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.

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.

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.

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.

Understanding the mechanisms of species diversity maintenance is crucial for appreciating community assembly and predicting responses to global climate change. Niche differentiation is one of the most important mechanisms underlying biodiversity maintenance across ecosystems. However, direct evidence for niche differentiation remains scarce in subtropical speciose forests. In this study, a 25-ha (500 m × 500 m) subtropical montane deciduous broadleaved forest dynamics plot in Shennongjia national park was developed to assess species-habitat associations across life history stages. Five habitat types were identified using multivariate regression trees and mapped to 625 20 m × 20 m quadrats. Torus-translation randomization tests identified species-habitat associations across life forms and life stages. Out of 105 species, 81 were significantly associated with at least one habitat type and 65 associated with elevation or convexity (49 species with elevation and 36 with convexity). Across all life stages, saplings were most strongly related to low elevation habitats, while juveniles and mature trees most often correlated with the “low convex slope” habitat type. Canopy and shrub species were positively correlated with the “high convex slope” and “low convex slope” habitat types, respectively. In conclusion, niche differentiation during regeneration (based on topographic heterogeneity) is essential for stable multi-species coexistence and the maintenance of biodiversity in subtropical speciose forests. Future studies are needed to examine how demographic rates shift along environmental gradients of convexity and elevation, providing a more in-depth understanding of niche differentiation in forest ecosystems.

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.

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.

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.

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.

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.

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.

Bark beetles are one of the greatest threats to coniferous forests in Europe. Pheromone traps are currently the most effective method of controlling the mass infestations that some of them are known to cause. However, the efficiency of pheromone traps has not yet been sufficiently researched, especially in relation to other important variables that are influenced by the current global changes. Ips typographus L. and Pityogenes chalcographus L. are two economically important bark beetle species that cause major damage to conifers in Serbia. In the present study, we evaluate the efficiency of two commonly used pheromone traps set during a three-year experiment in the Tara National Park in Serbia. During this period, 672,934 ind. of I. typographus and 2,597,578 ind. of P. chalcographus were caught. Our results show that wet traps were about 1.8 times more efficient than dry traps for both species studied. Furthermore, our results indicate that the optimal temperatures for bark beetle flight are between 22 °C and 26 °C, with substantial swarming behaviour occurring at 16.5 °C. At the same time, the data also show a negative correlation between the number of individuals caught and temperatures above 16 °C, suggesting that temperature is probably not the only key factor influencing bark beetle activity.

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.

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.

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.

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.

Forest insects and pathogens, in addition to fire, contribute to structural diversity by creating snags (dead trees) and dead tops on live trees or “topkill” in conifers throughout western North America. Snags and top-killed trees are important sources of wildlife habitat but quantifying their presence can be challenging at broad scales. Multiple approaches for detecting snags have been developed, but limited methods exist for detecting topkill via remote sensing. There is a need to monitor overlapping disturbances across time and space in addition to the structural diversity in mature and old-growth forests. We used airborne light detection and ranging (lidar) and associated field observations to map individual dead trees and live trees with topkill across a forest dominated by Pinus ponderosa in central Oregon. The lidar point cloud was segmented into individual tree objects (polygons representing tree crown extents as viewed from nadir), for which lidar metrics were computed. Lidar-detected tree objects were paired with 647 field-observed trees with corresponding tree health measurements, and a random forest classifier was developed that separated trees into: (1) live without topkill; (2) live with topkill; and (3) dead classes with 87% accuracy using lidar metrics. Tree mortality was mainly due to fire injury and native bark beetles, such as western pine beetle (Dendroctonus brevicomis), while topkill was primarily due to comandra blister rust (caused by the native fungal pathogen Cronartium comandrae). The classifier was applied to map individual tree health status for 46,444 tree objects from which tree health class density maps were created across the 229-ha study area. Approximately 43% were classified as live without topkill, 22% of trees were classified as live with topkill, and 35% of trees were classified as dead. This approach can be used to improve detection of topkill in conifers, along with tree mortality, caused by disturbances associated with forest insects and pathogens.

