Search Results (3,014)

Precision livestock farming requires monitoring approaches that extend beyond static activity thresholds to enable dynamic, animal-level decision support. A time-aware machine learning framework was developed to transform RFID-derived goat activity records into an interpretable behavioral monitoring system. Time-stamped activity data were processed into temporal features such as lagged activity, rolling mean, activity change, and elapsed-time variables to capture short-term behavioral history. The framework integrated latent-state discovery, fuzzy-uncertainty analysis, transition modeling, supervised classification, short-horizon forecasting, and dashboard-based alert visualization within a predictive dashboard-based monitoring framework. Four latent behavioral clusters were identified, with the dominant cluster representing a stable low-activity baseline and accounting for 77.88% of observations. Boundary-zone analysis indicated that 7.30% of observations were in transitional regions, while fuzzy clustering classified 21.42% as uncertain or mixed-state points, suggesting gradual shifts in activity. Transition analysis revealed greater persistence in baseline states and lower persistence in high-activity/non-baseline states, which exhibited the highest volatility and entropy. Using nested time-blocked validation, Random Forest predicted future high-activity/non-baseline onset with AUC values of 0.869 and 0.841 for 5 and 10 min horizons, respectively. These results demonstrate that activity instability can be detected and forecasted over short horizons, supporting behavior-based monitoring. However, external biological validation is still required before implementation as a health- or disease-detection system. Full article

Climate change is increasingly altering forest ecosystems worldwide, reshaping species phenology, productivity, and resilience. In this study, we evaluate the phenoclimatic responses of European beech (Fagus sylvatica L.) forests across Europe by assessing their phenological responses to climate change across climatic zones and altitudinal gradients using remote-sensing data. We used 24 years of satellite-derived land-surface phenology and climate data to quantify phenological trends at 356 beech-dominant locations from the EUFGIS database, of which 274 remained after land-cover homogeneity and data-quality filtering. To reduce land-cover mixing at the MODIS resolution, we applied a land-cover homogeneity filter based on ESA WorldCover. The analysis was structured across the seven climatic zones in Europe. Phenological responses to climate change were assessed through climate–phenology sensitivity analyses and a composite phenoclimatic departure index integrating climatic trends, phenological shifts, and interannual variability. Phenological sensitivity varied across climatic zones and phenological phases. Temperature-related sensitivity was most evident in spring in several continental zones, whereas precipitation sensitivity was more apparent for growing-season length and autumn timing in some regions. The composite phenoclimatic departure analysis showed that regional profiles were not uniform across the European beech range. Although warming was widespread, precipitation trends, phenological shifts, and interannual variability differed strongly among zones. These findings demonstrate heterogeneous and location-specific phenoclimatic responses across Europe, but the departure index should not be interpreted as a direct measure of ecological vulnerability or risk. Full article

This study investigates the relationship between tree species diversity and soil quality in natural Pinus massoniana Lamb. forests, providing a scientific basis for the sustainable management of natural stands and the establishment of plantations. Located in Ganzhou City, Jiangxi Province, China, a subtropical region, the study focuses on natural P. massoniana forests. The Patrick richness index and Shannon–Wiener diversity index were each classified into three levels—low, medium, and high—to analyze how soil physicochemical indicators vary across these diversity levels. A minimum data set of key soil indicators was established to evaluate soil quality. Linear and nonlinear scoring functions were used to compare the relationships between the total data set and minimum data set-derived soil quality indices, and the most suitable scoring function for this study was selected. An obstacle factor diagnosis model was applied to identify the factors limiting soil quality improvement under different diversity levels. The results showed that (1) in the 20–40 cm soil layer, soil water content, maximum water-holding capacity, and total porosity significantly increased with rising Patrick richness and Shannon–Wiener diversity levels. (2) With increasing Patrick richness and Shannon–Wiener diversity levels, organic matter and total nitrogen in the 0–20 cm soil layer, as well as total potassium, available nitrogen, and pH in both the 0–20 cm and 20–40 cm soil layers, showed significant upward trends. In contrast, total phosphorus in both layers significantly decreased. Furthermore, available phosphorus in both layers significantly declined as Patrick richness increased. (3) The minimum data set based on the nonlinear scoring function can effectively substitute for the total data set (r = 0.791, R² = 0.63), showing better applicability for assessing soil quality changes in natural P. massoniana forests. Additionally, the soil quality index in the 0–40 cm soil layer significantly increased with rising Patrick richness levels. (4) At different Patrick richness levels, bulk density had the greatest impact on soil quality in the 0–40 cm soil layer, while organic matter had the least. A comprehensive analysis shows that there is a positive correlation between tree species diversity and soil quality in natural P. massoniana forests. In P. massoniana plantation practices, a near-natural management approach should be adopted to create relatively species-rich and complex coniferous and broad-leaved mixed stands, which is conducive to improving soil quality. Full article

