Search Results (592)

To study the soil moisture dynamics and rainfall infiltration characteristics of karst sloping farmland and their driving factors, an abandoned farmland was selected for this study, and five monitoring points (from the foot, S1, of the slope to the top, S5) were set along the terrain gradient. The volumetric water content data of the 0–40 cm soil layer was obtained through in situ monitoring for one year. The infiltration characteristics were quantified in combination with a staining tracer test, and the soil properties were determined. The results showed that the soil moisture content increased with the deepening of the soil layer, and there was significant slope differentiation. The moisture content in the downhill slopes (S1, S2) was significantly higher than that in the uphill slopes (S4, S5), and the annual average value of S5 was 27.4% lower than that of S1. The moisture difference (Δθ, the difference in moisture content between hillslope and flatland) changed from positive to negative from the foot of the slope to the top, indicating that moisture was transported downward along the slope surface. A dye tracer showed that from S1 to S5, the water transport pathway gradually shifted from exhibiting deeper vertical penetration and narrower lateral spread to showing shallower vertical penetration and wider lateral spread. The preferential flow index decreased from 46.6 ± 2.3% to 34.7 ± 2.1%, indicating a progressive reduction in rapid vertical channeling, while the lateral flow index reached its peak (21.4 ± 2.7%) in the middle of the slope (S3), suggesting enhanced horizontal water redistribution at this position. Correlation analysis indicated that soil bulk density was extremely significantly negatively associated with infiltration capacity, while capillary porosity, non-capillary porosity, total porosity, organic matter, and high aggregate content were extremely significantly positively associated with infiltration capacity. These results revealed that the topographic gradient affected soil moisture and water infiltration paths by regulating soil physical properties in this karst forest ecosystem. It should be noted that the research results are only applicable to one slope and should not be directly extended to all karst slope agricultural landscapes. Full article

Rapid urbanization and industrialization have profoundly affected aquatic ecosystems in urban rivers, with phytoplankton taxa and functional group composition being particularly sensitive to environmental changes. Field surveys were conducted in the Nanming River, Guiyang, in October 2018 and July 2019, with 33 sampling sites evenly distributed across the upstream, midstream, and downstream reaches. The results revealed that: (1) The phytoplankton community comprised 6 phyla, 53 genera, and 61 species, dominated by Bacillariophyta, Chlorophyta, and Cyanobacteria. The community was classified into 20 functional groups, among which B, D, MP, P, and S1 were dominant and exhibited clear spatial heterogeneity along the longitudinal gradient. (2) Analysis of variance indicated that physicochemical parameters were the dominant factors explaining the variation in phytoplankton taxonomic and functional groups, with their independent contribution significantly higher than that of anthropogenic disturbance indicators and geographical factors. Redundancy analysis further identified NH₄-N, TP, and TN as key environmental factors. Spearman’s correlation analysis further indicated that human activities alter ambient environmental conditions, which are significantly correlated with dissolved oxygen and chlorophyll a levels, thereby driving the differentiation of phytoplankton niches. (3) Functional group succession followed a distinct spatial pattern: upstream areas were dominated by groups P, SN, and Y, reflecting agricultural non-point source inputs; midstream areas were dominated by groups W1, H1, and S1, characteristic of urban complex pollution; and downstream areas were dominated by groups C and X1, indicating cumulative nutrient loading. Collectively, this study elucidates the driving mechanisms of phytoplankton dynamics in karst urban rivers and provides a scientific foundation for water quality monitoring, eutrophication risk pre-warning, and aquatic ecological restoration. Full article

