The establishment of plantations has become a critical approach for reducing greenhouse gas emissions, particularly in fragile environments with carbon sequestration potential. In karst areas, plantations based on fast-growing afforestation species made significant contributions to enhancing carbon sequestration. However, the impact of understory vegetation on carbon accumulation remains unclear. Especially, the carbon accumulation associated with litter produced during the replacement of understory species receives insufficient attention, which leads to the neglect of the carbon sequestration potential in plantations of karst areas. Leaf is a crucial organ that links the litter production. To explore how leaf traits adapt to competitive environments and drive litter carbon accumulation during understory species replacement, this study observed leaf traits and litter carbon content changes in three types of plantations in the Liujiang River Basin, a typical karst area. A total of 37 sampling plots were selected for field investigation over a two-year period. Leaf traits, species diversity, vegetation coverage, and litter carbon characteristics in understory vegetation were measured. Variance analysis, allometric equations, and path analysis were used for data analysis. The results showed that most understory species adopted a biomass conservation strategy under high-coverage conditions (> 44.27%) and expanded competitive leaf area under low-coverage conditions (< 44.27%). However, Bidens pilosa and Miscanthus floridulus exhibited strong competitiveness during understory species replacement. They showed an expansion of competitive leaf area under high-coverage conditions. This competitive strategy reduced species diversity and community specific leaf area. But the rapid expansion of Bidens pilosa and Miscanthus floridulus increased understory vegetation coverage, and their increased specific leaf area facilitated leaf shedding, resulting in significant litter weight accumulation (P < 0.05), thereby enhancing litter carbon content per unit area. These competitive strategies were key driving factors for the increase in litter carbon content per square meter, which reached a maximum of 49.6% higher than that in natural grasslands. And the maximum increase in litter carbon accumulation derived from understory vegetation reached 3.37 times from 2023 to 2024 in plantations. In the understory vegetation of plantations, the competitive strategies reflected by leaf adaptation of key competitive species are critical factors influencing litter carbon accumulation. Future research could deeply explore the carbon sequestration effects resulting from the dynamic changes in competition within the understory vegetation of plantations.

The forestry landscape is being climatically redefined due to global warming. Limited knowledge is available on whether introduced pine species will be viable for plantation forestry in South Africa. Existing global circulation models were scaled down to a finer resolution by incorporating historical climate data, global positioning, and terrain conditions (terrain scaling). Terrain scaling of mean annual maximum temperature (MAT-max), minimum temperature (MAT-min), and median annual precipitation rainfall (MAP-median) was statistically significant, achieving R² values of 0.70, 0.78 and 0.90, respectively. Decadal climate change was analyzed for the period ranging from 2020 to 2060. Future decadal temperatures were found to increase and were generally greater in high-altitude regions compared to low-altitude regions. MAT-max increased by up to 1.7 °C and MAT-min by 0.4 °C by 2060. MAP-median decreased by up to 10% by 2060, with high-rainfall areas in low-altitude regions being more greatly impacted. Climate suitability was determined for Pinus elliottii, P. taeda, P. patula and the hybrid P. patula × P. tecunumanii by harnessing existing species-specific climate threshold models for the region. Current and future conditions were found to be most suitable for P. patula × P. tecunumanii plantations. Isolated climate niches with warmer, drier conditions were best suited for P. patula plantations, while warm, humid conditions favoured P. elliottii plantations. None of the current and future climatic conditions were suitable for P. taeda plantations. A similar approach can be applied to forestry regions globally to enable pre-emptive tree breeding and the introduction of new forest species due to climate change. Similar content being viewed by others

Mental health issues among college students have become a significant challenge in higher education, with the pervasive stigma surrounding mental illness significantly undermining the effectiveness of traditional psychological interventions. Forest therapy, as an innovative approach combining nonpharmacological treatment with contactless intervention, effectively circumvents the interference of stigma associated with illness, demonstrating potential in alleviating psychological stress and promoting physical and mental well-being. This study developed a standardized forest therapy intervention protocol and employed a controlled experimental design. It introduced fractional exhaled nitric oxide (FeNO) as a stress-related biomarker to systematically analyze its stress-relieving mechanisms. Additionally, physiological indicators including peripheral oxygen saturation (SpO₂), heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were selected, while psychological indicators were validated using the Pittsburgh Sleep Quality Index (PSQI) scale and the tension (TEN), depression (DEP), and fatigue (FAT) dimensions from the Profile of Mood States (POMS) scale (The terms TEN, DEP, and FAT appearing in the following text are all indicators for assessing stress-related negative emotions). Experimental results reveal: (1) DBP significantly improved in all experimental groups, with heart rate trends inversely correlated between low-to-moderate stress and high-stress groups. Stress and depression scores decreased across all groups, with particularly marked improvements in the high-stress group. (2) The effects of forest therapy exhibit stress-level dependence: High-stress individuals achieve physical and mental improvements by suppressing hypothalamic–pituitary–adrenal (HPA) axis hyperactivity and synergizing attention restoration with stress reduction mechanisms; low-to-moderate stress individuals benefit more from cognitive restructuring and parasympathetic optimization. The high-stress group demonstrated a unique response pattern of “depression alleviation+elevated FeNO” suggesting a potential mechanism involving suppression of HPA axis hyperactivity and restoration of nitric oxide (NO) synthesis. This indicates a dual mechanism during stress relief involving both reversal of pathological inhibition and restoration of physiological function. This validates that forest therapy, while improving psychological states, can positively influence bodily functions through specific physiological mechanisms.

Assessment of soil organic carbon (SOC) dynamics is often inadequately represented in empirical measurements because of the significant heterogeneity in soil structure and physico-chemical properties. Partitioning soil carbon (C) emissions into autotrophic and heterotrophic respiration is essential for understanding CO₂ flux sources, but inconsistencies in their magnitude and responses reveal a knowledge gap in partitioning methodologies and their impact on respiration estimates. Utilizing data from an eight-yr field mesocosm study in a temperate oak forest, we computed C emissions from multiple components based on the metabolic theory. Our theoretical calculations of soil C emissions from various treatments were validated against periodic field measurements of soil respiration over an eight-year period. The optimized computations, which included annual precipitation data and accounted for biomass C from litter, roots, and microbes, closely aligned with field measurements of soil respiration across varying treatments. These results showed that fine root and microbial biomass jointly drove temporal variations in soil C emissions, while interannual precipitation variability plays a secondary role. This study confirms the feasibility of using metabolic theory to quantify soil C emissions and highlights the critical role of fine roots and soil microbial biomass, emphasizing the need for a deeper understanding of these factors in SOC budget assessments.

Forest fragmentation is a key ecological process influencing the functions of forest ecosystems, particularly in the context of rapid urbanization. However, at the national scale, the regional changes of forest fragmentation and its effects on forest carbon sequestration capacity (CSC) remain unclear in urban agglomerations. Based on the established Forest Fragmentation Index (FFI), this study assessed the regional heterogeneity of forest fragmentation and systematically analyzed the nonlinear response of CSC to FFI, using a piecewise linear regression model, an XGBoost-SHAP framework, and PLS-SEM. We found that the average FFI across all urban agglomerations was 0.45, with 54.96% of the area exhibiting moderate fragmentation (FFI = 0.4 − 0.6). The average FFI in urban agglomerations was highest in subtropical monsoon climate (SMC) zones and lowest in temperate continental climate (TCC) zones. CSC showed a distinct spatial pattern of “stronger in low latitudes and coastal (eastern) regions, weaker in high latitudes and inland (western) regions”. Nationally, 34.4% of the regions exhibited CSC levels ranging from 400 to 600 g·m⁻²·a⁻¹, with the highest mean CSC in SMC and the lowest in TCC. We identified clear FFI thresholds affecting CSC across different climate zones: 0.48 in TCC, 0.39 in temperate monsoon climate (TMC), and 0.36 in SMC. While low levels of fragmentation may have marginal positive effects, high fragmentation significantly threatens CSC. Moreover, in the TCC zone, temperature was the dominant driver, with FFI enhancing CSC primarily through positive pathways mediated by temperature and leaf area index (LAI). In contrast, in the TMC and SMC zones, evapotranspiration (ET) was the dominant factor, and FFI suppressed CSC by reducing LAI and ET. This study reveals the complex mechanisms by which forest fragmentation, coupled with multiple factors, drives CSC, providing scientific insights for urban forest management and carbon neutrality policies.