Mushrooms, forest fruits and game meat represent three major categories of forest foods in Czechia. This study compares their acquisition mechanisms, market visibility and value-chain positions and provides reference-year, category-specific physical estimates and stage-specific indicative economic values. The analysis integrates pooled national survey data on mushrooms and forest fruits from 2021 to 2025 (N = 5025), a 2022 survey extension on game meat (N = 1000), qualitative interviews with 12 stakeholders in the Czech game-meat value chain conducted by the research team between 2023 and 2024, and official hunting statistics. In the 2024 reference year, mushrooms and forest fruits were estimated through household-collected quantities, whereas game meat was estimated as gross carcass-weight equivalent at the primary procurement stage. The three categories together represented an indicative stage-specific economic value of approximately EUR 324.3 million, but their physical quantities are interpreted as product-specific estimates rather than as directly equivalent units of provisioning value. Mushrooms showed the strongest household-collection profile: 70.4% of respondents reported collection and 20.1% reported purchase. Forest fruits displayed a more mixed acquisition pattern, with particularly high purchase shares for blueberries and raspberries. Collection and purchase were largely independent for mushrooms, whereas complementary relationships prevailed among forest fruits. Game meat had an indicative primary procurement value of EUR 33.57 million and reflected a regulated hunting-based value chain. The findings identify a differentiated forest-food system in which socio-economic significance is shaped by product-specific relationships among household acquisition, market access, value-chain organisation and stage-specific value creation. Full article

Moderate-resolution leaf area index (LAI) retrieval over heterogeneous landscapes is affected not only by unresolved subpixel composition in coarse-resolution predictors, but also by structural bias in supervisory labels aggregated from higher-resolution products. To address this issue, we developed a reference-guided two-stage workflow to improve LAI retrieval from the Visible Infrared Imaging Radiometer Suite (VIIRS). In the first stage, aggregated Sentinel-2 LAI was calibrated against Ground-Based Observations for Validation (GBOV) LP3 reference LAI using subpixel plant functional type (PFT) fractions and forest-sensitive hinge terms to generate corrected 500 m labels. In the second stage, a random-forest model was trained using VIIRS spectral reflectance, viewing geometry, vegetation indices, texture, and subpixel compositional variables. Model development was based on 2020–2021 data from 11 U.S. GBOV sites. Performance was evaluated by same-site temporal transfer to 2019 and 2022 and by strict leave-one-site-out (LOSO) validation. Label calibration improved agreement with GBOV from a coefficient of determination (R²) of 0.752 and a root mean square error (RMSE) of 1.110 to an R² of 0.908 and an RMSE of 0.676. Under LOSO validation, the final model achieved an R² of 0.901 with an RMSE of 0.703. On the 2019/2022 overlap subset shared by the final VIIRS retrieval, the official VNP product, and the GBOV reference, the final model achieved an R² of 0.905 and an RMSE of 0.609, compared with 0.755 and 0.978 for the official VNP product. These results show that reference-guided label correction, combined with explicit subpixel compositional information, can substantially improve VIIRS LAI retrieval over mixed pixels within the evaluated study domain. Full article

How do microorganisms in litter and soil affect litter decomposition in a temperate forest? Here, we conducted an 18-month laboratory experiment to assess the decomposition of pure Robinia pseudoacacia, pure Platycladus orientalis, and mixed R. pseudoacacia–P. orientalis litters under four treatments, namely “no microbe” (NM), “litter microbes” (LM), “soil microbes” (SM), and “litter and soil microbes” (LM + SM). Results demonstrated that, compared with SM, LM significantly enhanced the litter weight-loss rate and elevated the potential activities of lignocellulolytic enzymes at 180 days, and this was accompanied by lower cellulose and hemicellulose contents. Structural equation modeling indicated that microorganisms may directly or indirectly influence weight mass loss, partly by regulating these potential enzyme activities that are associated with changes in the litter organic matter composition. Across three forest stands, microbial treatments significantly affected litter decomposition. The standardized direct path coefficients linking microorganisms to the litter-mass-loss rate from highest to lowest were LM + SM, LM, and SM, indicating a synergistic effect between LM and SM that promotes decomposition through coordination. Taxonomically, most bacterial genera differed significantly among microbial treatments, whereas most fungal genera did not. Notably, the standardized direct path coefficient linking bacteria to litter mass loss was larger than that for fungi in both the SM and LM + SM groups. Additionally, field decomposition was faster than in the laboratory, with distinct microbial communities, verifying the environmental modulation of decomposers and the home-field advantage. This study clarifies microbial mechanisms underlying litter decomposition and provides a theoretical basis for forest ecosystem stability and sustainable management. Full article

