Search Results (338)

Thismia species are non-photosynthetic plants entirely dependent on fungal partners for carbon and nutrients. While their arbuscular mycorrhizal associations are well-documented, bacterial symbiont roles remain unexplored. Using 16S rRNA gene amplicon sequencing, we investigated endophytic bacterial communities in T. gardneriana, T. javanica, and T. mirabilis from geographically distinct locations in Thailand. Despite geographic separation, Thismia spp. consistently harbored bacterial compositions taxonomically and functionally distinct from surrounding soil microbiomes. Root endospheres were significantly enriched in Pseudomonadota and Bacteroidota, particularly Puia, while showing reduced compositional dynamics of Acidobacteriota and Planctomycetota. Bacterial communities in Thismia roots were markedly distinct from surrounding soil, while root endosphere communities from geographically distinct habitats clustered together regardless of spatial separation. Mantel and partial Mantel tests confirmed that host species identity, not geographical location, was the primary predictor of root bacterial community structure. Functional prediction analyses suggested root-associated communities were enriched for nitrogen cycling pathways, particularly nitrogen fixation and nitrate reduction. The selective enrichment of Bacteroidota, known for nitrogen fixation and phosphate mobilization, suggests these bacteria provide critical nutritional support in nutrient-poor forest floor environments. Isolated root strains belonged exclusively to Bacillota, including Neobacillus with plant growth-promoting traits. Our findings highlight the importance of tripartite plant–fungal–bacterial interactions in Thismia nutritional ecology. Full article

The accessibility of flying drones (unmanned aerial vehicles) presents reproducible and cost-effective methods to monitor submerged aquatic vegetation. In particular, drone-borne topobathymetric LiDAR provides high-resolution (cm-scale), three-dimensional information about the geometry and structure of surveyed areas, allowing for quantification of vegetation volume in addition to bathymetry. For seagrasses, this information can advance research regarding the structure of canopies in relation to blue carbon storage and biodiversity. Here, we demonstrate how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in Norway. After classifying LiDAR points using a Random Forest model, we created a Digital Terrain Model of the sea floor and a Digital Surface Model of the eelgrass canopy. From these models, we showed that eelgrass canopy volume can be estimated (between 862 and 1099 m³ across the small study area) and the above-ground carbon stock in living tissue can be quantified (between 96 and 122 kg C). To our knowledge, this is the first study to utilise drone-borne LiDAR to quantify the habitat volume and carbon-storage potential of a marine habitat-forming species like eelgrass, demonstrating a novel methodology for providing reproducible and high-resolution data of submerged aquatic habitats. Full article

We investigated bryophyte communities in mature beech forests (Fagus sylvatica L.) and Austrian pine plantations (Pinus nigra J.F. Arnold) on Fruška Gora Mountain (northern Serbia) to examine how stand structure and edaphic conditions influence trait composition and functional diversity. Environmental predictors included soil pH, soil temperature, herbaceous cover, and shrub density, while collinear structural variables were summarized using principal component analysis into a composite structural–moisture gradient. Community–environment relationships were analyzed using redundancy analysis (RDA) with restricted permutations, trait–environment coupling using RLQ and fourth-corner analysis, and functional diversity using Rao’s quadratic entropy (RaoQ). The RDA indicated significant effects of all predictors. RLQ revealed a structured multivariate coupling between bryophyte traits and environmental gradients. Functional diversity was higher in beech forests than in pine plantations, increasing with shrub density and decreasing along the structural–moisture gradient. Overall, plantation stands supported functionally more homogeneous bryophyte assemblages, highlighting the importance of stand structural complexity for maintaining forest-floor bryophytes’ functional diversity. Full article

