Search Results (507)

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

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

Forest structural heterogeneity is widely recognized as a key driver of biodiversity, yet its effects on different components of understory diversity across spatial scales remain insufficiently understood, particularly when assessed using high-resolution remote sensing techniques. In this study, we examined the relationship between forest structure and understory plant diversity along secondary forest succession using high-density uncrewed laser scanning (ULS) data. We derived a suite of LiDAR-based structural metrics describing canopy height, vertical heterogeneity and canopy openness, and evaluated their association with both taxonomic and functional diversity at alpha- and beta-scales, through linear mixed models and distance-based redundancy analysis. Structural metrics showed a limited explanatory power for local (alpha) diversity, with generally weak relationships across taxonomic and functional indices. In contrast, forest structure consistently explained variation in beta diversity, with canopy openness and vertical heterogeneity emerging as the strongest predictors of community dissimilarity. These findings suggest that forest structure plays a greater role in shaping differences in species composition among stands than in determining within-stand diversity. Overall, our findings highlight the potential of ULS-derived structural metrics to capture spatial patterns of understory diversity, while underscoring the importance of accounting for multiple environmental drivers and scale-dependent processes. Full article

The ecological stoichiometric traits of forest floor material nutrient resorption in forest ecosystems. However, systematic insights into nutrient allocation and scaling during the transformation of plant organs to forest floor material remain limited. This study examined six representative tree species on the southern slope of the Qilian Mountains, quantifying the carbon (C), nitrogen (N), and phosphorus (P) contents and their stoichiometric characteristics in leaves, branches, and mixed forest floor material. Allometric relationships between N and P were analyzed using the standardized major axis regression. Coniferous species exhibited a conservative strategy with high C/N and C/P ratios, whereas broad-leaved species and mixed forests exhibited nutrient-enrichment strategies. During the organ-to-forest floor material transformation, N and P contents significantly reduced, whereas C/N and C/P ratios increased, indicating strong nutrient resorption. N and P were positively associated with plant organs and forest floor material, with isometric relationships (b = 1.06 and 0.98 for plant organs and forest floor material, respectively). Because the slopes did not differ significantly from 1 (p > 0.05), the N:P ratio remained relatively constant, with no significant P limitation. This indicates that tree species regulate forest floor material quality through divergent nutrient utilization strategies, modulating nutrient cycling. This study provides a theoretical foundation for the ecological restoration and stand structure optimization of alpine forests. Full article

Non-industrial private forestlands (NIPF) have often been subjected to logging practices that remove the highest quality trees of the highest value species, leaving behind less-desirable stems and species; a practice termed high-grading or selective harvesting. Timber stand improvement (TSI) can be used to correct high-grading practices by removing poorly-formed or low-value tree species in order to promote the growth of higher value trees and species. The felled trees may be removed for biomass fuel or left in place. At study sites in southwestern Virginia, we monitored tree growth across experimental TSI with biomass removal, TSI cut-and-leave felled stems, and control plots in mixed-pine hardwood forests from 2012–2025, measuring diameter at breast height (DBH) for multiple species. Scarlet Oak (Quercus coccinea) and Yellow Poplar (Liriodendron tulipifera) had the largest growth increments during the study period, while Black Gum (Nyssa sylvatica) and Hickory species (Carya spp.) showed consistently low growth. Larger trees tended to grow at faster rates, consistent with allometric expectations. The two TSI treatments had similar growth increments and were 60%–100% higher than control plots over the tree blocks of treatments in this study. Mortality at the longest-term measured block was more than twice as high as TSI plots. These results suggest that TSI can reduce competition for light and nutrients promoting diameter growth, whereas untreated plots may experience resource limitations that suppress growth and increase mortality. The study provides a baseline for understanding forest dynamics and highlights the importance of management interventions in maintaining productivity and structural diversity in selectively-logged forests. Full article

