Search Results (771)

Microplastic (MP) and nanoplastic (NP) pollution has emerged as a pervasive and still insufficiently quantified pressure on terrestrial ecosystems, yet its consequences for wild herbivores remain incompletely understood. As key links between primary producers and higher trophic levels, wild herbivores occupy a critical ecological position and may serve both as exposed receptors and as biological vectors of plastic contamination. This manuscript presents a narrative review that synthesizes recent advances in understanding the physiological, behavioural, and ecological implications of MP and/or NP exposure in free-ranging herbivorous mammals, integrating evidence from field surveys, experimental studies, ecological modelling, and supportive mechanistic findings from livestock and experimental mammalian systems. Available evidence indicates that MPs and NPs are consistently detected in wild herbivores from both human-modified and protected landscapes, demonstrating widespread terrestrial exposure. Reported biological effects include oxidative stress, digestive dysfunction, inflammatory and immune responses, altered gut microbial communities, impaired nutrient assimilation, and organ-level damage, although much of the mechanistic evidence derives from controlled laboratory or livestock-based studies rather than direct wildlife investigations. Behavioural responses remain comparatively underexplored, particularly in large-bodied herbivores, with limited evidence for altered foraging, habitat use, and stress-related behaviours. At the ecosystem level, emerging studies suggest that herbivores may contribute to the landscape-scale redistribution of MPs and NPs through movement and faecal deposition, with potential downstream effects on soil processes, nutrient cycling, and plant–herbivore interactions. However, the current evidence base is constrained by major methodological and conceptual limitations, including the lack of standardized detection and reporting protocols, limited ecological realism in exposure studies, taxonomic and geographic biases, and poor resolution of long-term population-level and food-web consequences. Overall, the available literature indicates that MP and NP pollution represent a multifaceted and emerging risk to wild herbivores and the ecosystems they inhabit. Future research should prioritize standardized contamination-controlled monitoring, non-invasive faecal surveillance, ecologically realistic chronic exposure studies, and integrated conservation frameworks that recognize wild herbivores as sentinel species for terrestrial plastic pollution. Full article

There exists a complex and intimate interplay between ecosystem services and human well-being. This coordination not only concerns regional sustainable development but also depends on the structural balance of various service functions within ecosystems. Therefore, based on three-phase data from 2000 to 2020, this study investigates the coupling coordination relationship between ecosystem services and human well-being in the Northeast Black Soil Region, along with its driving factors and influence pathways. Key ecosystem services and human well-being levels were quantified, introducing a two-level coupling coordination model: D1 (coordination between total ecosystem service provision and human well-being) and D2 (coordination between internal ecosystem service balance and human well-being). Results indicate that: (1) From 2000 to 2020, the Ecosystem Service Index showed an initial rise followed by a decline. Synergistic relationships among ecosystem services strengthened, while trade-offs between cultural services (Shannon diversity index) and other services persisted. High human well-being zones were highly concentrated in provincial capitals, indicating the gradual formation of a priority development pattern. (2) The coupling coordination level of D2 was significantly weaker overall than that of D1. Compared to the overall supply level, the coordination of internal ecosystem service functions was a more critical factor constraining regional comprehensive development. (3) Landscape patterns are the primary factor governing the coupling relationship between regional ecosystem services and human well-being. Future efforts should focus on optimizing landscape configurations to enhance both human well-being and ecosystem coordination. This study contributes to a better understanding of the relationship between ecosystem services and human well-being from the perspectives of both aggregate coordination and internal balance, and also provides valuable insights for research and management measures in regions characterized by intensive agricultural development and rapid urbanization. Full article

The unchecked expansion of tea plantations onto steep, forest-adjacent slopes in subtropical mountains engenders a conflict between agricultural productivity and ecosystem integrity, particularly by exacerbating habitat fragmentation and soil erosion. While precise monitoring is essential to navigate this trade-off for sustainable management, accurate inventorying remains a challenge due to the plantations’ strong phenological variability, heterogeneous canopy structures, and high spectral confusion with surrounding vegetation. This study proposes a feature-optimized deep learning framework for mapping and characterizing tea plantations in complex landscapes, using Xinyang City, China, as a study area. The framework integrates multi-temporal Sentinel-1/2 observations with a sequential Jeffries-Matusita (JM)-Pearson feature filtering strategy. This approach effectively condenses a 132-variable high-dimensional pool (including optical spectra, vegetation indices, textures, and SAR polarimetry) into a compact 28-feature subset (a 78.8% reduction), preserving critical phenological and structural cues while minimizing redundancy. These optimized predictors drive a hybrid VGG16–UNet++ segmentation network, which couples transfer-learning-based semantic encoding with detail-preserving dense skip fusion. Extensive experiments across 18 model–feature configurations demonstrate that the optimal setting achieves an Overall Accuracy of 97.82%, an F1-score of 0.9093, and a mean IoU of 0.7968. Notably, the method significantly reduces misclassification in rugged, cloud-prone terrain, yielding a User’s Accuracy of 91.14% for tea. Based on the generated wall-to-wall map, we derived two decision-support indicators: multi-threshold steep-slope exposure and a normalized tea–forest interface density. This framework provides actionable, high-precision spatial products to support slope-based zoning, ecological restoration, and sustainable management in fragile mountain agroforestry systems. Full article

