Search Results (4,816)

As a core area for soil and water conservation on the Loess Plateau and a national primary shale oil production zone, Qingyang City faces an increasingly acute contradiction between its inherently fragile ecological base and energy development activities. From the dual perspectives of ecological regulating services and production-supporting services, this study selected six key ecosystem services—habitat quality (HQ), soil retention (SR), carbon storage (CS), water yield (WY), food supply (FS), and grassland forage supply (GS)—to comprehensively assess their spatiotemporal evolution, trade-off/synergy relationships, and driving mechanisms from 2000 to 2020. The results indicate: (1) Significant changes occurred in the total amounts and spatial patterns of all ecosystem services during 2000–2020. HQ showed a fluctuating upward trend, while SR, FS, and GS increased overall; by contrast, CS and WY generally declined. (2) Ecosystem services exhibited a differentiated pattern characterized by “intra-category synergy and inter-category trade-off.” Regulating and supporting services were generally dominated by synergistic relationships, although clear differences remained among specific service pairs; provisioning services generally showed trade-offs with regulating services, among which the trade-offs between FS–HQ and between FS–GS were the most pronounced, whereas FS–CS showed a certain degree of synergy. (3) Driving force analysis revealed a continuous decline in the influence of natural factors and a sharp intensification of human activity factors. Groundwater level and land-use intensity became core drivers of pattern shifts, with their explanatory power increasing significantly. The study reveals that ecosystem services in Qingyang have rapidly transitioned from being dominated by natural hydrothermal conditions to being profoundly reshaped by energy development activities, exposing the region to the ecological risk of a “resource curse.” These findings provide a scientific basis and management insights for achieving coordinated development between resource exploitation and ecological conservation in ecologically fragile areas of the Loess Plateau. Full article

Madagascar’s expanding blue economy is largely underpinned by seaweed aquaculture, particularly Kappaphycus alvarezii (Cottonii), which offers an alternative to declining small-scale fisheries and strengthens the resilience of coastal socio-ecosystems. Ensuring the sustainability of this economic activity requires effective ecological monitoring of aquaculture sites and surrounding habitats. This study examines and compares the performance of two imaging configurations—an RGB composite derived from a subset of multispectral images capturing red (650 nm), green (560 nm), and blue (450 nm) bands; and a five-band multispectral (MS) image encompassing blue, green, red, red-edge (730 nm), and near-infrared (840 nm) bands—combined with a Random Forest (RF) classification model, for benthic habitat mapping in a seaweed cultivation context. High-resolution orthomosaics (2 cm/pixel) enabled the discrimination of Kappaphycus cultivation plots from three shallow-water habitats: (i) ‘benthic macrophytes’, which comprise: seagrass meadows and benthic macroalgal; (ii) ‘sandy bottom’ and (iii) ‘green algae’. The RF classification achieved an overall accuracy of 87% (Kappa = 0.82) across ~10 hectares. Producer’s accuracy exceeded 80% for Kappaphycus cultivation, green algae, and sandy bottom for both the RGB and MS datasets, indicating strong classification performance. However, early-stage seaweed was occasionally misclassified as benthic macrophytes, likely due to its low biomass and weak spectral signature. This UAS-based approach provided a robust and cost-effective framework for monitoring off-bottom seaweed farms and associated natural habitats. This approach supports sustainable aquaculture development and integrated coastal management in Madagascar and comparable tropical reef socio-ecosystems. Full article

Artificial intelligence (AI) is transforming logistics and supply chain management by enhancing operational efficiency, predictive analytics, and decision-making capabilities; however, the determinants of AI adoption in emerging logistics ecosystems remain insufficiently understood. This study develops and empirically examines an integrated framework combining technology-organization-environment (TOE), task-technology fit (TTF), and unified theory of acceptance and use of technology (UTAUT) to explain AI adoption in Thailand. Using survey data from 500 logistics and supply chain professionals, covariance-based structural equation modeling (SEM) was employed to validate the measurement model and test the proposed relationships. The results show that technological, organizational, and environmental factors significantly influence AI adoption at the organizational level, while task and technology characteristics enhance task-technology fit at the operational level. At the behavioral level, performance expectancy, effort expectancy, and social influence positively influence behavioral intention, which in turn drives AI adoption, with facilitating conditions also exerting a direct effect. These findings indicate that AI adoption is shaped by a cross-level mechanism involving structural conditions, operational alignment, and individual acceptance, offering theoretical and practical insights for advancing digital transformation in logistics contexts. Full article

