Search Results (1,378)

Pines (genus Pinus) are a diverse group of conifers widely distributed across the Northern Hemisphere, comprising more than 110 species worldwide. In Europe, pines are a defining component of forests, occupying environments that range from dry Mediterranean lowlands to high-elevation alpine zones. They support a wide spectrum of ecosystem services, from timber and non-wood products to protection, biodiversity, and cultural values. In this review, we bring together ecological traits, silvicultural practices, and management considerations for ten European pine species. This review examines ten major European pine species, including Scots pine (Pinus sylvestris), maritime pine (P. pinaster), stone pine (P. pinea), Aleppo pine (P. halepensis), black pine (P. nigra), mountain pine (P. mugo), Swiss stone pine (P. cembra), Turkish pine (P. brutia), Bosnian pine (P. heldreichii), and Macedonian pine (P. peuce). Together, these species span a wide range of European environments, with Pinus sylvestris showing the broadest distribution. Despite the extensive literature on pine species, information on their ecological, silvicultural, and management attributes remains fragmented across disciplines. To bridge this gap, this review draws on an expert-curated core of scientific and technical literature complemented by a structured literature search adapted from the PRISMA framework, compiling within a single framework their main ecological and silvicultural characteristics, altitudinal range, growth patterns, stand diversity, and management practices and the principal goods and ecosystem services they provide. The comparative analysis highlighted distinct functional groups among European pines, illustrating contrasting adaptive strategies across environmental gradients, with altitudinal distributions ranging from sea level to 2400 m. Mediterranean species, such as P. halepensis, P. pinaster, and P. brutia, generally showed higher drought and fire adaptation, whereas mountain taxa such as P. cembra, P. mugo, and P. heldreichii displayed greater cold tolerance but increased vulnerability due to restricted climatic niches and slow regeneration. Broadly distributed species, such as P. sylvestris and P. nigra, exhibited intermediate ecological strategies, with responses strongly influenced by regional climatic conditions. By integrating evidence on distribution patterns, functional traits, growth patterns, and management practices, this review provides a comparative framework to support adaptive forest management and informed species selection, helping to safeguard forest functions and their associated benefits under ongoing climate change. Full article

The Taklimakan Desert Highway shelterbelt is the world’s largest ecological protection system established along a highway in a shifting desert environment and plays a critical role in mitigating wind-blown sand hazards and ensuring transportation safety. However, its long-term stability and protective capacity after more than two decades of operation remain insufficiently understood. In this study, Landsat imagery from 2005 to 2025 was used to monitor the long-term evolution of the shelterbelt along the Middle Section (~180 km) of the Taklimakan Desert Highway. A Random Forest classifier was employed to extract shelterbelt distribution, and classification results were validated using high-resolution Google Earth imagery and unmanned aerial vehicle observations. To quantify shelterbelt condition, a Shelterbelt Stability Index (SSI) was developed by integrating fractional vegetation cover (FVC), connectivity index (CI), percentage of landscape (PLAND), and perimeter-area fractal dimension (FRAC). The shelterbelt experienced initial seedling decline from 2005 to 2011, followed by progressive restoration during 2011–2020 and finally entered a stable saturated stage after 2020. Affected by saline water drip irrigation, wind-sand erosion and pipeline clogging, the overall vegetation condition deteriorated continuously before 2011. After targeted irrigation regulation, optimization of planting patterns and replanting measures were implemented; the degradation trend was reversed, contributing to the sustained improvement of vegetation thereafter. Significant spatial heterogeneity was observed along the highway, with certain sections maintaining high continuity and vegetation coverage, while others exhibited fragmentation, local discontinuities, area shrinkage, and increasing structural complexity. The proposed SSI effectively captured long-term structural dynamics and identified vulnerable sections subject to degradation. This study provides new insights into the life-cycle evolution of desert highway shelterbelts and offers scientific support for the sustainable management of ecological protection systems in arid environments. Full article

