spatial pattern of “higher in the north and east, lower in the south and west,” with significant spatial clustering (Moran’s I = 0.730, p < 0.001). At the regional scale, climate factors were the dominant drivers, with potential evapotranspiration (q = 0.810) and mean annual temperature (q = 0.794) exerting the strongest explanatory power. At the provincial scale, dominant factors varied: topographic controls in Liaoning, climate–topography interactions in Jilin, and climate dominance in Heilongjiang. Anthropogenic footprint had limited overall influence but showed amplifying effects in certain local areas. These findings highlight the multi-scale, multi-factor nature of SOM heterogeneity and underscore the need for region-specific management strategies. Full article

Biodiversity conservation in South Korea faces increasing challenges from alien plant invasions. These invasions threaten endemic species uniquely adapted to specialized habitats, making it crucial to understand their ecological interactions. This study quantitatively compared the species composition, ecological niches, and species turnover patterns of alien and endemic plants in South Korea using data from the National Ecosystem Survey. Non-metric multidimensional scaling (NMDS) and multi-response permutation procedure (MRPP) analyses revealed significant compositional heterogeneity between groups. Kernel density estimation (KDE) revealed niche overlap in water-related factors (precipitation, water yield), but clear separation in topographic and climatic variables (altitude, slope, temperature). Alien plants exhibited broader niche breadths, confirming their ecological generalist traits, whereas endemic species displayed narrower niches confined to specialized habitats. Zeta diversity analysis indicated slower species turnover in alien species, suggesting niche assimilation and habitat homogenization. Both groups fit a power-law model, emphasizing deterministic environmental filtering. These findings highlight the ecological risks posed by alien species to stability of endemic plant communities and underscore the importance of targeted, science-based management strategies. Full article

Slope stability is a critical factor in the mining industry, directly impacting operational safety and economic performance. In large open-pit mines, slope failures can cause work stoppages and significant financial losses. Regions like Mongolia, with their complex topography, irregular geometries, and heterogeneous rock conditions, present a particular challenge for assessing slope stability. Conventional two-dimensional (2D) slope stability analysis and deterministic approaches have limitations in accounting for these complex topographies, irregular pit geometries, and lateral resistance forces. For a large open-pit mine in Mongolia, this study applied three-dimensional (3D) analyses with varying slope widths, using both limit equilibrium and finite element methods, to achieve a more reliable stability assessment under complex topographic conditions. To further enhance the reliability of evaluations under heterogeneous rock mass conditions, probabilistic approaches were employed alongside traditional deterministic methods. This enabled a more accurate estimation of safety factors and the identification of potential failure zones. The comparative study results demonstrate that 3D and probabilistic analyses consistently show 17–20% higher factors of safety and lower probabilities of failure than conventional 2D deterministic analyses. These findings highlight the effectiveness of these advanced methods for reliable slope stability assessment in complex geological conditions. Ultimately, the results underscore the importance of incorporating 3D and probabilistic analyses for more accurate and reliable assessments in complex open-pit mining, thereby contributing to improved safety and optimized operational efficiency. Full article

This study evaluates the impact of incorporating lateral mass density variations into geoid models for Java, Indonesia, aiming to enhance the accuracy of regional geoid determinations. Geoid models have traditionally used a constant density assumption; however, Java’s varied topography and geological complexity suggest that density variability may significantly influence geoid accuracy. Employing the Stokes–Helmert method combined with the remove–compute–restore (RCR) technique, we calculated geoid models using both constant density and laterally variable density from the UNB TopoDens model. The models were validated against GNSS/leveling data, showing that while lateral density variations had limited effects along flat topographic profiles, they introduced notable discrepancies in regions with considerable elevation changes. Specifically, variable density models exhibited discrepancies of up to 30 cm in regions with complex terrain, underscoring the importance of selecting appropriate density models for precise geoid computations in heterogeneous landscapes. Nonetheless, a comprehensive validation using geometric geoid models is required to confirm the accuracy improvements across the entire region. Full article

Accurate soil organic carbon (SOC) estimation is critical for assessing ecosystem services, carbon budgets, and informing sustainable land management, particularly in ecologically sensitive mountainous regions. This study focuses on modelling the spatial distribution of SOC within the heterogeneous volcanic landscape of the Nevado de Toluca (NdT), central Mexico, an area spanning 535.9 km² and characterised by diverse land uses, altitudinal gradients, and climatic regimes. Using 29 machine learning algorithms, we evaluated the predictive capacity of three key variables: land use, elevation, and the Normalised Difference Vegetation Index (NDVI) derived from satellite imagery. Complementary analyses were performed using the Bare Soil Index (BSI) and the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2) to assess their relative performance. Among the tested models, the Quadratic Support Vector Machine (SVM) using NDVI, elevation, and land use emerged as the top-performing model, achieving a coefficient of determination (R²) of 0.84, indicating excellent predictive accuracy. Notably, 14 models surpassed the R² threshold of 0.80 when using NDVI and BSI as predictor variables, whereas MSAVI2-based models consistently underperformed (R² < 0.78). Validation plots demonstrated strong agreement between observed and predicted SOC values, confirming the robustness of the best-performing models. This research highlights the effectiveness of integrating multispectral remote sensing indices with advanced machine learning frameworks for SOC estimation in mountainous volcanic ecosystems Full article