Drunken horse grass (Achnatherum inebrians) plays a vital role in ecological restoration and grassland sustainability in Northwest China, but its ecological functions are increasingly threatened by emerging fungal diseases. In 2024, a leaf spot disease characterized by brown lesions with yellow halos was observed on drunken horse grass in Gansu Province, China. The causal pathogens were identified as Alternaria alternata and Alternaria infectoria based on morphological characterization, pathogenicity tests, and multi-locus phylogenetic analysis (ITS, TEF, GPD, RPB2, Alt a 1, endoPG, and OPA10-2). Preliminary fungicide sensitivity assays revealed that tetramycin and difenoconazole had the strongest inhibitory effects against mycelial growth in vitro. The EC₅₀ values for tetramycin were 0.0755 mg/L (A. alternata) and 0.2175 mg/L (A. infectoria), while for difenoconazole, they were 0.1023 mg/L (A. alternata) and 0.0599 mg/L (A. infectoria). To our knowledge, this is the first report of Alternaria species infecting the host plant, drunken horse grass, providing an essential basis for the effective management of this disease and the protection of grassland ecosystems. Full article

Hyperconnected IoT ecosystems have become crucial for organizational operations; yet, existing governance structures remain fragmented, are technology-centric, and not well-equipped to manage the risks, compliance pressures, and resilience needs of IoT. This paper presents an integrated, theory-based information security governance model that is tailored for IoT-driven organizations. A conceptual synthesis is performed through integrating five theoretical anchors: governance theory, socio-technical systems theory, risk governance theory, institutional/compliance theory, and resilience/adaptive capacity theory. These theoretical lenses are used to derive essential governance constructs and to develop a modular architecture tailored to IoT security needs. The model’s validity is grounded in theoretical integration rather than empirical testing, consistent with the nature of conceptual research. The integrated model provides six interdependent governance dimensions: strategic governance, operational governance, technical oversight, compliance alignment, risk governance, and resilience/adaptation, anchored by an ecosystem coordination layer. It provides structured decision rights, continuous risk monitoring, regulatory legitimacy, and native adaptive capabilities toward dynamic cyber-physical threats. This research addresses a known gap in the literature on IoT governance by providing an integrated, theoretically validated governance model that systematically connects the rationale and operational mechanisms of governance for resilient, future-proof IoT adoption. The model is further operationalized through a five-level maturity structure, enabling organizations to assess and progressively enhance governance capabilities. Full article

Inadequate drainage and the application of salty irrigation waterinduced salinity stress, poses a major constraint to agricultural productivity, especially in saline–arid regions. Shallow subsurface drainage has emerged as a potential technique for salinity management; however, its implications for crop physiological and biochemical responses remain unclear. Therefore, a two-year lysimetric study was undertaken in a split-split plot design investigating cut-soiler-based preferential shallow subsurface drainage (PSSD), soil type (saline sandy loam and normal silty clay loam), and irrigation water salinity levels (4, 8 and 12 dS m⁻¹) to evaluate the effectiveness of rice-residue-filled cut-soiler PSSD in mitigating salinity stress in pearl millet and mustard crops. The cut-soiler PSSD reduced root-zone salinity to around 60.0% by the end of experimentation. Lower root-zone salinity under cut-soiler PSSD alleviated osmotic and ionic stress by reducing hydrogen peroxide (11.0–14.6%), membrane injury (22.7–40.8%), lipid peroxidation (20.0–25.0%), proline accumulation (26.0–37.0%) and improving the Na⁺/K⁺ ratio (44.0%). Antioxidant enzyme activities were also significantly moderated under the cut-soiler PSSD. These physiological and biochemical improvements resulted in significant increases in grain and seed yield of pearl millet (23.5%) and mustard (31.4%), respectively. The findings of this study indicate that cut-soiler PSSD is an effective strategy to mitigate salinity stress at the physiological and biochemical level and offers sustainable management strategies for salt-affected agro-ecosystems. Full article