Mangroves, seagrass beds, and salt marshes represent key Blue Carbon Ecosystems (BCEs) that serve as vital carbon sinks, playing a crucial role in climate change mitigation. However, accurately quantifying blue carbon sequestration in these ecosystems remains challenging due to diverse environmental conditions, inconsistent methodologies, and substantial uncertainties. With the increasing urgency of global climate targets, reliable accounting methods are important for shaping policies and integrating blue carbon into carbon markets. In light of current needs, this review examined a range of carbon accounting methods, including isotopic methods, Unmanned Aerial Vehicles (UAVs), Remote Sensing (RS), modeling approaches (e.g., DeNitrification–DeComposition model (DNDC) and climate models), direct measurements (e.g., biomass sampling and eddy covariance), and Machine Learning (ML). Each method offers distinct advantages but also exhibits significant limitations, particularly in terms of cost, scalability, and spatial resolution. Moreover, the variability in carbon burial rates, methane (CH₄) and Nitrous Oxide (N₂O) emissions, and methodological assumptions were the sources of the greatest uncertainty. Although regional initiatives—such as Verra, Japan’s BlueCredit, Australia’s Blue Carbon Accounting Model (BlueCAM), and China’s Ministry of Natural Resources (MNR)—have implemented standardized procedures, a globally consistent framework is still lacking. Current blue carbon accounting methods face considerable uncertainties, mainly due to variations in environmental conditions, measurement techniques and Greenhouse Gas emissions (GHG), which limit their effectiveness in climate mitigation strategies and carbon credit markets. Therefore, future efforts should focus on integrating advanced technologies like RS, ML, and microsubs to harmonize global protocols, and improving ecosystem-specific data. Addressing these methodological gaps and strengthening monitoring frameworks will be pivotal for scaling up the role of BCEs in climate policy and carbon finance.

Remote sensing technology has become increasingly effective for forest mapping but its operational use in forest management and planning is still in its infancy. One of the most critical concerns is that remotely sensed forest attributes are not compatible with those traditionally defined in forestry practice. Tree species composition as a fundamental forest attribute is referred to by per-species tree volume or basal area proportion in conventional forestry but is quantified as tree counts or canopy cover percentage in remote sensing. These differences in the definition of tree species composition imply a barrier for effectively applying remote sensing in forestry decision-making. This study developed a remote sensing framework to derive tree species composition in a mixed-species, complex forest landscape based on tree attributes obtained by integrating UAV LiDAR and hyperspectral data. We classified 11 tree species with machine learning and obtained F-score values of 0.43–0.95. By incorporating tree species into tree diameter at breast height (DBH) prediction models, DBH was estimated with accuracy much higher than a general model of all tree species. The magnitude of increase in DBH-estimation accuracy was proportional to tree species-classification accuracy. Consequently, species composition coefficient estimation error was largely below 20% in the plots where forest type classification accuracy exceeded 90%. The error propagation from tree crown detection to DBH modeling cannot be overlooked for the integrated use of UAV LiDAR and hyperspectral data toward automatic, model-imbedded forestry-oriented surveys.

The soil desiccation tends to occur after afforestation in arid and semi-arid areas, exacerbated by increasing precipitation uncertainty under climate change. To clarify the water consumption patterns and drought response mechanisms of key silvicultural species, a manipulated drought experiment (50% throughfall exclusion) was conducted in Robinia pseudoacacia plantation on the Loess Plateau of China in 2021–2022. The stem sap flow, soil moisture, surface runoff and meteorological variables were monitored to investigate the drought responses of sap flux and transpiration. The sap flow under drought peaked 1 − h earlier diurnally and one month earlier seasonally than in the control after 1 − a drought treatment, as showed heightened sensitivity to increasing air temperature. Furthermore, the daily stand transpiration (T) of drought exhibited stronger dependence on soil moisture, relative humidity and vapour pressure deficit than in control. In this study, T calculated by the original Granier equation and by the revised method (accounting for inactive xylem length) was still underestimated by 65% and 41%, respectively, compared to the water balance method. After applying a calibrated correction coefficient (× 2.875) to the original Grainer equation, the mean T of R. pseudoacacia plantation was 209.60 mm for control and 144.15 mm for drought treatment during May to August 2021–2022. Nevertheless. both treatments extracted more than 70% of soil water from the 100–200 cm soil layer, suggesting an intensification of deep soil desiccation, particularly under drought stress. This study contributes to further understanding the water consumption characteristics of plantations to drought on the Loess Plateau, providing a scientific basis for sustainable forest management in semi-arid area.

Forest therapy has emerged as a promising intervention for chronic health conditions, yet the underlying mechanisms that govern its efficacy remain poorly understood. The study’s objective was to explore the therapeutic potential for four chronic diseases- hypertension, diabetes, chronic obstructive pulmonary disease (COPD) and subhealth through the interaction of environmental and structural factors of three subtropical forest types of deciduous broadleaf, evergreen broadleaf, and mixed coniferous-broadleaf forests in Zhejiang Province, China. The forest environmental factors we have selected include the biogenic volatile organic compounds (BVOCs), illumination, temperature, humidity, wind speed, CO₂, ozone (O₃), PM2.5, PM10, and negative air ions (NAI) in forest stands. The forest structural factors we have selected include diameter at breast height, tree height, clear bole height, canopy density, leaf area index, stand density, altitude, aggregation index, competition index, and mingling index. Physiological and psychological indicators of four chronic diseases groups were used as dependent variables. The methods of random forest analysis and factor importance ranking were employed to identify the predominant drivers of forest therapy efficacy. The key findings indicate that beneficial BVOCs promoted therapeutic effects across all four patient groups, with the antihypertensive effect is significant in the hypertension group (P ≤ 0.01). Environmental factors, especially humidity and O₃, had negative impacts on therapeutic outcomes across all four groups. In contrast, illumination and NAI had positive therapeutic effects on three groups but not the subhealth group Structural factors, especially stand density and altitude were key drivers of treatment effectiveness. Forest type was also crucial, with deciduous broadleaf forests yielding the best outcomes for four chronic conditions. The three forest types all exhibited significant therapeutic effects on emotional scores (P ≤ 0.001). In addition, deciduous broadleaf forest significantly reduced systolic blood pressure (P ≤ 0.001), while evergreen broadleaf forest significantly reduced systolic and diastolic blood pressure (P ≤ 0.001). This study provides critical insights into the interactions between the forest environment, structures, and therapeutic effects, offering a foundation for optimizing forest therapy strategies and forest management practices to improve public health. The findings have implications for personalized therapeutic interventions and sustainable forest management in subtropical regions.

Interactions between land-use and environmental drivers of soil quality in fragile ecosystems remain insufficiently quantified. The impact of land-use types and environmental factors on soil quality in the Loess Plateau was assessed here using a minimum data-set-based soil quality index. Data were collected from 99 plots across mixed forests, forests, shrublands, and grasslands. Principal component analysis was applied to construct the minimum data set with bulk density, capillary water-holding capacity, soil organic carbon, natural moisture content, and total phosphorus as core evaluation indicators. Using an obstacle factor diagnostic model and partial least squares path modeling, we identified soil moisture as the primary limiting factor. Grassland soil quality was lowest in Wangwazi (0.42), Changcheng (0.47), and Shenmu (0.21), but shrubland and grassland soil quality was highest in Hengshan (0.30, 0.27) and Yuyang (0.42, 0.53) (P < 0.05). These results provide guidance for sustainable land management in ecologically fragile regions.

