Search Results (1,534)

Arbuscular mycorrhizal fungi (AMF) play a vital role in the restoration of tropical forests by enhancing soil fertility, facilitating plant establishment, and improving ecosystem resilience. This study presents a comprehensive bibliometric analysis of global scientific output on AMF in the context of ecological restoration, based on 3835 publications indexed in the Web of Science and Scopus databases from 2001 to 2024. An average annual growth rate of approximately 9.45% was observed, with contributions from 10,868 authors across 880 journals. The most prominent journals included Mycorrhiza (3.34%), New Phytologist (3.00%), and Applied Soil Ecology (2.79%). Thematically, dominant research areas encompassed soil–plant interactions, phytoremediation, biodiversity, and microbial ecology. Keyword co-occurrence analysis identified “arbuscular mycorrhizal fungi,” “diversity,” “soil,” and “plant growth” as core topics, while emerging topics such as rhizosphere interactions and responses to abiotic stress showed increasing prominence. Despite the expanding body of literature, key knowledge gaps remain, particularly concerning AMF–plant specificity, long-term restoration outcomes, and integration of microbial community dynamics. These findings offer critical insights into the development of AMF research and underscore its strategic importance in tropical forest restoration, providing a foundation for future studies and informing ecosystem management policies. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

Earthquake-triggered landslides pose significant hazards to lives and infrastructure. While existing seismic landslide models primarily focus on seismic and terrain variables, they often overlook the dynamic nature of hydrologic conditions, such as seasonal soil moisture variability. This study addresses this gap by incorporating satellite-based soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission into the assessment of seismic landslide occurrence. Using landslide inventories from five major earthquakes (Nepal 2015, New Zealand 2016, Papua New Guinea 2018, Indonesia 2018, and Haiti 2021), a balanced global dataset of landslide and non-landslide cases was compiled. Exploratory analysis revealed a strong association between elevated pre-event soil moisture and increased landslide occurrence, supporting its relevance in seismic slope failure. Moreover, a Random Forest model was trained and tested on the dataset and demonstrated excellent predictive performance. To assess the generalizability of the model, a leave-one-earthquake-out cross-validation approach was also implemented, in which the model trained on four events was tested on the fifth. This approach outperformed comparable models that did not consider soil moisture, such as the United States Geological Survey (USGS) seismic landslide model, confirming the added value of satellite-based soil moisture data in improving seismic landslide susceptibility assessments. Full article

Root phenology significantly influences ecosystem processes yet remains poorly characterized across biomes. This study synthesized data from 59 studies spanning Arctic to tropical ecosystems to identify woody plants root phenological patterns and their environmental drivers. The analysis revealed distinct climate-specific patterns. Arctic regions had a short growing season with remarkably low temperature threshold for initiation of root growth (0.5–1 °C). Temperate forests displayed pronounced spring-summer growth patterns with root growth initiation occurring at 1–9 °C. Mediterranean ecosystems showed bimodal patterns optimized around moisture availability, and tropical regions demonstrate seasonality primarily driven by precipitation. Root-shoot coordination varies predictably across biomes, with humid continental ecosystems showing the highest synchronous above- and belowground activity (57%), temperate regions exhibiting leaf-before-root emergence (55%), and Mediterranean regions consistently showing root-before-leaf patterns (100%). Winter root growth is more widespread than previously recognized (35% of studies), primarily in tropical and Mediterranean regions. Temperature thresholds for phenological transitions vary with climate region, suggesting adaptations to environmental conditions. These findings provide a critical, region-specific framework for improving models of terrestrial ecosystem responses to climate change. While our synthesis clarifies distinct phenological strategies, its conclusions are drawn from data focused primarily on Northern Hemisphere woody plants, highlighting significant geographic gaps in our current understanding. Bridging these knowledge gaps is essential for accurately forecasting how belowground dynamics will influence global carbon sequestration, nutrient cycling, and ecosystem resilience under changing climatic regimes. Full article