The Hengduan Mountains, a global biodiversity hotspot, harbor numerous endemic plant species shaped by complex topography and microclimatic variation. However, increasing habitat fragmentation due to human activities threatens narrowly distributed species such as Cycas panzhihuaensis. To investigate its habitat suitability and inform conservation, we applied the MaxEnt model, Geodetector, and Zonation to predict potential distribution, identify key environmental drivers, and delineate priority conservation areas. Our results show that only 18.36% of the region constitutes suitable and highly fragmented habitat, primarily concentrated along the dry–hot valleys of the Jinsha and Yalong Rivers, and it is shrinking while shifting southward and southeastward under climate change. Elevation emerged as the dominant driver (q = 0.45), with strong interaction effects among topographic, climatic, soil, and anthropogenic factors, highlighting the role of environmental synergies in shaping habitat heterogeneity. Priority conservation areas covered 32% of suitable habitat and overlapped only 6.17% with existing protected areas, indicating a spatial conservation gap. These findings emphasize the need to incorporate microhabitat heterogeneity and environmental interactions in conservation planning and support the adoption of micro-reserve strategies to complement existing reserves. Our study provides a practical framework for protecting vulnerable montane species and offers insights into plant distribution dynamics in topographically complex regions. Full article

The accurate monitoring of short-term bathymetric changes in shallow waters is essential for effective coastal management and planning. Machine Learning (ML) applied to Unmanned Aerial Vehicle (UAV)-based multispectral imagery offers a rapid and cost-effective solution for bathymetric surveys. However, models based solely on multispectral imagery are inherently limited by confounding factors such as shadow effects, poor water quality, and complex seafloor textures, which obscure the spectral–depth relationship, particularly in heterogeneous coastal environments. To address these issues, we developed a hybrid bathymetric inversion model that integrates digital surface model (DSM) data—providing high-resolution topographic information—with ML applied to UAV-based multispectral imagery. The model training was supported by multibeam sonar measurements collected from an Unmanned Surface Vehicle (USV), ensuring high accuracy and adaptability to diverse underwater terrains. The study area, located around Lazarus Island, Singapore, encompasses a sandy beach slope transitioning into seagrass meadows, coral reef communities, and a fine-sediment seabed. Incorporating DSM-derived topographic information substantially improved prediction accuracy and correlation, particularly in complex environments. Compared with linear and bio-optical models, the proposed approach achieved accuracy improvements exceeding 20% in shallow-water regions, with performance reaching an R² > 0.93. The results highlighted the effectiveness of DSM integration in disentangling spectral ambiguities caused by environmental variability and improving bathymetric prediction accuracy. By combining UAV-based remote sensing with the ML model, this study presents a scalable and high-precision approach for bathymetric mapping in complex shallow-water environments, thereby enhancing the reliability of UAV-based surveys and supporting the broader application of ML in coastal monitoring and management. Full article

Land use/land cover (LULC) data serve as a critical information source for understanding the complex interactions between human activities and global environmental change. The subtropical karst region, characterized by fragmented terrain, spectral confusion, topographic shadowing, and frequent cloud cover, represents one of the most challenging natural scenes for remote sensing classification. This study reviews the evolution of multi-source data acquisition (optical, SAR, LiDAR, UAV) and preprocessing strategies tailored for subtropical regions. It evaluates the applicability and limitations of various methodological frameworks, ranging from traditional approaches and GEOBIA to machine learning and deep learning. The importance of uncertainty modeling and robust accuracy assessment systems is emphasized. The study identifies four major bottlenecks: scarcity of high-quality samples, lack of scale awareness, poor model generalization, and insufficient integration of geoscientific knowledge. It suggests that future breakthroughs lie in developing remote sensing intelligent models that are driven by few samples, integrate multi-modal data, and possess strong geoscientific interpretability. The findings provide a theoretical reference for LULC information extraction and ecological monitoring in heterogeneous geomorphic regions. Full article

The assessment of microplastics (MPs) in terrestrial ecosystems has garnered increasing global attention due to their accumulation and migration in soils, which may have potential impacts on soil health, biodiversity, and agricultural productivity. However, research on their distribution and interactions in soil remains limited, especially in tropical regions. This study aimed to characterize MPs extracted from tropical soil samples and relate their abundance to soil and terrain attributes under different land uses (forest, grassland, and agriculture). Soil samples were collected from an experimental farm in Lavras, Minas Gerais, Southeastern Brazil, to determine soil physical and chemical attributes and MP abundance in a micro-watershed. These locations were also used to obtain terrain attributes from a digital elevation model and the normalized difference vegetation index (NDVI). The majority of microplastics found in all samples were identified as polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and vinyl polychloride (PVC). The spatial distribution of MP was rather heterogeneous, with average abundances of 3826, 2553, and 3406 pieces kg⁻¹ under forest, grassland, and agriculture, respectively. MP abundance was positively related to macroporosity and sand content and negatively related to clay content and most chemical attributes. Regarding terrain attributes, MP abundance was negatively correlated with plan curvature, convergence index, and vertical distance to channel network, and positively related to topographic wetness index. These findings indicate that continuous water fluxes at both the landscape and soil surface scales play a key role, suggesting a tendency for higher MP accumulation in lower-lying areas and soils with greater porosity. These conditions promote MP transport and accumulation through surface runoff and facilitate their entry into the soil. Full article