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures. Full article

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB. Full article

Land leveling deep-seated landslides for agricultural use alters soil profile integrity and soil functionality. In the mid-20th century, such interventions in the Transylvanian Basin (Romania) involved grading and converting landslide bodies into arable land. This study evaluates the consequences of interventions on soil physicochemical properties and erosion susceptibility in the case of two deep-seated landslides. Soil samples collected from leveled landslide bodies were analyzed for pH, total nitrogen, available phosphorus (P-AL), available potassium (K-AL), calcium carbonates, humus content, and texture. The results, in the case of the two studied deep-seated landslides, indicate contrasts between areas where the Ah horizon is preserved and where leveling exposed the C horizon or parental material at the surface. Exposed zones exhibit reduced nitrogen and humus content, altered textures, and higher carbonate influence, indicating lower fertility potential despite 65 years of pedogenesis. Spatial assessment using Sentinel-2-derived NDMI and USLE-based erosion modelling confirms increased moisture stress and higher erosion susceptibility in areas with exposed substratum. These findings demonstrate that the leveling of the two studied deep-seated landslide bodies, although effective in expanding arable surfaces, leads to persistent soil degradation patterns and reduced agro-ecological resilience. Sustainable cultivation of such terrains requires targeted soil conservation measures, including erosion control and adapted land management practices. The results provide important implications for land-use planning in landslide-prone agricultural landscapes. Full article

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

The Western Songnen Plain, a critical yet ecologically fragile grain-producing area, is facing sustainability risks arising from rapid land use changes, which demand scientific assessment and regulation. From an ecological security standpoint, this study synthesizes multiple data sources, including GlobeLand30 data, climate, topography, and soil data. Based on the assessment of water conservation, soil conservation and biodiversity maintenance, combined with minimum cumulative resistance model (MCR) and the CLUMondo model, this study comprehensively reveals the dynamic evolutionary patterns of land use in the Western Songnen Plain over the past two decades, concurrently analyzed the spatial heterogeneity pattern of ecosystem services, and further simulated land use changes under natural growth, farmland protection, and ecological security scenarios. According to the results, the grassland area decreased significantly, while cropland and construction land continued to expand. Water conservation, soil conservation, and habitat quality displayed remarkable regional differences, with high values predominantly situated in wetlands, grasslands, and mountainous regions. In contrast, low values exhibited strong spatial correspondence with regions of heightened anthropogenic disturbance. Although the cropland protection scenario promoted agricultural intensification, it reduced ecological heterogeneity. In contrast, the ecological security scenario achieved a higher patch density (0.408) and landscape diversity (1.142) compared to the natural growth scenario, with moderate increases in aggregation. This study identified 27 ecological pinch points, 24 ecological barrier points, and 97 ecological corridors, which provide direct support for regional water and soil resource protection and further underpin the constructed ecological security pattern of “two belts, three zones, and multiple nodes”. These findings have important reference significance for optimizing regional land use structure and maintaining the stability of terrestrial ecosystems in the Western Songnen Plain. Full article

The pervasive environmental dispersal of glyphosate has established this herbicide as a dominant anthropogenic xenobiotic, necessitating advanced bioremediation strategies to restore soil integrity. This study assessed the bioremediation efficacy of biosurfactants produced by Serratia ureilytica BM01-BS in glyphosate-contaminated soils, establishing their adsorption dynamics and ecotoxicological safety. The strain S. ureilytica BM01-BS gave a biosurfactant yield of 3.7 g·L⁻¹ with promising surface properties, utilizing babassu (Attalea speciosa) waste as the sole nutrient source. Whole-Genome Sequencing and Biosynthetic Gene Cluster mining identified a Nonribosomal Peptide Synthetase cluster homologous to rhizomide-type lipopeptides responsible for biosurfactant production. Bioremediation assays in glyphosate-contaminated soils demonstrated a removal efficiency exceeding 95% in approximately 60 min, outperforming the synthetic surfactant SDS (20–30% efficiency). Kinetic and isothermal modeling suggest that the bioremediation process is governed by chemisorption, adhering to a pseudo-second-order model (R² = 0.998) with a maximum adsorption capacity of 845 µg·kg⁻¹. Fourier-Transform Infrared spectroscopy confirmed that the biosurfactant effectively removes glyphosate and restores the soil’s mineral integrity, as evidenced by the complete disappearance of glyphosate-associated phosphonic and carboxylic bands. Ecotoxicological assessments verified the environmental safety of the bioremediation process. These findings position the BM01-BS biosurfactant as a sustainable, biodiversity-based adjuvant for enhancing ecological resilience in glyphosate-impacted landscapes. Full article

