Search Results (11,428)

Soil-borne diseases represent a major constraint on the sustainable cultivation of morel mushrooms (Morchella spp.), yet the microbial ecological mechanisms driving disease occurrence and progression remain poorly understood. In this study, we conducted comparative metagenomic analyses of rhizosphere and root-adhering soils associated with healthy and diseased Morchella crops from two major production regions in China, aiming to elucidate shifts in microbial community composition, assembly processes, and functional potential. Disease conditions were linked to pronounced microbial dysbiosis, with community assembly shifting from stochastic to deterministic processes, particularly within fungal communities under host selection and pathogen pressure. Co-occurrence network analysis revealed substantial reductions in connectivity, modularity, and clustering coefficients in diseased soils, indicating the loss of ecological stability and keystone taxa. Functional annotations using CAZy, COG, and KEGG databases showed that healthy soils were enriched in genes related to carbohydrate metabolism, aerobic respiration, and ecosystem resilience, whereas diseased soils exhibited higher abundance of genes associated with stress responses, proliferation, and host defense. Furthermore, elemental cycling analysis demonstrated that healthy soils supported pathways involved in aerobic carbon degradation, nitrogen fixation, phosphate transport, and sulfur oxidation, while diseased soils favored fermentation, denitrification, phosphorus limitation responses, and reductive sulfur metabolism. Collectively, these results highlight the importance of microbial functional integrity in maintaining soil health and provide critical insights into microbiome-mediated disease dynamics, offering a foundation for developing microbiome-informed strategies for sustainable fungal crop management. Full article

Urban green hearts provide essential ecosystem services, including carbon sequestration, water purification, and hydrological regulation. The Green Heart Area of the Chang-Zhu-Tan Urban Agglomeration in Hunan Province, China, is the largest globally, and plays a critical role in regional water management. These functions are increasingly threatened by intensive land-use, while soil, as the foundational ecosystem component, mediates water retention, nutrient cycling, and erosion resistance. This study examined the effects of four land-use types—cropland, plantation, arbor woodland, and other woodland—on soil particle composition and key nutrients (organic carbon, total nitrogen, and total phosphorus). Statistical comparisons among land-use types were performed. Results indicated that silt was the dominant soil fraction across all land-uses (64–72%). Arbor woodland exhibited significantly higher sand content (29%) compared to cropland (19%; p < 0.05), suggesting improved water permeability and erosion resistance. Cropland showed elevated nutrient levels, with TN (1450.32 mg·kg⁻¹) and TP (718.86 mg·kg⁻¹) exceeding both national averages and those in arbor woodland. Coupled with acidic soil conditions (pH 5.23) and lower stoichiometric ratios (C/N: 10.82; C/P: 35.67; N/P: 3.29), these traits indicate an increased risk of nutrient leaching in croplands. In contrast, arbor woodland displayed more balanced C:N:P ratios (C/N: 12.21; C/P: 48.05; N/P: 3.84), supporting greater nutrient retention and aggregate stability. These findings underscore the significant influence of land-use type on soil ecological functions, including water infiltration, runoff reduction, and climate adaptability. The study highlights the importance of adopting conservation-oriented practices such as reduced tillage and targeted phosphorus management in croplands, alongside reforestation with native species, to improve soil structure and promote long-term ecological sustainability. Full article

Large language models (LLMs) such as Chat Generative Pre-Trained Transformer (ChatGPT) are increasingly used across domains, yet their generic training data and propensity for hallucination limit reliability in safety-critical fields like forestry. This paper outlines the conception and prototype of ForestGPT, a domain-specialised assistant designed to support forest professionals while preserving expert oversight. It addresses two looming risks: unverified adoption of generic outputs and professional mistrust of opaque algorithms. We propose a four-level development path: (1) pre-training a transformer on curated forestry literature to create a baseline conversational tool; (2) augmenting it with Retrieval-Augmented Generation to ground answers in local and time-sensitive documents; (3) coupling growth simulators for scenario modeling; and (4) integrating continuous streams from sensors, drones and machinery for real-time decision support. A Level-1 prototype, deployed at Futa Expo 2025 via a mobile app, successfully guided multilingual visitors and demonstrated the feasibility of lightweight fine-tuning on open-weight checkpoints. We analyse technical challenges, multimodal grounding, continual learning, safety certification, and social barriers including data sovereignty, bias and change management. Results indicate that trustworthy, explainable, and accessible LLMs can accelerate the transition to Forestry 5.0, provided that human-in-the-loop guardrails remain central. Future work will extend ForestGPT with full RAG pipelines, simulator coupling and autonomous data ingestion. Whilst exemplified in forestry, a complex, safety-critical, and ecologically vital domain, the proposed architecture and development path are broadly transferable to other sectors that demand trustworthy, domain-specific language models under expert oversight. Full article