Understanding root decomposition dynamics is essential to address declining carbon sequestration and nutrient imbalances in monoculture plantations. This study elucidates how forest gaps regulate Pinus massoniana root decomposition through comparative methodological analysis, providing theoretical foundations for near-natural forest management and carbon–nitrogen cycle optimization in plantations. The results showed the following: (1) Root decomposition was significantly accelerated by the in situ soil litterbag method (ISLM) versus the traditional litterbag method (LM) (decomposition rate (k) = 0.459 vs. 0.188), reducing the 95% decomposition time (T_0.95) by nearly nine years (6.53 years vs. 15.95 years). ISLM concurrently elevated the root potassium concentration and reconfigured the relationships between root decomposition and soil nutrients. (2) Lower-order roots (orders 1–3) decomposed significantly faster than higher-order roots (orders 4–5) (k = 0.455 vs. 0.193). This disparity was amplified under ISLM (lower-/higher-order root k ratio = 4.1) but diminished or reversed under LM (lower-/higher-order root k ratio = 0.8). (3) Forest gaps regulated decomposition through temporal phase interactions, accelerating decomposition initially (0–360 days) while inhibiting it later (360–720 days), particularly for higher-order roots. Notably, forest gap effects fundamentally reversed between methodologies (slight promotion under LM vs. significant inhibition under ISLM). Our study reveals that conventional LM may obscure genuine ecological interactions during root decomposition, confirms lower-order roots as rapid nutrient-cycling pathways, provides crucial methodological corrections for plantation nutrient models, and advances theoretical foundations for precision management of P. massoniana plantations. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydrometeorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience. Full article

Over the last decade, the scientific community has increasingly focused on forest-based practices for human well-being (FBPW), a term that includes all forest activities (e.g., forest bathing, forest therapy, social outdoor initiatives) important for improving people’s health and emotional status. This paper aims to develop a quantitative literature review on FBPW based on big data analysis (text mining on Scopus title and abstract) and PRISMA evaluation. The two techniques facilitate investigations across different geographic areas (major areas and geographical regions) and allow a focus on various topics. The results of text mining highlight the prominence of publications on FBPW for the improvement of human health in East Asia (e.g., Japan and South Korea). Furthermore, some specific themes developed by the literature for each geographical area emerge: urban green areas, cities, and parks in Africa; sustainable forest management and planning in the Americas; empirical studies on physiological and psychological effects of FBPW in Asia; and forest management and FBPW in Europe. PRISMA indicates a gap in studies focused on the reciprocal influences of forest variables and well-being responses. An investigation of the main physiological indicators applied in the scientific literature for the theme is also developed. The main strengths and weaknesses of the method are discussed, with suggestions for potential future lines of research. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

This article examines the role of managed ecosystems, and particularly forests, in achieving carbon neutrality in Russia. The range of estimates of Russia’s forests’ net carbon balance in different studies varies by up to 7 times. The. A comparison of Russia’s National GHG inventory data for 2023 and 2024 (with the latter showing 37% higher forest sequestration) is presented and explained. The possible changes in the Long-Term Low-Emission Development Strategy of Russia (LT LEDS) carbon neutrality scenario due to new land use, land use change and forestry (LULUCF) data in National GHG Inventory Document (NID) 2024 are discussed. It is demonstrated that the refined net carbon balance should not impact the mitigation ambition in the Russian forestry sector. An assessment of changes in the drafts of the Operational plan of the LT LEDS is presented and it is concluded that its structure and content have significantly improved; however, a delay in operationalization nullifies efforts. The article highlights the problem of GHG emissions increases in forest fires and compares the gap between official “ground-based” and Remote Sensing approaches in calculations of such emissions. Considering the intention to increase net absorption by implementing forest carbon projects, the latest changes in the regulations of such projects are discussed. The limitations of reforestation carbon projects in Russia are provided. Proposals are presented for the development of the national forest policy towards increasing the net forest carbon absorption, including considering the projected decrease in annual net absorption by Russian forests by 2050. The role of government and private investment in improving the forest management of structural measures to adapt forestry to modern climate change and the place of forest climate projects need to be clearly defined in the LT LEDS. Full article