Research Highlights: This study quantifies aboveground biomass for Oreopanax oroyanus and Escallonia resinosa in an Andean relict forest and examines their conservation relevance related to the scale of emissions voluntarily reported by small corporate emitters. Background and Objectives: Andean relict forests face severe fragmentation and degradation. This research evaluates carbon stocks in the Bosque de Zárate Reserved Zone (Peru) and explores how these findings may inform climate mitigation and conservation initiatives by examining their potential alignment with emissions voluntarily reported by Peruvian firms participating in a carbon disclosure system. Materials and Methods: A total of 27 plots were evaluated between 3034 and 3200 m a.s.l., tree height and diameter (DBH ≥ 10 cm) were measured for key species, and biomass was estimated using a pantropical allometric equation. Landsat imagery (1985–2025) was analyzed to assess long-term vegetation conditions, while Dynamic World land cover and Sentinel-1 radar (2018–2025) were used to assess forest cover and canopy structure changes. Voluntarily reported emissions of Peruvian firms participating in the “Carbon Footprint Peru” system (2012–2024) were analyzed to contextualize the forest results in the potential corporate interest in climate mitigation in Peru. Results: Total aboveground carbon stock for the altitudinal belt in the study area was 919.4 Mg C (18.6 Mg C ha⁻¹), equivalent to 3374.2 Mg CO₂, with Escallonia resinosa accounting for approximately 71% of the estimated stock. Multi-decadal satellite observations indicated persistent forest cover within the evaluated belt, while analysis of voluntarily reported corporate emissions identified numerous service-sector firms with annual emissions below 100 Mg CO₂ eq, providing context for the potential scale of future conservation-financing initiatives. Conclusions: Relict forests offer relevant localized carbon storage linked to other ecosystem services. Providing field-based carbon data may support the development of locally relevant community-led initiatives meaningful to climate-financing initiatives. However, the existing carbon stock does not by itself represent a source of carbon credits, and carbon capture-specific studies would need to be implemented to fully assess the mitigation capacity of these ecosystems. Full article

National parks in densely populated regions face growing pressure to shift from boundary-based protection toward cross-boundary landscape governance, yet connectivity between wildlands within and outside the national park remains understudied. China’s large population and marked spatial variation in anthropogenic pressure make it a particularly informative setting for investigating this issue. We developed a rewilding-oriented framework for wildland network modeling, applied to Baishanzu National Park and its surrounding counties in China. By integrating Boolean overlay, wilderness continuum analysis, and circuit theory, we systematically delineated wilderness patches, corridors, and pinch points. We identified 143 wilderness patches covering 1102.79 km² (14.06% of the study area), dominated by small fragments indicative of severe landscape fragmentation. High current-density zones are concentrated along continuous montane forest belts, valley-ridge transition zones, and narrow inter-patch passages. Twenty-six pinch points were identified, primarily within Baishanzu National Park, southeastern Jingning County, and the northern Yunhe–Longquan interface, alongside 15 internal corridors within the park. Rewilding strategies for montane national parks in human-dominated landscapes may benefit from extending beyond boundaries. Cross-boundary wildland network modeling offers a methodological reference for corridor planning and rewilding practice globally. Full article

The wildland–urban interface (WUI) refers to areas where forests with little human influence and other vegetation types intersect with human development. Ecosystem management in the WUI presents special challenges. Human dwellings and other infrastructure can constrain management options, such as timber harvesting, prescribed burning, and managing wildlife. Humans can also negatively affect ecosystem functioning through the introduction of invasive plant and animal species, wildfire ignition, habitat fragmentation, and attracting nuisance wildlife species. Although there are WUIs throughout the world, this review will focus mostly on the eastern United States, where a high population density and maturing forests highlight exceptional challenges to forest management. Land ownership in the WUI in this region is dominated by non-industrial private forestlands with diverse ownership objectives and is often distributed in relatively small parcels, creating difficulties for coordinated forest management. Landowners and loggers often plan timber harvests without input from forest managers. On the frontlines of WUI management are extension agents, state and local agencies, non-government organizations, and consulting foresters. Public education, cost-share agreements, and local government engagement are needed to conserve and manage forests in the WUI. Full article

