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 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

Different plants exist in preferences for ammonium (NH₄⁺) and nitrate (NO₃⁻) as the dominant N source, reflecting their adaptation to habitat environments. Elucidating plant specific nitrogen preferences and influencing factors is crucial for ecosystem management under climate change. In this study, we synthesized 216 observations from ¹⁵N isotopic tracer studies of diverse forests and grasslands in China to elucidate variations in and factors influencing soil inorganic nitrogen uptake by woody and herbaceous plants. Woody plants had significantly higher uptake rates and proportional contributions of ¹⁵NH₄⁺ than ¹⁵NO₃⁻ (P < 0.05), while herbaceous plants exhibited a contrary trend (P < 0.05). Mean annual temperature played a significantly role in regulating both ¹⁵NH₄⁺ and ¹⁵NO₃⁻ uptake rates in woody and herbaceous plants, followed by mean annual precipitation, mean annual inorganic nitrogen deposition rates, and soil total nitrogen content (P < 0.05). In addition, the key factors influencing nitrogen preference of woody and herbaceous plants were woody plant functional types (evergreen and deciduous plants) and soil inorganic nitrogen content, respectively. Based on the different functional types of woody plants, evergreen plants preferred ¹⁵NH₄⁺, while deciduous plants preferred ¹⁵NO₃⁻. The results reveal inorganic nitrogen patterns by different plant types in China and that mean annual temperature is a key determinant of plant nitrogen uptake rates. Thus, by matching species-specific nitrogen preferences with the environmental conditions of different regions, valuable insights can be gained to improve the uptake of inorganic N by different plant types and cope with N limitation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The improper handling of outliers in the analysis of variance (ANOVA) presents a persistent challenge in forestry research, which may lead to biased results, inflated Type I error rates, and obscured scientific signals. The current practice is often an ad hoc method, potentially driven by a need to achieve statistical significance rather than principled scientific reasoning. This Editorial paper addresses this systemic issue by proposing a structured, step-by-step framework for the diagnosis and management of outliers. The framework guides researchers to first investigate the cause of an outlier (data error, measurement error, or genuine extreme value), then statistically assess its impact on ANOVA results and assumptions, and finally, make a transparent decision on its treatment. We strongly advise against the statistically problematic practice of replacing outliers with the mean of other replicates, as it violates data integrity and obscures true variability. Instead, we recommend robust alternatives, including data transformation, non-parametric tests, or the use of trimmed means. This approach aims to uphold statistical robustness and scientific integrity, thereby improving the rigor of forestry research and its publications.

The therapeutic effects of forests have become a highlighted research focus in global health studies in recent years. The distinctive microclimate conditions and landscape aesthetics of forests have already been demonstrated to be beneficial for the rehabilitation of people’s physical and mental health. To advance the development of local forest therapy programs in the southern Taihang Mountain Range of North China, we investigated the therapeutic potential of four typical local forests: species-rich Pinus tabulaeformis forest (PTF) and Pinus bungeana forest (PBF), and species-poor Platycladus orientalis forest (POF) and Robinia pseudoacacia forest (RPF). This was done by assessing microclimate conditions and landscape aesthetics related to human health in these forests, while key stand characteristics determining landscape aesthetics were also explored. Microclimate conditions were monitored from June to October 2023, with monthly data collected from the 1st to the 4th of each month between 09:00 and 11:00 AM and diurnal variations recorded every two hours from 08:00 to 20:00 during September 1–4, while temperature, humidity and wind speed variables were integrated into a comprehensive climate comfort index (CCCI) as a surrogate for human comfort. Landscape aesthetics were evaluated with both eye-tracking technology and Scenic Beauty Estimation (SBE) method, which served as proxies for landscape visual perception. A structural equation model (SEM) was also developed to identify key forest characteristics determining landscape visual perception. The results showed that PM2.5 and bacterial concentrations in PTF and PBF were relatively lower than those in POF and RPF during summer and autumn. The CCCI values of PTF and PBF were 3.87 and 4.51, respectively, indicating superior air quality. Eye-tracking analysis revealed that PBF had the longest total gaze duration and highest number of gazes, possibly due to its trails, which provide high accessibility. SEM revealed that green coverage, tree height, shrub density, and herbaceous diversity jointly determined landscape visual quality (path coefficients: 0.98; 0.61; 0.71; –0.91), suggesting that integrated stand structure optimization is key to optimizing and enhancing these forests for their therapeutic values. These findings not only characterized the microclimate and aesthetic features of these forests but also identified key stand characteristics that affect their landscape aesthetics, providing a scientific basis for optimizing forest management works for forest therapy planning in the Taihang Mountains.

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.

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.

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.

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.

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.

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.

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.

Timber quality modeling is essential for value oriented forest management since the traditional, volume only yield models often ignore internal defects (notably knots) and overestimate usable wood. In this study, we developed an integrated framework to quantify knot-free and knotty core volumes in Korean pine (Pinus koraiensis) plantations in Northeast China. The framework couples a re-parameterized Kozak (For Chron 80:507-515, 2004) taper equation with a bark factor model to convert outside- to inside-bark diameters and two height-dependent func tions to describe sound- and loose-knot vertical distribu tion. Nonlinear mixed-effects models were employed with climatic, stand, competition, and tree predictors (e.g., DBH , CR , Hegyi index,SI , CMD ) to partition each stem into knot free and knotty core sections (sound- and loose-knot) based on the heights to crown base ( HCB ) and lowest dead branch ( HDB ), ensuring consistency with observed stem–crown structure. Model evaluation showed high accuracy for all three curves ( RMSE 0.70–1.04 cm; MAE 0.72–0.98 cm), supporting reliable prediction of stem profiles and internal knot distributions. We further identified the drivers of sec tion yields at tree and stand scales: at the tree scale, climate, stand, competition, and tree attributes contributed compa rably to knot-free yield; for the loose-knot (lowest-quality) section, climate (51%) and tree factors (37%) dominated. At the stand scale, knot-free proportion was accurately pre dicted using only basal area ( BAS ), dominant height ( H_dom ), and mean annual temperature ( MAT ), with an RMSE of 0.03. The framework delivers fast, reliable estimates of timber quantity and quality under varying stand and climate condi tions, supporting higher-value management of Korean pine.

