Search Results (759)

Alpine forests are key regulators of soil biogeochemical cycles, yet the extent to which forest type constrains soil microbial diversity and soil quality in high-elevation regions remains insufficiently resolved. Here, we assessed how contrasting alpine forest types influence the taxonomic composition and diversity of soil microbial communities, identified the dominant environmental drivers, and evaluated soil quality along the southern slope of the Qilian Mountains. Six forest types were examined, including four monospecific stands (Picea crassifolia, QQ; Betula spp., HS; Juniperus przewalskii, YB; and Pinus tabuliformis, YS) and two mixed formations (mixed conifer–broadleaf, ZKHJ; and mixed broadleaved, KKHJ). Bacterial and fungal communities were characterized using Illumina high-throughput sequencing, while structural equation modeling (SEM) was used to identify primary drivers of diversity and principal component analysis (PCA) was applied to construct the minimum data set (MDS) for soil quality evaluation. Mixed forests consistently exhibited higher bacterial and fungal alpha diversity than pure stands. Environmental gradients were the strongest positive drivers of microbial diversity, whereas soil chemical properties and vegetation-related biotic factors exerted partially negative effects. Soil quality index (SQI) values ranked as follows: KKHJ (0.55) > ZKHJ (0.49) > YB (0.48) > HS (0.46) > YS (0.44) > QQ (0.43). The mixed broadleaved forest reached Grade IV (upper-intermediate level) soil quality, whereas the other forest types were classified as Grade III (intermediate). Mixed forests showed stronger capacities for organic matter accumulation and nutrient retention. These findings indicate that promoting mixed forest stands is critical for improving soil structure, nutrient retention, and microbial diversity in this alpine region. Accordingly, forest management should prioritize the development of mixed forests to enhance overall soil quality. Full article

Forest canopy near-infrared reflectance and mass-based canopy nitrogen concentration (canopy %N) have been shown to be positively correlated. While the mechanisms underpinning this relationship remain unresolved, the broad range of wavelengths involved points to structural properties that influence scattering and covary with %N. Despite this, efforts that have focused on commonly measured structural properties such as leaf area index (LAI) have failed to identify a causal mechanism. Here, we sought to understand how lidar-derived canopy traits related to additional properties of foliar arrangement and structural complexity modulate the effects of leaf spectra and leaf area index (LAI) on canopy reflectance. We developed a leaf layer spectra model to explore how canopy reflectance would change if complex foliage arrangements were removed, compressing the canopy into optically dense, uniform stacked layers while maintaining the same leaf area index. Model results showed that LAI-weighted leaf reflectance saturates at a leaf area index of approximately two for needleleaf species and four for broadleaf species. When upscaled to estimate plot-level canopy reflectance in the absence of structural complexity (NIRr_LAI), results showed a strong positive relationship with canopy %N (r² = 0.86), despite a negative relationship for individual leaves or “big-leaf” canopies with an LAI of one (NIRrL, r² = 0.78). This result implies that the relationship between canopy near-infrared reflectance and canopy %N results from the integrated effects of canopy complexity acting on differences in leaf-level optical properties. We introduced an index of relative reflectance (IRr) that shows that the relative contribution of structural complexity to canopy near-infrared reflectance (NIRr_C) is related to canopy %N (r² = 0.55), with a three-fold reduction from potential canopy near-infrared reflectance observed in stands with low %N compared to a two-fold reduction in stands with high %N. These findings support the hypothesis that the correlation between canopy %N and canopy reflectance is the result of interactions between leaf traits and canopy structural complexity. Full article

Afforestation is recognized as a highly effective strategy for enhancing ecosystem carbon sequestration. However, the changes and drivers of soil organic carbon (SOC) following afforestation are still debated due to climate differences. Clarifying these responses is critical for improving the effectiveness of afforestation-based carbon sequestration strategies. In this study, we analyzed nine 20-year-old afforestation sites (coniferous and broad-leaved) along a Chinese climatic gradient to quantify SOC and its fractional changes following farmland-to-forest conversion, and to identify the dominant factors controlling SOC sequestration across climatic gradients and forest types. The results showed that afforestation enhanced SOC (5.1%–210.5%, p < 0.05) in humid and semi-humid regions, but showed no significant effect in semi-arid regions, and it even reduced SOC in arid regions (−19%–−53.8%). Across all climatic zones, mineral-associated organic carbon was the dominant contributor to SOC accumulation throughout the entire soil profile (0–60 cm). Climatic-scale analyses based on the aridity index determined that root and litter C/N ratios were the primary drivers of SOC sequestration in coniferous forests, whereas in broad-leaved forests, they were more strongly controlled by soil physicochemical properties, particularly total nitrogen, bulk density, and soil water content. This study identified that SOC responses to afforestation are strongly mediated by climate and forest type, which is helpful for managers to take targeted measures to increase soil carbon sequestration in forest management. Full article