Tropical forests, renowned for their exceptional biodiversity, often thrive despite inherently low soil phosphorus (P) availability. However, a comprehensive synthesis of the mechanisms that facilitate the coexistence of diverse species, and how these mechanisms respond to P addition, remains poorly understood. This review consolidates research findings on how tropical forest biodiversity is sustained under low P conditions, how P addition influences the overall biodiversity system, identifies research gaps, and suggests future directions. The relationship between P and biodiversity is complex: while P-limited forests support high diversity, P addition may lead to species disappearance, raising the question of why some forests that maintain high species diversity under P limitation continue to do so, while others experience a decline in diversity following P addition. Despite P limitation, forests can support high species diversity through adaptive strategies such as resource partitioning and P-use efficiency, which enable diverse communities to flourish. In low-P environments, species conserve P through resorption from older tissues and allocation to leaves, promoting photosynthesis and growth. These species exhibit lower specific leaf area and higher leaf dry matter content. While functional diversity is constrained, species diversity remains high as species adopt similar strategies. Specialized root traits, including finer roots and mycorrhizal symbioses, facilitate P uptake in low-P soils. However, P addition may lead to competitive exclusion, with species adapted to P-rich conditions outcompeting low-P specialists. Some species may dominate early successional stages by rapidly utilizing available P, suppressing other species, and reducing biodiversity over time. Anthropogenic P additions, such as agricultural fertilization and erosion, can intensify this effect, further decreasing species diversity and altering community composition, including fauna and microbial components of the forest. Due to the complexity and variability of tropical environments, critical knowledge gaps remain in understanding how diverse forest components, soil organisms, and environmental conditions interact with P addition, particularly at local and regional scales. Long-term studies, especially in less accessible or underfunded tropical regions, are essential to improve understanding of species interactions, resource partitioning, and biodiversity functioning under P-limitation.

Urban green spaces have positive effects on both physical and mental wellbeing. However, few studies have focused on the trends and thresholds of the effects of different influences on restorative benefits when viewing scenes featuring plant communities. We measured subjective evaluations and objective physiological data from 44 participants who viewed images of plant communities in the yellow to green hue range to compare differences in restorative benefits among plant communities at different visual distances, as well as quantifying the influencing factors involved. The following results were found: (1) Coniferous and multi-layered plant communities were found to provide greater restorative benefits, and the restorative benefits grew with increasing visual distance. (2) Shape and color characteristics were significantly correlated with restorative benefits, but the relationship is not simply linear. (3) The restorative benefits were found to be greatest when crown proportion was 61.23%, trunk proportion ranged from 4.11 to 13.70%, and the value of color index value ranged from 25.44 to 35.56%; the restorative benefits gradually increased when sky proportion exceeded 12.95–13.19%, the fractal dimension exceeded 1.62–1.67, and hue index exceeded 91.64°–95.67°; additionally, the restorative benefits decreased when the saturation index increased. This study provides a scientific basis for the construction and improvement of plant landscapes in urban green spaces.

Drought stress severely impairs plant growth and development, and lignin plays a critical role in plant resistance. In this study, we investigated the physiological and molecular mechanisms underlying drought adaptation regulated by BpSPL2 in birch (Betula platyphylla Suk), using transgenic overexpressing lines (OE3 and OE5) and SRDX repressor lines suppressed BpSPL2 expression (S2 and S4). The results showed that, compared with the wild type, the drought tolerance of two-year-old OE3 and OE5 birch lines was significantly enhanced under natural drought conditions, with reduced accumulation of malondialdehyde (MDA) and H₂O₂ content, increased proline content, and elevated antioxidant enzyme activities. In contrast, S2 and S4 lines exhibited greater drought sensitivity. RNA-seq analysis revealed that, under both normal and drought conditions, differentially expressed genes (DEGs) between genotypes were consistently enriched in pathways related to phenylpropanoid biosynthesis, cell wall formation, and lignin synthesis. Overexpression of BpSPL2 significantly increased the expression of lignin synthesis-related genes in birch and promoted lignin accumulation, indicating that BpSPL2 enhances birch drought tolerance by promoting lignin synthesis. To uncover potential molecular mechanisms, we performed a multi-omics correlation analysis integrating transcriptome and DAP-seq data, identifying 12 DEGs in the phenylpropanoid biosynthesis pathway as potential target genes of BpSPL2. DAP-seq results revealed that both the BpHCT25 and BpCSE5 promoter regions contain conserved BpSPL2 binding motifs. RT-qPCR analysis demonstrated that BpHCT25 and BpCSE5 expression levels were significantly elevated in the OE5 line, whereas they were downregulated in the S4 line. Further validation using biochemical approaches such as yeast one hybrid (Y1H) and dual luciferase reporter (DLR) assays confirmed that BpSPL2 binds to the promoters of its downstream genes BpHCT25 and BpCSE5, thereby promoting their expression. In summary, we report a novel molecular mechanism in birch where BpSPL2 mediates lignin biosynthesis to regulate drought resistance. BpSPL2 serves as a potential target for drought-tolerant breeding in birch, and these findings may hold significant application value for developing molecular breeding strategies for drought-resistant forest trees.

Low-carbon synergistic governance in the forestry industry constitutes a critical component of agricultural engineering. It holds substantial practical significance for China in addressing the strategic challenges posed by the “dual-carbon” goals and fostering high-quality regional economic development. From the perspective of the digital economy and drawing on Germany’s experience, this paper investigates the logical framework, practical challenges, and implementation mechanisms through which the digital economy can synergistically enhance low-carbon development in forestry. The findings reveal that the upstream enabling pathway is realized via a forest information monitoring mechanism, a resource analysis and utilization management mechanism, and an ecological restoration management mechanism, all of which are underpinned by the operation of intelligent equipment and natural resource regeneration. The midstream pathway primarily focuses on a raw material management mechanism for forestry inputs during processing and a supply-chain synergy mechanism that facilitates cross-sectoral coordination. The downstream pathway centers on a forest product marketing mechanism and a consumer-oriented service management mechanism. In-depth analysis indicates that, under current conditions, the digital economy’s enablement of forestry for low-carbon objectives still faces governance challenges. These include data security risks for target entities, “market failure” in the digital transformation of enabling actors, and insufficient regulatory policies within the enabling framework, necessitating further targeted policy recommendations.

Deadwood is essential for maintaining forest ecosystem health and biodiversity. This study investigates and compares the abundance and characteristics of deadwood in Eastern spruce (Picea orientalis Link. At Carr) stands located within the Velikoy Forest Management Unit (FMU) and Karagol-Sahara National Park (NP) in northeastern Turkey. A total of 476 randomly selected sampling plots were assessed —236 in Velikoy FMU and 240 in Karagol-Sahara NP—based on stand type, canopy cover, and age class. Deadwood was classified into standing deadwood, stumps, and downed deadwood, with volumes measured for each category. To model deadwood volume, we applied both fixed and mixed non-linear models alongside a Generalized Linear Mixed Model (GLMM), incorporating stand parameters, physiographic variables, and geographical factors. Model performance was evaluated using adjusted R² (R²_{_adj}), root mean square error (RMSE), Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results showed that in Velikoy FMU, the composition of deadwood volume was 29.5% standing, 37.8% stumps, and 32.7% downed. In contrast, Karagol-Sahara NP exhibited 47.8% standing, 22.7% stumps, and 29.4% downed deadwood. Statistical analyses revealed significant positive correlations between total deadwood volume and factors such as basal area, stand density, stand volume, tree count, site index, stand age, and distance to settlements. However, physiographic variables like slope and aspect showed no clear association. The fixed-effects model yielded R²_{_adj} = 0.714, RMSE = 10.376 m³ ha⁻¹, AIC = 1584.9, and BIC = 1590.4. The mixed-effects model improved performance significantly with R2_adj = 0.787, RMSE = 5.815 m³ ha⁻¹, AIC = 1262.2, and BIC = 1267.4. The GLMM identified basal area, stand density, and distance to settlements as the most influential predictors of deadwood volume. The GLMM model further reduced -2LogL, AIC, and BIC to 737.7, 741.7, and 749.5, respectively, underscoring the effectiveness of incorporating random effects in modeling. These findings emphasize the value of deadwood in forest management and its relationship with structural stand attributes. The integrated modeling framework offers robust tools for informing biodiversity-oriented forest management and conservation planning. Enhancing basal area and stand density, particularly in areas distant from human settlements, can foster deadwood accumulation and support biodiversity.