Accurate soil thermal resistivity is crucial for real-time cable ampacity determination to maximize cable utilization. However, the determination of soil thermal resistivity involves solving a computationally expensive multi-physical field inverse problem where a high-fidelity model (HFM) is used for performance evaluations. Surrogate-assisted evolutionary algorithms (SAEAs) are computationally efficient for such problems; model management strategies (MMSs) are key to SAEAs. Nevertheless, most MMSs struggle to balance the computational cost and the search accuracy due to their reliance on fitness value errors. In fact, maintaining a similar function landscape between the surrogate and the HFM is more essential than achieving precise fitness values on the surrogate. Consequently, a rank-based hybrid MMS-driven two-stage SAEA is proposed. Stage 1 focuses on identifying promising regions. To ensure the similarity between the surrogate and HFM function landscape and thus guide the evolution accurately, a global MMS is proposed. Specifically, a new function landscape similarity metric is proposed to adaptively adjust the surrogate update frequency. A new rank-error-based individual selection strategy selects key individuals for exact evaluations to refine the surrogate similarity. Stage 2 performs a refined local search within the identified promising region, utilizing a local MMS to re-evaluate the optimum of a local surrogate built around the best solution searched in Stage 1. Optimization results confirm the proposed method’s superiority on test functions and a prototype cable. Full article

Although diazotrophy is compatible with low-carbon agriculture and aligned with sustainability goals, its benefits could be expanded by better leveraging associative plant–diazotroph partnerships. Host control, however, remains underemphasized despite increasing resolution of microbial determinants of colonization. Understanding how plants tune permissiveness across fluctuating mineral N landscapes is therefore central to explaining when microbial presence translates into measurable diazotrophic function and plant N gain. Here, we propose an N-mediated host gatekeeping framework that organizes existing evidence into four licensing windows: (i) spatial positioning of permissive sites, (ii) N-sensitive transcriptional thresholding, (iii) local immune tuning at the contact interface, and (iv) carbon energy arbitration sustaining fixation and N transfer. Our model predicts that moderate, spatially heterogeneous mineral N biases the root interface toward permissive states in which microdomain colonization can translate into measurable biological nitrogen fixation, whereas at either extreme one or more windows tend to close. In crops, soil heterogeneity and genotype-linked root functional traits act as filters shaping when functional engagement becomes possible. By reframing N as both a resource and a signal acting through host arbitration, this model clarifies how permissiveness can be tuned to better realize diazotrophic potential and support plant N gain under rational mineral N management. Full article

Identifying the specific sources of heavy metal accumulation in intensive agricultural landscapes is essential for ensuring soil sustainability and food security. In this study, we independently carried out a high-density regional geochemical survey and high-resolution field sampling in the Yangtze–Huaihe Watershed, Eastern China, and used the original sample dataset to distinguish between geogenic backgrounds and anthropogenic enrichments. By employing the APCS-MLR model, four distinct pollution sources were quantitatively identified: natural pedogenesis, agricultural activities, traffic emissions, and industrial inputs. Results demonstrated that while most heavy metal concentrations remained below national safety thresholds, Cd and Hg exhibited significant topsoil enrichment, signaling potential ecological risks. Source apportionment revealed that natural sources primarily controlled As, Cr, Ni, and Pb, with the contribution ranging from 41% to 70%. In contrast, traffic emissions (e.g., tire wear and fuel combustion) emerged as the dominant source for Cd (68%), Zn (55%), and Cu (34%), while industrial activities accounted for a substantial 89% of Hg accumulation via atmospheric deposition. Notably, despite the region’s intensive cultivation, agricultural practices played a surprisingly minor role in heavy metal accumulation. These findings highlight that the accumulations from traffic and industry now account for approximately 50% of the total heavy metal load in the region. Our results underscore the critical importance of high-resolution spatial data for precise source identification and suggest that implementing vegetative buffer zones and stricter industrial emission controls are imperative to mitigate further soil degradation in similar agricultural watersheds. Full article