Plant phenology is a sensitive indicator of ecosystem responses to climate change, yet its dynamics and drivers in subtropical montane forests remain poorly understood. Based on the continuous phenological monitoring of 12 dominant tree species from 2008 to 2022 in a mid-subtropical evergreen broad-leaved forest on Ailao Mountains, China, this study analyzed phenological shifts and their climatic drivers. The results show that, (1) unlike the widely reported trends in northern mid-to-high latitudes, spring phenophases (budburst and leaf-out) did not exhibit significant advancing trends, while autumn phenophases (leaf coloration and fall) remained stable; (2) water availability played a dominant role in regulating spring phenology, with both budburst and leaf-out showing significant negative correlations with winter-spring precipitation, and responses varied significantly across hydrological year types; and (3) the life form strongly influenced phenological strategies, with evergreen species exhibiting earlier spring phenology than deciduous species. This study highlights that in seasonally humid subtropical montane forests, water availability exerts a stronger control on phenology than temperature. Our findings underscore the necessity of incorporating precipitation variability and functional trait differences into assessments of forest phenology and ecosystem functioning under future climate change, providing a scientific basis for the conservation and adaptive management of subtropical forests. Full article

Schima superba, belonging to the genus Schima of Theaceae, is a common large tree in evergreen broad-leaved forests in subtropical regions of China. As one of the largest transcription factor families in plants, MYB transcription factors play an important role in plant stress response by specifically binding to cis-acting elements in different gene promoter regions to accurately regulate gene expression. However, there are few studies on MYB transcription factors in S. superba. The MYB transcription factor family of S. superba was found and examined in this study using the genomic and transcriptome data of the S. superba. A set of 220 MYB transcription factors was identified from S. superba and classified into four subfamilies. These transcription factors were unevenly distributed on 18 chromosomes of S. superba. The conserved domains of the same subfamily members are highly similar to the conserved motifs. The collinearity analysis between species showed that there were few orthologous genes located on chromosome 18 of S. superba. Numerous elements linked to phytohormone response, stress adaptation, and growth control can be found in the promoter regions of the S. superba MYB transcription factor family, according to an analysis of the promoter cis-acting elements. Verification via qRT-PCR showed that among 15 SsMYBs genes tested, SsMYB24 expression peaked at 96 h of drought stress, followed by a rapid downregulation upon rewatering to initial levels. This expression pattern aligned with the transcriptome data. This study is helpful to further identify the function of SsMYB transcription factors and provide a new molecular mechanism for improving drought tolerance of S. superba. Full article

Global CO₂ concentrations are gradually increasing, and forests, as the main terrestrial carbon pool, are attracting growing attention in mitigating climate change. However, the impacts of forest types, species diversity, structural diversity, and environmental factors on the carbon sequestration mechanisms of subtropical forests remain unclear. This study established 45 forest plots (20 m × 20 m) in Lishui City, aiming to investigate the relationships between forest diversity, environmental factors, and carbon storage of subtropical forests among different forest types. Results showed that coniferous forests had the lowest species diversity (0.86), which exhibited extremely significant differences from broad-leaved forests (1.47, p < 0.01) and coniferous broad-leaved mixed forests (1.58, p < 0.01). The carbon storage of broad-leaved forests was 97.50 t·ha⁻¹, which was higher than that of coniferous broad-leaved mixed forests (77.08 t·ha⁻¹) and coniferous forests (75.57 t·ha⁻¹). The carbon storage of coniferous forests was significantly positively affected by species diversity (p < 0.05). Tree height was the most significant structural diversity factor affecting forest carbon storage (p < 0.05). The results of the structural equation model (SEM) showed that the proportion of broad-leaved trees in forests and structural diversity had a significant positive effect on carbon storage (p < 0.01). Species diversity had a non-linear relationship with carbon storage. The ecological niche complementarity effect and selection effect interacted with changes in species diversity. When the species diversity was lower than 1.12 (Shannon–Wiener index), the ecological niche complementarity effect dominated and promoted carbon sequestration; when it was above this threshold, the selection effect dominated and weakened carbon sequestration. This study recommends prioritizing the planting of broad-leaved tree species during afforestation and paying attention to the current status of forest diversity. Full article