Musk deer are forest-dwelling artiodactyls of high conservation value, but their wild populations remain under severe conservation pressure due to poaching driven by the demand for natural musk, together with habitat fragmentation and habitat loss. Efficient non-invasive image-based monitoring is therefore important for musk deer conservation; however, fine-grained detection of musk deer and visually similar artiodactyls in ecological images remains difficult because of background camouflage, vegetation occlusion, and high inter-class similarity. In this study, a fine-grained wildlife image dataset was constructed, and an RT-DETR-based framework, termed DSCF-DET, was proposed for automated detection in complex natural scenes. DSCF-DET integrates three task-oriented modules: DRPBlock for receptive-field-aware feature extraction, SASTE for sparse spatial encoding, and CBAFusion for cross-level feature fusion. On the constructed dataset, DSCF-DET achieved 91.4% precision, 86.2% recall, 88.7% F1-score, and 86.4% mAP50. Compared with RT-DETR-r18, it improved these metrics by 8.6, 12.1, 10.5, and 12.4 percentage points, respectively, while maintaining moderate model complexity. Visualization results showed more target-focused feature responses and reduced background-related activations. Cross-dataset experiments on an independent public wildlife dataset further suggested potential applicability to broader wildlife detection scenarios. These results indicate that DSCF-DET provides a computationally balanced approach for ecological image screening and intelligent musk deer monitoring. Full article

Forests cover nearly one-third of the Earth’s land surface and are subject to increasing anthropogenic pressure, including unauthorised construction, infrastructure expansion, and habitat fragmentation. Detecting buildings concealed beneath forest canopy is essential for environmental monitoring and territorial surveillance, yet optical satellite imagery fails under persistent cloud cover and dense vegetation, and no existing approach provides reliable detection across contrasting forest ecosystems. We address this gap by proposing a hybrid SAR-based detection framework that combines twelve physically interpretable Scattering-Informed SAR Features (SISF)—derived from electromagnetic scattering theory across amplitude, polarimetric, temporal, and texture dimensions—with a universal Convolutional Neural Network, integrated through probability-level fusion with isotonic regional calibration. Rather than relying on individual feature thresholds, the framework identifies buildings through their characteristic multidimensional scattering signature—a combination that remains discriminative across biomes where any single SAR descriptor would fail. The framework was evaluated on a novel 7721-object multi-biome benchmark spanning forest-steppe (Kazakhstan), boreal forest (Komi Republic, Russia), and tropical rainforest (Brazil, Pará). The final Fusion + Regional Calibration model achieved an overall F1-score of 0.803 on the held-out test set (N = 1545), outperforming single-model baselines by up to 17 percentage points. Regional F1-scores ranged from 0.727 (Kazakhstan) to 0.944 (Komi Republic), with detection performance remaining robust under partial canopy occlusion (F1 = 0.911, versus 0.903 for unobscured buildings). An empirical inverse relationship between hard negative proportion and detection F1-score was identified within each biome, with the 28–35% proportions present in our sampled regions reported as a preliminary observation rather than a generally optimal range—a dataset design finding not previously reported in the literature. The proposed framework provides a physically interpretable solution for SAR-based building detection under forest canopy, demonstrating consistent performance across three contrasting forest biomes, with direct applications to environmental monitoring and territorial surveillance in forested regions. Full article

Land-use conflicts in non-urbanized areas are fundamentally governance problems rather than purely environmental ones: sustainability outcomes increasingly depend on integrated, cross-sectoral decision-making reconciling forestry, agriculture, tourism, infrastructure, and urbanization within a finite land resource. This study examines whether statutory land acquisitions by a public forest administration can be understood as such a governance instrument rather than simply forest-area expansion, using an original transaction-level database of 911 land purchases by the Polish State Forests (Lasy Panstwowe) through statutory pre-emption rights between 2022 and mid-2026. The database covers 2806.1 hectares and, for the 903 transactions with a determinable price, approximately 114.2 million PLN. Using descriptive statistics, concentration indices (Gini, Herfindahl–Hirschman), an exploratory hedonic-style log–log regression of unit price on parcel area with location and year fixed effects, and a spatial-autocorrelation analysis (Moran’s I), the study examines spatial concentration, price differentiation, and parcel-size effects. Results show pronounced sub-regional concentration (county-level Gini = 0.615, more than double the voivodeship-level value of 0.284), a systematic price premium for parcels below 0.5 ha, and a dominant role of location over parcel size in explaining price variation (R-squared rising from 0.042 to 0.198 with location fixed effects); these are descriptive associations rather than causal estimates, given the absence of parcel-level quality covariates and a fully specified spatial–econometric model. The findings support interpreting statutory pre-emption purchases as a market-based institutional mechanism contributing to boundary rationalization, ownership consolidation, and mediation of competing land-use pressures, with implications for county-level monitoring and cross-sectoral coordination with spatial planning. Full article