Platycladus orientalis (L.) Franco seed orchards play an important role in sustainable forestry in China but balancing genetic gain and genetic diversity remains a significant challenge. Two key factors influence the success of seed orchards: parental breeding value and gamete contribution, as they determine both the genetic gain and diversity of the seed crops produced. This study aimed to optimize breeding strategies by analyzing parental breeding value, gamete contribution, and genetic gain across two growth periods (89 families in 2008 and 52 families in 2021). We evaluated height, diameter at breast height, and stem volume of progeny in a primary seed orchard, uncovering significant genetic variation among families. Interestingly, no correlation was found between growth traits and gamete contribution, indicating their independence. Using comprehensive scoring and PCA-biplot analysis, we consistently identified several elite families with superior growth performance in both years. We propose an optimal breeding strategy that combines 30% selective harvesting and 50% selective thinning to effectively balance genetic gain and genetic diversity, addressing a critical goal in tree improvement programs. The selected families and optimized strategy provide a scalable framework not only for P. orientalis but also for other conifer species globally, enhancing both productivity and genetic diversity in afforestation efforts.

Research on the sensitivity of carbon isotope composition (δ¹³C) dynamics to temperature (Q₁₀) and its influencing factors in the process of ecosystem respiration (Re) can accurately predict the trend for ecosystem carbon release with global warming for assessing ecosystem carbon sequestration capacity. We used stable isotope techniques to monitor canopy CO₂ concentration and δ¹³C in a cold-temperate Larix gmelinii forest in Northeast China. δ¹³C values were also analyzed in plant and soil samples across five stand types. The sensitivity of δ¹³C dynamics to temperature during Re and the main factors affecting the variation in Q₁₀ values were determined. Carbon isotope composition of ecosystem respiration (δ¹³C_Re), autotrophic respiration (δ¹³C_Ra), and heterotrophic respiration (δ¹³C_Rh) decreased with increase in temperature, and δ¹³C_Ra < δ¹³C_Re < δ¹³C_Rh. The contribution of Ra and Rh to Re were 51.57 and 48.42%, respectively. Temperature and precipitation had inhibitory effects on Q₁₀, whereas soil organic carbon and total nitrogen had stimulatory effects. Autotrophic respiration is the dominant pathway for carbon release in this ecosystem. Heterotrophic respiration, and particularly maintenance respiration, are more temperature-sensitive. Rising temperatures and precipitation reduce the δ¹³C sensitivity to temperature.

Cunninghamia lanceolata (Lamb.) Hook, a key species for forest plantations in subtropical China, is experiencing a critical decline in productivity due to management practices like long-term successive rotation. Within the C. lanceolata ecosystems, the vigour of the dominant trees reflects their growth potential under the prevailing site conditions. This is crucial for informing management strategies aimed at optimizing plantation productivity. This study focuses on dominant individuals of C. lanceolata, employing their basal area increment in the final year as a quantitative indicator of stand vigour. A dual-dimensional evaluation framework integrating crown structure and tree rings was developed to investigate the underlying mechanisms by which crown structural parameters, stand density and tree age influence stand vigour. This was based on stem analysis data from 76 dominant trees sampled across six southern Chinese provinces. Tree ring data were combined with crown structural parameters including length, width, ratio, volume, shape ratio, and crown projection ratio. A multi-method analytical framework incorporating correlation analysis, difference testing, subgroup analysis, and linear threshold regression modeling was employed to systematically examine these interactions. The results demonstrated that: (1) crown length exhibited a significant positive correlation with basal area increment, while crown shape and projection ratios had significant negative effects. (2) Middle-aged stands (11–20 years) and low-density stands (≤ 1000 ind. ha^− 1) exhibited the highest vigour, with significantly greater basal area increment compared to other age classes and density gradients. (3) Linear threshold regression analysis identified a critical threshold for the clear bole ratio at approximately 0.5. Staying below this value optimizes crown morphology and boosts vigour. Therefore, silvicultural management of C. lanceolata plantations should prioritize density regulation to alleviate inter-tree competition, complemented by precision pruning during the critical 11−20-year phase. Strategic control of the clear bole ratio is recommended to enhance stand vigour.