The spatial distribution of flammable tree species directly influences forest fuel structure and fire risk patterns. However, mixed pixels limit the ability of conventional classification methods to characterize continuous within-pixel variation in species composition, thereby constraining fine-scale forest mapping. To address this issue, this study developed a subpixel mapping framework for flammable tree species in Yajiang County, Sichuan Province, by integrating Sentinel-2 time-series data with a spectral mixing–unmixing strategy. Using 2019 Sentinel-2 time-series data and National Forest Inventory (NFI) data, temporal mixed samples with known abundance fractions were generated using a linear spectral mixing model. An XGBoost-based collaborative multi-regression framework was then applied to estimate the proportions of different tree-species endmembers within complex forest pixels. Quantitative evaluation using synthetic mixed samples showed that the model achieved stable unmixing performance across different random mixing scenarios. The best performance was obtained under the Mixed 2 scenario with a sample size of 250 K, reaching an R² of 0.821. The resulting maps revealed continuous spatial variation in the abundance and composition of flammable tree species. Mountain pine was the most widespread and dominant species, followed by spruce and mountain oak, whereas birch and fir mainly exhibited localized patchy distributions. An additional NFI-based categorical evaluation assessed the consistency of the final maps with real forest inventory records. The identification accuracies were 93.95% for pure stands and 91.22% for mixed stands, while the species classification accuracies were 87.28% for pure stands and 84.41% for dominant species in mixed stands. The proposed framework provides useful spatial information for regional forest fuel assessment and fire risk management. Full article

Weed competition, persistent seed banks, and management costs can limit crop productivity in organic farming. A two-year field experiment was conducted in southern Finland to evaluate the effects of pea (Lathyrus oleraceus Lam.), oats (Avena sativa L.), and camelina (Camelina sativa (L.) Crantz.), grown as pure stands and as three-crop mixtures at varying seeding densities, on weed diversity and suppression. The seeding densities (%) were 50:20:30 and 33:33:33 of the pure stand density of pea, oats, and camelina in 2022 and 50:50:50 and 33:33:33 of the pure stand density in 2023. Weed diversity was assessed at five sampling times, species were identified and analyzed for biomass, richness, Shannon-Wiener index (H), evenness, and dominance. Weed diversity and suppression varied with crop composition, growth stage, and seasonal conditions. In 2022, the 33% mix had the highest H (2.22) and evenness (0.77), enhancing weed suppression while controlling dominance. In 2023, pure oats had the highest H (1.65) and evenness (0.87), and pure peas had the lowest H (1.41) and evenness (0.67). Although pure oat stands provided the strongest weed suppression, crop mixtures enhanced species diversity and evenness, suggesting potential for more balanced weed management in organic systems, with short-term results indicating potential benefits for weed control. Full article

The southern pine beetle (SPB) is a serious pest of pine forests from Central America to the eastern United States, with a recent range expansion into the northeastern United States. Efforts to detect and monitor SPB activity began in 1960 as part of an overall integrated pest management system to limit its impact to southern pine forests. The ubiquity of SPB’s pine hosts in the southern United States, in the form of plantations and natural mixed stands, along with the regular occurrence of SPB outbreaks over a vast region, makes SPB a leading driver of overall forest health across this region. We review the past and current methodology for collecting SPB-related pine mortality and outbreak data using aerial and ground survey techniques and remote sensing via satellite imagery. We show how historical and ongoing measurements of SPB abundance, from pheromone-baited traps and aerial surveys, are used to forecast near-term probabilities of outbreaks with a statistical model (actualized through a public URL) that captures the natural tendency of SPB populations to be very high or very low. Insect forecasts can also be combined with maps of the host distributions to generate predictions of short-term regional risks and longer-term tree mortality forecasts via the US Forest Service’ National Insect and Disease Risk Map (NIDRM). Because the measurements of insect abundance and impact outcomes have become part of continuing forest management operations, statistical models can continue to be improved and there is self-reinforcing feedback between models and management. Improved understanding and monitoring of prominent insect pests that impact abundant tree species is a pathway to managing forest health more broadly. Full article

Selecting appropriate tree species is crucial for mitigating soil acidification and restoring biogeochemical cycles in subtropical acid rain regions. The objective of this study was to elucidate the influence of species selection on litter nutrient dynamics and its implications for soil carbon (C) and nitrogen (N) cycling. To achieve this, three forest types were examined at the Tieshanping Forest Farm (Chongqing, China). Twelve plots were established, including pure stands of Pinus massoniana Lamb. or Cinnamomum camphora (Linn) Presl, and mixed stands of these species. Litterfall was collected monthly (December 2020–November 2021) to determine pH, C, N, phosphorus, potassium, lignin, and cellulose contents, alongside potential nutrient returns and stoichiometric ratios. Results indicated that while total annual litterfall production did not differ significantly among the forest types, their seasonal dynamics varied distinctly, with C. camphora and P. massoniana peaking in spring and summer, respectively. Furthermore, C. camphora stands exhibited significantly higher annual P and K returns. Conversely, P. massoniana litter was characterized by the highest C:P ratio and mean annual lignin content (344.78 mg g⁻¹), indicating lower decomposability that may restrict organic C turnover and N release. Consequently, the nutrient-rich and readily decomposable litter of C. camphora is more effective than P. massoniana at alleviating soil acidification and facilitating healthier C and N cycling. These findings highlight the critical role of aboveground litter quality in driving belowground soil C and N dynamics, providing a vital scientific basis for species selection during ecological restoration in acid rain-affected areas. Full article