Mapping individual dead trees from aerial imagery is useful for assessing habitat quality, monitoring forest health, identifying risks to infrastructure, and guiding forest management strategies. Dead trees can be identified in summertime aerial imagery but computer-assisted methods are needed for efficient mapping across large areas. This study evaluated pixel- and object-based unsupervised classification and Deep Learning for mapping individual dead trees using summertime true-color aerial imagery. The study area was located in Rhode Island forests which had high rates of deciduous tree mortality due to a Spongy moth outbreak in 2015–2017. The unsupervised approaches included spectral and morphological filters to reduce commission error. The Deep Learning approach used a training/validation dataset with 22,504-point features representing dead trees. The pixel-based method had the best performance (F1 = 0.84), the object-based method had slightly lower performance (F1 = 0.79), and Deep Learning had the worst performance (F1 = 0.67). The pixel-based method had the added benefits of being automatable and not requiring training data whereas the object-based method could not be automated and Deep Learning required a large training dataset. The study showed that dead deciduous trees can be mapped from true-color aerial imagery and that an automated classification can yield high accuracy.

Carbon sequestration is an important management objective in China’s plantation forests, which are the most extensive in the world. Planning of plantation management often employs simulators that use either tree- or stand-level models. The use of individual-tree models is increasing in Heilongjiang Province, northeastern China, as these models enable flexible analyses of different stand structures, species compositions, and cutting types. So far, management optimization with individual-tree simulators has considered only the carbon storage of living tree biomass. This study proposed a method for expanding the simulators to accommodate the carbon storage of dead organic materials (DOM) and wood products. A dead tree is transferred to the dead-tree list, and a cut tree is transferred to the harvested tree list. Each new dead tree is characterized by the biomass of stem, branches, foliage, and roots, and the annual decomposition rate of each biomass component. A harvested tree is partitioned into harvest residue and wood product components. A residue component obtains biomass and the annual decomposition rate, and a wood product component obtains biomass and the yearly product disposal rate. From these lists, the remaining biomass of any DOM or product category can be easily computed for any future year. The method can be used in any country. In the example analyses conducted for Chinese larch (Larix gmelinii) plantations, the live tree carbon stock accounted for 75–80% of the total carbon storage, and wood products accounted for a maximum of 10–12%. Increasing the importance of carbon storage decreased cutting and led to longer rotations. Maximizing carbon storage as the sole objective resulted in very low harvest and long rotations (> 200 years).

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.

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.

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.

The Saihanba Forest Farm (SHB), China’s largest plantation base, has experienced significant habitat quality services (HQS) changes following six decades of afforestation. This study developed an integrated “CLUE-S–InVEST–BBN” framework to simulate and optimize HQS spatial patterns under alternative land use/cover change (LUCC) scenarios for 2035. Using Landsat imagery (2002–2022) and twelve biophysical and socio-economic drivers, we projected LUCC under three scenarios: ecological protection (EP), natural development (ND), and economic development (ED). HQS exhibited a “decline-then-recovery” trend from 2002 to 2022, with forests and grasslands contributing 79.6% and 48.2% of total HQS, respectively. The EP scenario projected 89.96% forest cover by 2035 with the highest mean HQS (0.8925), while the ED scenario showed a 1.54% decrease due to urban expansion. Bayesian belief network analysis identified LUCC, NDVI, road proximity, and precipitation as primary HQS determinants and delineated three management zones: key optimization, ecological conservation, and general management. This framework provides a replicable approach for enhancing HQS and supporting sustainable land-use planning in ecologically sensitive mountainous forest areas.

Afforestation and reforestation, when aligned with site-specific ecological and socioeconomic conditions, can enhance ecosystem functions and services (ESs). In the Mediterranean, European black pine is widely used in such projects. While management strategies to maximize timber yield are well studied, the economic valuation of multiple ESs and their trade-offs remains limited. This study employed a process-based forest growth model, incorporating climate, soil and stand structure, to assess the effects of thinning intensity and frequency on the provision and economic value of ESs, namely carbon sequestration, erosion control and recreational/aesthetic value, in Italian black pine stands. Results show that while intense and frequent thinning boosts growth, optimal economic outcomes were achieved with 25% basal area removal every 25 years, yielding €57,000–69000 ha^–1, about 30% more than high-intensity, short-rotation regimes. Non-provisioning ESs declined with heavier thinning (up to 22% loss between 15 and 35% intensity) and improved with longer thinning intervals (up to 18% gain from 10 to 25 years). Strikingly speaking, aesthetic and carbon sequestration benefits dominated total value, accounting for up to 99%, regardless of regime. These findings underscore the importance of long-term, balanced thinning strategies to optimize both wood production and broader ESs. The modeling approach offers practical guidance for multifunctional forest management, supporting more sustainable and economically viable decisions. While tailored to Italy’s context, the insights are relevant to policy and practice across Mediterranean and comparable forest systems.

Optical greenness indices, such as the fraction of absorbed photosynthetically active radiation (fAPAR), are critical in constraining and guiding the modelling of forest carbon storage. However, as optical sensors have limited penetration capacity, it remains uncertain whether greenness indices accurately reflect the true response of aboveground biomass (AGB) to local climatic conditions. In this study, we integrate a wall-to-wall AGB dataset derived from microwave remote sensing to examine the consistency between AGB and fAPAR in their climatic responses. Meanwhile, we use an AGB-fAPAR Difference Index (AFDI) to quantify the driving mechanisms underlying their divergent responses, which is defined as the difference between AGB and fAPAR after standardization and normalization. We find that AGB is negatively associated with local precipitation, whereas fAPAR exhibits a positive correlation, leading to pronounced response differences in AFDI across precipitation gradients. Micro-topography contributes 75% to the spatial patterns in AGB and fAPAR (with a correlation coefficient of 0.75), further shaping AFDI through changes in elevation and slope. Using the AFDI, we demonstrate that topography-driven surface water redistributes water availability beyond local precipitation — proximity to surface water shows a significant positive relationship with higher AFDI, yet radiation after topographic correction shows no significant contribution. Moreover, more complex tree species composition and near-mature stand age amplify the AFDI due to their impact on vertical structure. Our results suggest that AGB and fAPAR exhibit inconsistent responses to precipitation. Local topographic effects, hydrological dynamics, and forest composition and age structure collectively drive the disparity between AGB and fAPAR, highlighting the constraints of greenness indices in capturing forest biomass dynamics and providing a new perspective to enhance the accuracy of forest carbon dynamics predictions.

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.

Close-range remote sensing (CRRS) technologies are increasingly used in forestry, but there is a lack of awareness of the challenges, needs and expectations of both service providers and end users. We used a customised online questionnaire to interview professionals in the field, recruited through direct (existing networks) and indirect channels (social media). The main barriers we identified include the cost of equipment, the complexity of data processing workflows and insufficient access to specialised training. Our findings emphasise the need for interdisciplinary collaboration, the development of more intuitive and user-friendly tools and the expansion of specialised training programmes. The results of the questionnaire suggest that stronger partnerships between industry and academia should be encouraged to drive innovation and knowledge sharing. In addition, the development of standardised protocols for CRRS applications and the creation of accessible educational resources proved essential to support both novice and experienced users. Scientific conferences are the most important platform to gather all stakeholders in one place, and have underutilised potential to narrow the gap between theory and application. The recommendations we have made aim to facilitate the widespread adoption and efficient utilisation of CRRS technologies in practical forestry.

