Search Results (8,340)

In northeast British Columbia (BC), Canada, industrial forestry is gradually converting 10+ M ha of broadleaf–conifer (mixedwood) boreal forest to conifer plantations. This is due in part to governmental free-to-grow (FTG) regulations, which specify a minimum competition-free radius around conifer crop trees. FTG implementation is a poor investment; it reduces stand biodiversity and productivity and infringes on Indigenous Treaty Rights. Trials were established on three geographically separated boreal sites with no stand management (brushing) since planting. The goal was to determine the effect of FTG criteria on conifer growth in mixedwoods compared to growth of pure conifer stands using the BC Government growth model TIPSY (Table Interpolation Program of Stand Yield) projections. At trial establishment, less than a third of trees were FTG. The number of FTG trees increased at the last measurement but only reached 50 percent on one site. After a decade, conifer DBH (diameter at breast height) growth and stand productivity met or exceeded the model projection regardless of the initial FTG status. The DBH relative growth rate (RGR) indicated that spruce DBH growth was not impacted by competitors. These observations suggest that brushing on similar sites to meet timber objectives is likely unnecessary. Maintaining mixedwood stands supports greater biodiversity and carbon storage, and this approach better aligns with an Indigenous world view and Treaty Rights. There is an opportunity in northeast BC to shift forest management from conifer-based performance metrics to prioritizing ecological resilience and long-term forest health and productivity. Full article

Spatholobus suberectus is a medicinal and edible plant widely recognized for its pharmacological potential. Although its stems have been extensively studied and utilized, its leaves are often discarded as agricultural waste, leading to significant resource underutilization. To promote the sustainable valorization of these leaves, this study aimed to provide a predictive evaluation of their bioactive constituents and pharmacological potential. Leaves of S. suberectus were collected at six growth stages (January, March, May, July, September and November). A total of 6750 metabolites were identified, primarily comprising amino acids and derivatives (26.74%), organic acids (15.33%), and bioactive secondary metabolites, including flavonoids and phenolic acids (27.98%). Metabolic profiling revealed clear seasonal patterns, allowing the classification of the six harvest months into three distinct stages: January and March (G1), May and September (G2), and July and November (G3). Among these, the G1 stage was notably enriched in defensive secondary metabolites, particularly flavonoids and phenolic acids. To predict the bioactivity of these metabolites and elucidate potential mechanisms of action, network pharmacology and molecular docking analyses were employed. Network pharmacology and molecular docking were employed to predict anti-inflammatory mechanisms. From the metabolome, 83 potential bioactive compounds were screened, interacting with 306 targets. Network analysis identified 60 core anti-inflammatory targets (e.g., TNF, AKT1, PTGS2, STAT3) that were significantly enriched in MAPK and PI3K-Akt pathways. Molecular docking revealed strong binding affinities, with pelargonidin showing the highest affinity for PTGS2 (−11.72 kcal/mol). Candidate metabolites peaked in January, and extracts from this period exhibited notable COX-2 inhibitory activity (IC50 = 16.41 μg/mL). This research provides essential chemical characterization and preliminary bioactivity evidence to support the valorization of S. suberectus leaves and identifies January as the optimal harvest time to maximize their bioactive potential. Full article