Regional sustainable development requires integrated assessments that capture ecosystem function, risk exposure, and recovery capacity. Conventional two-dimensional frameworks based on ecosystem services (ESs) and landscape ecological risk (ERI) often overlook the self-regulation potential of ecosystems following disturbance. This study proposes that incorporating RES as a third zoning dimension enables functional differentiation between areas that share similar ES–ERI profiles but differ substantially in recovery capacity, thereby revealing management priorities that a conventional two-dimensional framework cannot detect. This study develops a three-dimensional zoning framework integrating ES, ERI, and ecological resilience (RES) in the main urban area of Ganzhou City, a representative hilly city in southern China. Land-use dynamics from 1990 to 2020 and under four 2050 scenarios were simulated using a coupled PLUS-InVEST approach. Differentiated ecological zones were delineated, and the optimal-parameter geographic detector (OPGD) was applied to examine driving factor interactions. Results indicate that cultivated land and forestland dominated the study area throughout the period. ES supply remained favorable with stage-wise fluctuations, while ERI showed progressive convergence of high-risk patches toward the central basin. RES exhibited a sharp decline in higher-resilience areas during 1990–2000 (91.0%), followed by partial recovery during 2010–2020 (47.3%). The three-dimensional zoning delineated 35.9% of the area as Ecological control zones that may require priority intervention. Driver analysis revealed that DEM, precipitation, and river proximity, along with their interactions, strongly influenced regional ecological patterns. The proposed framework extends conventional ES-ERI assessments and provides spatial guidance for differentiated ecological management in hilly regions. Full article

Forest fires pose a considerable threat to the security of ecosystems and human society, rendering accurate assessments of fire severity critical for ecological recovery and effective fire management. The differenced Normalized Burn Ratio (dNBR) has been employed to evaluate forest fire severity; however, it presents notable uncertainties owing to variations in data sources, temporal phases, and environmental factors. To address these challenges, this study analyzed 10 forest fires occurring between 2006 and 2023 in central Yunnan Province, China. First, a rapid sampling method utilizing very high-resolution imagery was developed to assess the performance of dNBR classification under varying conditions. Second, the study identified the optimal post-fire observation window and compared classification thresholds and accuracy between Landsat and Sentinel-2 imagery in assessing fire severity. Finally, the research explored the impacts of topographic correction and pre-fire vegetation differences on classification outcomes. The findings revealed the following: (1) Imagery captured in the spring of the fire year, characterized by minimal vegetation interference, demonstrated the highest classification stability and superior capability for identifying high-severity burns. (2) Landsat outperformed Sentinel-2 in regional accuracy (0.92 vs. 0.87), and direct threshold transfer between sensors resulted in a 39% underestimation of high-severity areas, underscoring the necessity for sensor-specific calibration. (3) Topographic correction provided limited practical benefits, merely yielding a marginal improvement in accuracy (+1.44%) with the SCS+C model in steep terrain, and was generally unnecessary. (4) The influence of pre-fire vegetation was discovered to be threshold-dependent: dNBR performed reliably in forests with pre-fire NDVI > 0.5, while adjusted approaches were solely recommended for sparse or heterogeneous vegetation. Overall, this study establishes a systematic framework for optimizing dNBR-based severity assessment, enhancing its accuracy and operational utility in forest fire management. Full article

Urban trees deliver multiple ecosystem services. However, rapid climate change may alter species-specific growth suitability, necessitating climate-informed planting and management. We developed 1 km grid-based ensemble species distribution models (ensemble SDMS) for 18 tree species widely planted in South Korean cities and projected growth suitability under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 across four future periods (2021–2040, 2041–2060, 2061–2080, 2081–2100) relative to a historical baseline (2000–2019). We quantified multidimensional redistribution signals from SDM outputs, including binary suitable area changes, centroid displacement, latitudinal boundary shifts, and mean suitability changes, using multivariate climatic predictors and complementary environmental variables. These indicators were integrated to classify species responses into four management-relevant types: Stable, Northward Expansion, Poleward Shift, Range Contraction. Model performance was generally high (AUC = 0.74–0.97). Although the median change in suitable area remained near 0%, interspecific variability increased toward later periods and under stronger forcing, with the largest dispersion under SSP3-7.0 (2041–2060). Stable type was most frequent overall (36.8–63.2%), but Northward Expansion increased to 42.1% under late-century SSP3-7.0, and Range Contraction reached 36.8% under mid-century SSP3-7.0. This indicator-based typology provides a practical basis for decision-support tools to prioritize climate-adaptive urban tree selection, replacement, and monitoring. Full article