To clarify the carbon sinks and their uncertainty components in the temperate secondary forest ecosystem (mosaics of natural secondary forests and plantations). We selected three typical forest stands including secondary mixed broadleaved forest (T1-MBF), secondary Mongolian oak forest (T2-MOF), and larch plantation (T3-LPF). The net primary productivity (NPP) and soil heterotrophic respiration (R_h) were monitored for 4 years (2020–2023) by both inventory and chamber methods, and net ecosystem productivity (NEP, carbon sink; NEP = NPP − R_h) and its uncertainty were further calculated for three forest stands. The results showed that the NEP were 1.99 ± 1.78, 1.87 ± 2.06 and 2.68 ± 1.42 t ha⁻¹·a⁻¹ for T1-MBF, T2-MOF, and T3-LPF, respectively, with high relative uncertainties (89.33%, 109.98% and 52.93%, accordingly). Specifically, fine root NPP dominated uncertainty (58.05–78.63%), followed by R_h (2.20–30.45%) and leaf (2.47–9.58%), while stable pools (e.g., stem, coarse root) contributed minimally. Importantly, to improve reliability, we developed a revised method to estimate low-uncertainty carbon sink, which focuses on low-uncertainty carbon pools, including stem, coarse root, and soil carbon. Low-uncertainty carbon sinks reached 1.82 ± 0.21, 1.79 ± 0.34, and 2.56 ± 0.54 t ha⁻¹·a⁻¹ for T1-MBF, T2-MOF, and T3-LPF, respectively. Notably, relative uncertainties dropped sharply to 11.52%, 18.77%, and 21.15%. This study provides a robust framework for quantifying low-uncertainty carbon sinks in the temperate secondary forest ecosystem by integrating resilient carbon pools, significantly improving estimation reliability while reducing uncertainty by 60.0–87.1% compared to conventional approaches.

Understanding tree mortality is crucial to understand forest dynamics and is essential for growth models and simulators. Although factors such as competition, drought, and pathogens drive mortality, their underlying mechanisms remain difficult to model. While substantial attention has focused on selecting appropriate algorithms and covariates, evaluating individual tree mortality models also requires careful selection of performance criteria. This study compares seven different metrics to assess their impact on model evaluation and selection. Results show that candidate models exhibited varying performances across metrics and that the choice of metric significantly influences the selection of the best model. When no confusion matrix was available, the area under the precision-recall curve (AUCPR) emerged as a more reliable alternative to the area under the ROC curve (AUC), offering a more informative assessment for imbalanced datasets. When a confusion matrix was available, Cohen’s Kappa coefficient (K) and Matthews correlation coefficient (MCC) outperformed accuracy-based metrics, providing a fairer evaluation of both live and dead tree classifications. These findings emphasize the importance of choosing appropriate evaluation standards to enhance mortality model assessment and ensure reliable predictions in forestry applications.

Soil aggregates are crucial for evaluating soil quality and fertility, as they significantly contribute to the storage of soil organic carbon and support the functional stability of forest ecosystems. Nitrogen and phosphorus deposition can influence the carbon cycle within forest ecosystems, thereby altering nutrient inputs that affect soil microbial communities and enzymatic activities. In this study, soil samples were gathered from the 0–20 cm depth in both Broad-leaved Korean pine (Pinus koraiensis) forests and KPP, which were representative in northeast China. After wet screening, soil aggregates of 4 different fractions were obtained. Soil aggregates of > 2, 2–0.25, 0.25–0.053 and < 0.053 mm were included. Corresponding forest litter was added to each fraction, and three levels of nitrogen and phosphorus (NP) addition involving low (L), medium (M), and high (H) along with a control treatment (CK), were applied. A 360–d laboratory incubation experiment was conducted under controlled conditions. The following parameters were measured: soil total organic carbon (TOC), microbial carbon (MBC), dissolved organic carbon (DOC), readily oxidized carbon (ROC), particulate organic carbon (POC), mineral bound organic carbon (MOC), light group organic carbon (LOC), mean weight diameter (MWD), geometric mean diameter (GMD), to assess alterations in soil carbon storage and the stability of soil aggregates. High NP addition markedly decreased soil aggregate stability and inhibited soil mineralization. However, it enhanced soil organic carbon accumulation, particularly DOC and LOC, in most aggregate fractions, while potentially suppressing MBC in small aggregate fractions. Carbon stabilization mechanisms differed between forest types: natural forests relied more on large aggregates (> 0.25 mm), whereas plantation depended more on microaggregates (< 0.25 mm). Consequently, soils dominated by large aggregates are better suited for M and L NP additions, whereas soils rich in microaggregates should avoid H NP inputs. Overall, the results indicate that medium and low levels of NP enhance aggregate stability and promote mineralization rates, whereas H NP application compromises structural stability and suppresses mineralization. These results enhance our understanding of how nitrogen and phosphorus deposition influence the organic carbon cycle in temperate forest ecosystems.

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.

Tropical forests contribute over half of the global primary productivity, playing a critical role in regulating the global carbon cycle. In recent decades, global tropical forests have shown a widespread increase in vegetation cover. However, how tropical forests respond to spatiotemporal climate change under increasing vegetation cover remains a critical question, limiting the development of effective conservation strategies. To address this, we evaluated the sensitivity of tropical forests to spatiotemporal climate variability using the vegetation sensitivity index (VSI) across greening in 2000–2021 and identified dominant climate drivers based on solar-induced chlorophyll fluorescence, enhanced vegetation index, and leaf area index data. Results indicate that over 84% of global tropical forests show an increase in vegetation cover, while a decrease in vegetation cover appeared in the southern Congo and southeastern Amazon. VSI showed a latitudinal gradient, with high values (> 60) near the equator and lower ones (< 40) in higher latitudes. Tropical forests in the Congo had the highest VSI, followed by the Amazon and Southeast Asia. VSI decreased in over half of the Amazon tropical forests, whereas the Congo and Southeast Asian forests showed comparable proportion of pixels with decreasing and increasing VSI trends. Notably, tropical forests typically exhibited increasing variability, indicated by the detrended variance along the greening trend gradient. In addition, tropical forests are highly vulnerable to climate change in the western Amazon, equatorial Congo Basin, and equatorial Southeast Asia based on the aspects of VSI magnitude and VSI trend. Precipitation is the dominant climate driver regulating global tropical forest variability to climate change, followed by temperature and solar radiation. Temperature dominated tropical forests variability in Southeast Asia. Our findings highlight the instability and vulnerability of tropical forests under climate change despite widespread increase in vegetation cover.