Forests play a critical role in global carbon sequestration, however the mechanisms linking biodiversity to carbon sinks across environmental gradients remain poorly understood. Using 735 permanent plots across subtropical China’s Zhejiang and Fujian provinces, we investigated how elevation mediates biodiversity-carbon relationships (BCRs) in natural forests compared to plantations. Our results show that natural forests maintained 16% higher carbon sequestration and had 23% lower mortality than plantations, with peak productivity at mid-elevations (400–800 m). Community-weighted specific leaf area (CWM_SLA) and tree size inequality (Gini coefficient) explained 43.6% of the carbon sink variation, while Shannon diversity showed negligible effects (P > 0.05). Structural equation modeling revealed that initial carbon stocks mediated BCRs, particularly in natural forests, with plantations showing significant carbon-mortality trade-offs at low and mid- elevations. Significant BCRs were only at low elevations, where CWM_SLA and Gini coefficients negatively affected carbon sinks, providing no support for consistently positive BCRs across elevation zones. To optimize forest carbon sequestration, we suggest species selection based on complementary functional traits, increasing the complexity of stand structure in medium and high elevation areas, and planting stress-resistant genotypes at low elevations to reduce mortality. This study provides insight for optimizing carbon-biodiversity co-benefits in subtropical forest restoration.

Global climate change is impacting organisms and ecosystems on a wide scale, with increasingly visible effects. This ongoing process is anticipated to significantly threaten species and populations, especially plants that lack mobility, potentially causing large-scale losses in the near future. To mitigate these impacts, it is essential to understand how long-lived forest trees will respond to climate shifts and to facilitate necessary migration mechanisms through human intervention. This study aims to model the suitable habitat distribution of Scots pine (Pinus sylvestris), a crucial forest tree species in Türkiye, under two climate scenarios (SSP245 and SSP585) for the present and future years (2040, 2060, 2080, and 2100) using the Maxent entropy model, with mapping support from ArcGIS software. Habitat suitability was analyzed with 21 parameters (19 bioclimatic and 2 topographic). Jackknife test results indicated that Mean Temperature of the Driest Quarter (Bio9) and Mean Temperature of the Warmest Quarter (Bio10) were the most influential parameters on the species’ distribution. The findings showed that under the SSP245 scenario, the suitable habitat for Scots pine is projected to decline to 83.63% of its current range by 2060, then increase to 106.02% by 2100. For the SSP585 scenario, the area is projected to decrease to 81.89% by 2060 and reach 96.13% by 2100. Populations in Türkiye’s southern and Marmara regions face high risks of near-total loss. To sustain Scots pine in new suitable habitats, adjustments to current forest management plans and silvicultural practices are needed to align with climate change projections.

Fine-scale classification data of forests and grasslands are essential for ecological protection, natural resource management, and environmental monitoring. In extensive mountainous regions, strong spectral mixing between forests and grasslands complicates the accurate delineation of their boundaries, while the lack of effective characteristics limits the refinement of forest type classification. This study proposes a fine classification method utilizing Landsat TM/ETM + /OLI data (1991–2021) to construct long-term temporal characteristics. A characteristic set of NDVI time series (NDVI standard curve, NM, and NP) was developed to delineate forests and grasslands and identify their transition (forest–steppe ecotone) based on NDVI differences. Meanwhile, seasonal metrics—seasonal variation characteristics of canopy closure were then extracted to classify 5 forest types, and the distribution above the tree line was used to identify 2 grassland types. Distinct from forests or grasslands, 6 additional land cover types were mapped using Maximum Likelihood Classification (MLC) based on spectral characteristics, producing an integrated 14-class land cover map. The final classification achieved User’s Accuracy (UA) values of 94.85%, 87.80%, and 84.72% for forests, grasslands, and the forest–steppe ecotone, respectively. Compared with classifications using only NDVI standard curve or spectral characteristics, the proposed NDVI characteristic set improved accuracy by 4.93 and 11.92% for forests, 7.50 and 12.43% for grasslands, and 12.98 and 34.52% for the ecotone. All forest and grassland subtypes exceeded 80% accuracy. Compared with existing datasets, such as GlobeLand30, CLCD, and FROM-GLC (2017), the method more precisely captured the forest-steppe ecotone and enhanced the refinement of forest and grassland classification, improving forest classification accuracy by 8.07%, 6.87%, and 8.77%, and grassland accuracy by 7.36%, 25.96%, and 12.66%, respectively. The current study establishes a basis for investigating the physical attributes of the land cover classification model. This study offers novel concepts for developing efficient remote sensing classification characteristics for land cover.

The stability of forest productivity is shaped by biodiversity and species asynchrony, the temporal variation in species’ productivity, and also by stand structural attributes, yet their contributions to forest restoration remain unclear. Here, we assessed how optimizing structural complexity can enhance stability by analyzing 13-year data from 201 restored subtropical plantation plots. Using structural equation modeling, the effects of tree diversity (species richness and functional and phylogenetic diversity), stand structure, functional composition, topography, and soil nutrient availability on productivity stability were quantified. Our results indicate that higher tree species richness, functional and phylogenetic diversity, structural complexity, and stand density positively influenced productivity and its temporal stability primarily via increased species asynchrony. Stands dominated by acquisitive species, characterized by higher specific leaf area, and leaf nitrogen and phosphorus levels, exhibited greater stability. In contrast, increased elevation and steeper slopes negatively affected stability, whereas higher soil nutrient availability had a positive effect. These findings highlight the critical roles of tree diversity and stand structure in achieving both high productivity and stability in the restoration of subtropical forests. Maintaining a proportion of acquisitive species and establishing dense, structurally complex, and functionally diverse stands can therefore be an effective strategy to enhance forest productivity and its resilience.

Tree trunk cross-sections are essential for forest condition analysis. Handheld laser scanning (HLS), a portable three-dimensional digitization technology, has captured considerable interest, due to the efficiency brought by its portability and flexibility. However, the accuracy of HLS is limited by its lightweight design, leading to point clouds with a thickness of 3 to 8 cm for tree trunk surfaces. This reduces the accuracy of cross-section characterization. Here, we propose a Kalman filter-based reconstruction algorithm to improve HLS-derived tree trunk cross-sections. The method transforms the point cloud from Cartesian to polar coordinates based on a density-based reference direction, and applies Kalman filtering for reconstruction. Diameter at breast height (DBH) is then calculated using a simulated diameter tape. Validation with HLS data at different accuracy levels shows that the proposed algorithm outperforms traditional geometric fitting methods, providing more accurate representations of irregular trunk cross-sections. It achieves a higher intersection over union of 86.98 ± 7.67% vs. 84.33 ± 11.08%, and a lower root mean square error in DBH estimation (1.96 cm vs. 2.48 cm). This approach enhances HLS accuracy and provides a promising solution for advancing portable laser scanning technology, which brings opportunity to revolutionize traditional field inventories.