The expansion of Moso bamboo (Phyllostachys edulis) significantly alters the spatial configuration of surrounding trees, leading to dynamic shifts in the spatial distribution of coarse woody debris (CWD). Investigating the spatial patterns of CWD during bamboo expansion can reveal the dynamic mechanisms of forest communities in this process, thereby providing scientific insights for forest management and conservation. In this study, conducted within the Yangjifeng Biodiversity Monitoring Large Plot, all trees with a diameter at breast height (DBH) ≥ 1 cm within the plots were tagged and important variables were measured, including DBH, tree height, and spatial attributes. Coarse woody debris (CWD) with a DBH ≥ 10 cm was also measured, including DBH, diameters at both ends, length, and spatial attributes. Based on the importance values of tree species in each 20 m × 20 m plot—where an importance value comprehensively measured a species’ relative abundance, frequency, and dominance in the community—the sample plots were divided into three continuous sample transects: evergreen broad-leaved forest (EBF), bamboo–broadleaf mixed forest (BMF), and Phyllostachys edulis forest (PEF). Ripley’s ‘g’-function was employed to analyze the spatial patterns and associations of CWD across these three forest types. A random sampling approach was used to collect CWD samples for the measurement and calculation of carbon storage. Three key findings emerged. (1) With the expansion of Moso bamboo, the biomass and carbon storage of standing dead trees both decrease. The biomass is highest in EBF, followed by BMF and PEF. However, carbon storage is greatest in BMF and lowest in PEF. (2) With the expansion of Moso bamboo, the distribution of CWD became increasingly uniform in PEF; analysis of the overall spatial pattern of CWD indicated that with increasing spatial scale, CWD changed from an aggregated to a random distribution across all three forest types, with a pattern scale of approximately 10 m. In EBF, the CWD of Moso bamboo exhibited a random distribution at all spatial scales. Additionally, CWD across different diameter classes, decay stages, and types changed from aggregated to random distributions as the spatial scale increased, with a reduction in aggregation intensity correlated with larger diameter classes. (3) Finally, we found an almost exclusively negative spatial association between living trees and CWD across all scales, and this negative correlation may be attributed to the absence of new tree growth following tree mortality. Collectively, our findings demonstrate that during Moso bamboo expansion, the spatial distribution of CWD changes from aggregated to random, while maintaining a significantly negative spatial association with living trees. This reveals the dynamic changes in the spatial patterns of CWD during community development, thereby providing a scientific basis for the forest management of subtropical evergreen broadleaf forests and bamboo management and control. Full article

Soil nematodes are integral to soil micro-food webs and serve as sensitive bioindicators of soil ecological condition. However, how forest vegetation and soil properties interact to shape nematode community assembly, network structure, and functional stability remains inadequately understood. Using 18S rRNA gene amplicon sequencing coupled with phylogenetic null modeling, we examined soil nematode communities across four forest types along a succession gradient. Although taxonomic diversity (e.g., Shannon and Pielou indices) differed significantly among forest types, network topology and stochastic assembly processes were more closely associated with community restructuring and co-occurrence patterns. This suggests that, while diversity is not irrelevant, network- and assembly-based metrics provide complementary and often more sensitive indicators of nematode community responses to forest type. Co-occurrence network analysis revealed that mixed forests fostered more complex and potentially stable networks, whereas plantations formed dense but potentially vulnerable networks. Assembly processes were not dominated by strong deterministic selection (|βNTI| ≤ 2 for most comparisons), a pattern consistent with undominated processes (e.g., ecological drift, weak environmental filtering). Dispersal limitation played a negligible role in this system. Partial Least Square Path Modeling identified spatial factors and key soil properties (e.g., pH, electrical conductivity, soil water content, and organic carbon) as primary drivers of community structure. Our findings indicate that assessing soil food web health should integrate network analysis and stochasticity metrics rather than rely solely on taxonomic diversity. For sustainable forest management, mixed-species stands are preferable to monoculture plantations, and maintaining soil physicochemical heterogeneity is critical for community stability. 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