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.

Chinese fir (Cunninghamia lanceolata) is a key resource in China’s timber industry. However, there are no site-specific cultivation guidelines for producing large-diameter trees and enhancing forest productivity and timber quality while addressing the complexities of Chinese tropical terrains. This study identifies key site factors using the Boruta algorithm and establishes diameter growth curves through K-means clustering and mixed-effects modeling to estimate how many years are required to reach target diameters. It is based on sample plot data from Chinese fir plantations across seven provinces in subtropical China. The results show that: (1) soil type and depth, and elevation have a strong influence on growth; (2) of the six basic growth models compared, the Korf model performed best, with the coefficient of determination (R²) of 0.6154. The predictive accuracy was significantly improved by introducing a mixed-effects model, which increased the R² value to 0.7535; (3) at a designated harvesting age of 26 years, diameters at breast height (DBH) were approximately 30, 27, and 26 cm under STG6, STG4, and STG8, respectively, all exceeding the large-size timber threshold (DBH ≥ 25 cm), demonstrating strong cultivation potential; and, (4) to attain a 25 cm DBH, STG6, STG4, and STG8 required 18, 21, and 24 years, respectively. Growth rate slowed significantly after 30 years, suggesting that the rotation period should be limited to within 30 years to optimize management efficiency. In this study, we developed a robust growth prediction model suitable for diverse site conditions and propose a site-specific management strategy that provides the basis for the precision cultivation and sustainable management of large-size Chinese fir plantations.

Soil bacteria, by participating in the soil nutrient cycling process, directly or indirectly regulate tree regeneration and survival. We established a 1-ha plot in the mixed forest of Picea asperata and Larix principis-rupprechtii and conducted a location-based survey of the regeneration seedlings, combining with collecting soil samples from 75 sampling points. The results showed that (1) Regeneration seedlings (n = 275) showed a patchy distribution, with density decreasing across height classes and smaller seedlings aggregating over shorter distances. As the scale increasing, the distribution pattern tends to become random; (2) Soil bacteria and nutrients also exhibited spatial heterogeneity and primarily shaped by structural spatial factors (range: 8.4–17.1 m); (3) Bacterial effects on tree regeneration were indirect by mediating through changes in soil nutrient availability (pc = 0.28). Specifically, bacteria competed with seedlings for beneficial macronutrients (pc = 1.19*, –0.85*) but mitigated toxicity by absorbing harmful micronutrients (pc = –0.48*, –0.75*). These findings highlight the role of bacteria-mediated nutrient dynamics in shaping regeneration patterns in warm-temperate mixed forests.

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.

The survival of urban forests is increasingly challenged by prolonged droughts which adversely affect the function of urban trees. Drought resistance in tree species is determined by their plant-water relationship. There are significant differences in xylem structure between ring-porous and diffuse-porous species, and these structures are closely linked to their hydrodynamic functional traits. This study examined the relationship between branch xylem anatomy and hydrodynamic traits in two timber species and analyzing xylem samples from eight broadleaved species. A water-efficiency safety trade-off was observed in diffuse-porous species, while ring-porous species adapt to their environment by adjusting water transport traits and varying tissue types. Two distinct hydraulic strategies were identified: ring-porous species with high water demand, formed a large conduit area, and axial parenchyma to improve water transfer efficiency, while increasing the thickness of the conduit wall to improve the implosion resistance. Diffuse-porous species formed an independent conduit distribution pattern with greater conduit density and proportion of conduit tissue hydraulic security. The physiological roles of conduit structures system directly determine the dynamic balance between efficiency and safety of water transport in the xylem hydraulic system, spatial distribution and the allocation of resources to thin-walled and fibrous tissues. Overall, woody species in urban environments exhibit considerable variation in drought tolerance, forming complex three-dimensional systems where conduit structure, spatial distribution and functional tissue allocation work together to determine their drought resistance strategies.

Forest measurements, including genetic trials, have relied on traditional measurement methods, an approach affected by different types of errors. To assess genetic trials, Terrestrial Laser Scanning (TLS) devices offer potential to improve accuracy. This study aimed to imple ment an approach for analyzing forest genetics trial measure ments using TLS data. A 15-year-old Pinus taeda L. progeny test in North Carolina USA was assessed using both TLS data and traditional field measurements. Accuracy was assessed using adjusted R², bias, percent bias, and RMSE. Genetic parameters were estimated via BLUP for diameter at breast height (DBH). The R²_adj values were 0.56 for DBH and 0.29 for total height (HT). Field-measured DBH had higher heritability (h² = 0.32) than raw TLS data (h² = 0.17). However, “cleaned” TLS estimates (DBH_R) improved herit ability (h² = 0.27) and showed stronger phenotypic correla tion with DBH_F (R = 0.84) than DBH_L (R = 0.75). GCA predictions using BLUP showed high correlation (R = 0.92) between field and TLS DBH estimates. Estimated gains using DBH_F were 11.3% and 12.1% for selecting the top 1st progeny (30 families) and the top 1st and 2nd progenies (15 families), respectively. Estimated gains using DBH_F were 11.3% and 12.1% for selecting the top 1st progeny (30 fami lies) and the top 1st and 2nd progenies (15 families), respec tively. Corresponding gains from DBH_L were 6.9% and 9.6%, and from DBH_R, 8.5% and 10.3%. The results demon strate that TLS, combined with the proposed methodology, is a reliable alternative for genetic analysis in forest trials.

Accurate prediction of plants’ flowering onset date (FOD) is vital for maintaining ecosystem functions and boosting forestry economic gains. While the Spring Warming (SW) model is commonly used to predict flowering phenology, its traditional fixed setting of the heat accumulation threshold (HAT), measured by growing degree days (GDD), fails to account for the spatial variation in preseason thermal requirements reported in previous studies. This limitation reduces the accuracy of FOD predictions across large spatial areas. In this study, we hypothesized that the HAT in the SW model varies spatially with habitat-specific temperature due to thermal acclimation. To test this, we systematically quantified the spatial differences in HAT using observed FOD data of Robinia pseudoacacia, which is a keystone species for afforestation and a vital nectar source, from 58 stations across China between 1963 and 2008. We identified the key temperature variables influencing HAT variability and developed a simplified, spatially dynamic HAT scheme. The updated SW model, incorporating this variable HAT, was evaluated with cross-site FOD observations. Results showed significant variation of HAT across different climate zones. A geodetector analysis found that the mean temperature from February to May was the main factor driving HAT heterogeneity, supporting our hypothesis. Additionally, spatial factors such as elevation and longitude also contributed to HAT variation alongside thermal factors. Incorporating this spatially variable HAT, predicted from preseason temperatures, into the SW model significantly improved FOD prediction accuracy, decreasing the root mean square error (RMSE) by 11.91% compared to a model with a constant HAT. Future climate scenario predictions indicated that the SW mode with the fixed HAT underestimated FOD advances in warmer areas and overestimated the rate of change, especially when compared to the heterogeneous HAT model. Overall, we emphasize the importance of considering spatial thermal acclimation in broad-scale flowering onset predictions.

