Search Results (1,745)

Wooden, nature-based barrier structures are widely implemented after wildfire in Mediterranean forests to reduce runoff connectivity and trap sediment, yet their ecological footprint on early plant recovery remains poorly quantified in Greece. We assessed two-year vascular plant recovery in forest landscapes burned during the 2021 wildfire season (Parnitha, Attica; Mavrolimni, Corinthia/Peloponnese) using repeated field surveys in 2022 and 2023. Sixteen permanent plots were established within operational rehabilitation works and assigned to the dominant structure types: wattles (brush/branch piles), contour-oriented hillslope log barriers, and channel log dams. In each year, vascular plant composition and recovery endpoints (species richness and diversity indices, density, cover, and aboveground biomass) were quantified using standardized quadrat sampling. Vegetation cover and biomass increased strongly from 2022 to 2023 at both sites, indicating rapid early reassembly. Against this dominant year effect, structure type was associated with pronounced biodiversity and compositional differences, most clearly in Parnitha where log barriers exhibited markedly reduced diversity in 2022 and community turnover patterns differed among structures. Plot-level PERMANOVA on Bray–Curtis dissimilarities calculated from log(x + 1)-transformed abundances did not detect a statistically significant structure type effect in either year (p > 0.05), whereas descriptive Bray–Curtis heatmaps suggested compositional contrasts among structure type × year combinations. Indicator–species analysis further identified a limited set of taxa associated with specific structures, suggesting provisional structure-linked microsite filtering during early assembly. By quantifying community composition and indicator taxa alongside structural recovery, this study provides operational-scale evidence that common wooden post-fire measures may be associated with early biodiversity signals in the first two years after fire, although these patterns should be regarded as provisional given the short monitoring period and limited replication. Incorporating these signals into post-fire land management can improve intervention design and placement, aligning risk reduction with biodiversity recovery in Mediterranean landscapes. Full article

Nitrogen (N) loss poses a significant threat to global climate stability and ecosystem sustainability. Afforestation, as a key ecological restoration strategy, regulates soil N cycling processes by modulating soil microbial community structure. However, a systematic synthesis of how afforestation influences microbial-mediated N loss remains limited. To address this gap, this study conducted a bibliometric analysis using CiteSpace software, based on 104 relevant publications indexed in the Web of Science Core Collection from 1997 to 2025, to comprehensively map the knowledge structure, research hotspots, and evolutionary trajectories in the field of afforestation-driven microbial regulation of soil N loss. The results reveal three developmental phases: initiation (1997–2005), growth (2006–2020), and stabilization (2021–2025). China contributed the highest number of publications (40), while the United States exhibited the greatest academic influence; the Chinese Academy of Sciences and the Russian Academy of Sciences clusters have emerged as core research institutions. Notably, keyword and citation analyses revealed that research hotspots have shifted from process-oriented measurements, including N mineralization and N₂O emissions, toward a deeper exploration of microbial community structure, biodiversity, and functional mechanisms. This study presents the bibliometric synthesis of microbial N loss mechanisms under afforestation, revealing a paradigm shift from environmental driers to microbial diversity. These insights inform microbial forest management strategies that balance N retention with carbon sequestration. Full article

Machine learning-based Network Intrusion Detection Systems (NIDSs) typically optimize uniform metrics such as accuracy and F1-score, overlooking the asymmetric cost structure of real-world security operations, where a missed attack (False Negative (FN)) far outweighs a false alarm (False Positive (FP)). We propose a cost-sensitive threshold optimization framework based on XGBoost, using a 10:1 FN-to-FP cost ratio derived from established cost models. We first demonstrate that the default threshold of 0.5 is suboptimal and that a globally optimized threshold of 0.08 substantially reduces total cost. However, a single global threshold cannot accommodate the heterogeneous detection characteristics of diverse attack types. We therefore introduce Per-Class Thresholding, which assigns independently optimized thresholds to each attack class. Evaluated on CIC-IDS2018 and UNSW-NB15 across five independent random seeds, our method achieves a 28.19% cost reduction over the Random Forest baseline on CIC-IDS2018, demonstrating that attack classes undetectable under the global threshold—including DDoS attack-LOIC-UDP (100%), DoS attacks-SlowHTTPTest (99.79%), and FTP-BruteForce (98.16%)—can achieve near-complete cost elimination through individual per-class threshold search. Cross-dataset validation on UNSW-NB15 further confirms that per-class thresholding consistently improves class-level detection, with cost reductions of 74.10% for Reconnaissance, 69.06% for Backdoor, and 54.42% for Analysis attacks. These results confirm that class-specific threshold calibration is essential for cost-effective intrusion detection. Full article