Understanding forest dynamics requires reliable inventories that assess tree- and stand-level characteristics. Traditionally, this has relied on field measurements such as diameter at breast height (DBH), height, and crown attributes, but these methods are labor-intensive and spatially limited. Remote sensing, particularly Light Detection and Ranging (LiDAR), has expanded forest inventory capacity by generating three-dimensional structural information. Mobile laser scanning (MLS), a recent adaptation, offers flexible, high-resolution data collection, though its performance across complex forests is still being evaluated. This study assessed the effectiveness of MLS in detecting individual trees and estimating DBH in mixed-species forests of the Northeastern United States. We also evaluated the influence of tree- and plot-level characteristics on detection accuracy and DBH estimation. Results showed an 85.2% tree detection rate, a 23.5% commission rate, and a DBH root mean square error (RMSE) of 1.98 cm (9.65%). Among the variables tested, tree DBH was the only significant predictor of detection probability; tree density and relative density had minimal effect. These findings demonstrate that MLS can achieve precise DBH estimation when trees are correctly identified, but false detections remain a limitation. Further methodological improvements are needed to enhance accuracy in structurally complex forests and advance MLS for operational forest monitoring. Full article

The fallow deer (Dama dama) population on the island of Rhodes, Greece, constitutes one of the oldest extant free-ranging populations of this species. Robust population estimates are crucial for effective conservation and management. This study aimed to estimate deer population density, using a combination of direct and indirect approaches and to evaluate their relative performance. Surveys were conducted during the rutting season (October 2022) across mixed habitats, shrublands, and forests using Daytime Distance Sampling (DDS) and Spotlight Distance Sampling (SDS). Indirect estimates were derived using the Faecal Standing Crop (FSC) method during the same period, and, in January 2023, a revisit allowed estimation through the Faecal Accumulation Rate (FAR) method. All approaches indicated that mixed habitats supported the highest densities (7.85–10 individuals/km²), whereas shrublands (2.24–2.64 individuals/km²) and forests (0.65–2.96 individuals/km²) showed lower densities. Distance sampling methods performed best in mixed habitats, while FSC yielded more accurate and precise estimates than FAR across all habitats according to the Relative Net Precision index. These findings highlight the value of integrating complementary density methods and support the combined application of SDS and FSC for long-term population monitoring and evidence-based management. Full article

Soil organic carbon (SOC) storage in forests is governed by complex interactions between site conditions and vegetation. This study quantifies SOC stocks across a gradient of Target Management Sets (TMS) in the Czech Republic (Central Europe) to evaluate the baseline storage capacity of distinct ecological sites and the modifying effects of dominant tree species, specifically Norway spruce and European beech. Utilizing large-scale spatial data, linear mixed-effects models, and piecewise structural equation modeling (pSEM), we analyzed SOC stratification across middle (≈400–600 m a.s.l.) and higher (≈600–800 m a.s.l.) elevational zones. The results indicate that while overall SOC stocks inherently increase with elevation due to climatic constraints, tree species dictate the vertical carbon distribution within the soil profile. Specifically, conifers (i.e., Norway spruce and Scots pine) accumulate SOC primarily in the organic layer, whereas broadleaves (mainly European beech and oak) translocate and stabilize carbon in deeper mineral horizons. The pSEM analysis revealed that beech functions as a ‘calcium pump’, increasing topsoil pH and driving calcium-mediated SOC stabilization in mineral soils. This mechanism is highly effective at middle elevations but partially overridden by abiotic limits at higher elevations. We conclude that inherent site conditions (TMS) determine total SOC capacity, whereas tree species management controls SOC stability. Although no significant differences were observed in total SOC stocks between conifers and broadleaves at the same sites (medians of total SOC ranged from approx. 5 to 16 kg·m⁻², depending on the site), converting purely coniferous stands into broadleaves represents an effective strategy for long-term mineral SOC stabilization, particularly in middle-elevation sites. Full article