Under the dual pressures of climate change and human activities, the frequency and intensity of global wildfires have significantly increased. While seasonal differences profoundly affect the intensity and spatial patterns of wildfire driving factors, past research has largely focused on annual scales, with insufficient attention paid to the dynamic changes and deeper impacts of driving factors in the seasonal dimension. Taking seasonal variations as the core entry point, this study integrated cross-border resources in the Sino-Mongolian border area, adopted satellite fire point data from 2001 to 2022, fused multi-source data including meteorological, topographic, vegetation, socioeconomic and anthropogenic activity data, incorporated meteorological data under three future climate scenarios, and compared the applicability of six models (Logistic Regression (LR), Gompit Regression (GR), Random Forest (RF), Boosted Regression Trees (BRT), XGBoost, and Support Vector Machine (SVM)) in wildfire prediction on the Mongolian Plateau. The results indicate that the Boosted Regression Trees model is the optimal model. Daily average relative humidity (Hum) and yearly average wind speed (Y_win) are the primary driving factors. The eastern provinces of Mongolia, Khovd Province, Selenge Province, and Hulunbuir City in China are identified as extremely high-risk areas for wildfires, with an increasing trend in wildfire incidents on the Mongolian Plateau in the future. This study improves the analysis of fire risk level zoning to accurately identify the spatial characteristics of high-risk areas and clarifies critical thresholds through the marginal benefit analysis of driving factors. Based on this, differentiated early warning systems can be initiated in conjunction with the specific conditions of high-risk areas, supported by targeted prevention and control measures, enhancing the foresight and effectiveness of wildfire risk management in cross-border regions.

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.

Accurate tree height measurement is crucial for assessing forest health and management strategies, as it directly correlates with biomass and carbon storage capabilities. Thus, drones are increasingly used in forest remote sensing due to their high spatial resolution and operational flexibility. They support individual tree detection and detailed structural mapping, yet the reliability of tree height estimates remains influenced by multiple factors. We present a meta-analysis of 36 case studies to evaluate how environmental complexity and sample size influence tree height estimation accuracy. Forest environments, characterised by heterogeneous canopy structures, occlusions, and species diversity, were associated with higher errors and more significant variability. Conversely, plantations and urban sites yielded lower and more consistent errors, reflecting their structural simplicity and spatial regularity. Although lidar and image-based methods performed similarly in forests (1.58 m vs. 1.69 m mean error), lidar clearly outperformed image matching in plantations (0.44 m vs. 0.91 m). Regarding sample size, mean errors were not consistently lowest in larger datasets, but studies with more than 300 trees exhibited the lowest variability, indicating more stable performance. These findings highlight that both environmental structure and sample size affect the robustness of drone-based height estimation. We recommend the implementation of standardised workflows that account for environmental and technical limitations, including terrain complexity, sensor configuration, and weather conditions. Although drones provide considerable benefits, their successful application depends on careful mission planning and grounded operational expectations. Addressing these challenges through improved mission planning and methodological consistency is essential to ensure the robustness and scalability of drone applications in long-term forest monitoring.

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.

Key differences in biological legacy exist between clear-cutting and natural disturbances. One way to enhance similarity is by preserving structural features of old forests, such as retention trees, within harvested areas. Norwegian forest certification standards set by the Programme for the Endorsement of Forest Certification (PEFC) and the Forest Stewardship Council (FSC) require both the preservation and mapping of retention trees within harvested area for eventual reporting in a central database. This study, conducted in a managed forest in southeast Norway, evaluates the accuracy of retention tree identification, including density and volume predictions, using airborne laser scanning (ALS) data with low (2 pulses m^–2) and high (approximately 100 pulses m^–2) pulse densities, with and without spectral data. We also assess whether ex-situ reference data, such as diameter–height measurements from sample plots in similar forests or species annotations from aerial images, can fully or partially replace in-situ data collected within harvested stands.Three reference datasets were used, fully or partially: (1) 630 in-situ retention trees across 27 stands (for species classification and diameter at breast height (DBH) prediction), (2) 1064 ex-situ sample trees (for DBH prediction), and (3) 150 ex-situ annotated segments (for species classification). Using an individual tree segmentation approach with adaptative local maxima window size and regeneration height filtering, 65% of the in-situ retention trees were correctly identified, increasing to 74% when excluding snags. ALS at 2 pulses m^–2 alone provided reliable total density and volume predictions, while spectral data improved species-specific accuracy. Species classifications remained consistent across data source (kappa = 0.556 for in-situ retention trees, 0.519 for ex-situ annotated segments), but DBH were underpredicted with ex-situ sample trees (RMSE = 9.4 cm, MSD =  − 4.6 cm) compared to using 40 in-situ retention trees (RMSE = 8.8 cm, MSD = 0.2 cm). We recommend sampling approximately 40 in-situ retention trees to calibrate diameter-height models and using ex-situ annotated segments for species classification. This approach, based on low-density ALS and orthophoto datasets, meets the PEFC requirement to provide the locations of retention trees and may also support retrospective detection, thereby contributing to semi-automated certification reporting and virtual audits. In the eventuality of a publicly accessible database of measured retention trees were available, in-situ sampling for diameter-height model calibration could be omitted.

Phenology is crucial for assessing the effect of climate change on the survival and growth dynamics of temperate and boreal plants. Warmer temperatures induce earlier budbreak, possibly increasing the risk of late frost, while warmer winters may fail to fulfill the chilling requirement delay budbreak. In our study, we simulate early spring warming on the seedlings and branch cuttings of sugar maple (Acer saccharum Marsh.) from two provenances (Cantley, more southern, and Duchesnay, more northern) originating from different bioclimatic zones in Quebec, Canada. We assessed budbreak in seedlings and branch cuttings after transfer to controlled forcing temperatures (15 or 20 °C) on two dates (DOY 61 and 115). We also calculated chilling accumulation using three commonly applied models including the Chilling Hours, Utah, and Dynamic models. We tested either direct transfer from natural conditions or transfer after a period in artificial chilling temperatures (4 or 7 °C). Seedlings transferred to 20 °C on DOY 61 required 12 additional days to complete budbreak compared to those transferred to the same temperature on DOY 115. The northern provenance (Duchesnay) completed budbreak 11 d faster than the southern provenance (Cantley). Seedlings exposed to 7 °C chilling and 20 °C forcing performed budbreak 7 d faster than those submitted to 4 °C chilling and 15 °C forcing, and 4 d faster than seedlings at 4 °C chilling and 20 °C forcing. The tested chilling metrics models were not able to fully explain the difference in budbreak timing between the treatments. No difference in budbreak was found between branch cuttings and seedlings, validating the branch cuttings as a reliable proxy for phenological studies. Our findings demonstrate the role of chilling and forcing accumulation on budbreak during late winter and early spring. We also show that current chilling models need to be modified to incorporate subzero temperatures to better represent and predict budbreak in boreal and northern temperate species. Warming during winter and spring could advance the timing of budbreak in sugar maple, thus lengthening the growing season, but possibly exposing the trees to damage by late frosts. The warmer provenance (Cantley) showed later budbreak, suggesting a potential for spring frost avoidance that is relevant from a forest management perspective.