Mangrove ecosystems play a critical role in climate regulation, carbon sequestration, and pollution mitigation. However, their long-term resilience to accelerating sea-level rise (SLR) under global climate change scenarios remains uncertain. Vertical soil accretion is a critical factor in determining the vulnerability of mangrove wetlands to SLR. In this study, vertical soil accretion rates in a mangrove wetland in the Beilun estuary were measured using a ²¹⁰Pbex dating method. Based on recently acquired data and previously available data, we conducted the first systematic assessment of SLR risk in mangrove wetlands in the Guangxi coastal zone in the context of increasing global climate change and extreme weather. The results show that the vertical soil accretion rate of 6.72 ± 1.91 (4.22–10.54) mm/a in the Beilun estuary is slightly higher than SLR rate in the Guangxi coastal zone. Concurrently, our results indicate that mangroves with thriving root systems enhance soil accretion through biotic controls in the Beilun estuary, while significant changes in soil sources and hydrodynamic forces during the 1980s and 2000s contributed to adaptation to SLR. Additionally, by linking sedimentation dynamics with SLR projections, we reveal that current accretion rates in some mangrove areas in the Guangxi coastal zone are insufficient to offset the projected SLR by the end of 2050 and 2100. This finding offers a new perspective on the traditional assumption of inherent resilience in mangroves while revealing the adaptive capacity of mangroves in the Beilun estuary and Guangxi coastal zone under projected SLR scenarios. It underscores the need for integrated management strategies that balance sediment supply maintenance and ecological restoration, which are critical to ensuring the long-term resilience of mangrove ecosystems, in line with sustainability principles. Full article

Mount Falakro in Northern Greece historically hosted populations of the Balkan-endemic butterfly Polyommatus orphicus and its larval host plant Onobrychis alba. In this study, we surveyed six historically confirmed localities during the peak flight period of P. orphicus in 2024, but neither the butterfly nor the host plant were detected. While the historical data on both species are scarce and often imprecise, our field observations indicate severe habitat degradation, dominated by overgrazing and suspected climate-driven shifts. Habitat conditions were assessed qualitatively, with special attention to limestone substrates previously known to support O. alba. Although definitive absence cannot be statistically confirmed, the lack of detection in previously occupied sites raises urgent concerns about possible local extinction. Our findings suggest that both species may already be extirpated from parts of their former range. This case study underscores the conservation relevance of absence data and highlights the importance of site-based monitoring in mountainous ecosystems undergoing rapid environmental change. Long-term surveys, regulated grazing, and post-disturbance habitat restoration are urgently needed to clarify the conservation status of these species and guide future management strategies. Full article

This study evaluates the degree to which small and medium sized enterprises (SMEs) are prepared to adopt smart manufacturing in contrast to large enterprises, a transition that depends on the effective use of the Internet of Things, artificial intelligence (AI), and advanced analytics. While many large multinational companies have already integrated such technologies, smaller firms still struggle because of tight budgets, limited technical expertise, and difficulties in scaling new systems. To capture these realities, the investigation refines the Initiative Mittelstand-Digital für Produktionsunternehmen und Logistik-Systeme (IMPULS) Industry 4.0 readiness model, which was initially developed to help German SMEs, so that it aligns with the circumstances faced by smaller manufacturers. A thorough review of published work first surveys existing readiness and maturity frameworks, highlights their limitations, and guides the selection of new, SME-specific indicators. The framework gauges readiness across six dimensions: strategic planning and organizational design, smart factory infrastructure, lean operations, digital products, data-driven services, and workforce capability. Each dimension is operationalized through a questionnaire that offers clear benchmarks and actionable targets suited to the current resources of each enterprise. Weaving strategic vision, skill growth, and cooperative support, the approach offers managers a direct path to sharper competitiveness and lasting innovation within a changing industrial landscape. Additionally, a separate Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is provided for each dimension based on survey data offering decision-makers concise guidance for future investment. The proposed adaptation of the IMPULS framework, validated through empirical data from 31 SMEs, introduces a novel readiness index, diagnostic gap metrics, and actionable cluster profiles tailored to developing-country industrial ecosystems. Full article