Faced with both global climate change and rapid urbanization, understanding how the built environment affects surface runoff is essential for strengthening urban hydrological resilience. However, elucidating the nonlinear and interactive effects of three-dimensional buildings and two-dimensional green spaces on surface runoff potential in urban blocks remains a scientific challenge for precise flood-mitigation spatial planning. Using six typical waterlogging-prone blocks in Shenyang as case studies, this study constructs a morphological index system for urban gray–green spaces and reveals the nonlinear effects of each index on surface runoff potential using an interpretable Random Forest (RF)–SHAP model. The results indicate that the RF model reliably captures the complex spatial patterns of simulated local surface runoff potential (R² = 0.723–0.825). At the block scale, the surface runoff response exhibits a dual character: it is predictively dominated by three-dimensional morphological dominance and two-dimensional base regulation. Three-dimensional building morphology generally demonstrates pronounced unidirectional thresholds and high-value saturation in model prediction. In particular, when the core indicator, building spatial congestion degree (B_SCD), crosses a critical threshold, surface runoff potential rises sharply. The coefficient of variation in building height (B_HVC) shows a “V-shaped” reversal in areas of extreme surface runoff potential, whereas two-dimensional green space indicators display clear asymmetric critical points. Significant reductions in surface runoff potential appear only when green space scale (G_LPI), boundary complexity (G_LSI), or fragmentation (G_PD) exceed specific model-identified thresholds. Furthermore, the study demonstrates marked interactive effects between the morphologies of gray–green spaces. High surface runoff risk from dense, large buildings can be substantially offset by large green space patches (G_LPI) with highly complex boundaries (G_LSI). The advantage of vertically staggered buildings (B_HVC) requires a green space base with low fragmentation (G_PD) to realize a gray–green synergistic mitigating effect. These findings provide theoretical and methodological support for enhancing the hydrological resilience of urban blocks. Full article

Accurate crop type mapping in Sub-Saharan Africa is a challenging task, due to the presence of smallholder farming systems characterized by fragmented landscapes and heterogeneous cropping practices. Persistent cloud cover, particularly significant during the cropping season, systematically limits the exploitation of optical satellite image time series, making things even harder. This study proposes MSF-Net (Multimodal Sentinel Fusion Network), a convolutional neural network-based late-fusion framework that combines Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral time series for multi-class crop classification in the complex agricultural landscapes of central and northern Benin. The model was evaluated across six sites and three growing seasons (2022–2024) covering 12 land cover classes and compared with a Sentinel-2-only Temporal Convolutional Neural Network (TempCNN), a SAR-only baseline (S1-Branch), an ablated version of the proposed method, and two external state-of-the-art multimodal architectures, TSViT and TWINNS. MSF-Net achieved the highest or joint-highest overall accuracy in 10 of 14 site–year configurations, with overall accuracy ranging from 82.61% to 91.15% and kappa coefficients from 0.79 to 0.89, consistently outperforming both external baselines across all site–year configurations. The largest gains over TempCNN reached up to 30 percentage points for spectrally ambiguous classes such as Shrubby Savannah, Cotton, and Open Forest. In addition, MSF-Net produced more spatially coherent maps, with reduced salt-and-pepper noise, improved parcel-level homogeneity, and fewer modality-specific artefacts. These results demonstrate the value of SAR-optical fusion for operational crop monitoring in tropical West Africa. Full article