Phase change materials (PCMs) offer potential for passive thermal regulation in building envelopes through latent heat storage; however, their effectiveness remains strongly climate-dependent, and configurations optimized for one region often underperform in others. Existing evaluation approaches rely largely on location-specific simulations or surrogate models with limited climate transferability. This study develops a physics-informed, climate-aware machine-learning framework to assess PCM-integrated wall assemblies across diverse climates. A structured dataset of 720 EnergyPlus simulations was generated across nine PCM materials, ten ASHRAE climate zones, two placement configurations, and four thickness levels using automated model generation and batch simulation through Eppy-based workflows. Ensemble-based models (XGBoost, LightGBM, CatBoost, Random Forest) were trained under climate-grouped validation to predict total annual energy consumption, peak cooling demand, and peak heating demand. The models achieved high predictive accuracy for total annual energy use (R² ≈ 0.98–0.99) and peak cooling demand (R² ≈ 0.93–0.96), outperforming statistical, climate-only, and PCM-agnostic baselines. In contrast, peak heating demand showed low predictability (R² ≤ 0.26), indicating limited sensitivity to PCM parameters under the studied configuration. These results demonstrate that climate-aware validation enables defensible cross-climate PCM assessment, supporting energy demand reduction and sustainable envelope design decisions aligned with global building decarbonization goals. Full article

Background/Objectives: Poly(ADP-ribose) polymerase 1 (PARP1) is an important therapeutic target in DNA repair-deficient cancers, but discovery of new inhibitors remains constrained by scaffold convergence, tolerability limits, and acquired resistance. This study aimed to develop an interpretable, reliability-stratified cheminformatics workflow for PARP1 potency prioritization and structure-based follow-up. Methods: A curated ChEMBL dataset of 3339 PARP1 inhibitors was encoded using RDKit 2D descriptors and Avalon fingerprints (1143 initial features), then reduced to 132 informative variables by Random Forest-based feature selection. Five regression models were optimized, including a stacked ensemble. Model interpretation was performed using permutation feature importance and SHAP. External near-domain corroboration was assessed using a stringent PubChem similarity expansion (Tanimoto > 0.90) around sub-10 nM seed compounds, followed by comparison with retrievable experimental PARP1 activity values. Top scaffold-diverse candidates were further evaluated by complementary docking against PARP1 (PDB: 4R6E) using AutoDock Vina and cavity-guided docking through the SwissDock platform. Results: The stacked ensemble achieved the best held-out performance (test R² = 0.723; RMSE = 0.610 pIC₅₀ units), with 83.7% of test predictions within ≤0.75 pIC₅₀ units and only 2.7% exceeding 1.5 pIC₅₀ units. PubChem similarity expansion retrieved approximately 32,450 analogs, of which 3349 were predicted to have IC₅₀ ≤ 10 nM. Among 366 compounds with retrievable experimental PARP1 activity values, predicted versus experimental pIC₅₀ showed a positive association (R² = 0.124; Pearson r = 0.479), with RMSE = 0.491 and MAE = 0.330 pIC₅₀ units. Three ligands—CID 168873053, CID 175154210, and CID 172894737—showed the strongest complementary docking support and pocket-consistent poses relative to niraparib. Conclusions: This workflow provides a transparent and practically useful framework for near-domain PARP1 inhibitor prioritization. The combined QSAR, explainability, external corroboration, and docking strategy supports shortlist generation for experimental follow-up. Full article