Accurate and transferable estimation of forest carbon is essential for operational forest management and national greenhouse gas reporting, yet it remains challenging because of variation in stand structure and site conditions. Unmanned aerial vehicle-based light detection and ranging (UAV-LiDAR) provides detailed structural information for modelling tree-level carbon, but model transferability across sites is often limited. In this study, we compared three modelling approaches—a linear mixed-effects model (LMM), a generalised additive model (GAM), and Random Forest (RF)—within a unified framework of multi-site, locally post hoc calibrated, and fully local model-fitting strategies. Using data from 20 radiata pine (Pinus radiata D. Don) plantation stands across New Zealand (35,201 trees), a leave-one-site-out (LOSO) framework was used to assess multi-site model transferability and support post hoc calibration, while local models were evaluated using repeated within-site train/test splits. We also evaluated how prediction accuracy changed with increasing local sample size and compared random tree selection with plot-based sampling. Multi-site models showed poor generalisation, with mean relative RMSE ranging from 35.9% to 56.9% and substantial site-level bias. Applying post hoc calibration to the multi-site model using a 50-tree sample reduced prediction error by 30 to 60% (mean relative RMSE 22.8–25.0%) and substantially reduced bias across sites. The fitting of fully local models with the same sample size yielded only modest further improvements (mean relative RMSE 21.9–23.1%). Gains in accuracy were minimal with increasing sample sizes above 50 trees for post hoc calibration and 175 trees for the fully local models, and differences in accuracy between sampling strategies were small. These results show that post hoc calibration of multi-site UAV-LiDAR models with a small local sample provides a practical and efficient approach for tree-level carbon estimation in plantation forests. Full article

Salt-bridge kefir-derived microbial fuel cells (MFCs) provide a low-cost platform for studying fermentation-linked electrical output, but their behavior is often evaluated through isolated current or voltage traces rather than integrated reactor-state evidence. This study assessed laboratory-scale double-chamber MFCs operated under fed-batch conditions with a kefir-derived mixed consortium and molasses-based substrate. Thirty-three independent reactors, including graphite- and graphene-anode configurations, were monitored from day 0 to day 20, generating 693 reactor-day observations. Electrical, redox, temperature, substrate-related, UV–Vis soluble-phase, baseline sequencing, endpoint SEM, FTIR functional-group evidence, and semimechanistic descriptors were integrated to diagnose reactor evolution and predict fixed-condition current output. Current declined from 0.8985 to 0.1133 mA, residual glucose-equivalent decreased from 5.3124 to 0.0127 g L⁻¹, and the glucose-consumption fraction reached 0.9977. Fixed-condition apparent power decreased from 0.8636 to 0.0856 mW, while cumulative charge and cumulative apparent energy averaged 595.02 C and 456.69 J per reactor. FTIR bands supported carbohydrate/EPS, organic-acid, and proteinaceous-matrix signatures consistent with a fermentation–redox–biofilm cascade. The random-forest model showed strong grouped cross-validation performance (R² = 0.956, RMSE = 0.082 mA, MAE = 0.060 mA, slope = 1.009, r = 0.978). This work supports state-aware current and fixed-condition power-output prediction in kefir-driven MFCs without claiming maximum power-density or complete electrochemical characterization. Full article

Sustainable management of the invasive tree Hovenia dulcis (H. dulcis) in the Mixed Ombrophilous Forest (MOF) is crucial for reconciling biodiversity conservation with income generation for smallholders. This study developed a species-specific predictive growth model for H. dulcis and simulated management scenarios across three properties with contrasting invasion intensities. By integrating stem quality, phytosanitary status, and individual growth rates into tree selection criteria, we evaluated trade-offs between timber yield and structural recovery under Continuous Cover Forestry (CCF) principles. The mixed-effects growth model demonstrated high predictive performance (marginal R² = 0.89, RMSE = 2.05 cm), confirming H1 and validating its application as a decision-support tool for long-term silvicultural planning. Results confirmed H2: no single standardized management approach proved appropriate across all sites, as invasion intensity, stand density, and diameter distribution varied substantially among properties and directly determined the most suitable harvesting strategy. In highly invaded stands (Property I), intensive harvesting of 61 trees yielded the highest commercial volume (Vc = 21.84 m³), while in more preserved forests (Property II), conservative selection of 26 trees (Vc = 9.53 m³) prioritized structural quality. Structural recovery periods ranged from 1 to 7 years depending on harvesting intensity, with removal of stagnant large-diameter trees reducing passage time for remaining individuals. Targeting sawlog-quality trees (dbh > 25 cm) was 3.35 times more profitable than firewood production, providing a significant economic incentive for smallholders. These findings demonstrate that property-specific H. dulcis management can transform a biological threat into a productive resource, fostering MOF restoration through active and sustainable use. Full article