Soil organic carbon (SOC) and soil inorganic carbon (SIC) are two key components of soil total carbon (STC) pools. However, most studies have focused excessively on SOC, while research on SIC remains limited, especially with regard to different pools of particulate (POM) and mineral-associated organic matter (MAOM) in humid regions. Here, a 13-year field experiment was conducted in the farmland of Jiangjin District, Chongqing, to explore the variations of inorganic carbon in POM (POM-IC) and MAOM (MAOM-IC) in humid subtropical soils under long-term fertilization. Four fertilization regimes were arranged in this field experiment: high-rate fertilization (1050 kg N, 480 kg P₂O₅, and 255 kg K₂O ha⁻¹ yr⁻¹), conventional fertilization (480 kg N, 180 kg P₂O₅, and 255 kg K₂O ha⁻¹ yr⁻¹), zero nitrogen fertilization (0 kg N, 180 kg P₂O₅, and 255 kg K₂O ha⁻¹ yr⁻¹), and zero phosphorus fertilization (480 kg N, 0 kg P₂O₅, and 255 kg K₂O ha⁻¹ yr⁻¹). Soil samples were collected from surface soil (0–15 cm) and subsoil (15–30 cm) to determine STC, SOC, SIC, organic carbon in POM (POM-OC) and MAOM (MAOM-OC), POM-IC, and MAOM-IC. Results showed that SOC accumulation under high-rate fertilization was primarily associated with increased POM-OC. Compared with the zero nitrogen treatment, the other three fertilization regimes significantly decreased subsoil SIC, which was primarily associated with reduced MAOM-IC. High-rate fertilization increased the contributions of POM-OC to SOC and POM-IC to SIC, respectively, yet reduced the corresponding contributions from MAOM. Linear relationship analysis revealed that POM-OC was more sensitive to fertilization regimes than MAOM-OC. However, responses of POM-IC and MAOM-IC to fertilization regimes were roughly equivalent. This is of great significance for understanding the stabilization mechanisms of SIC. This study highlights the non-negligible MAOM-IC loss in subsoil induced by nitrogen fertilization in humid subtropical soils. Given that STC was the highest under high-rate fertilization, this treatment is recommended. This study is of great significance for improving the understanding of soil organic carbon and inorganic carbon dynamics in humid regions. Full article

With the accelerated pace of urbanization, the substantial reduction in natural vegetation, water bodies, and wetlands has disrupted ecosystem structures, leading to significant declines in biodiversity. Blue-green spaces play a crucial role in maintaining urban habitat quality and supporting species diversity. As a sensitive indicator group to changes in the ecological environment, spatial variations in bird richness can provide important insights into changes in urban ecosystems and habitats. Therefore, a systematic investigation of the relationship between the landscape patterns of blue-green spaces and bird richness in ecologically complex regions is of great significance for achieving sustainable urban development and biodiversity conservation. This study focuses on Southwest China, utilizing bird richness data and blue-green space landscape pattern indicators. By integrating Random Forest (RF) models with Shapley (SHAP) methods, it quantitatively analyzes the relationship between blue-green space landscape patterns and bird richness in typical complex ecological regions. Results indicate nonlinear associations between blue-green landscape patterns and bird richness, with green spaces exerting a stronger overall influence than blue spaces. Edge density (ED) in green spaces demonstrated markedly higher feature importance than other landscape indicators. Within green spaces, ED and class area (CA) showed stronger associations with bird richness, while within blue spaces, CA and Percentage of Landscape (PLAND) provided more prominent explanatory power for bird richness. By clarifying the nonlinear responses and differentiated roles of blue and green landscape patterns, this study provides quantitative evidence for optimizing blue-green spatial planning and promoting biodiversity conservation in ecologically complex regions. Full article

Accurate mapping of aboveground biomass (AGB) in mountainous forests is essential for carbon stock assessment and ecological management, yet remains challenging due to the difficulty of linking local high-precision observations with regionally continuous coverage. To address this issue, we developed a hierarchical framework integrating local reference construction, UAV–GEDI bridging, footprint-level modeling, and regional continuous mapping, applied to the mountainous forests of Simao District, Pu’er City, Yunnan Province, China. Field plot measurements and UAV-borne LiDAR data were first used to construct a local AGB reference product, which was then transferred to the GEDI footprint scale through geolocation correction and footprint-scale quality control, yielding 252 valid bridging samples across three UAV flight zones, with approximately 65% originating from the TYH zone. Among five candidate models evaluated for GEDI footprint-level AGB estimation, the Stacking ensemble model performed best, with a pooled out-of-fold R² of 0.736 and RMSE of 24.15 Mg ha⁻¹, and was subsequently applied to 89,579 GEDI footprints across the study area. For regional continuous mapping, the empirical Bayesian kriging regression prediction (EBKRP) scheme combining Landsat TCW, Sentinel-2 IRECI, and the Sentinel-1 polarization ratio achieved the best external validation performance, with R² of 0.622 and RMSE of 26.05 Mg ha⁻¹ based on 61 independent field plots. These results indicate that the proposed hierarchical framework effectively bridges local high-precision observations and regional continuous AGB mapping in complex mountainous forest environments, offering a systematic methodological reference for GEDI-based forest carbon monitoring. Full article

