Search Results (3,564)

Forests play a critical role in Earth’s ecosystem, yet monitoring their long-term, large-scale spatiotemporal dynamics remains a significant challenge. This study addresses this gap by developing an integrated framework to map annual forest distribution in Hunan, China, from 1999 to 2023 at a high resolution of 30 m. Our methodology combines multi-temporal satellite imagery (Landsat 5/7/8/9) with key environmental variables, including digital elevation models, temperature, and precipitation data. To efficiently reconstruct historical maps, training samples were automatically derived from a reliable 2023 forest product using a transferable logic, drastically reducing manual annotation effort. Comprehensive evaluations demonstrate the robustness of our approach: (1) Qualitative analyses reveal superior spatial detail and temporal consistency compared to existing global forest maps. (2) Rigorous quantitative validation based on ∼9000 reference samples confirms high and stable accuracy (∼92.4%) and recall (∼91.9%) over the 24-year period. (3) Furthermore, comparisons with government forestry statistics show strong agreement, validating the practical utility of the data. This work provides a valuable, accurate long-term dataset that forms a scientific basis for critical downstream applications such as ecological conservation planning, carbon stock assessment, and climate change research, thereby highlighting the transformative potential of multi-source data fusion and automated methods in advancing geospatial monitoring. Full article

The integration of sustainable development into biology education has been a growing area of interest. Biology education for sustainability is considered through competencies and skills, taking different dimensions of knowledge into account. Solving problems requires not only knowledge but also communicative and strategic activity. Thus, biology education must emphasize the main visions of scientific literacy proposed in the literature, supporting students to understand society and everyday socioscientific challenges at the local as well as at the global level, and to deal with differing scientific results and uncertain information. However, there are very few studies from a holistic didactic viewpoint on the implementation of sustainable education (SE) in biology education in the context of teacher education and school education for promoting a sustainable future. This study addresses this gap via a bibliometric and content analysis of the literature (n = 165 and 131, respectively) based on the categories of the sustainable development goals (SDGs), subject aims, learning objectives, content knowledge, teaching methods, competencies and skills, and assessment methods. The literature analyzed emphasizes the environmental and social SDGs, the development of students’ factual and conceptual knowledge and learning, interactive teaching and learning methods, critical thinking and reflection, and summative and formative assessment methods. There is much less attention on economic and institutional SDGs, scientific skills, environmental attitudes, knowledge creation, strategic thinking and empathy, and diagnostic assessment methods. Compared to earlier studies performed in the 2010s, teaching and learning methods have become more diverse in contrast to the earlier focus on teacher-centered methods. Overall, the conclusion is that biology education must evolve beyond content mastery to integrate ethical, technological, and transdisciplinary dimensions—empowering learners not only to understand life but to sustain it—aligned with quality education (SDG 4), good health and well-being (SDG 3), and life on land (SDG 15). The findings also suggest that powerful knowledge needs to be emphasized for providing essential insights into ecosystems, biodiversity, and the processes that sustain life on Earth. They also highlight the importance of regular evaluations of teaching and learning for detecting how pedagogical and didactic approaches and strategies have supported students’ learning focused on sustainable development. Full article

Bacterial vaginosis (BV) is a leading cause of genital discomfort among women globally, and it arises from dysbiosis of the vaginal ecosystem characterized by the overgrowth of pathogenic bacteria. Current therapeutic strategies primarily rely on antibiotics and/or probiotics, which demonstrate clinical efficacy but are frequently associated with limitations such as antimicrobial resistance, high recurrence rates, and incomplete restoration of a healthy vaginal microbiota. Inspired by the success of fecal microbiota transplantation in gastrointestinal disorders, vaginal microbiome transplantation (VMT) from healthy donors has emerged as a potential alternative therapeutic approach for BV. However, experimental and early clinical studies indicate that VMT efficacy is not uniform across individuals, with considerable inter-individual variability in treatment outcomes. Host genetic factors, baseline vaginal microbial composition, immune status, and environmental influences are likely to modulate therapeutic success, underscoring the need for personalized interventions. This article critically evaluates the shortcomings of existing standardized treatments, highlights the potential advantages and challenges of VMT, and discusses emerging, precision-based therapeutic strategies for BV in light of recent research advances and ongoing clinical trials worldwide. Full article