Climate change intensifies the challenges surrounding water cycling and vegetation dynamics in arid desert ecosystems, calling for detailed observations to decode adaptive plant strategies and support restoration efforts. This study analyzes interannual variations in water budgets and vegetation coverage in two distinct desert systems—K. foliatum (midstream) and R. songarica (downstream)—within the Heihe River Basin from 2016 to 2021. We uncover a pronounced ecohydrological contrast: the K. foliatum ecosystem displays substantial soil moisture variability alongside high precipitation and evapotranspiration rates, leading to a soil water deficit. In contrast, the R. songarica ecosystem maintains minimal moisture fluctuation under extreme aridity, yet records a slight water surplus. Notably, vegetation coverage in K. foliatum closely correlates with soil water storage, precipitation, and evapotranspiration, whereas R. songarica exhibits no significant hydrological coupling, implying a pulsed response to episodic rainfall. Groundwater recharge emerges as a key compensatory mechanism against rainfall shortages in midstream regions. These findings underscore the need for region-specific management—prioritizing groundwater conservation downstream and intelligent irrigation regulation midstream—offering a science-backed pathway for restoring and managing water resources in arid inland basins under climate change. Full article

Microrefugia can be critical in mediating biological responses to climate change, but the location and characteristics of these habitats are often poorly understood. Groundwater-dependent ecosystems (GDEs) represent critical microrefugia for species dependent on cool, moist habitats. However, knowledge of the distribution and stability of GDE microrefugia remains limited. This challenge is typified in the Pacific Northwest, where poorly studied cliff-face seeps harbor exceptional biodiversity despite their diminutive size (e.g., ~1–10 m width). To improve knowledge about these microrefugia, we regionally modeled their distribution and stability. We searched for cliff-face seeps across 1608 km of roads, trails, and watercourses in Washington and Idaho, while monitoring water availability plus air and water temperatures at selected sites. We detected 457 seeps through an iterative process of surveying, modeling, ground-truthing, and then remodeling the spatial distribution of seeps using boosted regression trees. Additionally, we used linear and generalized linear models to identify factors linked to seep thermal and hydrologic stability. Seeps were generally most concentrated in steep and low-lying areas (e.g., edges of canyon bottoms), and were also positively associated with glacial drift, basalt or graywacke bedrock types, high average slope within 300 m, and low average vapor pressure deficit. North-facing slopes were the best predictor of stable air and water temperatures and perennial seep discharge; low-lying areas also predicted stable seep water temperatures. These findings improve possibilities to manage seep microrefugia in the Pacific Northwest and safeguard their associated biodiversity under climate change. Lastly, our iterative method adapts techniques commonly used in species distribution modeling to provide an innovative framework for identifying inconspicuous microrefugia. Full article