The sensitivity of vegetation productivity and ecosystem respiration to precipitation, namely S_GPP and S_ER, is an intrinsic characteristic of vegetation and a key metric for understanding the variations in ecosystem carbon cycle under changing climate. Previous studies typically treat S_GPP or S_ER independently, overlooking their potential interrelationship. Besides, beyond the direct effects of precipitation, temperature likely plays a significant role in shaping both S_GPP and S_ER. In this study, we leveraged the FLUXNET2015 dataset to analyze the global spatiotemporal patterns of S_GPP and S_ER, and applied a mixture regression model to investigate their hydrothermal regulations. The results showed that S_GPP and S_ER varied greatly across biomes, with the higher values in arid ecosystems, while forest ecosystems exhibited relatively low values. Temporal analysis indicated that S_GPP and S_ER significantly increased over time in shrubland, while S_ER in evergreen needleleaf forest and S_ER in grassland significantly increased and decreased, respectively. The relationship between S_GPP and S_ER decoupled with increasing leaf area index, with a breakpoint occurring at 1.61 m² m⁻². Across ecosystem types, the decoupling of S_GPP and S_ER was primarily observed in closed-canopy forests, with hydrothermal conditions identified as the key drivers of this phenomenon. Specifically, in forests, S_GPP was predominantly regulated by the joint influence of temperature and precipitation, whereas S_ER was mainly controlled by precipitation alone. Across all ecosystems, S_ER shifted from being co-regulated by precipitation and temperature to predominantly precipitation-driven as mean annual precipitation (MAP) exceeded 1000 mm, while SGPP transitioned to primarily temperature-dependent above 1500 mm MAP. This study underscores how hydrothermal conditions shape the complex S_GPP-S_ER interplay, providing critical insights for future forest ecosystem research.

Mixed-species plantations potentially alter the stability of soil organic carbon (SOC), playing a crucial role in SOC sequestration. However, how tree species mixtures affect root and rhizosphere soil characteristics and further shape rhizosphere SOC stability are not fully understood. In this study, the effects of mixed-species plantations on rhizosphere SOC stability through root exudation, root morphological traits, rhizosphere properties and microbial biomass carbon were investigated in a Pinus massoniana monoculture and two paired plantations interplanted with Erythrophleum fordii (a nitrogen-fixing species) and Castanopsis hystrix. Our findings show that when interplanting with C. hystrix, root exudation of P. massoniana increased significantly, which was positively correlated with increases in mass proportion (+ 38% and + 11%) and carbon contents (+ 77% and + 12%) of large and small macro-aggregates in the P. massoniana rhizosphere. This suggests that root morphological traits and exudation inputs largely affected P. massoniana rhizosphere SOC stability. When interplanting with E. fordii, there was no significant increase in root exudation and no correlations between rhizosphere aggregate mass proportion, carbon content and root exudation rates, while available nitrogen attributed to P. massoniana rhizosphere SOC stability. Our results suggest divergent mechanisms underlying P. massoniana rhizosphere SOC stability in mixed-species plantations with different companion species, and highlight the critical role of selecting appropriate companion species in improving SOC stabilization of mixed-species plantations.

Ecological restoration is a crucial measure to address environmental degradation and climate change, while ecological efficiency provides a comprehensive framework to evaluate restoration outcomes. Using panel data from 26 provinces in China (2003–2023), this study develops a forestry-oriented ecological efficiency evaluation system, encompassing resource and socio-economic inputs, expected outputs, and undesirable outputs. The super-efficiency SBM model is applied to assess the ecological efficiency of China’s Shan-Shui Initiative system, while Moran’s I, gravity center migration, and standard deviation ellipse methods are employed to characterize the spatiotemporal evolution of ecological efficiency. The results show a significant increase in ecological efficiency since 2015, with notable disparities among provinces. Eastern provinces generally exhibit higher ecological efficiency, while central and northeastern regions lag behind. The spatial clustering of ecological efficiency is particularly pronounced after 2014 (Moran’s I = 0.3607, p < 0.001), indicating the effectiveness of cross-provincial coordinated ecological interventions. Furthermore, in recent years, the fluctuations of the ecological efficiency gravity center have decreased, while the area of the standard deviation ellipse has expanded, and high-efficiency zones have broadened, reflecting a growing balance in ecological efficiency across regions. The spatial balance of ecological efficiency across China shows positive trends, with significant improvements in the northwest and stabilization in the eastern regions. Key state-owned forest areas, such as Heilongjiang, Jilin, and Inner Mongolia, exhibit heterogeneous trajectories but generally maintain lower efficiency levels. This study underscores the need for region-specific governance and continuous ecological investment to enhance forest and landscape restoration. It provides quantitative evidence for the effectiveness of China’s Shan-Shui Initiative and explores the implications of integrated ecological restoration models, such as the Shan-Shui Initiative, for global ecological governance.

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.

Lodgepole pine (Pinus contorta ssp. latifolia) is the most widely distributed and commercially valuable subspecies among the four recognized subspecies of P. contorta. This study explored the genetic potential of seven quantitative traits using a comprehensive 14-provenance trial that included family structure, with collections from five geographic zones across British Columbia. The analysis focused on wood specific gravity, branch characteristics (angle, diameter, and length) as well as growth parameters (height, diameter, and volume). While inter-provenance differences within zones remained generally modest at age 12, substantial variation at the family level within provenances became apparent, showing significant genetic diversity within populations. Heritability estimates showed considerable variation across both traits and geographic regions, reflecting the complex genetic basis of these characteristics. Quantitative genetic differentiation (Q_st) for all traits except branch angle indicated that natural selection rather than genetic drift drove the evolution of these traits. The lack of significant isolation-by-distance effects indicated that patterns of genetic variation were not geographically structured in a straightforward linear way. Correlation analyses among traits uncovered important evolutionary trade-offs, with specific gravity showing negative associations with growth traits and branch length, but no link to branch angle. Path analysis pinpointed branch length as a key mediating factor directly decreasing wood density while also enhancing growth performance. These results highlight the hierarchical nature of trait co-evolution, where branch angle develops independently, and branch length plays a central role in mediating covariations among other features. The comprehensive results improve our understanding of lodgepole pine’s genetic potential and offer valuable insights for breeding programs aiming to balance growth performance with wood quality objectives.