Quercus baronii Skan (Q. baronii) is an ecologically important tree species in arid and soil erosion-prone areas of northern China, and also holds significant potential as a bioenergy tree species, providing substantial ecological benefits. Global climate change has profoundly influenced the suitable habitats and habitat fragmentation of Quercus baronii forests. This study employed the Maximum Entropy (MaxEnt) model to project the current and future suitable habitats of Q. baronii forests, along with their trends of contraction and expansion. Concurrently, composite landscape indices were used to assess the fragmentation of these suitable habitats. The results indicate that the suitable habitats for Q. baronii forests are primarily located in the eastern part of Northwest China, the northern part of Central China, and the southern part of North China. Minimum temperature of the coldest month (bio6), annual precipitation (bio12), and temperature seasonality (bio4) emerged as the primary determinants of habitat suitability. Under three future climate scenarios, the centroid of suitable habitats for Q. baronii forests is projected to shift towards higher latitudes in the northwest, with the elevation of suitable habitats also gradually rising in tandem with increased carbon emissions. Under low carbon emission scenarios, the extent of suitable habitat for Q. baronii forests is expected to expand; under medium and high carbon emission scenarios, it is expected to first increase and then decline. Although over two-thirds of the suitable habitat for Q. baronii forests is projected to remain relatively intact, future suitable habitats are expected to be more fragmented compared to the present. This fragmentation is projected to intensify with increasing carbon emissions, primarily occurring at the edges of the suitable areas. The results of this study lay the groundwork for both the preservation of forest biodiversity and the ecological conservation and sustainable management of temperate broad-leaved forest ecosystems. Full article

Plant diversity is a key indicator of ecosystem structure, function, and restoration status, yet its rapid assessment remains challenging in sandy ecosystems where vegetation is sparse, spatially heterogeneous, and strongly affected by exposed soil backgrounds. In such environments, conventional greenness-based spectral indices may not adequately capture species-level variation because plant communities are controlled not only by photosynthetic biomass but also by soil moisture, micro-topography, and dune-related habitat heterogeneity. This study evaluated the potential of Sentinel-2-derived spectral indices for estimating plant α-diversity in the Hunshandak Sandland, northern China. Based on field observations from 888 plots collected during 2017–2024, four α-diversity metrics—species richness, Shannon–Wiener index, Simpson index, and Pielou evenness index—were calculated and compared with 21 spectral indices using correlation analysis, partial least squares regression (PLSR), and random forest (RF) models. The results showed that model performance varied substantially among diversity metrics. Species richness was estimated with the highest accuracy, whereas Shannon–Wiener, Simpson, and Pielou indices showed weaker predictability, indicating that remotely sensed spectral indices were more sensitive to species number than to abundance distribution and evenness. Moisture- and soil-background-sensitive indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BSI/BRI), and Chlorophyll Absorption Ratio Index (CARI), showed relatively stable relationships with plant diversity across different vegetation gradients. Although the overall explanatory power was moderate rather than high, the results demonstrate the practical value of Sentinel-2 spectral indices for regional screening of plant diversity patterns in sandy ecosystems. This study provides empirical evidence for biodiversity monitoring and ecological restoration assessment in semi-arid sandy landscapes and highlights the need to integrate environmental covariates, multi-source remote sensing, and phenological information in future studies. Full article

The eastern coast of China, characterized by a pronounced climatic gradient and diverse ecosystems, is an ideal region for exploring the spatiotemporal dynamics of carbon fluxes and their drivers. Based on observations from eight flux tower sites, together with meteorological, remote sensing, and ecohydrological variables from 2001 to 2022, this study developed Back Propagation (BP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models to estimate regional gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP). Among them, RF performed best, achieving validation R² values of 0.92, 0.84, and 0.83 for GPP, ER, and NEP, respectively, and was therefore selected for regional upscaling. The regional mean GPP, ER, and NEP were 1578.38, 1286.05, and 334.56 g C m⁻² yr⁻¹, respectively, indicating that the region functioned as a net carbon sink during the study period. GPP, ER, and NEP exhibited a clear spatial gradient, with higher values in the south and lower values in the north. Total regional NEP increased from 344.12 Tg C in 2001 to 517.73 Tg C in 2022, reflecting a continuous strengthening of terrestrial carbon sink strength. Forests contributed most to the regional carbon sink, while the ecosystem-level NEP contribution of croplands increased over time; by contrast, the total carbon sink of wetlands declined because of area loss. These results suggest that ecological restoration, vegetation greening, and land cover optimization jointly enhanced the carbon sink along the eastern coast of China. These findings have important implications for ecological management and green low-carbon development along the eastern coast of China. Full article