Regression-based aboveground biomass (AGB) prediction from Earth-observation data often compresses the upper tail of the biomass distribution, yet the relative effectiveness of geospatial residual correction and ensemble learning in fragmented mountain landscapes remains unclear. Thus, we compared the two paths for reducing high-value underestimation: geospatial residual reconstruction using empirical Bayesian kriging regression prediction, and feature-space optimization using Stacking ensemble learning. SHapley Additive exPlanations (SHAP) interpreted feature contributions, and quantile regression forests (QRF) converted high-AGB point estimates into prediction intervals. Results show that geospatial optimization brought limited gain because residual spatial autocorrelation was weak (Moran’s I = 0.10), whereas Stacking improved overall R² from 0.75 to 0.79 and reduced high-AGB bias (AGB > 80 t/ha) from −13.56 to −5.49 t/ha. This improvement was mainly attributed to complementary heterogeneous learners, with XGBoost capturing the primary non-linear trends, SVR extrapolating to correct high-AGB errors, and RF providing minor marginal calibration. SHAP analysis suggests that LiDAR-derived cubic mean height (Elev_curt_mean_cube) was the dominant feature explaining high-AGB variability, with a threshold response consistent with biomass-height allometry. QRF achieved coverage of 92.8% with a mean interval width of 74.51 t/ha, while coverage in the high-AGB subset was 84.2% with a mean width of 90.50 t/ha. The proposed comparison-and-diagnosis framework provides an interpretable approach for selecting an appropriate correction pathway and supports forest carbon monitoring, carbon accounting, and management decisions in complex mountain ecosystems. Full article

Acute myeloid leukemia (AML) remains a therapeutic challenge due to complex oncogenic networks, including the often-undruggable MYC pathway. Here, we present an integrated in silico framework combining transcriptomic analysis, machine learning, and molecular dynamics (MD) simulations to explore potential therapeutic approaches targeting vault RNA1-1 (VTRNA1-1) in AML. RNA-seq profiling revealed that VTRNA1-1 depletion is associated with a profound disruption of the MYC and FOXM1 regulatory axes. To highlight compounds capable of recapitulating this transcriptomic signature, we developed a machine learning pipeline utilizing a Random Forest classifier trained on a fully compiled L1000FWD database subset. Virtual screening of approved drugs predicted the anthelmintic niclosamide as a top candidate (98.17% mimic probability). Explainable AI further rationalized this prediction by highlighting specific fragments within niclosamide’s salicylanilide core. Furthermore, a 200 ns MD simulation indicated favorable computational stability of niclosamide bound to the p62 (SQSTM1) ZZ domain. The complex showed rapid structural convergence (ligand RMSD plateauing at 1.65 nm) without dissociation, while maintaining strict receptor compactness (steady Radius of Gyration and solvent-accessible surface area) and a persistent interaction network of ~73 close atomic contacts. These findings suggest that niclosamide may function as a stable physical “lid” over the p62 ZZ domain, occluding its N-degron-binding cleft. Taken together, our computational framework highlights niclosamide as a promising candidate for AML drug repurposing, providing a hypothesis-generating foundation that warrants rigorous experimental validation. Full article

Sanitary sewer collection systems are among the least observable urban infrastructure assets, with most utilities operating fewer than one sensor per several hundred pipes; placement drives operational value. We develop DPP-SP, a Descriptive–Predictive–Prescriptive Sensor-Placement framework that links machine-learning failure prediction with risk-weighted maximum-coverage placement and apply it to a 33,349-pipe sewer system. The geographic information system (GIS) topology is rebuilt, raising the largest connected component from 29.8% to 89.4% of nodes. Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a multilayer perceptron (MLP) are trained on combined 2020–2025 failure data; RF achieves the highest receiver-operating-characteristic area under the curve (ROC-AUC) of 0.7626 and supplies per-pipe risk weights, while repeated stratified cross-validation confirms this model ranking and permutation-importance and SHAP analyses identify pipe age and length as the dominant risk drivers. A budgeted maximum weighted coverage problem is solved over 680 candidate sites using greedy, genetic algorithm (GA) and tabu search (TS). At $K=48$, RF with greedy covers 32.26% of network risk against an 11.73% baseline, a 174.9% improvement; all three optimizers converge on the same solution. Extending to $K=400$ exposes a 56.58% coverage ceiling—set jointly by residual network fragmentation and the upstream detection range, and specific to the baseline candidate set and radius—and a six-radius sensitivity study (200–2500 m) identifies detection range as the most influential design parameter over the ranges tested. Risk coverage can be nearly tripled by redeploying the existing 48 stations without purchasing additional sensors. Full article