Tailoring ecological restoration policies to local conditions, such as determining whether to prioritize afforestation programs or grassland restoration, is critical for the sustainable implementation of China’s Grain for Green Project (GGP). However, there remain significant knowledge gaps to understanding the divergent hydrological responses of these programs. This study integrates remote sensing and meteorological data to detect spatiotemporal variations of evapotranspiration and its influencing factors. The eco-hydrological dynamics between two distinct vegetation restoration zones are compared: the northwestern region (grassland restoration) and the southeastern region (afforestation). After implementation of the GGP, a notable greening trend with a significant increase in leaf area index was observed, exhibiting spatial heterogeneity in both regions. At the same time, evapotranspiration showed a distinct NW–SE increasing gradient. Regional drivers also diverged: evapotranspiration in the southeast was primarily leaf area index-driven, while in the northwest, it was controlled by precipitation-to-potential evapotranspiration ratios. This water-carbon coupling disparity amid regional climate variability caused contrasting water yield declines, significant in the southeast compared to the insignificant yields in the northwest. This study contrasts forest versus grassland restoration at a regional scale. The findings underscore ecohydrological feedbacks from ecological restoration, advocating spatially adaptive ecosystem management to balance water-carbon trade-offs.

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.

Forest restoration plays an essential role in mitigating climate change, conserving biodiversity, and maintaining ecosystem services. Altough global initiatives emphasize the urgency of developing effective strategies for ecosystem recovery, assessing the success of these strategies remains a considerable challenge. In this context, we investigated the growth dynamics of Cedrela odorata L. (Meliaceae) over more than 160 years in two Atlantic Forest areas in Rio de Janeiro, Brazil, characterized by distinct historical recuperation pathways: active reforestation in Parque Nacional da Tijuca (PNT) and natural regeneration following land protection in Reserva Biológica do Tinguá (RBT). Dendrochronological analyses revealed significant differences in age structure and tree size between areas. Trees in PNT were older and larger (169 years, DBH 78 cm) than those in RBT (132 years, DBH 56 cm), reflecting differences in management intensity. Higher growth rates during early development of trees in PNT are likely associated with silvicultural practices implemented during reforestation. However, from the 1940s onwards, a convergence in growth rates indicates structural recovery in both areas. This pattern reflects increased competition among trees, characteristic of more advanced stages of forest dynamics. After 1990, both areas experienced a decline in growth, potentially linked to increasing urban-related environmental stressors. Overall, our results highlight the resilience of C. odorata and emphasize the importance of forest recuperation efforts in both areas, demonstrating their long-term success. Additionally, they underscore the effectiveness of restoration practices in PNT, as demonstrated by the acceleration of structural recovery in the forest, with older trees, increased above-ground biomass, and radial increment rates comparable to those of mature forests. This success also implies the restoration of ecosystem services, as sought in the historical recovery proposals for these two tropical forest areas.

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.

Taper models are widely used to estimate log assortments and, consequently, forest yield from inventory data. However, for Pinus taeda, few studies have employed mixed-effects taper models that explicitly account for the hierarchical structure of forestry data and heterogeneity of variances. This study addresses this gap by developing and evaluating mixed-effects taper models based on modified versions of Kozak’s (1969) equation. The models incorporate random effects at the farm/forest region, stand, and tree levels and allow for different variance structures, enabling them to capture the heterogeneity commonly observed in P. taeda stands. Diagnostic procedures using least confounded residuals were applied to assess model adequacy. Compared with traditional fixed-effects taper models, the selected mixed-effects model achieved superior performance, including reduced bias, improved fit across stem sections, and better predictive accuracy. Additionally, in Appendices, we provide a tutorial outlining the computational procedures in R software for statistical modeling of data related to this species within the mixed-effects model framework.

Global climate change intensifies temperature extremes, yet empirical insights into forest species’ post-heat resilience mechanisms remain scarce. Climate extremes are projected to increase in frequency and intensity, making species resilience a critical determinant of forest ecosystem stability and biodiversity conservation under global change scenarios. Following the unprecedented 2022 Chongqing heatwave, we investigated resilience across 79 woody species along an altitudinal gradient. Given that reproductive investment strategies may be crucial for post-disturbance recovery, we hypothesized that the trade-offs between reproductive and vegetative investment may better predict species resilience under extreme heat conditions. Integrating Bayesian structural equation modeling-an advanced statistical approach that enables simultaneous analysis of multiple causal pathways and accounts for measurement uncertainty through probabilistic inference, we analyzed how vegetative traits (height, wood density and specific leaf area) and reproductive traits (seed mass, output and allocation) influence resilience. The results reveal: (1) specific leaf area (SLA) and seed mass negatively influenced species’ resilience, and seed mass was a particularly strong predictor of resilience across 79 species; (2) in 46 evergreen species, reproductive allocation at twig level enhanced resilience, while vegetative-reproductive trade-offs collectively explained resilience patterns; and (3) evergreen species adopted a bet-hedging strategy, dynamically adjusting trait allocations to balance survival and regeneration under thermal stress. This study advances community-scale resilience understanding by identifying seed mass as a key reproductive trait governing post-heat recovery, highlights seed size ecology under climate extremes, and informs post-disaster forest recovery strategies. In ecological afforestation, the findings may underscore the urgency of integrating trait-based ecology into forest management. Small-seeded species may be prioritized in post-heat stress recovery due to their stronger resilience capacity; evergreen species require balanced vegetative-reproductive resource allocation, while deciduous species with small SLA be favored. Long-term and multi-generational effects following extreme heat events require further exploration, and detailed technical specifications and operational guidelines represents the necessary next step for practical application.

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.

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.