Coupled nitrogen (N) and phosphorus (P) cycling in calcareous karst soils, and how it responds to long-term land use and restoration, remains poorly quantified. We compared seven plots on a karst slope platform: three development types (degraded disturbed land, pasture, and fruit orchard) and four restoration types (planted evergreen forest, planted deciduous forest, evergreen–deciduous mixed forest, and naturally restored forest). Gross N transformation rates were measured by ¹⁵N isotope dilution, alongside soil properties, available P, microbial biomass N, and enzyme activities. Conservation plots generally had higher soil organic carbon, total N, microbial biomass N, and enzyme activity than development plots; soil organic carbon peaked under naturally restored forest (57.5 g·kg⁻¹) and was lowest in disturbed land (47.9 g·kg⁻¹). Naturally restored forest also showed the highest gross nitrification and total N mineralization, whereas disturbed land had the weakest ammonification, with negative gross rates pointing to N immobilization. Available P (up to 15.9 mg·kg⁻¹) tracked alkaline phosphatase activity, organic carbon, and total N rather than total P. Across the alkaline range (pH 7.2–7.8), random forest models ranked ammonium and enzyme activity, not pH, as the main predictors of N mineralization. Long-term land use and restoration were thus associated with consistent differences in karst soil N and P supply. Full article

Aerobic ceramic membrane bioreactors (AeCeMBR) have shown great potential in treating wastewater (WW) from the textile industry; however, their operation faces challenges such as process variability, membrane contamination, and the need for accurate prediction of treated water quality under varying conditions. In this study, chemical oxygen demand (COD) and turbidity were selected as key indicators, as they directly reflect organic load removal and solids separation efficiency in MBR systems. The effect of four operational parameters: hydraulic retention time (HRT), organic loading rate (OLR), mixed liquor suspended solids (MLSS), and transmembrane pressure (TMP), was investigated using a response surface methodology (RSM) based on a Box–Behnken design. A random forest (RF) model coupled with Monte Carlo simulation (MC) was also developed using 174 experimental data points to enhance predictive power and quantify uncertainty. The RSM model showed strong agreement with experimental results (coefficient of determination (R²) > 0.95), achieving approximately 96% removal for both COD and turbidity, with validation errors of less than 2%. MC simulation (10,000 iterations) was applied to assess the effect of ±10% variance under operating conditions, providing a probabilistic view of system performance. The RF-MC framework demonstrated high predictive accuracy, with strong correlations between predicted and observed values (R² = 0.92 for COD and 0.97 for turbidity) and low uncertainty. Overall, this study proposes an integrated RSM, RF–MC approach for AeCeMBR systems, providing a robust and uncertainty-aware framework for process optimization and performance prediction under changing operating conditions. Full article

Pinus pinaster Ait. forests have potential for resin tapping, a forestry activity that complements timber production and may increase the profitability of maritime pine stands. However, the viability of this co-production remains uncertain due to the potential effects of resin tapping on wood characteristics. The present study aimed to investigate the effects of resin tapping on the wood characteristics of maritime pine, in order to infer possible changes in wood quality, its utilisation, and, consequently, its value. The study was based on samples collected in Tresminas from resin-tapped trees (37.2 ± 6.0 years old and mean height of 15.8 ± 1.4 m) subjected to the traditional Portuguese resin tapping method for four consecutive years, and from non-resin-tapped trees (37.5 ± 8.9 years old and mean height of 14.1 ± 2.2 m). Samples were collected from different positions along the stem of resin-tapped trees (incision side, opposite side, and 50 cm above the last tapping incision) and compared with samples obtained from non-resin-tapped trees. Wood density, modulus of elasticity (MOE), modulus of rupture (MOR), extractives content, growth ring width and the number and area of resin ducts were evaluated. The effects of resin tapping on wood properties were assessed by comparing resin-tapped and non-resin-tapped trees, as well as different sampling positions within resin-tapped trees, using linear mixed-effects models. Mean comparisons were performed using Tukey’s test at a 95% significance level. No significant effects of resin tapping were observed on MOE or MOR between resin-tapped and non-resin-tapped trees. Wood from the incision side showed higher density (0.596 g·cm⁻³) and higher extractives content (7.49%). Resin-tapped trees produced a greater number of resin ducts after tapping; however, their area did not change. No significant differences were found in growth ring width between resin-tapped (1.75 mm) and non-resin-tapped trees (1.80 mm), although resin-tapped trees presented slightly narrower rings on average. Resin tapping in P. pinaster did not promote relevant changes in wood properties that would compromise its mechanical and physical performance. Although some alterations were detected, these were predominantly localised and restricted to the region adjacent to the tapping incision. Full article