Atmospheric nitrogen (N) deposition is an important global change driver in forest ecosystems, yet its long-term effects on belowground microbial communities in cold-temperate coniferous forests remain insufficiently understood. In this study, endpoint shotgun metagenomic sequencing was used to evaluate bulk soil microbial communities after 12 years of experimental N addition in a Larix gmelinii-dominated forest in the Greater Khingan Mountains of northeastern China. Four treatments were included: control (0 kg N ha⁻¹ yr⁻¹), low N (25 kg N ha⁻¹ yr⁻¹), medium N (50 kg N ha⁻¹ yr⁻¹), and high N (75 kg N ha⁻¹ yr⁻¹). Microbial alpha diversity did not differ significantly among treatments, although moderate N addition showed a tendency to maintain relatively higher richness and diversity. In contrast, beta-diversity analysis indicated clear shifts in community composition along the N addition gradient. Pseudomonadota, Acidobacteriota, and Actinomycetota dominated the microbial communities, with Pseudomonadota tending to increase under N enrichment, whereas some oligotrophic groups showed reduced relative abundance. Functional annotation showed that metabolism-related genes remained dominant across treatments, and carbohydrate-active enzyme profiles suggested altered microbial potential for complex carbon decomposition under long-term N input. Nitrogen addition also modified the abundance patterns of some antibiotic resistance genes and mobile genetic elements, although overall resistome differentiation among treatments remained limited. These results provide endpoint metagenomic evidence that long-term N addition can reshape bulk soil microbial community composition and selected functional potentials in cold-temperate coniferous forest soils, even when overall alpha diversity remains relatively stable. Full article

Controlling operation-stage carbon emissions (CE) from transport buildings is crucial for China’s dual-carbon goals and the ecological security of the Qinghai–Tibet Plateau (QTP), and the sustainable development of plateau transport infrastructure. For plateau railway passenger stations (RPS), limited monitoring and distinctive high-altitude, cold-climate operations make daily CE prediction difficult with conventional measurement- or simulation-based methods. This study develops a machine-learning approach based on a Monte Carlo synthetic database and derives engineering-standard formulas for direct use. Building scale, meteorology and passenger flow volume (PFV) were compiled for 12 representative RPS, and a large synthetic database of daily carbon emission was generated under multiple distribution constraints. With daily mean temperature, heating degree days, altitude, station floor area and PFV as inputs, four models were trained and assessed using mean absolute error, root mean square error, mean absolute percentage error (MAPE) and R². The results show that random forest (RF) performed best, achieving ~6% MAPE and R² > 0.99 on the test set, and markedly lower errors than multivariable linear regression. Interpretation of RF via feature importance and partial dependence shows that floor area, altitude and PFV dominate emissions and exhibit nonlinear response patterns. To improve transparency and transferability, ridge regression was used to fit a linear surrogate to RF predictions, producing engineering-standard formulas for daily and annual operation-stage CE. The formulas retain most predictive accuracy while requiring only readily obtainable variables, enabling rapid estimation and scenario analysis for cold, high-altitude RPS. The proposed workflow provides a replicable pathway for operational CE assessment in data-scarce regions and supports low-carbon planning, design and operation of RPS on the QTP, thereby contributing to more sustainable infrastructure development in high-altitude regions. Full article

The energy utilization of residual woody biomass is a relevant strategy for the decentralized energy transition and local waste management in rural areas. The objective of this study was to characterize (physically, chemically, and energetically) five types of residual biomass: pine branches, huinumo (this material refers to the long, thin pine needles that, after drying and falling, form a layer on the forest floor), cherry branches and leaves, and grass waste generated in the community of San Francisco Pichátaro, Michoacán, Mexico, in order to evaluate its viability for the production of densified solid biofuels. A comprehensive analysis was conducted, including moisture content, higher heating value, proximate characterization, structural chemical analysis (using the Van Soest method), elemental CHONS analysis, ash microanalysis (by ICP-OES), and a multicriteria analysis with normalized energy and compositional indicators. The results showed that huinumo and cherry leaves were the most outstanding biomasses, presenting the highest heating values (20.7 MJ/kg) and low moisture and ash contents. Pine branches obtained the most balanced results, characterized by their equilibrium in fixed carbon and lignin, as well as their low potassium content. The multicriteria analysis showed that there is no absolute optimal biomass; however, it indicates that pine branches and huinumo are the most robust feedstocks for the production of briquettes or pellets. The results confirm the significant technical and environmental potential of local lignocellulosic residues for the production of solid biofuels and for contributing to sustainable energy solutions at the local scale. Full article