COP29 stepped forward in operationalizing the critical Paris Agreement Article 6 mechanism, which poses new opportunities for forest-based carbon crediting projects. However, these projects, especially those with forest conservation activities, once deemed promising nature-based solutions to climate change, have been facing unique over-crediting challenges, which raised significant concerns in the public and academia. This paper provides recommendations for adopting a dynamic matched baseline accounting approach to enhance integrity and rebuild trust in the industry and the upcoming new global carbon market.

Accurate quantification of forest structural parameters, such as tree height (H), crown vertical projection area (CPA) and crown volume (CV), is essential for precise estimation of forest carbon sequestration, monitoring succession dynamics, and improving carbon cycle models. In natural forests characterized by high species diversity and complex stand structures, the capability of terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to measure forest structural parameters across different tree heights for coniferous and broadleaved species, remains unevaluated under the influence of canopy shading effects. This study investigated deciduous broadleaf -Korean pine forests by integrating TLS and UAV-LS point clouds using geographic coordinates and combining inventory data to identify tree species from individual tree point clouds. The fused point cloud of forest structural parameters served as a baseline dataset to evaluate TLS and UAV-LS accuracy during the period of no leaf cover. The results showed a strong correlation between TLS and UAV-LS with the fused point cloud (R² = 0.96–0.99) TLS and UAV-LS had greater accuracy in measuring H, CPA and CV for coniferous trees than for broadleaf trees, with smaller D-rRMSE differences for conifers (0.7%–3.6%) than for broadleaves (1.1%–21.1%) Across height categories, TLS maintained relatively stable rRMSE values except when height exceeded 25 m, where rRMSE increased. Conversely, UAV-LS showed a significant reduction in rRMSE and RMSE as height increased (88.0% to 1.4%) These results highlight the greater stability of TLS than UAV-LS in measuring the structural parameters of the forest during the period of no foliage.

Developmental noise (DN) is random phenotypic variation often estimated by fluctuating asymmetry (FA), which is considered a biomarker of environmental stress. However, data on DN/FA stress responses in plants, including woody ones, are contradictory. This analysis examines DN stress reactions in plants in terms of the hormesis concept (low-dose stimulation and high-dose inhibition). It is shown that various low-dose stressors (environmental-related impacts, pollutants and even biotic factors) causing hormesis reduce DN/FA levels, demonstrating the plant’s ability to actively control DN through regulatory mechanisms. It is hypothesized that this DN regulation can be potentially based on the control of the reactive oxygen species (ROS) production, since ROS regulate growth, development, and stress responses in all aerobic organisms. Excessive ROS during stress causes biomolecule oxidation and fluctuations in cellular processes, including developmental gene expression, thereby increasing DN/FA levels. Hormesis may reduce DN levels by overactivating transcription factors of antioxidant enzyme genes. DN stress sensitivity, including its ability to hormesis, depends some important aspects that are often overlooked: the functional importance and complexity of the trait, the trait selection history, the trait developmental plasticity, the species ecological strategy, the development stage, and trade-offs. The significant complexity of DN stress responses requires using a set of traits to assess DN/FA, instead of a single trait, along with physiological and biochemical plant stress indicators to identify trade-offs. New approaches to environmental stress indication based on DN/FA levels are needed, which count hormesis and trade-offs. Hormesis can increase fitness by reducing DN levels of functionally significant traits. Thus, hormesis may be a mechanism for regulating the trajectory of trait development by controlling DN levels with stressful environments.

Knots are common wood defects that severely reduce the mechanical performance and market value of timber. To identify the key drivers of knot diameter in Larix olgensis and determine its nonlinear response mechanisms, this study, within the framework of tree resource allocation trade-offs and stand competition, integrates a generalized additive model (GAM), SHapley Additive exPlanations (SHAP)–based machine learning models, and structural equation modelling (SEM) to build a multi-dimensional analytical framework spanning nonlinear effect identification, threshold detection, and causal pathway analysis. The results show that diameter at breast height (DBH), tree height (H), sound knot length (SKL), knot height (KH), and knot tilt angle (TA) are the main positive drivers of KD, whereas increasing stand density (SD) exerts a significant suppression on knot expansion through intensified resource competition. By organically combining predictive and mechanistic models, this study reveals several biologically meaningful thresholds and further clarifies how stand structure influences knot development. These findings provide a quantitative basis for optimizing stand density and crown structure to control knot size, thereby improving wood quality and promoting the sustainable management of L. olgensis plantations, and also offer a transferable methodological framework for quantitative studies of knot characteristics in other plantation tree species.

Climate change has exacerbated midday environmental stress, prompting plants to mitigate tissue damage through photosynthetic reduction-a phenomenon termed plant midday depression. Modern technological advancements now facilitate large-scale collection of high-frequency data on carbon–water fluxes and environmental factors. The widespread occurrence of plant midday depression introduces significant uncertainties in terrestrial carbon dynamics estimation and prediction. However, systematic understanding of this phenomenon remains insufficient. In this review, we elucidated the physiological mechanisms governing plant midday depression, and summarized its variability. We further assessed current methodologies for evaluating and predicting plant midday depression. Additionally, we discuss recent advances in understanding the complex interactions between climate change drivers and midday depression, identifying persistent challenges in this research domain. This review aims to establish a foundational framework for future researchers and to elucidate the current scientific understanding. A comprehensive understanding of plant midday depression is essential for accurate prediction of terrestrial ecosystem carbon dynamics under evolving climatic conditions.

While tree species in phosphorus (P) impoverished subtropical forests exhibit divergent foliar P allocation strategies to maximize phosphorus-use efficiency, how these strategies integrate with the leaf economics spectrum (LES) and are modulated by leaf habit (deciduous vs. evergreen) remains unclear. Five foliar P fractions involving inorganic, metabolite, nucleic acid, lipid, and residual were quantified alongside LES traits across 10 deciduous and 22 evergreen woody species grown in standardized monocultures under uniform conditions. Deciduous species had 30% higher total foliar P than evergreens due to 50% and 35% greater metabolite P and nucleic acid P fractions, respectively. Evergreens allocated more P to residual fractions associated with higher leaf mass per area and conservative traits, whereas deciduous species linked lower residual P to rapid photosynthetic turnover. Evergreen species reduced allocation to inorganic P and residual P, together with increased nucleic acid P allocation, corresponding closely with their LES positioning. In deciduous species, however, LES variation was largely independent of foliar P allocation patterns. Importantly, deciduous species exhibited 31% higher photosynthetic P-use efficiency (PPUE) than evergreens. Overall, PPUE was shaped by the combined effects of leaf habit and LES, with foliar P fractions the primary proximate driver and LES functioning as the central mediator. These findings demonstrate that leaf habit governs the physiological coupling of regional LES trade-offs with foliar P biochemistry, underpinning niche differentiation in P-limited soils and enhancing trait-based predictions of nutrient utilization across tree functional types.

Plant functional traits are key for understanding the adaptive strategies to environments, and fine roots play a crucial role in nutrient acquisition. Examining the variation of functional traits of fine root and soil physicochemical properties, and investigating their coupling relationships and dominating factors, could provide a theoretical foundation for ecological restoration in the burned forest. We established 12 plots within Pinus tabuliformis Carrière forests subjected to light, moderate and severe fire severities. Through detailed analysis of fine roots and soil physicochemical properties, we evaluated the variations and coupling effects in fine root functional traits and soil properties using the Coupling Coordination Degree Model and Partial Least Squares Path Modeling. Our results showed significant differences in the functional traits of fine root and soil physicochemical properties across fire severities (P < 0.05). The coupling coordination degrees between fine root functional features and soil physicochemical properties ranged from 0.4 to 0.6, with the following order: unburned, moderate, severe and light severity. Forest fire negatively impacted the coupling coordination degree indirectly, primarily influenced by the direct positive effects of fine root morphological traits (e.g., specific root length) and soil nutrient properties (e.g., nitrogen and available phosphorus). The synergistic recovery of fine root-soil systems in Pinus tabuliformis forests was most pronounced following moderate fire severity, showing a medium-level coordination degree. For light-severity fires, enhancing fine root morphological characteristics through soil warming is recommended. In contrast, it is suggested to apply appropriate nitrogen and soil fertilizers for improving soil conditions after severe fire.