Elevated ozone (O₃) levels pose a threat to tree physiology, with responses varying among genotypes. This study investigated O₃ tolerance in two poplar clones (107 and 546) exposed to elevated O₃ (E-O₃), focusing on photosynthetic performance and stomatal dynamics. Clone 107 demonstrated remarkable resilience, with only minor late-season reductions in light-saturated photosynthesis (A_sat), maximum photosynthetic rate (A_max), and chlorophyll content under E-O₃. However, clone 546 exhibited severe impairment, including dysfunctional photosynthetic light-response curves with a reduction in stomatal limitation and increase in biochemical limitation, indicating shifted constraint dominance. Moreover, there was stomatal paralysis characterized by sluggish kinetics (prolonged t_90gopening, reduced SL_max) and loss of bell-shape-like g_s-PPFD response. Results also revealed collapsed instantaneous water-use efficiency (_iWUE) due to decoupled stomatal conductance (g_s) and CO₂ assimilation. The g_s response remained static under elevated O₃ without corresponding photosynthetic benefit, leading to simultaneous carbon starvation and water loss. E-O₃ also induced chlorophyll degradation and premature senescence in clone 546, suggesting chronic oxidative damage. These findings demonstrate that the sensitivity of clone 546 stems from systemic failure in stomatal regulation and biochemical compensation, while the tolerance of clone 107 reflects maintained photosynthetic stability. The results indicate that SL_max and _iWUE dynamics can be utilized as key diagnostic indicators of O₃ stress in trees and highlight the importance of selecting resistant genotypes such as clone 107 for O₃-polluted regions.

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.

Precise phenological regulation is critical for temperate trees to survive the winter. However, the underlying mechanism is still unclear. Here, we found that Pag4CL3 coordinately modulates lignin biosynthesis and melatonin accumulation in 84 K poplar (Populus alba × P. glandulosa). Overexpression of Pag4CL3 or Pag4CL5 increased the lignin content in stem but reduced plant growth. In contrast, knockout of either gene reduced stem lignin monomers, promoted growth, and improved cold tolerance, with Pag4CL3 mutants (4cl3) exhibiting more pronounced resistance. PagSNAT2, which encodes a key enzyme in melatonin (MT) biosynthesis, is markedly upregulated in the 4cl3 mutant. Consistent with this, overexpression of PagSNAT2 promoted MT accumulation in 84 K poplar, and the 4cl3 mutant exhibited significantly higher MT levels in both autumn dormant and spring sprouting buds compared to the wild-type. Yeast two-hybrid (Y2H) and luciferase complementation assays further confirmed that Pag4CL3 directly interacts with PagSNAT2. Additionally, low temperature inhibited the binding of transcription factor PagDRS1 to the Pag4CL3 promoter and attenuating its suppression of melatonin synthesis. This study thus unveils a cold-responsive PagDRS1–Pag4CL3–PagSNAT2 regulatory module that balances structural formation and stress adaptation in trees, providing a theoretical basis and breeding strategy for developing poplar varieties with enhanced biomass and winter resilience.

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.