Ectomycorrhizal (ECM) fungi form key symbioses with forest trees, strongly regulating plant nutrition and stress tolerance. This review synthesizes how ECM fungi redistribute plant-fixed carbon (C) in soil, interact with soil organic matter (SOM), and mediate the uptake and allocation of nitrogen (N), phosphorus (P) and other macro- and micronutrients. We highlight mechanisms underlying ECM enhanced organic and mineral N and P mobilization, including oxidative decomposition, enzymatic hydrolysis, and organic acid weathering. Beyond C-N-P dynamics, ECM fungi also enhance acquisition and homeostasis of elements such as K, Ca, Mg, Fe, and Zn, reshaping host nutrient stoichiometry, productivity, and soil microbial community composition. We further summarize multi-layered mechanisms by which ECM improve host plant resistance to pathogens, drought, salinity–alkalinity, and heavy metal stresses via physical protection, ion regulation, hormonal signaling, aquaporins, and antioxidant and osmotic adjustment. Finally, we outline research priorities, such as using trait-based, multi-omics, and microbiome-integrated approaches to better harness ECM in forestry and ecosystem restoration. Full article

Rapid urbanization is fragmenting ecological spaces in megacities, threatening biodiversity and ecosystem services. Yet, it remains unclear whether, and under what conditions, urban ecological networks (ENs) can recover robustness once heavily disrupted. This study aims to (i) develop a dynamic assessment framework that couples network robustness and connectivity, and (ii) apply it to examine how ENs evolve under sustained urbanization and shifting policy regimes. Using multi-period data for Shenzhen, China (2000–2025), we simulate deliberate and random attacks on patches and corridors to derive data-driven thresholds that grade the importance of ecological elements, and integrate these with graph-based connectivity metrics to track changes in network structure and node centrality over time. Shenzhen’s EN exhibits a typical “fragmentation–reconfiguration–optimization” pathway, with a “rapid decline–deceleration–recovery” trajectory in robustness that closely aligns with the introduction of strict ecological control lines and subsequent restoration initiatives. The results show that targeted protection of residual core habitats, combined with strategic reconnection and infill greening in the urban interior, can reverse earlier losses in network robustness. The proposed robustness-informed framework provides operational guidance for prioritizing protection, restoration, and optimization of ecological space, and offers a transferable approach for adaptive EN planning in high-density tropical and subtropical megacities. Full article

Nest-site selection in birds is a hierarchical process shaped by environmental filters operating across multiple spatial scales. In species that depend on burrows excavated by ecosystem engineers, understanding how these filters interact is essential for effective conservation. We evaluated nest-site selection by the Burrowing owl (Athene cunicularia) within colonies of the Mexican prairie dog (Cynomys mexicanus) in the southern Chihuahuan Desert using a multiscale analytical framework spanning burrow, site, colony, and landscape levels. During the 2010 and 2011 breeding seasons, we located 56 successful nests and paired each with an inactive non-nest burrow within the same colony. Eighteen structural and environmental variables were measured and analyzed using binary logistic regression models, with model selection based on an information-theoretic approach (AICc) and prior screening for predictor collinearity. Nest-site selection was associated with greater internal burrow development and reduced external exposure at the burrow scale, proximity to satellite burrows and low-to-moderate vegetation structure at the site scale, higher densities of active prairie dog burrows at the colony scale, and reduced predation risk and agricultural disturbance at the landscape scale. The integrated multiscale model showed substantially greater support and discriminatory power than single-scale models, indicating that nest-site selection emerges from interactions among spatial scales rather than from isolated factors. These findings support hierarchical habitat-selection theory and underscore the importance of conserving functional Mexican prairie dog colonies and low-disturbance grassland landscapes to maintain suitable breeding habitats for Burrowing owls in the southern Chihuahuan Desert. Full article

Heavy metal pollution in forest and agroforestry soils represents a persistent environmental challenge with direct implications for ecosystem functioning, food security, and human health. In tropical and subtropical regions, intense weathering, rapid organic matter turnover, and dynamic redox conditions strongly modulate metal mobility, bioavailability, and long-term soil vulnerability. This review synthesizes current knowledge on the sources, biogeochemical mechanisms, ecological impacts, monitoring approaches, and restoration strategies associated with heavy metal contamination in forest and agroforestry systems, with particular emphasis on tropical landscapes. We examine natural and anthropogenic metal inputs, highlighting how atmospheric deposition, legacy contamination, land-use practices, and soil management interact with mineralogy, organic matter, and hydrology to control metal fate. Key processes governing metal behavior include sorption and complexation, Fe–Mn redox cycling, pH-dependent solubility, microbial mediation, and rhizosphere dynamics. The ecological consequences of contamination are discussed in terms of soil health degradation, plant physiological stress, disruption of ecosystem services, and risks of metal transfer to food chains in managed systems. The review also evaluates integrated monitoring frameworks that combine field-based soil analyses, biomonitoring, and geospatial technologies, while acknowledging methodological limitations and scale-dependent uncertainties. Finally, restoration and remediation strategies—ranging from phytotechnologies and soil amendments to engineered Technosols—are assessed in relation to their effectiveness, scalability, and relevance for long-term functional recovery. By linking mechanistic understanding with management and policy considerations, this review provides a process-oriented framework to support sustainable management and restoration of contaminated forest and agroforestry soils in tropical and subtropical regions. Full article