Afforestation of former agricultural land is widely promoted as a strategy to mitigate climate change and support sustainable land management. However, soils subjected to long-term cultivation often retain chemical legacies that may persist for decades after land-use change, influencing soil functioning and ecosystem development. This study investigates the persistence of selected agricultural soil chemical properties following afforestation using a chronosequence approach. Post-agricultural soils afforested for 15, 40, and 80 years were examined on Dystric Brunic Arenosols developed from sandy parent material. Composite samples were collected from forest-floor horizons (Ol and Ofh) and upper mineral horizons (A and B). The analyzed parameters included organic carbon (Corg), total nitrogen (Nt), sulfur (S), soil pH, hydrolytic acidity (Ha), exchangeable base cations (EBC), and cation exchange capacity (CEC). The results show that agricultural soil legacies persist for several decades after afforestation. Soils under the 15-year-old stand were characterized by higher exchangeable calcium, higher base saturation, and lower hydrolytic acidity, reflecting the persistence of historical liming. With increasing stand age, soil acidity increased, and base-cation concentrations declined, while organic carbon accumulated mainly in forest-floor horizons. These findings highlight the importance of considering agricultural soil legacies when evaluating the sustainability of afforestation and its role in long-term ecosystem services. Full article

Precise orbital floor reconstruction requires personalised surgical templates that combine high geometric fidelity with manufacturing efficiency. This study presents and validates the TARMM procedure, developed to optimise the production of polylactide (PLA) templates. A key innovation is the integration of advanced machine learning algorithms (Random Forest) and Mitchell–Netravali interpolation to reduce medical reconstruction artefacts, as well as the implementation of Material Extrusion (MEX) technology with Variable Layer Height (VLH). This strategy minimises the stair-step effect on complex anatomical curvatures while maintaining high process throughput. The results demonstrate that the TARMM procedure ensures a geometric error within ±0.1 mm. A strong linear correlation (r = 0.99) was found between layer height and surface roughness (Sa), indicating that a 0.07 mm layer in critical areas significantly improves template morphology and facilitates the contouring of titanium meshes. The clinical validation across 21 cases confirmed a 30 min reduction in surgical preparation time. The developed method serves as a low-cost, high-precision alternative to photopolymerization technologies, contributing to modern 3D printing applications in maxillofacial surgery. Full article

Energy consumption optimisation has emerged as a critical need in Autonomous Mobile Robots (AMRs). Conventional A* implementations typically minimise path distance, neglecting energy-relevant factors such as directional changes and trajectory smoothness that significantly impact battery life and operational costs. This work proposes two energy-aware A* variants trained on empirical data from the KUKA KMP 1500 platform, where energy consumption is measured as battery SoC depletion: A*-RF, which integrates a Random Forest (RF) model directly into the cost function, and A*-MOD, which approximates the energy model through RF feature importance weights, achieving linear computational complexity O(nf). The RF model predicted energy consumption with an RMSE below 1.5% relative error, identifying travel distance and rotation angle as the dominant energy factors. Experimental validation across 42 path planning scenarios on a real industrial factory floor demonstrates that A*-MOD reduces energy consumption by up to 58.91% and improves operational autonomy by 2.21 times, with statistically significant improvements (p < 0.01) across all evaluated metrics. Full article