Understanding the environmental fate of microplastics (MPs) in agricultural soils remains a major challenge, particularly under field conditions where soil structure and hydraulic processes jointly regulate particle transport and retention. This study investigated whether hydro-physical soil functioning can explain the distribution and accumulation of MPs in pistachio orchard soils from a semi-arid region of southeastern Türkiye. A total of 42 soil samples were analyzed for MP abundance, size distribution, and morphology, together with key hydro-physical properties including texture, porosity, bulk density, aggregate stability, organic matter content, and soil water retention characteristics. To identify the dominant controls on MP occurrence, explainable machine learning approaches combining Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and SHAP (SHapley Additive exPlanations) analysis were employed. Microplastic abundance differed among management systems. Former landfill or construction sites represented the largest proportion of the total recorded microplastic abundance (40.9%), followed by conventionally managed (25.2%), manure-amended (24.5%), and sewage-sludge-amended orchards (9.4%). Median microplastic abundances were 1433, 667, 4633, and 633 particles kg⁻¹ soil, respectively. Fine-sized MPs constituted the dominant particle fraction and exhibited strong associations with pore-system characteristics, indicating that pore-size compatibility governs their retention and mobility within the soil matrix. Morphology-specific analyses further revealed contrasting relationships between soil hydro-physical properties and individual MP forms, suggesting distinct retention pathways for granules, films, fragments, and fibers. Explainable AI analysis identified organic matter, silt content, bulk density, and water retention characteristics as the most influential predictors of MP occurrence. Among the tested models, RF demonstrated superior predictive robustness and generalization capacity. The findings demonstrate that hydro-physical soil functioning plays a central role in determining microplastic fate in agricultural soils and highlight the value of interpretable machine learning frameworks for uncovering the mechanisms underlying contaminant retention and redistribution. Integrating soil structural indicators with explainable artificial intelligence offers a promising pathway for improving microplastic risk assessment in agroecosystems. Full article

Soil erosion on sloping cropland is a major threat to agricultural sustainability and ecological security in mountainous regions, yet its spatial distribution and landscape-level structural characteristics remain insufficiently quantified. Taking Zhaotong in northeastern Yunnan, China, as a typical mountainous agricultural region in the upper Yangtze River Basin, this study integrated Sentinel-2 imagery, high-resolution reference data, field survey information, the Google Earth Engine platform, a random forest classifier, the Chinese Soil Loss Equation, and landscape pattern metrics to assess soil erosion on sloping cropland. The land use classification achieved an overall accuracy of 90.70% and a Kappa coefficient of 0.88, providing a reliable basis for sloping cropland extraction. Sloping cropland covered 4218.77 km², accounting for 84.64% of total cropland area, but contributed 1.84 × 10⁷ t·yr⁻¹ of annual soil loss, equivalent to 97.51% of total cropland erosion. The mean erosion rate of sloping cropland reached 4260.50 t·km⁻²·yr⁻¹, and 95.84% of sloping cropland exceeded the soil loss tolerance threshold. County-level analysis revealed strong spatial heterogeneity, with high erosion risks concentrated in northern and eastern mountainous counties. Intensive, Severe, and Extreme erosion occupied only 26.14% of the sloping cropland area but contributed 62.21% of total soil loss. Landscape metrics further showed that Moderate erosion had the highest patch density and edge density, indicating a critical fragmentation stage in erosion development. These findings support a tiered conservation strategy in which high-intensity patches are prioritized for immediate sediment reduction, while fragmented Moderate-erosion (2500–5000 t·km⁻²·yr⁻¹) areas receive preventive management. The proposed framework provides a useful approach for identifying erosion hotspots and supporting targeted soil and water conservation in mountainous agricultural landscapes. Full article