The 1980 eruption of Mount St. Helens (WA, USA) presented a unique opportunity to observe primary succession in a post-eruption landscape previously dominated by conifer forests. The eruption scoured soil and biological communities adjacent to the mountain, and species of conifers have generally been slow to colonize the nutrient-poor substrate surrounding the volcano. Further, different species of conifer establish and grow at different rates. The recent advancement of conifers in the post-eruption landscape has highlighted a research gap related to conifer growth patterns. We measured the height, age, and incremental growth of 472 trees representing three common conifers, Pseudotsuga menziesii, Abies procera, and Pinus contorta, on debris avalanche (80 sites) and pyroclastic flow (82 sites) disturbance zones of the 1980 eruption. We paired annual incremental growth with recent climate data. We found that height, age, and growth rates differ among species and sites. All species had higher growth rates on the debris avalanche deposit compared to the pyroclastic flow due to either climate or substrate. Climate influences were mixed, where one species increased growth with temperature, another declined, and another was unrelated. Nevertheless, more than 40 years after the eruption, we find rapid height growth in species with implications for future forests. Full article

Plant functional traits serve as vital tools for understanding vegetation adaptation mechanisms in changing environments. As the primary organs for nutrient acquisition from soil, fine roots are highly sensitive to environmental variations. However, current research on fine-root adaptation strategies predominantly focuses on tropical, subtropical, and temperate forests, leaving a significant gap in comprehensive knowledge regarding fine-root responses in rocky-desertification habitats. This study investigates the fine roots of Pseudotsuga sinensis across varying degrees of rocky desertification (mild, moderate, severe, and extremely severe). By analyzing fine-root morphological and nutrient traits, we aim to elucidate the trait differences and correlations under different desertification intensities. The results indicate that root dry matter content increases significantly with escalating desertification severity. Fine roots in mild and extremely severe desertification exhibit notably higher root C, K, and Mg concentrations compared to those in moderate and severe desertification, while root Ca concentration shows an inverse trend. Our correlation analyses reveal a highly significant positive relationship between specific root length and specific root area, whereas root dry matter content demonstrates a significant negative correlation with elemental concentrations. The principal component analysis (PCA) further indicates that the trait associations adopted by the forest in mild- and extremely severe-desertification environments are different from those in moderate- and severe-desertification environments. This study did not account for soil nutrient dynamics, microbial diversity, or enzymatic activity—key factors influencing fine-root adaptation. Future research should integrate root traits with soil properties to holistically assess resource strategies in rocky-desertification ecosystems. This study can serve as a theoretical reference for research on root characteristics and adaptation strategies of plants in rocky-desertification habitats. Full article

Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = −6.73%), H (CV = −52.68%), and LAI (CV = −63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential. Full article

Despite increasing interest in including indigenous and local people in forest restoration initiatives, their views on which species are most useful, or reasons behind not planting native tree species are often ignored. Focused on south-western Rwanda, this study addressed these knowledge gaps. We carried out 12 focus group discussions with village elders to determine the following: main benefits provided by native forests, the native species they prefer for different uses, and the main barriers to species’ cultivation. Then, considering other key information from the literature, we performed a ranking exercise to determine which native species had the greatest potential for large-scale tree planting initiatives. Our results show that native forests provide 17 benefits to local communities, some of which cannot be replaced by plantations with exotic species. Among the 26 tree species identified as most useful for timber, firewood, medicine and fodder, ten were ranked as with the greatest potential for restoration initiatives. Of these, two had not been included in recent experimental plantations using native species in Rwanda, and none were considered among the priority species for domestication in Africa. Overall, our study highlights the need to better connect the ecological and social dimension of forest reforestation initiatives in multiple contexts. Full article