Restoration plantations in subtropical regions, often established with fast-growing tree species such as Acacia auriculiformis A. Cunn. ex Benth and Eucalyptus urophylla S. T. Blake, are frequently developed on highly weathered soils characterized by phosphorus deficiency. To investigate strategies for mitigating nutrient imbalances in such ecosystems, a long-term (≥13 years) fertilization experiment was designed. The experiment involved three fertilization regimes: nitrogen fertilizer alone (N), phosphorus fertilizer alone (P), and a combination of nitrogen and phosphorus (NP) fertilizers. The objective of this study was to investigate the effects of long-term fertilization practices on soil quality in subtropical plantations using a soil quality index (SQI). Consequently, all conventional soil physical, chemical, and biological indicators associated with the SQI responses to long-term fertilization treatments were systematically evaluated, and a principal component analysis (PCA) was conducted, along with a literature review, to develop a minimum dataset (MDS) for calculating the SQI. Three physical indicators (silt, clay, and soil water content), three chemical indicators (soil organic carbon, inorganic nitrogen, and total phosphorus), and two biological indicators (microbial biomass carbon and phosphodiesterase enzyme activity) were finally chosen for the MDS from a total dataset (TDS) of eighteen soil indicators. This study shows that the MDS provided a strong representation of the TDS data (R² = 0.81), and the SQI was positively correlated with litter mass (R² = 0.37). An analysis of individual soil indicators in the MDS revealed that phosphorus addition through fertilization (P and NP treatments) significantly enhanced the soil phosphorus pool (64–101%) in the subtropical plantation ecosystem. Long-term fertilization did not significantly change the soil quality, as measured using the SQI, in either the Acacia auriculiformis (p = 0.25) or Eucalyptus urophylla (p = 0.45) plantation, and no significant differences were observed between the two plantation types. These findings suggest that the MDS can serve as a quantitative and effective tool for long-term soil quality monitoring during the process of forest sustainable management. Full article

Plastic pollution is an increasingly pressing environmental concern due to its persistence in ecosystems. To address this issue, this study evaluates polyethylene biodegradation and bioelectricity generation using Aspergillus ochraceopetaliformis in microbial fuel cells (MFCs). Single-chamber MFCs were designed (three) with carbon and zinc electrodes, where the fungus was cultivated in a nutrient-rich medium to enhance its metabolic activity. Parameters such as pH, power density, and FTIR spectra were monitored to assess plastic biodegradation. The results demonstrated a significant reduction in polyethylene mass and structure, along with a maximum generation of 0.921 V and 4.441 mA on day 26, with a power density of 0.148 mW/cm² and a current of 5.847 mA/cm². The optimal pH for fungal activity in the MFC was recorded at 7.059. Furthermore, FTIR analysis revealed a decrease in peak intensity at 1470 cm⁻¹ and 723 cm⁻¹, indicating structural modifications in the treated plastics. Furthermore, microbial fuel cells connected in series successfully powered an LED bulb, generating a maximum voltage of 2.78 V. These findings confirm the feasibility of using Aspergillus ochraceopetaliformis for biodegradation and bioelectricity generation, although practical applications require further optimization of system conditions and improvements in long-term stability. This research contributes to the development of biotechnological strategies for plastic waste management, sustainable integrating approaches with energy potential. Full article

The green landscapes of oasis cities play an important role in maintaining ecological security. However, these ecosystems face increasing threats from desertification and fragmentation, driven by intensifying climate change and rapid urbanization. Understanding the characteristics and driving mechanisms behind changes in green landscape patterns is crucial for advancing sustainable urban green space management. This study explores the spatio-temporal changes in the green landscape pattern in Urumqi during 1990–2020 using a random forest classifier. This study also applies geographical detectors and geographically weighted regression to comprehensively determine the driving mechanism and spatio-temporal nonstationarity. The results are as follows: (1) The landscape types are primarily dominated by unused land, urban green spaces, and construction land, accounting for more than 80%. The areas of urban green spaces, water bodies, cropland, and unused land decreased by 0.38%, 37.41%, 0.57%, and 4.58%, respectively, from 1990 to 2020. With rapid urbanization, construction land exhibited a significant expansion trend, and the degree of fragmentation of urban green spaces increased spatially over these 30 years. (2) From 1990 to 2020, each landscape index exhibited fluctuating characteristics. Overall, the Shannon’s diversity and evenness indices of the urban green landscapes exhibited an increasing trend. The contagion and connectivity indices exhibited a decreasing trend, decreasing from 50.894 and 99.311 in 1990 to 46.584 and 99.048 in 2020, respectively. (3) During these 30 years, the dynamics of urban greenery were affected by a combination of natural and social factors, with elevation determining the overall urban green distribution pattern. Precipitation and temperature dominate the urban green space changes in the north and south of Urumqi. Socioeconomic factors such as GDP, population, river distance, and town distance regulate the urban green space changes in the central built-up area. Full article