Planted forests serve as critical carbon sinks in climate mitigation strategies, yet balancing individual growth with stand-level carbon storage through age-specific density regulation remains a key knowledge gap. By integrating dendrochronological analysis (1003 increment cores from 532 trees) with longitudinal stand development data (50 permanent plots), we quantify moisture-mediated thresholds governing carbon dynamics in Larix principis-rupprechtii plantations. Our results showed that tree biomass dominated ecosystem carbon storage, accounting for over 95% of aboveground pools, with stand development exhibiting distinct phases: a linear carbon accumulation phase persisting until 70,000 tree-years/ha, followed by a carbon saturation plateau. Optimal balance between tree growth and carbon storage occurred at 27,000 tree-years/ha under baseline conditions, increasing to 34,000 tree-years/ha during normal/wet years but showing 20–35% reductions under drought stress. Moisture availability mediated these thresholds, with drought intensity exacerbating growth-carbon tradeoffs and significantly lowering operational targets. Our findings establish age-stratified density management curves that reconcile tree-level productivity with stand-level carbon storage across moisture gradients, providing actionable guidelines for adaptive silviculture in climate-sensitive plantations. These quantitative relationships enable forest managers to optimize stand density thresholds based on both stand age and projected climate conditions, offering a framework to maximize ecological and economic benefits in water-limited environments.

Ecological and anthropogenic changes have reduced the area of Central Asian riparian forests (tugai), involving dieback of Populus pruinosa Schrenk, one of the tugai’s principal tree species. In a tugai forest on the Zarafshon River, Central Uzbekistan, we investigated the role of environmental factors in P. pruinosa dieback by comparing one healthy and one proximate declining stand. We measured the widths of tree rings of the past 25 years (1999–2023), analyzed their carbon isotope ratios (δ¹³C; 2004–2023), determined physical and chemical soil variables, and retrieved data on groundwater depths and SPEI (Standardised Precipitation Evapotranspiration Index). Over the 25-year period, radial growth did not differ between healthy and declining trees, but tree growth of the declining stand decreased, and in the last 6 years (2018–2023), during and after 2 exceptionally dry years (2018 and 2019), radial increment was significantly smaller. Correlations between radial growth, δ¹³C and SPEI, indicative of drought stress, were only found in the declining stand’s trees. Soil of the declining stand had a higher clay content in the subsoil (30–60 cm), higher salt concentrations in the uppermost layer (10 cm) and in the subsoil, and a lower field capacity across the entire soil profile. There was no groundwater decline during the study period. For the first time, evidence is provided that a drought spell in combination with predisposing unfavorable soil conditions can cause tree dieback in Central-Asian tugai forests at a relatively short distance from the water table. Our study may also contribute to initiate further research for analyzing interrelationships between hydrological, edaphic, ecophysiological and meteorological factors in dieback processes of Central-Asian riparian forests, especially in regions that are strongly underrepresented in ecological research.

Forest litterfall is a key contributor to soil carbon accumulation. However, existing studies have primarily foused on site-level observations or annual-scale assessments, while the intra-annual dynamics and spatial distribution of forest litterfall at the national scale remain poorly understood. In turn, this limitied comprehensive spatiotemporal assessments of forest carbon sequestration capacity. In this study, we compiled 4,223 monthly litterfall observations from 88 forest sites across China and integrated multi-source environmental variables to develop a Transformer-CatBoost hybrid prediction model for estimating the spatiotemporal patterns of forest litterfall across three representatibe years (2002, 2009 and 2018), corresponding to major stages of ecological restoration efforts in China. Model evaluation demonstrated strong predictive performance (R² = 0.74), effectively capturing the nonlinear relationships driving litterfall dynamics. By incorporating national forest area changes in 2002, 2009, and 2018, the study further revealed the spatiotemporal evolution of forest structure under large-scale ecological restoration programs. Based on nationwide monthly-scale modeling results, we systematically characterized the spatial distribution and seasonal variation of litterfall production across China’s forests, with an anuual average of 547.04 ± 0.23 g m^⁻2 (or 479.13 ± 0.20 g m^⁻2 excluding January and December). Furthermore, using a fixed carbon conversion rate, we estimated national carbon content of forest litterfall at 290.4 Tg in 2002, 311.9 Tg in 2009, and 354.1 Tg in 2018, indicating a clear increasing trend. This study represents the nationwide, monthly-scale modeling and prediction of forest litterfall in China.