This is the first systematic review evaluating Black Sea plankton as biosensor organisms for Biological Early Warning Systems (BEWS)—real-time monitoring approaches that detect sublethal behavioral or physiological responses to pollutants before irreversible ecosystem damage occurs. The systematic literature review was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, ensuring methodological transparency and applicability. A total of 140 publications from databases (Web of Science Core Collection, Scopus, PubMed, and Google Scholar databases) were included in the final analysis. We assess nine native planktonic taxa as candidates for automated video-based water quality monitoring, using a multi-criteria framework encompassing biological sensitivity, technical detectability, and practical feasibility. Three species emerge as the most suitable candidates: Aurelia aurita as a universal indicator (sensitive to copper, surfactants, petroleum, and microplastics; its large size enables standard video detection); Acartia tonsa for trace contamination (reproductive toxicity at metal concentrations 4–33× below regulatory standards); and Mnemiopsis leidyi for metal-specific discrimination (bioluminescent responses: 650% Zn, 430% Cu, and 350% Hg at 0.001 mg/L). Analysis of 140 publications reveals critical gaps: 33% of species lack toxicological data, 95% of studies test single toxicants despite natural mixture exposure, and microplastic methodology varies 1000-fold in particle size. Threshold analysis suggests planktonic sublethal stress at “safe” concentrations under current standards, suggesting inadequate protection of marine food webs. A complementary monitoring approach integrating these species with computer vision algorithms offers autonomous early-warning capability for Black Sea environmental management. Full article

The Spur-thighed tortoise (Testudo graeca) is a long-lived terrestrial reptile listed as ‘Vulnerable’ on the IUCN Red List and protected under CITES Appendix II. As an ecosystem engineer, it plays a vital role in Mediterranean landscapes, yet it frequently faces anthropogenic pressures in urban environments. This study provides an ecological and ethological assessment of a captive T. graeca population (n = 42) in the historical Münire Madrasa Handicrafts Bazaar in Kastamonu, Türkiye. The methodology integrated spatial carrying capacity modeling (Boullon model), systematic ethogram-based observations (120 h), and ethnozoological surveys (n = 200). Spatial analysis revealed that the population exceeds the corrected Real Carrying Capacity (RCC ≈ 10) by four times (Overcapacity Index: 4.2) within the 70 m² area. Ethological findings documented chronic stress, with stereotypic pacing (H1) occupying 32% of the time budget, alongside a significant loss of anti-predator mechanisms due to anthropogenic habituation (İ1). While stakeholders (100%, 95% CI: 98.1–100%) perceive the tortoises as cultural symbols of abundance, the biological reality indicates severe welfare risks, including potential metabolic bone disease from a monotonous anthropogenic diet and a disrupted Ca:P ratio. The site is categorized as a ‘High-Constraint Interaction Zone’. We propose a management transition toward a monitored ‘Urban Wildlife Education Station’ to align local cultural values with international animal welfare and conservation standards. Full article

Managers of protected areas (PAs) face growing challenges to conserve biodiversity while responding to multiple land uses such as recreation, tourism, and resource extraction. These pressures are intensified by the impacts of climate change on ecosystems. This highlights the need for planning approaches that support decision-making in the short, medium, and long term. This article profiles Wells Gray Provincial Park as a case study to demonstrate how an ecosystem-based planning approach can be incorporated into PAs planning. Wells Gray is situated in a unique ecosystem in the interior of British Columbia (Canada). We present an innovative model that integrates land cover types, ecosystem mapping, and Biogeoclimatic (BGC) zones derived from the Biogeoclimatic Ecosystem Classification (BEC) system using GIS tools to identify ecosystems and their associated services as Critical Decision Factors (CDFs). By explicitly linking ecosystems, land cover, and spatial patterns, this approach supports the systemic inclusion of ecosystems in management decisions. To account for future uncertainty, BGC zones were projected under climate change scenarios to inform interpretations of potential ecosystem impacts. The results indicate that this integrated analysis can initiate strategic thinking and facilitate dialogue to collaboratively plan with stakeholders. This approach can improve ecosystem-based planning processes in PAs across Canada. Full article