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change. Full article

Northeast British Columbia (54–60° N latitude, 120–123° W longitude) has 10+ M ha of complex conifer–broadleaf forest, which is a unique forest type in the province. Current management practice is to remove competing broadleaf species to promote the growth of more commercially valued conifers. This approach ignores the species silvics and results in forest simplification, thus reducing species and structural diversity, habitat value, and overall stand resilience to future events such as climate change and wildfires. These practices also negatively impact traditional First Nation treaty rights. Three trials were established across the region in 5-to-18-year-old post-logging mixed species stands where broadleaves had not been removed. Competition-free radii of 0, 1, 2, and 4 m were established around white spruce (Picea glauca (Moench) Voss) crop trees. The objective was to investigate the impact of broadleaf (aspen Populus tremuloides Michx. or paper birch Betula papyrifera Marsh.) competition on crop tree growth with respect to the free-to-grow (FTG) standard. Except at extreme broadleaf densities (>10,000 SPH), crop tree DBH growth was not impacted when trials were established. After at least 11 growing seasons, except at the competition-free 4 m radius, DBH was not impacted by competition. Spruce DBH in the mixed stand at all radii was greater than the expected BC model projections for a pure spruce stand on these sites. Our findings suggest that the current FTG management approach in northeast BC only has a positive result if taken to an extreme. It has a low return on investment and reduces stand resilience and total productivity. An alternative forest management approach for the region is presented. Full article

Different approaches have been developed to assess the phenological dynamics of ecosystems. However, diverse data sources and extraction methods for assessing ecosystem phenology can result in discrepant and inaccurate results, especially across different types of vegetation under various climate classifications. Based on the phenology of dominant plant species (Phe_plant) obtained from ground monitoring in an alpine shrub meadow at Haibei Station (HBS) on the Qinghai–Tibetan Plateau and in a broad-leaved Korean pine forest at Changbai Mountain (CBF) in Northeastern China, we extracted vegetation phenology from the Normalized Difference Vegetation Index (Phe_NDVI) and photosynthetic phenology from gross primary productivity (Phe_GPP) using five common methods. These methods included Gaussian fitting, single logistic function fitting, double logistic function fitting, and smoothing techniques combined with fixed threshold and derivative-based determination approaches. There was no consistent interannual trend in either plant phenology or environmental factors at the two sites. Among the three types of plant phenology, a similar interannual pattern in the start of the growing season (SOS) was observed, whereas the interannual patterns for the end of the growing season (EOS) and the growing season length (GSL) were asynchronous. Compared to Phe_plant, both Phe_NDVI and Phe_GPP exhibited an earlier SOS, a delayed EOS, and consequently an extended GSL. The SOS derived from both Phe_NDVI and Phe_GPP was advanced by increasing spring temperatures at both sites, while the relationship between EOS and air temperature was relatively weak. The discrepancy between Phe_NDVI and Phe_GPP was more pronounced at CBF than at HBS, likely due to the complex vegetation composition and structure of the mixed forest. The different extraction methods produced more consistent and less variable estimates of SOS compared to EOS and GSL at both sites. Among the five methods, the dynamic threshold approach showed a relatively small difference between Phe_NDVI and Phe_GPP, suggesting that it could provide a more consistent estimate of plant phenology across the two sites. This study clearly reveals the inherent discrepancies associated with using different types of phenological data and the influence of extraction methods on phenology across different plant functional types. More attention should be given to improving the accuracy of EOS and understanding the influence of vegetation composition on phenological variation in future studies. Full article

Accurate assessment of stand structure is fundamental for elucidating the relationship between forest structure and ecological function, which is vital for enhancing forest quality and ecosystem services. This study, conducted in a 1 hm² plot of old-growth broadleaved-Korean pine forest in Changbai Mountain, integrated Terrestrial Laser Scanning (TLS), precise geographic coordinates, Quantitative Structure Models (QSM), and wood density data. This methodology enabled a precise, non-destructive quantification of key structural parameters—DBH, tree height, crown overlap, stand volume, and carbon storage—and the development of species-specific allometric equations. The results demonstrated that TLS-derived DBH estimates were 99% accurate, consistent across diameter classes. The overall crown overlap rate (DBH ≥ 5 cm) was 59.1%, decreasing markedly to 26.7% and 19.2% at DBH thresholds of 20 cm and 30 cm, respectively. Allometric models based on DBH showed higher predictive accuracy for stem biomass than for branches, and for broadleaved species over conifers. Notably, conventional models overestimated stem biomass while underestimating branch biomass by 1.34–92.85%, highlighting biases from limited large-tree samples. The integrated TLS-QSM approach provides a robust alternative for accurate biomass estimation, establishing a critical foundation for large-scale, non-destructive allometric modeling. Its broader applicability, however, necessitates further validation across diverse forest ecosystems. Full article