Soil total nitrogen (TN) and soil total phosphorus (TP) are key soil quality indicators and provide critical ecological functions in the grasslands. This study analyzed the driving factors of TN/TP in the grasslands of Qinghai Province based on Shapley additive interpretation (SHAP) analysis. Four machine learning methods, namely random forest (RF), XGBoost 3.2.0, support vector machine, and Cubist, were used to establish spatial prediction models for TN/TP. Vegetation factors (Net Primary Production and Normalized Difference Vegetation Index) and precipitation-related factors (Aridity Index and Mean Annual Precipitation) were the most important variables for TN, indicating plant productivity and precipitation are strongly associated with TN accumulation. Elevation and temperature-related factors (Mean Annual Temperature and evapotranspiration) were the most important variables for TP, demonstrating that elevation-mediated temperature was the major factor affecting the TP accumulation. XGBoost and RF were the optimal models for TN and TP, respectively. TN exhibited a decreasing spatial trend from east to west, while the northwestern and southwestern areas showed relatively higher and lower TP, respectively. Total TN and TP stocks were estimated to be 3.57 × 10⁸ t and 0.88 × 10⁸ t, respectively. This study provides data support and suggestions for sustainable soil nutrient management in the grasslands on the Qinghai-Tibet Plateau. Full article

Natural forest regeneration offers economic, ecological, and environmental advantages over artificial regeneration; however, its application is often constrained by uncertainties in stand development and management outcomes. Pre-commercial thinning (PCT), a key assisted natural regeneration practice, is widely used to regulate stand density and improve early stand development. Nevertheless, empirical evidence remains limited regarding how post-thinning residual density influences both tree growth and operational performance in high-density naturally regenerated Pinus densiflora stands. This study evaluated three residual density treatments (RD2000, RD3000, and RD5000) following PCT in naturally regenerated pine stands with an initial density of approximately 30,000 stems ha⁻¹. Diameter at breast height, tree height, and crown area were monitored annually over three years, while thinning productivity and operational costs were quantified during treatment implementation. Residual density significantly affected both biological and operational outcomes. The intermediate residual density (RD3000) showed the most consistent growth responses, whereas the lowest residual density (RD2000) resulted in suppressed growth. The highest residual density (RD5000) achieved the highest productivity and lowest operational costs despite moderate growth performance. These results indicate a trade-off between growth performance and operational efficiency and suggest that an intermediate residual density may provide a balanced strategy for managing naturally regenerated pine stands. Full article

Remote sensing technologies provide valuable geospatial data for analyzing environmental conditions and for supporting spatial ecological modeling across large, heterogeneous landscapes. In this study, multi-source remote sensing–derived environmental variables were integrated with ensemble machine learning techniques to model the habitat suitability of the common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ&K), Pakistan. Environmental predictors derived from satellite observations included land cover, vegetation condition, terrain attributes, and climate-related indicators. To ensure model reliability, multicollinearity among predictors was evaluated, and spatial clustering patterns of leopard occurrence records were examined using global spatial autocorrelation analysis. Two complementary machine learning algorithms, Maximum Entropy (MaxEnt) and Random Forest (RF), were implemented and integrated through a weighted ensemble approach to improve predictive accuracy and robustness. The ensemble model achieved high predictive performance with an area under the curve (AUC) value of 0.942, outperforming individual algorithms. The resulting habitat suitability map indicates that approximately 30% of the study region is highly suitable habitat, primarily in the northern and central districts, including Muzaffarabad, Neelum, Hattian, Poonch, and Sudhnutti. Variable importance analysis identified remotely sensed land cover, elevation, vegetation cover, slope, and temperature seasonality as the dominant predictors of habitat suitability, whereas anthropogenic indicators such as proximity to roads and population density had secondary effects in fragmented areas. The results demonstrate the potential of integrating remote sensing data and ensemble machine learning for spatial habitat modeling and wildlife conservation planning in mountainous ecosystems. Full article