Detecting rare ground-level obstacles (e.g., large boulders) in dense boreal forests from low-altitude UAV RGB imagery is challenging due to limited annotated data, strong background clutter, and expensive field labeling. This paper evaluates two complementary synthetic-data augmentation pipelines for low-data forestry object detection: segmentation-guided diffusion inpainting, where SegFormer-derived forest-floor masks constrain Stable Diffusion inpainting to plausible insertion regions, and simulator-based generation in Unreal Engine 5 with controlled domain randomization and automatic annotations. We conduct a ten-fold cross-validation study on a real UAV dataset of 64 images and report both accuracy and stability across folds. Compared to real-only training (mean mAP50 ≈ 0.579; mAP50-95 ≈ 0.350), inpainting improves mean performance (mAP50 ≈ 0.647; mAP50-95 ≈ 0.435) while substantially reducing cross-fold variance and lifting the worst-case fold from 0.301 to 0.619 in mAP50. Simulator augmentation yields slightly lower mean accuracy (mAP50 ≈ 0.546; mAP50-95 ≈ 0.344) but markedly improves robustness by mitigating collapse on difficult splits (minimum mAP50 0.496 vs. 0.301). These results indicate that carefully curated generative augmentation can reduce failure risk and improve generalization in extremely data-limited forestry detection settings without additional field data collection. Full article

Understanding the drivers of soil carbon efflux is critical for predicting forest carbon cycles under climate change. This study investigates how plant roots and phenology govern the spatio-temporal variability of boreal forest floor respiration (Rf) in an ectomycorrhizal-dominated forest. By analyzing stabilized soil carbon fluxes (NEE, Ra, and Rh) one year after root exclusion in northern Sweden, we challenge the passive physicochemical paradigm. Results show that the spatial distribution and magnitude of Rf are primarily driven by plant roots, with Ra accounting for >60% of total efflux. The collapse of respiration in trenched plots confirms the mycorrhizal bridge as the essential conduit for these spatial patterns. Regarding temporal variability, we identified a biological pulse driven by plant phenology. After temperature-normalization, Ra maintained a strong seasonal peak in July and August. Notably, static drivers like fine root biomass failed to explain spatial variation (R < 0.3, p > 0.05), whereas dynamic NEE showed significant positive correlations (R = 0.52, p < 0.0001). This holistic perspective suggests that the forest floor operates as an integrated metabolic continuum, where root activity and phenological pump are the main regulating factors on carbon release. Future models should reposition plant–fungal phenology as the primary engine of soil metabolism. Full article

Understanding how housing attributes are capitalized into prices is central to addressing urban affordability challenges. Using 2799 second-hand housing transactions from Wenzhou, China, this study examines residential price formation under pronounced spatial and structural heterogeneity. Multiple predictive models are evaluated within a unified 10-fold cross-validation framework. Results indicate that Random Forest delivers the strongest predictive performance, achieving a normalized mean squared error below 0.10 and explaining over 90% of out-of-sample price variation, substantially outperforming hedonic regression, regression trees, bagging, boosting, and support vector models. Permutation-based importance analysis identifies district location, building scale, and floor area as the dominant price determinants, while the influence of renovation quality, transportation access, and educational amenities varies across districts and dwelling types. These findings reveal strong nonlinearities and heterogeneous valuation mechanisms in rapidly urbanizing housing markets. Methodologically, the study demonstrates how interpretable machine learning complements traditional hedonic analysis, while providing policy-relevant insights into housing affordability dynamics in medium-sized Chinese cities. Full article