This study was carried out to investigate stand structure, growth dynamics, and carbon fluxes in Pinus rigida plantations of varying ages in South Korea. Field measurements across four mountain sites quantified diameter-class distributions, net primary productivity (NPP), soil respiration, and net ecosystem production (NEP). P. rigida exhibited normally distributed diameter structures in larger classes, whereas Quercus spp. showed reverse J-shaped patterns, indicating active regeneration and ongoing succession toward mixed broadleaved stands. Individual NPP was highest in P. densiflora (4.77 kg yr⁻¹) and P. rigida (4.31 kg yr⁻¹), while Quercus spp. displayed lower growth due to light limitation. Stand-level NPP peaked in 20–40-year-old stands (4.27–4.88 ton C ha⁻¹ yr⁻¹) and declined with age (2.30 ton C ha⁻¹ yr⁻¹). Soil respiration averaged 1.0 ton C ha⁻¹ yr⁻¹ and was strongly temperature dependent (R² = 0.56; Q₁₀ = 2.70). NEP on Mt. Galmi reached 4.38 ton C ha⁻¹ yr⁻¹, demonstrating substantial carbon sink capacity. These findings indicate that aging P. rigida plantations maintain ecosystem-level carbon uptake through successional compensation. Policy efforts should prioritize adaptive thinning, assisted natural regeneration, and long-term monitoring frameworks to accelerate the transition toward climate-resilient mixed forests and to strengthen national forest carbon neutrality strategies. Future research should integrate long-term carbon flux observations, species interaction modeling, and assessments of climate-driven disturbance regimes to refine management pathways for resilient mixed-forest landscapes. Full article

Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index (NDVI) is a key indicator for assessing the growth status of vegetation. However, the insufficiency of existing NDVI datasets in terms of spatiotemporal continuity has limited the accuracy of long-term vegetation change studies. This study proposed a machine learning-based downscaling framework that integrates the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI and the Global Inventory Monitoring and Modeling System (GIMMS) NDVI data to reconstruct a long-term, high-resolution NDVI dataset. Unlike conventional statistical fusion approaches, the proposed framework employs machine learning-based nonlinear relationships to generate long-term, high-resolution NDVI data. Three machine learning algorithms—Random Forest (RF), LightGBM, and CatBoost—were evaluated. Their performance was validated using the MODIS NDVI as reference, with the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (R) as evaluation metrics. Based on model comparison, the CatBoost model was identified as the optimal algorithm for spatiotemporal data fusion (R² = 0.9014, RMSE = 0.0674, MAE = 0.0445), significantly outperforming RF and LightGBM models and demonstrating stronger capability for NDVI spatiotemporal reconstruction. Using this model, a long-term, 1 km monthly GIMMS-MODIS NDVI dataset from 1982 to 2014 was successfully reconstructed. On the basis of this dataset, the spatiotemporal variation characteristics of vegetation in the TRSR from 1982 to 2014 were systematically analyzed. The research results show that: (1) The constructed long-series high-resolution NDVI dataset has a high consistency with MODIS NDVI data; (2) From 1982 to 2014, the NDVI in the TRSR showed an increasing trend, with an average growth rate of 0.0020/10a (p < 0.05). NDVI showed obvious spatial heterogeneity, characterized by a decreasing gradient from southeast to northwest. (3) The Yellow River source exhibited the most evident vegetation recovery, the Yangtze River Source area showed a moderate improvement, whereas the Lancang River Source area displayed little noticeable change. (4) Broad-leaved forests experienced the most significant growth, while cultivated vegetation displayed a marked tendency toward degradation. This study provides both a high-accuracy long-term NDVI product for the TRSR and a methodological foundation for advancing vegetation dynamics research in other high-altitude regions. Full article