Power load forecasting is essential for power system security and energy dispatch. With the increasing renewable integration, load patterns have become more volatile and uncertain, difficult for traditional forecasting methods to maintain high adaptability. To address this challenge, this study proposes the Llama3-QLoRA-GeoWeather framework, a novel power load forecasting approach based on the open-source large language model Llama 3.3 70B. The framework introduces a two-stage progressive fine-tuning strategy based on QLoRA, significantly reducing computational costs and allowing adaptation on constrained hardware. Moreover, geographic features from the OpenStreetMap ecosystem and meteorological data from OpenWeatherMap API are integrated to further enhance the forecasting performance. A comprehensive Llama3-PowerFrame enhancement framework for future power systems is also designed. Experimental results demonstrate that Llama3-QLoRA-GeoWeather achieves the best forecasting performance (MAPE = 1.16%), outperforming the state-of-the-art baselines. This corresponds to a reduction in MAE, RMSE, and MAPE by approximately 42.7%, 67.8%, and 42.3% respectively, providing a viable technical pathway for building the next-generation intelligent load forecasting system across multiple scenarios with high credibility and strong adaptability. Full article

The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes, high mobility, and dynamic channel variations make traditional HO schemes inadequate for UAV-assisted 6G networks. This paper presents a comprehensive review of existing HO optimization studies, emphasizing artificial intelligence (AI) and machine learning (ML) approaches as enablers of intelligent mobility management. The surveyed works are categorized into three main scenarios: non-UAV HOs, UAVs acting as aerial base stations, and UAVs operating as user equipment, each examined under traditional rule-based and AI/ML-based paradigms. Comparative insights reveal that while conventional methods remain effective for static or low-mobility environments, AI- and ML-driven approaches significantly enhance adaptability, prediction accuracy, and overall network robustness. Emerging techniques such as deep reinforcement learning and federated learning (FL) demonstrate strong potential for proactive, scalable, and energy-efficient HO decisions in future 6G ecosystems. The paper concludes by outlining key open issues and identifying future directions toward hybrid, distributed, and context-aware learning frameworks for resilient UAV-enabled HO management. Full article

Wildfires are increasingly frequent and intense due to climate change, resulting in degraded soils with diminished microbial activity, reduced water retention, and low nutrient availability. In many regions, previously restored areas face repeated burning events, which further exhaust soil fertility and limit the potential for natural regeneration. Traditional reforestation approaches such as seed scattering or planting seedlings often fail in these conditions due to extreme aridity, erosion, and lack of biological support. To address this multifaceted problem, this study proposes a living, biodegradable hydrogel that integrates an engineered soil-beneficial microorganism consortium, designed to deliver beneficial compounds and nutrients combined with endemic plant seeds into a single biopolymeric matrix. Acting simultaneously as a biofertilizer, soil conditioner, and reforestation aid, this 3-in-1 system provides a microenvironment that retains moisture, supports microbial diversity restoration, and facilitates plant germination even in nutrient-poor, arid soils. The concept is rooted in circular economy principles, utilizing polysaccharides from food industry by-products for biopolymer formation, thereby ensuring environmental compatibility and minimizing waste. The encapsulated microorganisms, a Bacillus subtilis strain and a Nostoc oryzae strain, are intended to enrich the soil with useful compounds. They are engineered based on synthetic biology principles to incorporate specific genetic modules. The B. subtilis strain is engineered to break down large polyphenolic compounds through laccase overexpression, thus increasing soil bioavailable organic matter. The cyanobacterium strain is modified to enhance its nitrogen-fixing capacity, supplying fixed nitrogen directly to the soil. After fulfilling its function, the matrix naturally decomposes, returning organic matter, while the incorporation of a quorum sensing-based kill-switch system is designed to prevent the environmental escape of the engineered microorganisms. This sustainable approach aims to transform post-wildfire landscapes into self-recovering ecosystems, offering a scalable and eco-friendly alternative to conventional restoration methods while advancing the integration of synthetic biology and environmental engineering for climate resilience. Full article