The San Andrés, Providencia, and Santa Catalina archipelago, located in the Colombian Caribbean, hosts diverse ecosystems, including coral reefs, mangroves, seagrass beds, and beaches, all of which are increasingly threatened by human activities. This research proposes a spatial analysis of ecological risk that integrates ecosystem vulnerability and anthropogenic pressures associated with land-use change to promote sustainable risk management. The vulnerability of island ecosystems was assessed by analyzing changes in cover across multiple time periods. At the same time, risks from anthropogenic pressures were determined based on marine protected area zoning and land-use planning regulations. Results show contrasting patterns: while several mangrove and beach sectors remained relatively stable, mangrove loss reached up to 65% in Providencia, and seagrass ecosystems experienced severe degradation, including a complete loss (100%) in western San Andrés. Risk maps indicate that the highest risk levels are consistently associated with Special Use Zones, where tourism infrastructure, navigation, and port activities are permitted. These findings highlight the importance of ecosystem-based risk management and adaptive governance in reducing anthropogenic pressures and preserving island ecosystem health. Full article

Soil health is essential for food security, ecosystem stability, and sustainable development, yet its spatial heterogeneity and driving mechanisms remain insufficiently understood at regional scales. This study investigates soil health in Heilongjiang Province, China. A Soil Health Index (SHI) was constructed using eight indicators covering physical, chemical, and biological properties based on multi-source datasets at 1 km spatial resolution. A random forest (RF) model was applied to identify key environmental drivers, and Moran’s I and Getis–Ord Gi* statistics were used to analyze spatial clustering. The results showed that SHI values ranged from 0.19 to 0.70, with a mean of 0.45. The RF model achieved strong performance (R² = 0.6666, RMSE = 0.03184, MAE = 0.02372), significantly outperforming linear regression (R² ≈ 0.17). Significant spatial clustering was observed, where “hotspots” refer to statistically significant clusters of high SHI values, and “coldspots” indicate clusters of low SHI values based on Getis–Ord Gi* analysis. Climate factors (temperature and precipitation) and elevation were the dominant drivers. Significant spatial clustering was observed, with clear hotspot and coldspot patterns. These findings provide spatial evidence for sustainable land-use planning and zonal soil management. However, the analysis is limited by data resolution and model interpretability, which may affect the representation of fine-scale variability. Full article

Nitrogen (N) loss poses a significant threat to global climate stability and ecosystem sustainability. Afforestation, as a key ecological restoration strategy, regulates soil N cycling processes by modulating soil microbial community structure. However, a systematic synthesis of how afforestation influences microbial-mediated N loss remains limited. To address this gap, this study conducted a bibliometric analysis using CiteSpace software, based on 104 relevant publications indexed in the Web of Science Core Collection from 1997 to 2025, to comprehensively map the knowledge structure, research hotspots, and evolutionary trajectories in the field of afforestation-driven microbial regulation of soil N loss. The results reveal three developmental phases: initiation (1997–2005), growth (2006–2020), and stabilization (2021–2025). China contributed the highest number of publications (40), while the United States exhibited the greatest academic influence; the Chinese Academy of Sciences and the Russian Academy of Sciences clusters have emerged as core research institutions. Notably, keyword and citation analyses revealed that research hotspots have shifted from process-oriented measurements, including N mineralization and N₂O emissions, toward a deeper exploration of microbial community structure, biodiversity, and functional mechanisms. This study presents the bibliometric synthesis of microbial N loss mechanisms under afforestation, revealing a paradigm shift from environmental driers to microbial diversity. These insights inform microbial forest management strategies that balance N retention with carbon sequestration. Full article