The Brazil nut tree (Bertholletia excelsa Bonpl.) is a non-timber forest product of great ecological and economic importance in the southwestern Amazonian countries. The study aimed to analyze the effects of habitat (natural tree-fall gaps, logged gaps, and field crops) on the survival and early growth of Bertholletia excelsa (Brazil nut) seedlings in the Peruvian Amazon. Seedlings were monitored every 2 months for 1 year to record survival and seedling growth. We used linear mixed models to evaluate the effects of habitat type, time, and their interaction on diameter and height growth. The non-parametric Kaplan–Meier method was used to estimate survival probability. This study showed that the survival of B. excelsa seedlings was significantly higher in canopy gaps than in crop fields. We also revealed the negative effect of canopy cover on the early height growth. Height growth of surviving B. excelsa seedlings was significantly higher in logging gaps than in all other habitats, and in natural tree-fall gaps it was significantly higher than in crop fields, whereas seedlings in logging gaps and crop fields had significantly higher diameter growth than those in natural tree-fall gaps. Our results illuminate the potential of enrichment planting using B. excelsa in logged tropical rainforest in the southeast Peruvian Amazon. Full article

China harbors a diverse array of macrofungi, yet its fungal diversity remains inadequately documented, particularly in under-explored regions such as the temperate forests of the northeast and the subtropical highlands of the southwest. In this study, four agaric species are reported as new records for China based on morphological observations and multilocus phylogenetic analyses. Phylogenetic analyses of the internal transcribed spacer (ITS) and nuclear large subunit (nrLSU) ribosomal RNA gene regions confirmed their generic and species-level placements. Cortinarius infidus was collected from mixed coniferous and broadleaf forests in Liaoning Province, Pseudolaccaria fellea from Pine-dominated forests in Liaoning Province, Volvariella clavocystidiata from pine-dominated coniferous forests in Liaoning Province, and Gerhardtia borealis from mixed coniferous–broadleaf forests in Guizhou Province. Comprehensive macro and micromorphological descriptions, color photographs, line drawings, scanning electron micrographs, and comparisons with closely related taxa and original literature are provided to confirm these identifications. These findings contribute to a better understanding of the distribution patterns of these genera and expand the known fungal diversity of China. Full article

Chinese fir (Cunninghamia lanceolata) is one of the most important fast-growing timber species in southern China and plays a critical role in regional carbon sequestration and timber production. Soil organic carbon (SOC) is a key component of the terrestrial ecosystem carbon pool, and its content, composition, and stability directly affect soil fertility, ecosystem service functions, and the ability to cope with climate change. This review summarizes the mechanisms by which density regulation and conifer–broadleaf mixed forest management affect the content, fractions and stability of SOC in Chinese fir plantations. Density regulation changes stand structure, litterfall, and roots, which can impact soil microbial activity, litter decomposition, and mineralization of soil organic matter. Conifer–broadleaf mixed planting and broader mixed-forest reconstruction, through introducing functionally distinct tree species, can optimize stand microenvironments, increase species diversity, improve litter quantity and quality, and diversify root exudates. These changes further regulate soil organic carbon (SOC) accumulation and its physicochemical stability. Based on the latest literature reports, we demonstrate that mixed-species stands with a moderate broadleaf proportion significantly enhance SOC sequestration relative to pure stands, driven by improved litter quality and soil pH neutralization that promote microbial necromass formation and aggregate-associated carbon stabilization. Optimal density regulation complements these benefits by facilitating understory development and root carbon input. Current research indicates that both density reduction and species mixing, as two independent silvicultural measures, can individually enhance soil organic carbon (SOC) stability in Chinese fir plantations. This review identifies key research gaps and provides theoretical foundations for carbon-oriented sustainable management of Chinese fir plantations. Full article