Background: The nasopharyngeal microbiome is crucial for respiratory health in mammals, yet it remains poorly characterized in the endangered forest musk deer (Moschus berezovskii), particularly in the context of disease. Methods: We compared the bacterial (16S rRNA) and fungal (ITS2) communities in the nasopharynx of healthy (n = 6) and clinically diseased (n = 6) individuals. Results: Although alpha diversity did not differ significantly, beta diversity (PCoA) analysis revealed distinct bacterial (PERMANOVA, R² = 0.165, p = 0.014) and fungal (R² = 0.577, p = 0.003) community structures between groups. The diseased group exhibited a significant increase in the bacterial phylum Proteobacteria (70.97% vs. 46.27%), primarily driven by the genera Bibersteinia and Pseudomonas. Fungal communities in the diseased group were dominated by a higher relative abundance of Ascomycota and Basidiomycota, with significant enrichment of Wallemia and Aspergillus. LEfSe analysis identified Pseudomonas and multiple fungal taxa (e.g., Wallemia, Aspergillus) as biomarkers for the diseased group. PICRUSt2 prediction indicated enrichment of pathways related to carotenoid biosynthesis and sphingolipid metabolism in the diseased state, while FUNGuild analysis suggested a higher abundance of animal/plant pathogen-related fungi. Conclusions: Symptomatic respiratory infections in forest musk deer are associated with significant dysbiosis of the nasopharyngeal microbiome, characterized by the marked enrichment of potential bacterial opportunists (e.g., Pseudomonas) and specific fungal taxa (e.g., Wallemia, Aspergillus), alongside distinct functional shifts in the microbiome. These findings provide the first integrated bacterial–fungal profile of the nasopharyngeal microbiome in this endangered species, and highlight potential microbial biomarkers associated with respiratory disease. Full article

The interaction between environmental variables influences patterns of diversity and the composition of communities along the elevational gradient. However, there is a lack of evidence regarding how these diversity patterns in Scolytinae change in response to environmental changes associated with elevation. This study aims to evaluate the influence of environmental changes along an elevational gradient on the diversity and composition of Ambrosia beetles, testing the hypothesis that species assemblages are primarily driven by the interaction between environmental variables and vegetation structure. We sampled Scolytinae at five sites (650–3360 m a.s.l.) on Tacaná Volcano from February 2018 to January 2019. Sampling was conducted using five trap types, including ethanol-baited Malaise traps and interception traps. Data were analyzed using Hill numbers for alpha diversity, Bray–Curtis indices for beta diversity, and canonical correspondence analysis to evaluate the relationship between Scolytinae species abundance and environmental variables. We recorded a high richness with 82 species, a peak in diversity at mid-elevations in mesic montane forests (p < 0.05). The Scolytinae species pool is structured in three local assemblages, corresponding to different elevational landscapes, environmentally structured. Different environmental variables displayed some correlation with species dynamics. However, these factors alone were insufficient to explain patterns of species diversity. Their influence appears to depend on interactions with site-specific characteristics. These results highlight that elevational gradients act as environmental filters structuring Scolytinae assemblages primarily through species turnover rather than nested species loss. Full article

Madeira’s agricultural sector is characterised by pronounced structural heterogeneity, land fragmentation, and increasing socio-economic and environmental pressures. However, comprehensive typological and spatial analyses remain limited, particularly in small island contexts. This study addresses this gap by providing a typological and spatial analysis of the agrarian structure in the Autonomous Region of Madeira, Portugal, using 2019 Agricultural Census data. An integrated framework combining Principal Component Analysis (PCA), Partitioning Around Medoids (PAM) clustering, and Random Forest validation—representing a novel approach in agrarian typology studies—is employed to identify three agricultural models: Intensive Subtropical Agriculture (24.1% of parishes), characterised by small holdings and high labour intensity; Extensive Traditional Agriculture (64.8%), featuring moderate farm size and diversified cropping; and Pasture-based Agriculture (11.1%), dominated by larger farms and low labour input. The results confirm significant structural trade-offs, including a strong inverse relationship between farm size and labour intensity (r = −0.653) and a negative correlation between specialisation and crop diversity (r = −0.673). Spatially, the models exhibit clear territorial differentiation, with subtropical systems concentrated in southern coastal areas and traditional systems prevailing in northern and interior regions. These findings support the hypothesis of a hybrid agrarian transition. Despite relying on cross-sectional data, the results provide a robust basis for targeted and place-based policy design within the Common Agricultural Policy (CAP) framework. Full article

Accurate individual tree crown identification is essential for urban forestry, yet existing datasets often lack exhaustive annotations and multi-temporal diversity. To address this limitation, an exhaustively annotated dataset was curated for crown instance segmentation, comprising 47,754 labeled individual crowns from approximately 110 species across three temporal phases. Anchored in a “crown geometry” labeling criterion focusing on upper-canopy individuals visible in the imagery, and the high-resolution imagery captured seasonal variations in shape, color, and texture, providing an empirical basis for within-site robustness. Utilizing this dataset, this study (1) compared five instance segmentation models; (2) evaluated their generalization capabilities across different temporal phases; and (3) tested a multi-temporal joint training strategy and a non-maximum suppression (NMS)-based fusion. The experiments revealed significant overfitting in single-temporal models. While ConvNeXt-V2 achieved a high segmentation mean Average Precision (Segm_mAP) of 0.852 within the same temporal phase, its performance dropped sharply to 0.361 across phases. Bi-temporal joint training significantly mitigated this issue, improving cross-temporal performance to 0.665 and further increasing within-phase accuracy to 0.874. In contrast, tri-temporal training reduced accuracy (0.748), demonstrating that effective generalizability depends on the strategic selection of complementary temporal phases rather than the mere accumulation of data. The multi-temporal training framework provided in this study could serve as a practical reference and a foundational benchmark for further urban forest structural monitoring research. Full article