Karst Mountain Areas (KMAs) are characterized by fragile geology, shallow soils, and high ecological sensitivity, rendering their Ecosystem Services (ESs) highly vulnerable to Land Use and Land Cover (LULC) change. However, the spatiotemporal evolution of ESs and the trade-offs and synergies among them remain poorly understood, particularly concerning the interplay between human activities and natural constraints in these complex landscapes. Taking the Wumeng Mountain Area (WMA) in southwestern China as a representative case, this study integrates multi-temporal LULC data (2000, 2010, and 2020) with the InVEST model to quantify the dynamics of four key ESs: Carbon Storage (CS), Habitat Quality (HQ), Soil Conservation (SC), and Water Yield (WY). An integrated analytical framework combining land use dynamic degree, intensity analysis, transition matrices, and grid-scale Spearman correlation analysis was developed to reveal ESs interactions. Results indicate that, despite substantial land use changes, most ES pairs exhibited synergistic relationships, and these synergies intensified from 2000 to 2020. The strengthened synergies, particularly among CS, SC, and HQ, are conducive to simultaneously achieving multiple ecological security goals, such as regional carbon sequestration, biodiversity conservation, and soil stability. However, the trade-offs observed between water yield and regulating services (CS, HQ) in 2000 highlight potential conflicts between ensuring water supply and enhancing other ecological benefits in fragile karst landscapes, offering a scientific caution for balancing water resource development with ecological protection. This study demonstrates that understanding ES interactions is not merely an ecological description but constitutes a critical scientific basis for optimizing land use and improving regional human well-being and sustainability. Full article

In the context of increasing climate variability and carbon neutrality targets, understanding the relative roles of climate and human activities is essential for accurately assessing vegetation carbon sink dynamics. This study develops a climate-controlled attribution framework to disentangle human-induced effects from natural climatic variability in Guizhou Province, a representative karst region of Southwest China. Using multi-source remote sensing and climate data from 2004 to 2023, net ecosystem productivity (NEP) was estimated, and its spatiotemporal dynamics were analyzed. A two-step attribution approach was applied to isolate climate-driven variability and quantify the contribution of anthropogenic activities. Results indicate that mean NEP increased significantly from 273 gC·m⁻²·yr⁻¹ in 2004 to 369 gC·m⁻²·yr⁻¹ in 2023, with a provincial average of 318 gC·m⁻²·yr⁻¹. Human activities are estimated to contribute a dominant share (approximately 60–75%), although uncertainties remain due to methodological limitations. Spatial analysis reveals pronounced heterogeneity, with stronger human-induced enhancement in eastern regions and mixed restoration–disturbance effects in ecologically fragile western areas. These findings suggest that ecological restoration policies in fragile karst ecosystems can generate amplified carbon sink responses beyond background climatic effects. These findings provide insights into understanding climate–carbon cycle interactions and improving region-specific climate mitigation strategies. Full article

Karst regions are commonly characterized by highly interwoven bare rock–bare soil–vegetation mosaics, strong coupling between surface and subsurface processes, and pronounced geomorphic fragmentation. Conventional land cover classification systems, which are primarily organized around land use patterns or generic ecological types, are often unable to accurately represent these key surface components and their roles in ecological processes. From the perspective of reconstructing classification semantics, this study proposes a Natural-Attribute-Based Karst Land Cover Classification framework (NALCC). The framework takes bare rock, bare soil, vegetation, water bodies, and impervious surfaces as primary classes, and further develops a hierarchical system consisting of subclasses, attribute labels, hierarchical coding, multi-scale organization, and parameter mapping with ecosystem service models. Compared with conventional land cover classification systems, the innovation of this framework lies not in increasing the number of categories, but in reconstructing the semantic organization of classification units, so that land cover classification can move beyond surface-type description toward the expression of process-sensitive information. The classification objective of NALCC is not to develop a universal land cover classification system, but to establish a process-oriented classification framework for ecosystem service monitoring, rocky desertification diagnosis, and governance zoning in karst regions, which can directly represent key surface components and their ecological-process significance. However, its regional transferability and mapping performance still need to be further validated through case studies in representative areas. Full article