Accurate individual tree crown (ITC) segmentation from unmanned aerial vehicle (UAV) imagery is important for fine-scale forest inventory, plantation management, and ecological monitoring. However, delineating ITCs in dense plantation environments remains difficult because crowns are strongly adjacent, canopy structures are highly homogeneous, and crown boundaries are often blurred, making it hard for existing methods to preserve both regional integrity and boundary continuity. This study proposes the Perceptual Segment-Anything Model with Multi-head Cross-Parallel Attention (Per-SAM-MCPA), a lightweight and effective framework for fine-grained ITC segmentation in dense plantation scenes. Based on a compact ResNet-50 backbone, the framework integrates perceptual target-aware representation, multi-scale detail enhancement, global contextual modeling, and semantic-boundary collaborative refinement to improve crown discrimination and structural consistency. A perceptual relation module is used to strengthen pixel-level semantic dependency modeling, and a Multi-head Cross-Parallel Attention (MCPA) mechanism is designed to capture long-range contextual interactions through orthogonally decomposed spatial attention, improving global geometric consistency with limited computational overhead. A Composite Constraint Loss (CCL) that combines a weighted cross-entropy loss, a structural similarity loss, and a boundary term based on Hausdorff distance is introduced to jointly optimize region-level segmentation quality and boundary fidelity. Experiments on the Catalpa bungei UAV dataset show that the proposed method achieves an intersection over union (IoU) of 87.3% and an F1-score of 91.0%, outperforming representative baseline methods such as SAM and Mask R-CNN while maintaining an inference speed of 35.7 FPS on a single GPU. These results indicate that Per-SAM-MCPA offers an accurate, efficient, and practical solution for ITC segmentation in dense plantation environments. Full article

The Yellow River Delta, a young alluvial plain in China, is experiencing severe land subsidence that threatens its ecological security and sustainable development. However, the driving mechanisms of this subsidence exhibit strong spatial heterogeneity, which traditional global models fail to capture. This study integrates high-precision subsidence measurements from Sentinel-1A imagery and SBAS-InSAR technology (2017–2023) with multi-source environmental factors (topography, geology, land use, precipitation) to propose a Spatially Adaptive Ensemble Learning Model with feature selection (SA-GSE). The model concatenates predictions from base learners (CatBoost, XGBoost, Random Forest) with spatial features (e.g., distance to salt pans, local topographic variance) to form meta-features, which are then input into a multilayer perceptron meta-learner. Through 5-fold spatial cross-validation, SA-GSE learns spatially dynamic base-model weights, implicitly adapting to regional variations in subsidence drivers. The model achieves an R² of 0.7810 and RMSE of 40.55 mm/yr on the test set, outperforming individual base models and ordinary stacking. Residual spatial autocorrelation is substantially reduced, with SA-GSE yielding the lowest Moran’s I (0.0334, p = 0.206) among all evaluated models, confirming effective capture of spatial heterogeneity. Driving force analysis reveals that distance to salt pans is the most important predictor (permutation importance: 0.4456), underscoring the dominant role of brine extraction-induced aquifer compaction. Lagged precipitation importance (0.3191) exceeds that of current precipitation (0.2453), indicating a recharge lag effect. SHAP interaction analysis uncovers a nonlinear “precipitation decoupling” mechanism in salt pan areas, where high precipitation paradoxically exacerbates subsidence. The resultant map of predicted subsidence rates highlights elevated rate zones in the northern salt pans and along the Guangli River. While the map does not represent a full risk assessment—as it does not include exposure or vulnerability—it provides a spatially explicit estimate of hazard likelihood. This ensemble framework yields novel perspectives on subsidence drivers in heterogeneous regions and can support land subsidence prevention and groundwater management planning. Full article