Clarifying the relationship between landscape patterns and runoff coefficient, along with identifying key influencing pathways, is crucial for formulating sustainable water resource management strategies. Since the launch of the Grain-for-Green (GfG) project in 1999, the landscape pattern of the Loess Plateau has been profoundly reshaped, altering regional rainfall-runoff processes. Assessment across 27 catchments selected in the central Loess Plateau demonstrated forest and grassland areas expanded by 738.8 km² and 480.4 km², respectively, paralleled by a 20.1% enhancement in vegetation coverage. Correspondingly, surface runoff decreased by 28.1–90.6% in the 2000s and 12.8–95.5% in the 2010s compared to the 1960s, with a similar decline in runoff coefficient. This study further developed a novel landscape unit mapping method, integrating vegetation coverage, land use, slope, and soil type to compute landscape metrics. Partial least squares regression (PLSR) and piecewise structural equation modeling (piecewiseSEM) were constructed to systematically analyze the linkage between landscape patterns and surface runoff. The constructed landscape metrics explained 64.6% of the variance in the runoff coefficient, with perimeter area fractal dimension (PAFRAC), mean perimeter-area ratio (PARA_MN), and aggregation index (AI) exerting significant influence. The findings provide a scientific basis for water resource management in regions with similar environmental characteristics. Full article

Using perfluorooctanoic acid (PFOA) as a representative highly mobile solute to isolate hydrological controls, we investigated how slope influences the partitioning of vertical and lateral transport pathways. While vertical percolation has been widely examined using conventional column leaching tests, lateral transport driven by topographic gradients remain insufficiently quantified under controlled conditions. Here, laboratory-scale inclined leaching experiments were conducted to resolve the distribution of solute transport among vertical leachate, lateral runoff, and solid-phase retention under systematically varied slope angles (0°, 4°, 9°, and 20°), flow regimes, and leaching volumes. Results show that solute migration shifted from vertical-dominated transport under flat conditions (91% at 0°) to lateral-dominated export at moderate slopes, with lateral pathways accounting for up to 75% of the recovered mass at 9°. This pathway shift was well described by an exponential partitioning model, f₁(α) = f_max (1 − e^−kα), where f_max = 0.80 and k = 0.34°⁻¹ (R² = 0.97), indicating a critical crossover threshold at approximately 4° slope. Flow regime interacted with slope angle to modulate lateral transport efficiency: slower flow enhanced lateral export at moderate slopes, whereas faster flow promoted peak lateral transport under steeper conditions. In contrast, solid-phase retention remained consistently low (5–9%) across all treatments, indicating that the observed redistribution patterns were primarily governed by hydrological pathway partitioning rather than sorption processes. These results demonstrate that even modest topographic gradients can fundamentally alter solute transport pathways in sloped soils. The slope-dependent pathway partitioning framework developed here provides a process-based basis for incorporating lateral transport into hillslope hydrological models and for improving assessments of contaminant redistribution in both managed and natural landscapes. Full article

The synergistic impacts of land use/land cover (LULC) transformations and weather pattern variabilities (WPV) represent a primary driver of hydro-geological instability, threatening agricultural productivity, soil conservation, and water quality. Disentangling the discrete contributions of these stressors to runoff and sediment yield (SY) remains a significant challenge, particularly in complex, confluence-proximal watersheds lacking major hydraulic regulations. This study investigates the Tirumakudalu Narasipura watershed in Karnataka, India, an agriculturally intensive system undergoing rapid peri-urbanization. Leveraging the process-based geospatial interface of the Water Erosion Prediction Project (GeoWEPP), we analyzed hydrological responses over a 24-year period (2000–2023) and projected future trajectories through 2030. To overcome the traditional constraints of GeoWEPP, which was developed for small-scale watersheds (<260 ha), we present a novel upscaling framework utilizing a multi-site multivariate temporal calibration of hydrological response variables to exploit its process-based precision in capturing distributed soil erosion and landscape heterogeneity. This approach is further reinforced by an ancillary data validation to minimize error propagation while model-upscaling. Our findings reveal projected increases in runoff and SY of 14.69% and 49.23%, respectively, between 2000 and 2030. Notably, the sub-decadal acceleration from 2023 to 2030 (17.32% for runoff and 18.51% for SY) underscores a shifting dominance where LULC-driven surface modifications now outweigh climatic variance in forcing hydrologic change. Furthermore, the study quantifies how anthropogenic interventions such as strategic crop selection, tillage intensity, and irrigation regimes act as critical determinants of topsoil preservation. These results provide a scalable, economically feasible framework for precision land stewardship and sustainable watershed management in rapidly developing tropical landscapes. Full article