Subtropical deciduous broad-leaved forests in eastern China form a key ecotone between temperate and subtropical biomes, yet their vegetation–environment relationships remain insufficiently understood. This study examined community structure, species diversity, and their associations with topographic and soil variables in a 25 ha forest dynamics plot in the Lushan Mountains. All woody plants with a diameter at breast height (DBH) ≥ 1 cm were surveyed, and detailed topographic attributes and soil physicochemical properties were measured. Community structure showed strong linkages with species diversity: tree-layer structural characteristics were generally negatively correlated with diversity, whereas in the shrub layer, density was negatively but height and DBH were positively correlated with diversity. Species diversity in the two layers was positively associated, while tree-layer structure was negatively related to shrub-layer diversity. Among topographic factors, altitude and the topographic solar radiation aspect index (TRASP) exerted the strongest influences on soil properties, with altitude negatively correlated with soil pH and available nutrients but positively correlated with C:N, C:P, and total carbon, and TRASP showing negative correlations with most nutrients except total phosphorus. Redundancy analysis revealed that topographic heterogeneity and soil conditions jointly shaped community structure and species diversity, with soil C:N ratio, altitude, pH, total phosphorus, and total carbon emerging as key drivers. These findings demonstrate that areas with high plant diversity do not always correspond to high soil nutrient content and underscore the importance of integrating both topographic and edaphic factors into biodiversity conservation and forest management in subtropical deciduous broad-leaved forests. Full article

The expansion ratio of bamboo forests has significantly influenced soil nutrient cycling in broad-leaved forests through alterations in vegetation composition. However, the extent to which varying expansion ratios of bamboo within broad-leaved forest areas (MRB) affect soil enzyme activities and stoichiometric ratio in karst regions with yellow limestone soil remains poorly understood. Therefore, we examined five MRB levels (<20%, 20–40%, 40–60%, 60–80%, and >80%) and three soil depths (0–20 cm, 20–40 cm, and 40–60 cm) to analyze variations in soil microbial resource limitation within a field experiment conducted in a karst region. The results revealed showed that root morphology was significantly less developed at both low (<20%) and high (≥80%) expansion ratios than at moderate expansion ratios (40–60%) and exhibited a decreasing trend with increasing soil depth. The soil TC (46.43 g·kg⁻¹), SOC (80.05 g·kg⁻¹) and N:P (0.96) reached their highest values in the 40–60% MRB, whereas the pH (4.61), BD (0.42 g·cm⁻³), AN (20.28 g·kg⁻¹) and C:N (45.51) were lowest at 0–20 cm. Moreover, the CBH and the E_C:N ratio at 40–60 cm depth in the 0–20% MRB were significantly 2.64 and 1.31 times greater than those at 0–20 cm depth in the 40–60% MRB. Mantel and structural equation modeling (SEM) analyses revealed that soil enzyme activity and stoichiometric ratios are indirectly influenced by soil bulk density (β = −0.156) and root characteristics (β = −0.630). Overall, both C and P limitations are present at the lowest MRB (<20%), whereas other MRB ranges exhibit only P limitation without C limitation. Our results highlight that soil nutrient availability in karst regions of Southwest China is influenced by vegetation structure. These findings provide a scientific foundation for achieving the green and sustainable management of bamboo forests within broad-leaved forest ecosystems. Full article

Understory vegetation is a critical component of forest ecosystems. Its removal can substantially alter litter decomposition processes, with cascading effects on carbon (C) and nutrient cycling in terrestrial ecosystems. However, the global response patterns of litter decomposition to understory removal and underlying controlling factors remain unclear. We conducted a meta-analysis of 330 observations from 29 peer-reviewed field litterbag studies to assess the effects of understory removal on litter decomposition. We evaluated the changes in decomposition rate, mass loss, and nutrient dynamics to quantify the impacts of understory removal on litter decomposition. We assessed the associated shifts in soil microbial communities, measured using phospholipid fatty acids (PLFAs), to examine how microbial responses mediate decomposition during understory removal. We examined whether canopy type moderated these responses and explored the key predictors of decomposition for understory removal. Understory removal significantly reduced litter decomposition rate and mass loss by an average of 29.6% and 14.8%, respectively, while increasing lignin remaining by 30.1%. Soil microbial biomass also declined, with total, fungal, and actinomycete PLFAs decreasing by 12.0%, 30.8%, and 27.5%, respectively. Across canopy types, understory removal decreased litter mass loss in both broadleaved and coniferous forests. However, the remaining N and P increased significantly in broadleaved forests but changed only marginally in coniferous forests. Random forest analysis showed that initial litter quality and variations in fungal biomass were the primary predictors of decomposition responses. Understory vegetation removal significantly suppresses litter decomposition by reducing fungal biomass, and interacting with litter quality constraints and canopy type strongly moderates these effects. This highlights the essential role of understory vegetation in sustaining nutrient cycling and microbial functioning in forest ecosystems and underscores its critical role in guiding sustainable forest management. Full article