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.

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

Despite the critical role of plant resource allocation and soil characteristics in plant survival across different altitudes in subtropical China, the detailed dynamics of these interactions had not been previously well-documented. This investigation endeavored to examine the linkage between the characteristics of plant community functions and the physical and chemical attributes of soil at differing elevations within the mountainous forests of the Jinhua Mountain area. The results indicated that as altitude increased, most soil nutrient and moisture traits showed a declining trend, with Bulk Density (SBD) and Total Phosphorus (TP) initially increasing, then stabilizing or slightly rebounding. Specific Leaf Area (SLA) increased from 101.5 to 153.8 cm² g⁻¹ with altitude increase, but Leaf Dry Matter Content (LDMC) and Potential Maximum Height (H_max) decreased (from 22.3 to 6.1 m). High-altitude shrub communities preferred environments with high SBD (6.8 g cm³) but limited moisture and nutrients, exhibiting traits of rapid nutrient uptake and photosynthesis, indicative of a fast-growing ecological strategy. In contrast, low-altitude tree communities displayed more conservative strategy traits. Redundancy Analysis (RDA) revealed that climate variables accounted for 53.09% of the variance in RDA1, highlighting the significant impact of mean annual temperature and precipitation on plant community traits. Soil variables, in contrast, explained 47.99% of the variance in RDA2. The Structural Equation Model (SEM) confirmed that the raised altitude enhanced plant nutrient acquisition capabilities while suppressing the plant's ability to retain soil nutrients, significantly reducing soil nutrient content. Furthermore, the decline in soil moisture retention capacity further promoted plant acquisition strategies, exacerbating soil nutrient scarcity in high-altitude regions. The findings of this study contributed to a more nuanced comprehension of the multifaceted interactions between plant communities and soil in subtropical ecosystems, providing a robust foundation for ecological management and strategies for preserving ecosystems.

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.

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.

Norway spruce, an ecologically and economically important conifer species, requires efficient propagation methods for mass production and for use in breeding programs. This review explores several propagation methods, including seed-based and vegetative approaches, with a particular emphasis on the cutting method. It examines key factors affecting rooting success, such as donor tree age, seasonal sampling effects, sample position within the crown, and surrounding rooting conditions. Unlike seed-based propagation, which faces major limitations, including long maturation times, irregular seeding year, and genetic variability, vegetative propagation methods can overcome the mentioned challenges. Vegetative propagation using the cutting method offers advantages such as genetic uniformity, higher genetic gain, and faster regeneration. Nonetheless, compared to seed-based propagation, its higher cost and obstacles, such as reduced rooting success in older trees and plagiotropism of cuttings must be considered. Hedging, serial propagation, and selecting the optimal sampling position within the crown can help overcome these constraints, and enhance rooting success. Achieving an acceptable rooting success rate even in older Norway spruce trees, and even without applying auxin hormones, presents a unique opportunity for propagating mature trees in breeding programs, especially for traits influenced by both additive and non-additive genetic effects. In addition, both additive and non-additive genetic effects can also be utilized through seed-based propagation methods, such as a complete diallel cross, which involves crossing all parental trees in every possible two-way combination and testing their progenies, leading to enhanced genetic gain. Somatic embryogenesis is an alternative propagation method that enables the long-term cryopreservation of cell lines and their mass propagation after evaluating the regenerated seedlings. By integrating different propagation methods, including cuttings, somatic embryogenesis, and seeds, Norway spruce breeding programs can be accelerated, enabling the efficient production and deployment of high-quality planting stock for both reforestation and breeding purposes.