Cyanobacterial toxins (cyanotoxins) threaten aquatic ecosystems and human health, yet the factors influencing their production and distribution in freshwater remain unclear. In north-central Tennessee, nutrient-rich runoff from agricultural and urban areas, combined with a karst landscape that supports drinking and recreational water use, heightens the need to understand cyanotoxin behavior. To examine cyanotoxin patterns, the U.S. Geological Survey and the Tennessee Department of Environment and Conservation monitored 18 sites, including two wells under the influence of surface water, every two weeks from September 2022 to November 2024. At least one cyanotoxin was detected at all sites, with the highest concentrations in deep reservoirs and lower levels in shallow systems. Most detections occurred during summer and fall, aligning with high temperatures and rapid-onset drought. Statistical analysis indicated that increased specific conductivity and pH raised the likelihood of detecting total microcystin, likely resulting from drought conditions and nutrient-laden runoff. Additionally, dissolved microcystin showed an inverse relationship with Cumberland River water levels, and principal component analysis showed that Secchi depth, chlorophyll a, pH, temperature, and conductivity explained most water quality variability. These results help increase understanding of cyanotoxin distribution and associated water quality conditions during detections to guide future freshwater cyanotoxin monitoring studies. Full article

Accurate land use and land cover (LULC) data are essential for effective environmental management and reliable ecological modeling within complex landscapes such as the karst region of Southwest China. While new 10 m resolution global LULC products (i.e., ESA WorldCover, ESRI Land Cover, and annual mode composite of Dynamic World (DW)) offer unprecedented spatial detail, their reliability in heterogeneous karst remains poorly understood. We evaluated the accuracy and spatial consistency of these products for 2021 in the karst regions across five provinces in Southwest China using 1416 reference points collected through stratified random sampling. The ESA WorldCover dataset outperformed the others, achieving the highest overall accuracy (79.39 ± 2.19%). ESRI’s shrub metrics, however, reflect the structural absence of this class from its 2021 product rather than classification error. ESA’s superior performance in preserving fine-scale features is consistent with independent global assessments of both the 2020 and 2021 versions. This superior performance is attributed to its integration of Sentinel-1 SAR with optical data, a finer minimum mapping unit (100 m²), and expert-driven post-classification corrections. While all products successfully identified dominant classes like trees, substantial confusion emerged among spectrally similar classes such as shrubs, grass, and crops. A key finding was the strong effect of landscape heterogeneity on accuracy. Classification accuracy was 19.37% lower at patch edges (67.38%) compared to patch interiors (86.75%). Furthermore, edge reference points contribute disproportionately to total errors. Critically, none of the three products currently provide a sufficient basis for shrub-focused ecological monitoring in this region: ESA rarely detected shrub cover, DW mapped extensive but largely inaccurate shrub areas, and ESRI eliminated the shrub class from its 2021 product. These results show that while global 10 m products provide valuable information, careful product selection and regional validation remain essential for heterogeneous karst environments. Future improvements should integrate multi-source data (optical + synthetic aperture radar), apply topographic compensation for shadow effects, and develop region-specific approaches for mapping vegetation transitions. Full article