Coastal saltmarsh wetlands play a pivotal role in global carbon and nitrogen cycling, yet the vertical distribution characteristics of sediment carbon and nitrogen and their regulatory mechanisms remain uncertain. Microbial biomass carbon (MBC) and nitrogen (MBN) serve as critical indicators of ecosystem functioning, representing the most labile organic fractions that directly mediate biogeochemical processes in coastal wetlands. We investigated Yalu River Estuary coastal wetlands in the northern Yellow Sea. Sediment cores (0–100 cm depth) were collected and stratified into 20-cm intervals to analyse physicochemical properties and carbon–nitrogen indicators, enabling quantitative assessment of vertical distribution patterns and environmental drivers. The key findings are as follows: (1) Both microbial biomass carbon (MBC) and nitrogen (MBN) exhibited significant depth-dependent decreases, with MBC decreasing sharply by 45% (90.42 to 60.06 mg/kg) in the 40–60 cm layer and MBN decreasing by 50% (7.50 to 3.72 mg/kg) in the 80–100 cm layer. Total carbon (TC) peaked in the 40–60 cm layer (6.49 g/kg), whereas total nitrogen (TN) continuously decreased (from 0.51 (surface) to 0.24 g/kg (bottom)). (2) Depth-specific controls were identified: Surface layers (0–20 cm) were governed by tidal scouring (causing TC loss) and pH buffering; subsurface layers (20–40 cm) were constrained by moisture content (MC) and bulk density (BD), with partial mitigation by labile TC; and deeper layers (40–100 cm) were dominated by chemical factors exhibiting TN limitation and high electrical conductivity (EC). Understanding these microbial biomass dynamics is particularly crucial for predicting how coastal wetlands will respond to climate change and anthropogenic disturbances, as MBC and MBN serve as sensitive early-warning indicators of ecosystem health. Notably, MBC and MBN in northern Yellow Sea coastal wetlands are regulated primarily by physical—biological interactions in surface sediments and chemical stressors in deeper layers, providing crucial theoretical foundations for precise wetland carbon sink assessment and sustainable ecosystem management. 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

Urban forests are natural habitat areas within urban ecosystems that enhance physical, mental, and social well-being. By integrating natural and cultural values into the urban landscape, these areas offer individuals opportunities to interact with nature and engage in various recreational activities. Recreational activities increase physical activity levels, help reduce stress, strengthen mental health, and foster social interaction, thereby significantly protecting and improving public health. This study aims to evaluate the recreational performance of urban forests—an essential component of the urban ecosystem—through a multidimensional approach. In this context, ecological (topography, vegetation, water resources, soil structure, climate), physical (accessibility, infrastructure, area size), social (activity diversity, usage intensity, community events), and cultural (landscape values, urban identity, conservation status of cultural landscapes) factors were considered as key indicators. Bursa Atatürk Urban Forest was selected as the study area, and the methodology integrated SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis with weighted multi-criteria decision-making techniques. In addition, the qualitative data obtained were supported by statistical analysis methods to reveal the relationships among the criteria quantitatively. Through this holistic approach, the recreational performance of the urban forest was evaluated scientifically, leading to the conclusion that the area’s strengths should be preserved, its weaknesses improved, and its cultural landscape values managed sustainably. The study provides a valuable decision-support framework capable of guiding strategic planning for the future. Full article

The Industrial Internet of Things (IIoT) is transforming industrial operations through connected devices and real-time automation but also introduces significant cybersecurity risks. Cyber threat intelligence (CTI) is critical for detecting and mitigating such threats, yet traditional centralized CTI approaches face limitations in latency, scalability, and data privacy. Federated learning (FL) offers a privacy-preserving alternative by enabling decentralized model training without sharing raw data. This survey explores how FL can enhance CTI in IIoT environments. It reviews FL architectures, orchestration strategies, and aggregation methods, and maps their applications to domains such as intrusion detection, malware analysis, botnet mitigation, anomaly detection, and trust management. Among its contributions is an empirical synthesis comparing FL aggregation strategies—including FedAvg, FedProx, Krum, ClippedAvg, and Multi-Krum—across accuracy, robustness, and efficiency under IIoT constraints. The paper also presents a taxonomy of FL-based CTI approaches and outlines future research directions to support the development of secure, scalable, and decentralized threat intelligence systems for industrial ecosystems. Full article