Castanopsis hystrix (C. hystrix) is one of the most dominant and ecologically important species in subtropical evergreen broad-leaved forests of China. Interactions between its root and rhizosphere microorganisms play a pivotal role in nutrient acquisition and in mediating plant response s to environmental stresses. In this study, high-throughput 16S ribosomal RNA (16S rRNA) sequencing combined with untargeted metabolomics was employed to systematically characterize the rhizosphere microbial community and root exudates in C. hystrix. The results showed that, compared with non-rhizosphere soil, bacterial diversity in the rhizosphere of C. hystrix was significantly reduced, while several specialized and potentially efficient taxa were selectively enriched, particularly Candidatus_Solibacter, Candidatus_Xiphinematobacter, and Candidatus_Koribacter, thereby reshaping a distinct rhizosphere-specific community structure. Metabolomic analyses further revealed that 129 metabolites were significantly enriched in the rhizosphere, including four major classes of compounds associated with plant stress resistance: lipids and lipid-like molecules, organoheterocyclic compounds, organic acids and derivatives, and phenylpropanoids and polyketides. The enrichment of these metabolites likely contributes substantially to stress tolerance in C. hystrix. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified six defense-related metabolic pathways, including pyrimidine metabolism, steroid biosynthesis, nucleotide metabolism, plant hormone signal transduction, ATP-binding cassette transporter (ABC transporters), and the biosynthesis of various plant secondary metabolites. Further correlation analysis and co-occurrence network analysis suggested that C. hystrix may potentially influence the enrichment of beneficial microorganisms through rhizosphere metabolites selectively, which could reduce the reliance on external nutrient acquisition and enhance the stress resilience of C. hystrix. Our study provides a comprehensive perspective for elucidating rhizosphere interaction networks and their ecological functions in C. hystrix, thereby enhancing our understanding of the environmental adaptability of dominant tree species in subtropical forests. Full article

Understanding how landscape structure affects nutrient pollution is essential for contemporary effective river basin management. This study examined the influence of landscape composition and configuration on concentrations of nitrate (NO₃⁻), nitrite (NO₂⁻), and ammonium (NH₄⁺) in 30 small lowland catchments of central–eastern Poland during the cold period. Water samples were collected monthly from September 2021 to April 2022, and land-use patterns were quantified using landscape metrics derived from high-resolution spatial data at the catchment scale and within riparian buffer zones. The results showed that the impact of land use on nitrogen concentrations was strongly dependent on both landscape type and spatial scale. Forests, meadows, wetlands, and water bodies generally acted as sink landscapes, reducing nitrate and nitrite levels. The effect was more pronounced in catchments where forest patches (mainly coniferous) covered a larger area, had greater total Edge Length, and were more complex in shape. It was advantageous when meadow patches were large, cohesive, and weakly fragmented. In contrast, arable land and built-up areas consistently functioned as source landscapes, contributing to higher nitrogen concentrations when characterized by a larger share, size (both), and aggregation degree of patches (arable land). Higher landscape diversity at the catchment scale was associated with lower nitrate and nitrite concentrations. Overall, land-use effects were best explained at larger spatial extents, especially the entire catchment and the 500 m buffer zone. These findings emphasize the need to integrate landscape structure and appropriate spatial scale into nutrient management strategies for lowland agricultural catchments. Full article