Wildfires severely disrupt soil nitrogen (N) cycling, yet the mechanisms driving this disruption in fragile karst forest ecosystems remain poorly understood. We investigated how wildfires affect soil N transformation dynamics and the microclimatic drivers of these dynamics in a karst forest. Using an in situ paired burned versus unburned plot design, we evaluated post-fire soil physicochemical properties, N fractions, and N-acquiring enzyme activities in the 0–10 cm soil layer. Wildfires significantly deteriorated the soil microenvironment, increasing mean soil temperature by 9.93% and bulk density by 36.66%, while sharply reducing soil water content, porosity, and saturated hydraulic conductivity. Consequently, the fires severely depleted total and organic soil N pools. Furthermore, N-acquiring enzymes (urease, protease, nitrate reductase, and nitrite reductase) initially declined in activity before gradually recovering. Notably, partial least squares structural equation modeling (PLS-SEM) revealed a fundamental shift in the drivers of nitrogen transformation. In unburned soil, abiotic climatic factors regulated N dynamics. After wildfire, enzyme-mediated biological processes controlled N dynamics, and these processes were constrained by altered soil physics. Restoring soil physical structure and stimulating enzymatic mineralization are therefore critical, rate-limiting steps for the recovery of soil N reservoirs in fire-prone karst landscapes. Full article

Forest above-ground biomass (AGB) estimation is crucial for evaluating carbon dynamics. Although optical and synthetic aperture radar (SAR) data provide complementary spectral and structural information, limitations in existing fusion approaches restrict AGB estimation accuracy. This study proposes a multi-source data fusion framework comparing two image fusion strategies—the conventional Hue-Intensity-Saturation Wavelet (HIS-Wavelet) method and a deep learning-based HIS-Non-Subsampled Shearlet Transform combined with Pulse Coupled Neural Network (HIS-NSST + PCNN) approach—for forest AGB estimation using Gaofen-1 (GF-1), Gaofen-2 (GF-2), and Gaofen-3 (GF-3) satellite imagery in a subtropical forest area of Yunnan Province, China. Three regression models—Multiple Linear Stepwise Regression (MLSR), K-Nearest Neighbor (KNN), and KNN with Fast Iterative Feature Selection (KNN-FIFS)—were systematically compared to evaluate estimation performance and justify model selection. Results indicate that the HIS-NSST + PCNN method outperforms HIS-Wavelet in fusion quality metrics, with the GF-2 Red-Near-infrared-Blue (RNB) band and GF-3 combination using HH co-polarization achieving the highest image quality. The optimal AGB retrieval was achieved with the GF-1RNB and GF-3 combination under HIS-NSST + PCNN (coefficient of determination (R²) = 0.80, root mean square error (RMSE) = 14.79 t/ha), improving R² by 0.07 and RMSE by 2.35 t/ha over HIS-Wavelet. However, for GF-2 + GF-3, HIS-Wavelet achieved marginally better inversion accuracy (R² = 0.71) than HIS-NSST + PCNN (R² = 0.69), indicating that superior fusion quality does not directly translate to higher inversion accuracy. Bootstrap resampling analysis (1000 iterations) confirmed the statistical robustness, with the optimal KNN-FIFS yielding R² = 0.800 (95% confidence interval (CI): 0.678–0.924) and RMSE = 14.79 t/ha (95% CI: 12.51–17.22 t/ha), showing non-overlapping confidence intervals with both benchmark models. These findings demonstrate that spectral complementarity between optical and SAR data plays a more critical role than spatial resolution alone in fusion-based AGB estimation, and that adaptive feature selection is essential for maximizing inversion accuracy. Full article