Leaf area index (LAI) is a critical parameter for characterizing canopy structures. The spatiotemporal patterns of LAI reflect the multi-scale adaptive mechanisms of forests, spanning from individual leaf expansion and population structure optimization to coordinated ecosystem functionality. However, research on the spatiotemporal dynamics of LAI remains limited, particularly in karst primary forests that feature complex community structures, high rock exposure rates, and heterogeneous soil distribution patterns. Our research focused on a 1.28 ha karst forest plot in southwest China, where we quantified LAI seasonality using the LAI-2200 analyzer during four critical phenophases: January (dormant period), April (growth period), July (maturation period) and October (senescence period). Structural equation modeling (SEM), variance partitioning and hierarchical partitioning analysis were employed to quantify the relative contributions of biological, abiotic and spatial factors (represent community spatial connectivity shaped by ecological processes including seed dispersal, resource distribution and competition) to LAI variation. The results revealed distinct seasonal fluctuations in LAI, with significant spatial autocorrelation occurring at characteristic scales of 11.4 m (dormant period), 20.1 m (growth period), 66.3 m (maturation period), and 19.2 m (senescence period), respectively. Spatial factors were the dominant factors in explaining LAI variation across growth, maturation and maturation periods, accounting for 32% to 80% of the explained variance. During the dormancy period, however, the proportion of deciduous trees was the most important factor (32%). The spatial factors exhibited a positive correlation with LAI during the growth and dormancy periods but showed an inverse relationship in other periods. Biological drivers differentially regulated LAI across growth, senescence and dormancy period. Tree height and deciduous species proportion reduced LAI, whereas crown width, density of small trees and stand density increased it. Potassium (inhibiting) and phosphorus (promoting) elements were the primary soil elements influencing LAI. Topographic factors (altitude, slope position and slope aspect) affected LAI through direct, indirect, or combined effects, with slope aspect being the dominant topographic factor. In summary, spatial distribution of LAI exhibited seasonal dependence, which was influenced by spatial, stand, soil and topographic factors, etc. This study identified the primary drivers of LAI in karst evergreen and deciduous broadleaf forests during different seasons, and simultaneously demonstrated the importance of long-term forest monitoring and targeted management strategies, while highlighting the necessity of incorporating spatial factors into LAI estimation and modeling frameworks.

The present study aims to determine the potential impact of recent past, present-day and future climate conditions—along with silvicultural interventions—on the “intrinsic Water Use Efficiency” (iWUE). iWUE, defined as the amount of carbon assimilated per unit of water lost through stomata, is a valuable metric that reflects the combined effects of climate change and forest management on carbon and water balance in forest ecosystems. We studied these effects on a European beech (Fagus sylvatica L.) forest, one of the most common tree species in Europe, in a unique pre-Alpine site in Italy subjected to different silvicultural treatments in the past. Therefore, we analyzed iWUE derived from the δ¹³C measured isotope for the period 2013–2019 under three different silvicultural schemes observed at the study site. Opposite to what was expected, no statistically significant differences were found on iWUE between the treatments (ANOVA: p-value = 0.21) with a mean value for all treatments ranging from 94 μmol mol^–1 and 98 μmol mol^–1. To explore future dynamics, we used a validated process-based biogeochemical model to simulate iWUE under two climate scenarios (RCP4.5 and RCP8.5) and the same three silvicultural treatments. Again, silvicultural practices showed little effect on iWUE, while differences were evident between climate scenarios and time periods. iWUE increased between the first (2019–2029) and last (2040–2050) decades of simulation by 20.9%, 20.5% and 19.5% for the “Control”, “Traditional” and “Innovative” treatments, respectively. In conclusion, in the past and for the next half-decade, silvicultural treatments, at least at the study site, may not influence much the iWUE of beech forests even if it will increase remarkably under climate change.

Understanding how the radial growth of major conifer species responds to climate change on the Tibetan Plateau is increasingly important. However, relevant studies in the middle of the Hengduan Mountains, eastern Tibetan Plateau have not been carried out. In this study, increment cores of Abies georgei, Picea likiangensis and Larix potaninii at the treeline (4260 m), and those of Pinus densata (3730 m), A. georgei (3560 m, 3330 m) and P. likiangensis (3560 m) from subalpine stands, were sampled. Radial growth responses of these conifers to climate change were analysed via dendroecological methods, and the impacts of climatic factors were investigated via partial least squares path modelling (PLS-PM) during the period 1952‒2023. Our results reveal that a rapid warming occurred after 1988, dividing the study period into two intervals: 1952‒1987 and 1988‒2023. A. georgei at the treeline showed improved growth whereas P. densata at the 3730 m site showed reduced growth during 1988‒2023. At the treeline, radial growth of the three conifers were significantly positively correlated with precipitation and the standardized precipitation evapotranspiration index (SPEI) in May/June, and A. georgei and P. likiangensis was negatively correlated with Tmax and Ped in May during 1988‒2023. Radial growth of P. likiangensis was negatively correlated with Tmax in the previous December during the same period. In the subalpine stands, tree ring chronologies of A. georgei at the 3560 and 3330 m sites, and P. densata at the 3730 m site were significantly positively correlated with May precipitation, P. likiangensis at the 3560 m site had significantly negative correlations with Tmax, Tmean, and Ped of the previous October over the period 1988‒2023. According to PLS-PM results, the warming-induced promotion of radial growth for A. georgei at the treeline was greater than the suppression by warming-induced drought. In contrast, warming had no significant effect on the radial growth of L. potaninii at the treeline and P. likiangensis at the 3560 m site. Early growing season drought caused by climate warming is the primary factor limiting the radial growth of subalpine conifers, and continued warming is expected to impact carbon sequestration and community composition in the Hengduan Mountains.

Moso bamboo [Phyllostachys edulis (Carrière) J. Houz.] expansion into adjacent forests affects plant species diversity and associations with soil microorganisms, which will likely have significant impacts on soil phosphorus (P) bioavailability. However, our understanding of how moso bamboo invasion changes soil P bioavailability and its linkage with fungal communities, particularly during expansion into different native forest types, remains limited. Here, we compared soil acid phosphatase (ACP) activity, available P (AP) content, four bioavailable P fractions (CaCl₂-P, citrate-P, enzyme-P and HCl-P), and fungal community composition among stands of moso bamboo forest (BF), bamboo-broadleaf mixed forest (MLF), bamboo-coniferous mixed forest (MCF), adjacent evergreen broadleaf forest and coniferous forest (CF). Our results indicate that moso bamboo invasion significantly altered bioavailability of soil P. Specifically, its invasion into CFs significantly increased the AP, CaCl₂-P, citrate-P, and HCl-P and reduced soil ACP activity, whereas enzyme-P content significantly increased in the MCF. In contrast, its invasion into broadleaf forests significantly reduced soil enzyme-P content and ACP activity and increased HCl-P content, whereas citrate-P content did not change. In the MLF, the contents of AP and CaCl₂-P significantly decreased after the invasion. The invasion also reshaped the composition of soil fungal communities; fungal biomass increased by 128.92% in broadleaf forests compared to 65.67% in the CF. The beta diversity and biomass of soil fungal communities in the CF invaded by moso bamboo were significantly correlated with various P forms, such as AP, citrate-P, and HCl-P, whereas in the BF, they were only significantly correlated with soil ACP activity. These findings demonstrate that the divergent responses of soil P fractions and fungal community traits are primarily driven by the forest type preinvasion, highlighting the importance of baseline ecosystem characteristics in predicting invasion outcomes.