Forest fires cause severe ecological and economic losses, so timely and accurate detection becomes crucial for effective prevention and control. Edge devices with intelligent algorithms can detect forest fires in real time. Current deep learning algorithms can achieve high accuracy, but they are not suitable for edge devices because they require substantial computing resources. To address this issue, this study proposes a real-time lightweight forest fire detection network (RLFNet) improved from YOLOv11n, with three key enhancements to the backbone, neck, and head. (1) A Parallel Multi-Scale Extraction Block (PMEB) improves C3k2 with a dual-branch parallel strategy to enhance multi-scale feature extraction efficiency; (2) a Bidirectional Cross Fusion Module (BCFM) replaces simple Concat with a context-aware cross-gating mechanism to suppress background noise and reduce false alarms; and (3) a Faster Inference Detection Head (FIDH) leverages structural re-parameterization and group normalization to boost inference efficiency while reducing parameters. In addition, a Layer-Adaptive Magnitude-based Pruning (LAMP) strategy is applied to further improve model’s computational efficiency. Experimental results on the self-constructed Diverse Fire Scenario (DFS) dataset demonstrate that RLFNet reduces parameters and GFLOPs by 25.2% and 20.6%, boosts mAP50 by 5.3%, and achieves an inference speed of 225 FPS, attaining the best accuracy and speed among the compared models. Validation on a public remote sensing dataset further confirms its strong generalization. These results indicate that RLFNet provides a high efficiency and lightweight solution for edge devices to real-time detect forest fires. Full article

Understanding the drivers of arthropod community diversity is essential for effective biodiversity conservation, particularly in human-dominated landscapes. True bugs (Hemiptera) are morphologically and ecologically diverse, differing in feeding strategy, specialization, and mobility, and thus occupy a wide range of habitats. We sampled true bugs using sweep nets across 257 plots in the Xishuangbanna Biodiversity Priority Area, Southwest China, including natural forests, planted forests, cultivated lands, and complex habitats comprising mosaics of these land-use types. We quantified morphological traits and assessed how habitat type and surrounding landscape structure at multiple spatial scales influence taxonomic and functional diversity. Species richness and abundance were lowest in cultivated lands, whereas functional diversity remained relatively consistent across habitats. Landscape composition and configuration significantly shaped community structure, with effects varying by habitat type and spatial scale. Overall, landscape heterogeneity promoted species richness and abundance, while connectivity and fragmentation showed contrasting effects depending on habitat context. Complex habitats and forests generally supported higher diversity under heterogeneous and well-connected landscapes. These results demonstrate that habitat characteristics and landscape structure jointly regulate true bug diversity, highlighting the need for habitat-specific landscape management that enhances heterogeneity and connectivity in natural and complex habitats while reducing isolation and simplification in human-modified systems. Full article

This study aimed to reveal the rhizosphere microbial community structure, carbon–nitrogen–phosphorus (C-N-P) nutrient cycling processes, and functional gene characteristics of Pinus massoniana and Schima superba in mixed forests. Furthermore, we sought to elucidate the microbial mechanisms by which mixed-species afforestation enhances soil quality improvement, providing a theoretical basis in soil microbiology for the cultivation of these mixed forests. The research subjects included pure P. massoniana plantations (CLPs), pure S. superba plantations (CLSs), and individual P. massoniana (HJP) and S. superba (HJS) trees within mixed plantations (HJLs). We collected rhizosphere and bulk soil samples to analyze their physicochemical properties and enzyme activities. Metagenomic sequencing was employed to profile the rhizosphere microbial communities and functional genes involved in C-N-P cycling. Furthermore, by integrating a functional gene co-occurrence network analysis with structural equation modeling (SEM), we systematically elucidated the coupling relationships among the stand types, soil properties, microbial communities, and nutrient cycling. Mixed planting significantly improved soil quality; compared to the CLP and CLS forests, the nitrate nitrogen (NO₃⁻-N) content in the mixed forest soils increased by 121.01% and 120.10% (p < 0.05), and the activity of urease (URE) also significantly increased by 123.99% and 49.56%, respectively. Mixing significantly altered the microbial community structure. In the bacterial community of the mixed forests, the abundance of nitrogen-fixing and potentially phosphorus-solubilizing bacteria from the genera Paraburkholderia and Burkholderia increased. In the fungal community, the arbuscular mycorrhizal fungus Rhizophagus, which possesses a nutrient absorption advantage, exhibited absolute dominance, with its relative abundance ranging from 14.84% to 88.81%. The abundances of genes associated with denitrification and phosphorus starvation regulation were significantly upregulated in the mixed forests; notably, the abundance of phosphorus starvation regulation genes in the HJSs was 18.84% higher than that in the CLSs. A co-occurrence network analysis demonstrated that the proportion of positive correlation edges in the HJP nitrogen cycling network reached as high as 75.0%, and the average degree of the HJS phosphorus cycling network (2.691) surpassed that of the CLSs. The structural equation modeling further revealed that the association strength between the fungi and phosphorus cycling genes in the mixed forests increased to R² = 0.915 (p < 0.01) from R² = 0.213 in the pure forests. This mixed planting practice transforms nutrient cycling from a resource-competitive mode to a microbially synergized mode, thereby forming an efficient endogenous nutrient cycling system. This synergistic rhizosphere microbial effect is a key internal mechanism for overcoming nutrient bottlenecks and should serve as a diagnostic indicator of soil recovery in the ecological restoration of degraded pine forests. Full article