Blue–green spaces serve as the core carriers of urban ecosystems, and their conservation and optimization have emerged as pivotal issues in territorial spatial planning and ecological governance. Taking Guilin, a national innovation demonstration zone for China’s Sustainable Development Agenda, as the study area, a deep learning-based DBDTAF-Net classification model is constructed using 2020 Sentinel-2 remote sensing imagery and AW3D30 Digital Surface Model (DSM) data. The model achieves a mean Intersection-over-Union (mIoU) of 86.05% on the test set and an IoU of 94.67% for rocky desertification areas. Based on the classification results, 21 derived indicators (including landscape patterns of BGSs) and six meteorological and topographic factors, alongside three core ecosystem service indicators—Aboveground Biomass (AGB), Net Primary Productivity (NPP), and soil conservation—are extracted to characterize their spatial patterns. The XGBoost-SHAP framework is employed to quantify the driving effects and threshold responses of BGS patterns on ecosystem services. The results indicate that (1) BGSs in Guilin display a spatial pattern of “green-dominated, blue-supplemented, generally contiguous yet locally fragmented,” and all three ecosystem services exhibit significant spatial clustering. (2) Landscape pattern factors of green spaces constitute the dominant influencing factors, with contribution rates ranging from 22.3% to 28.6%. Specifically, green space_COHESION demonstrates a stable linear positive effect. A green space ratio below 45% suppresses AGB, whereas exceeding 45% shifts to a positive effect and represents an efficient enhancement interval for NPP while exerting a continuously positive influence on soil conservation. A cultivated land proportion below 30% leads to a strongly increasing inhibitory effect on AGB and soil conservation, whereas its inhibition on NPP weakens beyond 20%. A construction land proportion exceeding 10% significantly suppresses NPP, and the inhibitory effect stabilizes above 20%. Green space patch density below 0.8 shows a pronounced negative effect, which diminishes above 0.8. Blue space factors exert relatively weak effects. (3) The ecosystem service supply capacity varies across functional zones in Guilin, with the ecological barrier zone performing the best, the modern agricultural zone performing moderately, and the six central urban districts of the Shanshui Metropolis Area exhibiting the lowest levels. This study provides a technical framework for high-precision extraction of urban BGSs and quantitative analysis of factors influencing ecosystem services, offers decision support for ecological conservation and restoration in Guilin, and furthermore proposes insights for the coordinated development of rational land resource utilization and ecosystem service enhancement in other karst cities. Full article

Arbuscular mycorrhizal fungi (AMF) are vital for nutrient cycling, but how lithology across bulk soil and the rock–soil interface influence AMF communities remains poorly understood. We investigated the effects of karst (dolomite, limestone) and non-karst (clastic rock) lithologies across bulk soil and the rock–soil interface on AMF diversity, community composition, and co-occurrence networks in southwest China. AMF diversity did not differ among lithologies or between bulk soil and rock–soil interface, whereas community composition showed significant differences across lithology. The relative abundance of Glomus was lower in karst than in non-karst, whereas Paraglomus showed the opposite pattern. Co-occurrence network analysis revealed that karst soils exhibited higher numbers of nodes and edges but lower network density, transitivity, betweenness centrality, and average path length compared to non-karst soils. Within the same dolomite and limestone, network properties were similar between the rock–soil interface and bulk soil. Soil pH, exchangeable Ca²⁺ and Mg²⁺, total nitrogen, and nitrate nitrogen were negatively correlated with Glomus and network properties (e.g., number of nodes and edges), while ammonium nitrogen showed positive correlations. Our results indicate that lithology exerts a stronger influence than soil compartment on AMF community composition and interspecific interactions, emphasizing the key role of lithological substrates in regulating AMF communities. Full article

Severe heavy metal contamination affects the karst landscapes of Guangxi Zhuang Autonomous Region, China, which are highly polluted and complex. However, integrated assessments of heavy metal sources, distribution, ecological risks, and speciation in karst agricultural soils remain limited. Additionally, there is a gap regarding the interactions between cadmium (Cd), zinc (Zn), and selenium (Se) in natural rice fields. This study employed the pollution load index (PLI), ecological risk index (RI), and Positive Matrix Factorization (PMF) models to evaluate the sources and characteristics of heavy metal contamination in farmland soils. The results showed significant pollution in agricultural soils of Guangxi karst due to Cd, chromium (Cr), copper (Cu), and nickel (Ni). Among these, Cd poses the highest ecological risk. Heavy metal accumulation in the surface soil far exceeds that in deeper layers, and the main sources of Cd were contributed from soil parent material and agricultural activities. Speciation analysis revealed the high bioavailability of Cd, while Zn and Se existed in more stable forms. Despite elevated soil Cd levels, rice grains remained within the safety limits. Using transmission electron microscopy (TEM), Cd was primarily detected in the cell walls of rice stems and husks, which was attributed to Zn’s competitive uptake, reducing Cd absorption and Se forming complexes with Cd to enhance its fixation. Statistical correlations revealed positive associations between Cd in soil and rice. Cd also demonstrated a positive correlation with Se, but a negative correlation with Zn, suggesting a synergistic mechanism between Zn and Se that acts to mitigate the absorption of Cd. This study provides practical guidance for managing farmland soil heavy metal contamination and protecting agricultural soil resources in the karst areas. Full article