Soil fauna are crucial for nutrient cycling and promoting plant growth. Plant species mixtures can enhance soil biodiversity and ecosystem functions, but their effects on soil fauna under changing water availability remain poorly understood. To address this gap, we combined a field experiment with a meta-analysis to examine how plant species mixtures influence springtail communities under varying water availability. In a field experiment in Ontario, Canada, we assessed springtail abundance, species richness, Simpson’s diversity index, and community composition in pure and mixed stands of trembling aspen (Populus tremuloides) and jack pine (Pinus banksiana) under ambient, reduced (− 25%), and increased (+ 25%) throughfall in young boreal forest. Tree mixtures enhanced springtail abundance and increased Simpson’s diversity index from − 8.3% under ambient water to + 12.3% under reduced water. Springtail community compositions varied significantly among stand types, with shifts in community composition strongly correlated with fine-root biomass and soil water content. A meta-analysis revealed the effects of plant mixtures on springtail abundance were more positive in sites with less precipitation. On the basis of these results, converting plant mixtures to monocultures will significantly decrease springtail abundance and diversity in areas with less water.

Temperate secondary forests play an important role in climate change mitigation and the global carbon cycle, but their variations and drivers of ecosystem carbon storage (ECS) during succession remain unclear. In this study, ECS (trees, shrubs, herbs, litter, coarse woody debris—dead or fallen trees and soil) and environmental factors (temperature, humidity and soil nutrients) were measured in four forest types: Abies nephrolepis and Pinus koraiensis, Fraxinus mandshurica and P. koraiensis, Tilia amurensis and P. koraiensis, and Quercus mongolica and P. koraiensis, in three successional stages (early shrub-grass lands, middle secondary forests and old-growth forests) in temperate Changbai Mountains, to reveal the dynamics of ECS and its allocation patterns during succession, and its formation mechanisms. The results show that: (1) ECS ranged from 49.0–66.1 to 153.8–197.0 and 308.2–446.4 Mg ha−1 in early, middle and late successional stages, respectively; (2) ECS of the four secondary forests recovered to 48.9% of old-growth forest levels after 40 years of succession; their ecosystem carbon sequestration potential ranged from 154.4 to 249.3 Mg ha−1, mainly contributed by vegetation (89.7–94.0%), whereas, soil contribution was smaller (6.0–10.3%).These secondary forests may take at least 100 year to recover to the level of old-growth forest ECS at the current recovery rate; (3) The proportion of vegetation increased with succession in ECS from 3.3–4.6% at the early succession to 74.2–82.8% at the late succession. Moreover, vegetation carbon storage mainly depended on a few pioneer tree species (49.1–66.4%) (middle succession stage) and the climax tree species P. koraiensis and 1–2 associated species (87.5–89.7%) (late succession stage). The contribution of dominant tree species to vegetation carbon storage was significantly greater than that of the tree species diversity; (4) The ECS and vegetation carbon storage were promoted by stand conditions (average DBH and stand density), while soil carbon storage was jointly driven by soil organic carbon and ammonium nitrogen and stand conditions. Our research indicates that temperate secondary forests have considerable carbon sequestration potential (mainly dependent on vegetation) during succession and strengthening the cultivation of the climax species P. koraiensis and associated tree species will help to realize this carbon sequestration potential and better cope with climate change.

Modeling mixed forest growth is challenging due to multiple tree species, uneven-aged structures, and complex environmental interactions. Traditional transition matrix models, widely used for predicting ingrowth, upgrowth, and mortality in mixed forests, rely on statistical approaches but have limited capacity to handle high-dimensional data. Machine learning has the potential, but there is limited application and a lack of comparison, especially in terms of model accuracy and biological plausibility. This study developed a climate-sensitive transition matrix growth model with the integration of machine learning (ML-Matrix) for mixed conifer-broad-leaved forests in northeastern China. Forest data were obtained from permanent plots in the 5th–9th National Forest Inventories. We evaluated five machine learning algorithms—random forest (RF), boosted regression trees (BRT), artificial neural network (ANN), support vector machine (SVM), and k‑nearest neighbor (KNN)—to model upgrowth, two-stage mortality, and recruitment. Results showed BRT excelled in modeling upgrowth of Pinus koraiensis and the first-stage mortality, while ANN excelled for upgrowth of other tree species. For the second-stage mortality, ANN performed best for two broad-leaved groups, while SVM outperformed for three coniferous groups. Optimal recruitment algorithms (SVM, BRT, or ANN) varied by species. Stand variables were the most important predictors with the relative important values of 85.1%–99.8% for upgrowth, 49.0%–88.4% for mortality, and 46.4%–84.3% for recruitment. Compared to the traditional statistical transition matrix (TS-Matrix), ML-Matrix achieved higher test-set accuracy for four tree species groups, excluding Pinus koraiensis. However, long-term simulations revealed TS-Matrix produced more biologically reasonable prediction of stand density, basal area and volume. This study highlighted the potential of ML-Matrix and the need for caution with its long-term projections for mixed forests under climate change.

The Hengduan Mountains, a biodiversity hotspot on the Tibetan Plateau, are undergoing rapid elevation-dependent warming, profoundly altering alpine forest dynamics. Larix species in this region dominate high-altitude treelines and represent the world’s lowest-latitude natural populations of larch forests. However, climate-growth relationships of Larix species at the whole regional scales have received little attention. To address this gap, we investigated the spatiotemporal variability of radial growth in Larix species and their climatic sensitivity across this region using a network of 26 tree-ring chronologies spanning 1960–2022. Hierarchical clustering identified three distinct geographical clusters (southwestern, central, and northeastern), revealing divergent growth trajectories and climate responses. Results demonstrated that growing-season temperature was the primary climatic driver of Larix radial growth, but its influence varied spatially: southwestern populations correlate strongly with May–August mean temperatures, central populations with May–August mean temperatures, and northeastern populations with April–June maximum temperatures. Each cluster exhibited unique growth trends and thermal sensitivities before and after the rapid warming since 1990. Meanwhile, an inter-individual response divergence became apparent under warming, reflecting intensified competition and microhabitat-scale stress, which highlights the limitations of traditional population-level climate response models that assume a uniform response. Spatial heterogeneity in climate-growth relationships reflected synergistic thermal-hydrological effects and species-specific adaptations, with warming enhancing carbon sequestration in moisture-sufficient areas but threatening high-elevation ecosystems through growth suppression and treelines instability. These findings underscore the need for regionally tailored conservation strategies to address climate-driven ecological imbalances in alpine forests.

Drought affects forest productivity and tree radial growth in multiple ways. Two major impacts are growth decline and loss of resilience, i.e., the capacity to recover normal growth rates after a drought, which may indicate impending death. Growth decline and dieback processes have been reported for Mediterranean conifers, but information for natural and planted stands under semi-arid conditions is still scarce, particularly across the increasingly arid Maghreb. We addressed this by assessing growth rates, variability and resilience indices in Algerian Aleppo pine (Pinus halepensis Mill.) stands under Mediterranean sub-humid to semi-arid conditions. Several climate variables and teleconnection patterns (NAO, North Atlantic Oscillation; WeMO, Western Mediterranean Oscillation) were investigated to determine the main drivers of growth decline. Growth resilience indices were calculated at site and tree levels and related to growth trends. Mean basal area increment (BAI) during 2000–2023 was 16.6 cm² a⁻¹. Negative BAI trends occurred for all sites since 2013, as aridification intensified. All stands showed growth decreases during dry years regardless of site conditions or growth rates. Growth was constrained by cold January conditions, dry conditions from the previous winter to summer, and elevated temperatures from late spring to late summer. Long (12-month) droughts peaking in summer suppressed growth, which was also inversely associated with NAO June indices. Growth decline responded to recovery and resistance indices during the 2012 and 2017 droughts. The results show that long-term aridification triggers growth decline despite short-term, post-drought recovery.