Mountain forests play a key role in buffering local climate, yet their climate sensitivity is seldom mapped in a way that is directly usable for spatial planning. This study investigates how phenological thermal and vegetation variability are organized within the forested landscape of Namyangju, a mountainous region in central Korea, and derives spatial indicators of forest climate sensitivity. Using monthly, cloud-screened Landsat-8/9 land surface temperature (LST) and normalized difference vegetation index (NDVI) images over a recent multi-year period, we calculated phenological coefficients of variation for 34,123 forest grid cells and applied local clustering analysis to identify belts of high and low variability. Forest areas where LST and NDVI variability simultaneously occupied the upper tail of their distributions (top 5%/10%/20%) were interpreted as climate-sensitivity hotspots, whereas co-located coldspots were treated as microclimatic refugia. Across the mountainous terrain, sensitivity hotspots formed continuous belts along high-elevation ridges and steep, dissected slopes, while coldspots were concentrated in sheltered valley floors. Notably, the most sensitive belts were dominated by high-elevation conifer stands, despite the limited seasonal fluctuation typically expected in evergreen canopies. This pattern suggests that elevation strongly amplifies the coupling between thermal responsiveness and vegetation health, whereas valley-bottom forests act as stabilizers that maintain comparatively constant microclimatic and phenological conditions. We refer to these patterns as “forest climate-sensitivity belts,” which translate satellite observations into spatially explicit information on where climate-buffering functions are most vulnerable or resilient. Incorporating climate-sensitivity belts into forest plans and adaptation strategies can guide elevation-aware species selection in new afforestation, targeted restoration and fuel-load management in upland sensitivity zones, and the protection of valley refugia that support biodiversity, thermal buffering, and hydrological regulation. Because the framework relies on standard satellite products and transparent calculations, it can be updated as new imagery becomes available and transferred to other seasonal, mountainous regions, providing a practical basis for climate-resilient forest planning. Full article

Existing Mediterranean reinforced concrete buildings with masonry infills exhibit critical seismic vulnerabilities, yet real-time damage detection capabilities remain limited. This study validates a novel dense piezoelectric transducer (PZT) network concept for early damage detection in deficient RC structures under progressive seismic loading. A three-dimensional single-story RC frame with brick infills, representative of pre-Eurocode Mediterranean construction (non-ductile detailing, inadequate transverse reinforcement), was tested at serviceability limit states (SLSs) (Phase A) using a dynamic pushover approach with the 1978 Thessaloniki earthquake record, progressively scaled from EQ0.1g to EQ1.1g within the GREENERGY vertical forest renovation project. The specimen featured 48 PZTs using electromechanical impedance (EMI) methodology, 12 accelerometers, 8 displacement sensors, and 20 strain gauges. Progressive infill deterioration initiated at EQ0.5g while steel reinforcement remained elastic (max 2350 μstrain < 2890 μstrain yield). Maximum inter-story drift reached 11.37‰ with negligible residual drift (0.204‰). The PZT network, analyzed through Root Mean Square Deviation (RMSD), successfully detected internal cracking and infill-frame debonding before visible manifestation, validating its early warning capability. Floor acceleration amplification increased from 1.26 to 1.57, quantifying structural stiffness degradation. These SLS results provide critical baseline data enabling the Phase B implementation of sustainable vertical forest retrofitting strategies for aging Mediterranean building stock. Full article

The issue of water disasters in the mining floor is extremely severe. Despite significant progress in the on-site monitoring and identification of water inrush channels, research on the spatial development characteristics of cracks and the temporal evolution patterns remains insufficient, resulting in the incomplete development of microseismic-based water disaster early warning theory and practice. Based on this, the present study first derives the expressions for the diameter and length of water inrush channels according to the damage characteristics of microseismic events and the glazed porcelain shape features of the channels. A theoretical model for the correlation between microseismic-water inrush volume is established, and the relationship between microseismic and water level is revealed. Analysis of field monitoring data further indicates that when high-energy microseismic features (such as single high-energy events and higher daily cumulative energy) are detected, the aquifer water level begins to decline, followed by high water inrush events. Therefore, a decrease in water level accompanied by high-energy microseismic features can serve as an important early warning marker for water disasters. Finally, advanced machine learning methods are applied, in which the optimal index combination for floor water inrush prediction is obtained through the genetic algorithm, and the weights of each index are determined by integrating the analytic hierarchy process with the random forest model. Field engineering verification demonstrates that the integrated early warning system performs significantly better than any single monitoring indicator, and all high-water-inrush events are successfully predicted within four days. Full article