Analysing pollen dispersal dynamics in natural ecosystems is essential to unravel the effects of meteorological conditions on pollen dispersion and decipher the reproductive strategies of threatened species. However, most aerobiological studies have been conducted in cities. In this study, the intradiurnal airborne pollen dynamics of Sierra de las Nieves National Park (southern Spain) were studied by applying a novel methodology based on the combination of decision trees and clustering to identify the meteorological drivers of intradiurnal pollen dispersion. To that end, pollen patterns were studied in the main pollen types of this protected area during 2018–2024. The days with high pollen detection for each pollen type were clustered according to their intradiurnal pattern. The meteorological conditions of the grouped days were analysed using decision tree algorithms to identify possible causes of their intradiurnal pollen pattern. The highest pollen detection usually occurred around 12:00 and 14:00 h. Most pollen types exhibited a peak during daylight, corresponding to the typical pattern of local pollen sources. Some pollen types, such as Castanea and Urticaceae, exhibited a nocturnal peak characteristic of distant pollen transport. Most pollen types had two different intradiurnal patterns triggered by different meteorological conditions, except Plantago, Poaceae and Quercus. The most relevant variables determining the intradiurnal patterns observed were the frequency of winds blowing from the northwest and northeast quadrants, relative humidity and maximum temperatures. Combining cluster analysis with decision trees proved to be of great utility to analyse the influence of weather conditions on intradiurnal pollen patterns.

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.

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.

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.

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 altered the global temperature regimes leading to warmer temperatures occurring earlier in spring in many temperate regions. This has induced an earlier budbreak making trees susceptible to late spring-frost events, however, information on species-specific late spring-frost tolerance is only available from observational studies. Here, we implemented a quantitative study on late spring-frost tolerance determined by in vitro leaf-level measurements of three temperate broad-leaved tree species via the assessment of the maximum quantum yield efficiency of the photosystem II (F_v/F_m). We investigated to what extent in vitro measurements conducted one day before a late spring-frost event can predict the in vivo damage caused by a cold snap. Fraxinus excelsior showed the lowest in vitro tested tolerance to late spring-frost, and the leaves lost 50% of F_v/F_m (LT50) at + 0.60 ± 0.26 °C. The damage induced by the cold snap the following day (minimum temperature of − 3.28 °C) was a fatal decline of F_v/F_m to 5.8% of the maximum. The other two species, namely Fagus sylvatica and Quercus robur, were characterized by LT50 of − 0.17 ± 9.99 °C and − 2.29 ± 1.11 °C, respectively. The cold snap induced less damage, F_v/F_m values declined to 46.9% and 53.5% of the maximum in the two species, respectively. The in vitro measurements precisely predicted the damage caused by the late spring-frost event. We suggest that in vitro estimated LT50 values can be used as a comparative leaf trait as it has high predictive power for tree species performance after late spring-frost.

The conversion of Norway spruce stands into mixed-species forests is currently one of the most pressing challenges to ensure the stability of forest ecosystems in Central Europe. Recently, direct seeding as a method of artificial regeneration and species (re-)introduction has received increased attention in forestry. Considering that environmental conditions have a strong influence on the growth performance of direct-seeded plants, we investigated how differences in soil and environmental conditions affect the growth performance of silver fir (Abies alba Mill.) and pedunculate oak (Quercus robur L.) seedlings. Our study focused on closed-canopy and open-canopy (canopy removal) Norway spruce stands in a low mountain forest in central Germany. Our data indicates that the growth performance of A. alba and Q. robur seedlings is mainly influenced by the availability of photosynthetically active radiation (PAR). The growth of A. alba increased with a higher PAR-ratio, whereas the photosynthetic efficiency, as measured by F_v/F_m (chlorophyll fluorescence), showed sensitivity to it. Conversely, the growth performance of Q. robur showed a linear increase with light availability. Nutrient availability was the second most important factor, while soil pH alone showed no significant effect. The volumetric water content showed no direct effect, though drought appeared to reduce growth. The results stress that A. alba is sensitive to abrupt changes in the light regime at this early stage of development, highlighting the key role of canopy longevity in facilitating growth. Q. robur, on the other hand, appears to be well suited to sites at high risk of canopy loss due to disturbance or where the canopy has previously been removed.