The indiscriminate expansion of rubber plantations in Southeast Asia has catalyzed extensive tropical primary deforestation, yet its cascading impacts on ecosystem services and the underlying eco-economic trade-offs remain inadequately explained. By integrating multi-source remote sensing data with ecosystem service valuation models, this study unveiled the spatial dynamics, ecological repercussions, and economic costs of primary forests converted to rubber plantations in Southeast Asia from 2001 to 2021. The results showed that 12.7% of the current 14.15 × 10⁶ ha rubber plantations were converted from primary forests, with Indonesia, Thailand, and Cambodia collectively contributing 70% of the conversion area. Cambodia exhibited the highest conversion intensity, with 35.3% of its rubber plantations displacing primary forests. Such land-use transformations precipitated catastrophic ecosystem services degradation in conversion, with 66.3–98.2% of areas experiencing declines. Although rubber cultivation generated 69.3 billion USD in economic benefits in conversion, this amount offset only approximately 1/19 of ecosystem service losses, which were valued at 1,344.9 billion USD. Indonesia, accounting for 46% of aggregate ecological value loss, is the regional degradation epicenter. Benefit-loss ratios remained alarmingly low (< 6%) in major producing countries, exposing considerable imbalances in eco-economic relationships. This study draws the attention of policymakers and researchers to the substantial ecological costs associated with rubber-driven primary forest deforestation and calls for collective efforts to explore balanced pathways for rubber expansion and ecosystem conservation.

The decomposition of litter by microbial communities is essential for ecosystem functioning. High nitrogen deposition, interacting with the flexible nutrient features of litter, can disrupt microbial succession. However, little is known about the specific links between microbial assembly and nitrogen addition, particularly in the in-situ litter layer. In a Korean pine (Pinus koraiensis) plantation, we investigated how eight years of nitrogen addition (0, 20, 40 and 80 kg ha⁻¹ a⁻¹) affect litter layer microbial community, assessing changes in abiotic properties and microbial community succession. The findings revealed complex influences of nitrogen addition on litter abiotic properties throughout the decomposition stage, such as contrasting influences on NH₄⁺ and NO₃⁻, where NH₄⁺ was elevated but NO₃⁻ was decreased. The effect on microbial community structure and assembly was highly stage-dependent. In the early stage, bacterial assembly was driven by stochastic processes (dispersal limitation). During the middle and late stages, high nitrogen addition shifted bacterial assembly from predominantly deterministic processes (heterogeneous selection) to stochastic processes (drift). However, it did not affect the predominance of stochastic processes during fungal assembly (dispersal limitation and drift). Thus, the influences of nitrogen addition on bacterial and fungal networks were inconsistent, with the stage-specific sensitivity differences between bacteria and fungi. Specifically, high nitrogen addition decreased bacterial stability and complexity, but promoted fungal stability over time. However, it did suppress fungal niche differentiation in the late stage. These results demonstrate that high nitrogen conditions influence litter abiotic properties and the associated microbial traits, such as community assembly, particularly during the late decomposition stage.

Accurate individual tree species classification is essential for forest inventory, management, and conservation. However, existing methods relying primarily on single-source remote sensing data (e.g., spectral, LiDAR, or RGB) often suffer from insufficient feature representation and noise interference, particularly in subtropical forests with high species diversity, leading to increased classification errors. To address these challenges, we proposed the Multi-source Tree Species Classification Fusion Network (MTSCFNet), a novel deep learning framework that integrates RGB imagery, LiDAR-derived feature maps, and GF-2 satellite data through a modified UNet backbone, which incorporates a three-branch encoder and a Triple Branch Feature Fusion (TBFF) module within a middle fusion strategy. We evaluated the MTSCFNet in Chinese-fir mixed forests located in the Shanxia Forest Farm, Jiangxi Province, China. The results showed that: (1) MTSCFNet outperformed four baseline models, achieving Macro F1 (0.78 ± 0.01), Micro F1 (0.93 ± 0.01), Weighted F1 (0.93 ± 0.01), a Matthews correlation coefficient (MCC) (0.89 ± 0.01), Cohen’s ĸ (0.89 ± 0.01), and mIoU (0.69 ± 0.01), with respective improvements of 4.05% in Macro F1, 1.89% in Micro F1, 0.09% in Weighted F1, 1.67% in MCC, 1.64% in Cohen’s ĸ, and 5.92% in Mean IoU over the second best model, SwinUNet; (2) Compared to the best two-source combinations (R + S, R + L), MTSCFNet achieved up to 1.50%, 3.28%, 3.42%, 6.72%, 6.76%, and 3.51% higher Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU, and up to 8.11%, 2.63%, 2.88%, 5.01%, 4.99%, and 11.48% improvements over single-source inputs, while also exhibiting the lowest variability, indicating strong robustness; (3) Under different fusion strategies, MTSCFNet with middle fusion surpassed early and late fusion by up to 15.31%, 3.74%, 3.99%, 7.66%, 7.76%, 22.33% and 24.13%, 5.76%, 6.20%, 11.48%, 11.57%, 32.96% in Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU, respectively, validating the effectiveness of feature-level multi-modal integration; (4) In cross-region transfer experiments, MTSCFNet demonstrated strong spatial generalizability, achieving average scores of 0.78 (Macro F1),0.87 (Micro F1), 0.86 (Weighted F1), 0.59 (MCC), 0.59 (Cohen’s ĸ), and 0.68 (mIoU), and outperformed SwinUNet by up to 38.80%, 9.40%, 18.58%, 22.48%, 26.17%, and 33.00% in Macro F1, Micro F1, Weighted F1, MCC, Cohen’s ĸ, and mIoU across varying forest densities. Overall, MTSCFNet offers a robust, accurate, and transferable solution for tree species classification in complex subtropical forest environments.

Global diversification of the structure and composition of artificial forests and partial or complete conversion to natural forests is an important task for improving long-term biodiversity and counteracting climate change. Larix kaempferi is a tree species used widely in forests throughout northeast Asia that plays an important role in converting artificial forests to mixed forests. However, the phylogenetic diversity (PD) of the species remains unclear. We investigated L. kaempferi forests formed in Gayasan National Park, South Korea, categorized the community types, and quantified species composition, PD, and phylogenetic community structure depending on the vegetation type. Furthermore, we explored the factors regulating biodiversity in L. kaempferi forests to provide insights for promoting forests with high structural diversity. We observed unique vegetation characteristics and community formation mechanisms depending on the local environment, with vegetation types located in valleys and at the bottom of slopes having the highest PD. We revealed how the structural properties and local conditions of forests affect phylogenetic community structure for each vegetation type, leading to competitive interactions and competitive exclusion. For all vegetation types, PD showed a gradually increasing trend with older stand age, but piecewise structural equation modeling analysis showed that topographic environmental factors are the main factors regulating PD. Our findings highlight the need to introduce customized management approaches suited to the characteristics of each community rather than using the same method for all communities. This approach is crucial because species composition, ecological properties, rate of succession, and surrounding environmental conditions differ between vegetation types. In addition, by presenting management strategies to improve biodiversity depending on vegetation type, we expanded existing knowledge on the conversion of artificial forests to mixed forests. Our study provides important insights into establishing strategies for managing artificial coniferous forests and the mechanisms of community formation with changes in species composition after forest creation.