The Sanjiangyuan Region is among China’s most critical ecological function zones and serves as an important habitat for rare wildlife species such as brown bears and snow leopards. Driven by factors including climate change and intensified human activities, human–wildlife conflicts have become increasingly frequent on the Qinghai–Tibet Plateau, threatening the living space of both herders and wildlife. This study centers on the Yushu Tibetan Autonomous Prefecture in Qinghai Province, integrating multi-source remote sensing data with field survey data, and employs the Maximum Entropy Model (MaxEnt) MaxEnt model and the BIOMOD2 framework to simulate high-risk areas for brown bear incidents. Results indicate that the BIOMOD2 ensemble model (EMca) achieved the highest predictive accuracy, with the Random Forest (RF) model demonstrating strong robustness among individual models. Digital Elevation Model (DEM), Soil Surface Moisture (SSM), Fractional Vegetation Cover (FVC), and Human Footprint (HFP) were identified as the primary factors influencing the spatial distribution of brown bear incidents. High-risk areas exhibited significant clustering, mainly concentrated in the southern and southeastern regions of Qumalai, Nangchen, and Chindu; the eastern part of Zadoi County; and the central and southern parts of Yushu City, particularly within the elevation range of 4304–4544 m, where human activity intensity is relatively low. The core high-risk zone is located along the Tongtian River in southern Qumalai County, demonstrating strong spatial connectivity. By investigating the spatial distribution patterns and driving mechanisms of brown bear incidents in Yushu Prefecture, this study offers some references for government agencies to formulate strategies that promote harmonious coexistence between humans and nature. Full article

The preservation of hydrobiological diversity is essential to ensuring the stability of the food chain and the sustainable development of high-Andean basins, which face increasing vulnerability to anthropogenic factors such as the construction of dams and reservoirs. In this study, multiple regression models, both linear and nonlinear, were developed to predict the Shannon–Wiener (H′) and Pielou (J′) indices of periphyton and macrobenthos using 21 water quality parameters and concentrations of nine metals in sediments. Samples of macrobenthos and periphyton were collected at seven monitoring stations during the dry and wet seasons between 2014 and 2025. For the analysis, linear regression models were compared with nonlinear machine learning models, specifically Gradient Boosting and Random Forest. Principal component analysis (PCA) revealed that variability of the basin’s ecosystem is dominated by geogenic factors (conductivity, boron, chlorides, and arsenic) and thermal influence. The Gradient Boosting model demonstrated superior predictive capacity (R² = 0.768 for macrobenthos) compared to linear models (R² = 0.354), successfully capturing the nonlinear responses of biota to stressors such as arsenic in sediments and temperature. It is concluded that natural chemical anomalies in the Titire River act as severe ecological filters, and that artificial intelligence shows promising results in the exploration of new applied tools for environmental management in extreme altoandine ecosystems. Full article