Conventional descriptive statistical approaches in per- and polyfluoroalkyl substance (PFAS) environmental forensics often fail under small-sample, ecosystem-level complexity, challenging the optimization of sampling, monitoring, and remediation strategies. This study presents an advance from passive description to adaptive decision-support for complex PFAS contamination. By integrating Bayesian optimization (BO) via Gaussian Processes (GP) with a Generalized Linear Mixed Model (GLMM), we developed a signal-extraction framework for both understanding and action from limited data (n = 18). The BO/GP model achieved strong predictive performance (GP leave-one-out R² = 0.807), while the GLMM confirmed significant overdispersion (1.62), indicating a patchy contamination distribution. The integrated analysis suggested a dominant spatiotemporal interaction: a transient, high-intensity perfluorooctane sulfonate (PFOS) plume that peaked at a precise location during early November (the autumn recharge period). Concurrently, the GLMM identified significant intra-sample variance (p = 0.0186), suggesting likely particulate-bound (colloid/sediment) transport, and detected n-ethyl perfluorooctane sulfonamidoacetic acid (NEtFOSAA) as a critical precursor (p < 0.0001), thus providing evidence consistent with the source as historic 3M aqueous film-forming foam. This coupled approach creates a dynamic, iterative decision-support system where signal-based diagnosis informs adaptive optimization, enabling mission-specific actions from targeted remediation to monitoring design. Full article

Pesticide residues from agriculture pose persistent threats to ecosystems and human health. Precipitation and surface runoff facilitate the transport of pesticide residues, leading to their subsequent accumulation in lakes and rivers. Microalgae-based bioremediation offers a promising and environmentally friendly approach for degrading and detoxifying these residues. This study employed liquid chromatography–mass spectrometry (LC-MS) to determine pesticide residues in various microalgal solutions. Using three-dimensional excitation-emission matrix (3D-EEM) spectroscopy and fluorescence regional integration (FRI), we quantified the dynamics of dissolved organic matter (DOM) and its relationship with pesticide degradation in the microalgal system. Over time, Tolypothrix tenuis exhibited the highest degradation rate for THX (95.7%), while Anabaena showed the most effective degradation for ATZ (53.8%). Based on structural analysis of degradation products, three potential degradation pathways for THX and ATZ under microalgae action were proposed. Moreover, the degradation process may also involve reactive oxygen species and intracellular enzymes. Hydroxylation and carboxylation were the primary reactions involved in THX degradation, leading to ring opening and subsequent mineralization. In ATZ, the initially removed groups included methyl and carbonyl groups, with the final products undergoing hydroxylation and subsequent mineralization to water and carbon dioxide. This study, conducted within the context of aquatic environmental protection, investigates the threat of pesticide residues to aquatic ecosystems. It further elucidates the associated environmental impacts and degradation mechanisms from a microalgal perspective. Full article

Soil salinization remains a critical constraint on global land sustainability, severely limiting agricultural output and ecosystem resilience. To address this issue, a field trial was implemented to investigate the interactive benefits of vermicompost (VC) and a novel soil conditioner derived from coal gasification slag-based soil conditioner (CGSS) in mitigating saline–alkali stress. The perennial forage grass Leymus chinensis, valued for its ecological robustness and economic potential under adverse soil conditions, served as the test species. Five treatments were established: CK (unamended), T1 (CGSS alone), T2 (VC alone), T3 (CGSS:VC = 1:1), T4 (CGSS:VC = 1:2), and T5 (CGSS:VC = 2:1). Study results indicate that the combined application of CGSS and VC outperformed individual amendments, with the T4 treatment demonstrating the most effective results. Compared to CK, T4 reduced soil electrical conductivity (EC) by 12.00% and pH by 5.17% (p < 0.05), while markedly enhancing key fertility indicators—including soil organic matter and the availability of nitrogen, phosphorus, and potassium. Thus, these improvements translated into superior growth of L. chinensis, reflected in significantly greater dry biomass, expanded leaf area, and increased plant height. Additionally, the T4 treatment increased soil microbial richness (Chao1 index) by 21.5% and elevated the relative abundance of the Acidobacteria functional group by 16.9% (p < 0.05). Hence, T4 treatment (CGSS: 15,000 kg·ha⁻¹; VC: 30,000 kg·ha⁻¹) was identified as the optimal remediation strategy through a fuzzy comprehensive evaluation that integrated multiple soil and plant indicators. From an economic perspective, the T4 treatment (corresponding to a VC-CGSS application ratio of 2: 1) exhibits a lower cost compared to other similar soil conditioners and organic fertilizer combinations for saline–alkali soil remediation. This study not only offers a practical and economically viable approach for reclaiming degraded saline–alkali soils but also advances the circular utilization of coal-based solid waste. Furthermore, it deepens our understanding of how integrated soil amendments modulate the soil–microbe–plant nexus under abiotic stress. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article