Forest structure is fundamental for linking ecological processes with management outcomes, and it influences key ecosystem services. However, the high cost and complexity of field data collection often limit the application of structural indices to small-scale studies, constraining operational assessments of forest multifunctionality. This study develops and tests an operational indicator of forest multifunctionality based on the structural heterogeneity index derived from forest management plans (FMPs). We analyzed the dendrometric data from 134 management units across 15 FMPs in the Lombardy region (Italy). Horizontal diversity was quantified using a Gini-based index, calculated from tree diameter-class distributions and combined with stand age, timber stock, and tree density using principal component analysis. Two orthogonal gradients emerged: a productivity gradient and a maturity–structural heterogeneity gradient. Generalized linear mixed models were used to assess their effects on carbon sequestration, timber yield, and touristic–recreational value. Structural heterogeneity was positively associated with all three functions, while productivity showed contrasting effects, particularly a negative relationship with recreational value. These results demonstrate that structural complexity and productivity are not necessarily in conflict and highlight the potential of FMPs as cost-effective data sources for operational, landscape-scale assessments of forest multifunctionality. Full article

Soil-dwelling predatory mites (Acari: Mesostigmata) are key components of decomposer-based soil food webs and contribute to the regulation of soil microarthropods, including agricultural pests. Despite their ecological and applied importance, the predatory mite fauna of Slovenia has remained poorly documented. This study provides the first systematic inventory of soil-dwelling mesostigmatid mites in Slovenia, based on standardized sampling conducted between July and October 2024 and between June and September 2025. Samples were collected from a range of organic substrates, including stable manure, compost, vermicompost, decomposing plant material and forest litter, and mites were extracted using a modified Berlese–Tullgren method. In total, 31 predatory mite taxa belonging to nine families were recorded, with all species except Macrocheles glaber being reported for the first time in Slovenia. Diversity analyses, based on species richness, Shannon index and minimum confirmed abundance, revealed clear differences in community structure among substrate types. Manure- and compost-based substrates showed the highest species richness and abundance, whereas forest litter supported lower diversity but more even communities. Several recorded genera include species with documented or potential relevance for the suppression of soil-dwelling pests such as Rhizoglyphus spp. These findings provide baseline data for future faunistic, ecological and applied research and improve our understanding of predatory mite communities in organically enriched agroecosystems. Full article

The East China Sea represents a critical coastal wetland region, characterized by complex geomorphology, heterogeneous land-cover composition, and diverse wetland types. Accurate delineation of coastal wetland extent is essential for ecosystem service assessment and sustainable coastal management, directly contributing to wetland-related Sustainable Development Goals (SDGs), particularly SDG 15, on ecosystem conservation and biodiversity protection. However, pronounced spectral similarity and structural heterogeneity among wetland classes pose substantial challenges to reliable classification. To address these challenges, this study developed a hierarchical classification framework integrating Random Forest, K-means clustering, and a decision tree classifier based on multi-source Sentinel-1 and Sentinel-2 imagery. Spectral, polarimetric, texture, and morphological features were systematically constructed to enhance class separability. Using this framework, a 10 m resolution coastal wetland map of the East China Sea was generated for 2023. The proposed approach achieved an overall accuracy of 91.32% and improved the discrimination of spectrally similar wetland types. Feature fusion reduced confusion among water-related classes, while object-based clustering improved the extraction of linear riverine wetlands. The resulting 10 m wetland map provides updated spatial information for ecological assessment and coastal management in the East China Sea. Full article

Hopea celebica Burck is an endangered dipterocarp endemic to Sulawesi, Indonesia, occurring in two ecologically contrasting habitats: karst and ultrabasic forests. These environments differ markedly in soil composition and topography, potentially driving ecological specialization and genetic divergence. To investigate the genetic variation and genetic structure of this species, we applied newly developed microsatellite (SSR) markers, together with the chloroplast DNA sequences of the trnL–trnF region. Genotypes at 15 SSR loci were determined for 255 individuals collected from six populations covering the range of the species’ distribution across karst and ultrabasic forests. Genetic diversity was consistently higher in karst than in ultrabasic populations. DIYABC and VarEff analyses revealed a historical bottleneck and earlier recovery in the karst populations. Analysis of molecular variance (AMOVA) revealed that 35% of the genetic variation was partitioned between habitat types (F_RT = 0.345, p = 0.001). Bayesian clustering (STRUCTURE), principal coordinate analysis (PCoA), and UPGMA dendrograms consistently showed two distinctive clusters corresponding to habitat type. Chloroplast haplotypes differed between populations in the karst and ultrabasic forests. These results suggest that populations in the karst and ultrabasic forests have undergone a long history of differentiation without migration. The strong habitat-related genetic structure likely reflects ecological isolation and early-stage speciation. We recommend treating the karst and ultrabasic populations as distinct conservation units to preserve the evolutionary potential and adaptive capacity of H. celebica under ongoing environmental change. Full article