Widespread tree mortality is increasingly associated with extreme drought, yet its mechanisms remain poorly understood. To address this, we investigated which indicators most accurately delineate the relationship between climatic stressors and satellite-derived metrics related to tree crown/forest canopy conditions and forest decline for conifers and evergreen broadleaves. The study was performed between 1990 and 2020 in woodlands of Cyprus dominated by Juniperus phoenicea, Pinus brutia, and Quercus alnifolia (endemic to Cyprus). Landsat 5, 7, 8 and 9 images were used to assess the condition of tree crowns via 8 remote sensing (RS) indicators (NDMI, EVI, GPP, LAI, NBR, NDVI, NDWI, SAVI) correlated, thereafter, with the SPI, SPEI and PDSI drought indicators. Our findings clearly outline that very severe drought conditions <−2 for the SPI-12 and the SPEI-12, or <−5 for the PDSI-12, exceeded the capacity of all 3 species to sustain healthy stands at habitats representing the xeric limits of their natural distribution range in Europe. Very low precipitation appears as the driving force. However, starting from the year of drought induced mortality and including the years that followed, their annual vegetation’s response via decadal monitoring was related to climate averaged over 4 to 7 past years (including the year of monitoring) depending on species. Observed multi-year associations are consistent with a ‘memory’ effect that may reflect cumulative depletion and slow recharge of deeper, root-accessible moisture pools; however, our remote-sensing indicators do not directly perceive subsurface storage. NBR and NDMI were significantly connected with climatic variability, as described by the SPI or SPEI for the J. phoenicea and by the PDSI for the P. brutia, before and at the first years after mortality has occurred. After some years after the mortality year, the decadal response of vegetation to climate was better described by the NDVI. Before oak mortality the RS indicators applied failed to capture the evergreen vegetation dynamic of Q. alnifolia dense stands. Thereafter, the NDVI provides the highest accuracy, as the oaks likely experienced more severe and faster defoliation than the conifers, reducing their vegetation density at levels detectable by the NDVI. Thus, our study highlights the importance of considering long-term drivers for tree foliage-defoliation status, dependent on species type and habitat-specific water availability. This understanding can explain the types of droughts (seasonal to multiyear) that can trigger tree mortality under climate change.

Internal wood defects pose a serious threat to forest health, reduce timber quality, and increase resource waste during processing, thereby constraining the sustainable development of the wood industry. Computed tomography (CT) enables non-destructive, high-resolution visualization of internal structures, yet accurate defect identification and quantitative characterization remain challenging. This study proposed an integrated framework for internal defect detection and 3D reconstruction in industrial wood applications. The proposed SCV-YOLOv8n model, built upon the lightweight YOLOv8n‑seg architecture, integrated a Spatial Pyramid Pooling Faster Cross Stage Partial Channel (SPPFCSPC) module and a Convolutional Block Attention Module (CBAM) to enhance feature extraction and improve segmentation robustness for complex defect textures. Furthermore, an improved Visualization Toolkit (VTK) pipeline was developed for high-precision 3D reconstruction. The upgraded VTK pipeline integrated Backbone-Enhanced Segmentation Refinement (BESR), Feature Connectivity Adjustment (FCA), and Boundary Curvature Guidance (BCG) submodules, which collectively refine boundary continuity, strengthen internal coherence, and preserve curvature consistency, resulting in more faithful representations of internal geometries. Experiments conducted on three typical internal defect types (decay, hollow, and knots) demonstrated that SCV‑YOLOv8n outperformed the baseline YOLOv8n-seg in both detection accuracy and segmentation consistency, while the improved VTK pipeline achieved higher 3D reconstruction fidelity and enhanced morphological realism. The 3D reconstruction results revealed that decay and hollow defects form interpenetrating cylindrical structures, whereas knots present irregular localized volumes. This framework provides an effective and practical method for accurate defect visualization, sawing path optimization, and resource utilization, supporting sustainable and intelligent wood processing.

Correction to: Journal of Forestry Research (2026) 37:34 https://doi.org/10.1007/s11676-025-01979-9 

The article “Methods for identifying topkill in conifers using airborne lidar to monitor structural diversity and disturbance”, written by Brent W. Oblinger, Benjamin C. Bright, Andrew T. Hudak and Kerri T. Vierling, was origi nally published under the copyright “This is a U.S. Gov ernment work and not under copyright protection in the US; foreign copyright protection may apply”. As a result of the subsequent decision to publish the article under the open access model, the article’s copyright notice was changed on 16 January 2026 to © The Author(s) 2026 and the article is now distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to The original article can be found online at https:// doi. org/ 10. 1007/ s11676- 025- 01979-9. * Brent W. Oblinger brent.oblinger@usda.gov 1 2 3 Forest Health Protection, USDA Forest Service, 63095 Deschutes Market Rd, Bend, OR 97701, USA Rocky Mountain Research Station, USDA Forest Service, 1221 South Main St, Moscow, ID 83844, USA Department of Fish and Wildlife Sciences, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844, USA the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this arti cle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4.0 

The original article has been corrected.

Our understanding of water cycle dynamics is limited owing to uncertainties regarding global evapotranspiration (ET) and its drivers under climate change. This study aimed to improve the precision of global ET estimates using the remote sensing Penman–Monteith (RS-PM) model and reveal the driving factors of ET variation using geographical detectors. The RS-PM model accurately predicted the 8-d ET, with results comparable to those of the Global Land Surface Satellite (GLASS) algorithm. The RS-PM ET estimates for evergreen broadleaf forests and mixed forests had relatively high accuracy, whereas those for shrublands and wetlands had large uncertainties. The RS-PM model overestimated ET in high- and mid-latitude regions of the Northern Hemisphere and underestimated ET in low-latitude regions, the Southern Hemisphere, and regions between 90° and 180° E. Net radiation (NR) was the dominant factor driving global 8-d ET variation, whereas leaf area index (LAI) acted as the dominant driver of annual ET variation across most climatic zones. Precipitation was the main driver of annual ET in semi-arid and arid regions, in southern Africa, and most of Asia, while NR, temperature, and vapour pressure deficit (VPD) dominated annual ET in regions characterized by limited solar energy and those at high latitudes. The effects of LAI-VPD interactions had high explanatory power for ET variation in most regions; however, interaction effects varied across latitudes and climatic zones. This study provides a valuable reference for improving the RS-PM model to predict global ET and discovers discrepancies in the dominant factors on ET variation across global regions.

Tropical cyclones routinely cause catastrophic damage to coastal forest ecosystems. However, critical knowledge gaps persist regarding intra-typhoon spatial dynamics—particularly how damage gradients vary with proximity to both storm eyes and shorelines within individual typhoon events. This hinders mechanistic understanding of windthrow patterns essential for developing targeted coastal resilience strategies. This study pioneers a systematic investigation into the spatial vulnerability patterns of Casuarina equisetifolia coastal shelterbelts through novel dual-distance analysis from typhoon epicenters and coastlines. Our research involved examining the damage patterns and extent inflicted by super-strong Typhoon No. 5 “Doksuri” in 2023 on trees at the typhoon’s center and its sub-center. The results of this study were obtained through function fitting and ANOVA, mainly by dividing different gradients and investigating the condition of damaged trees. (1) Damage to C. equisetifolia was more severe in the typhoon’s sub-center than in the center, and it intensified with increasing distance from the coast; (2) The number of severely damaged trees and the total number of damaged trees under different gradients at both locations generally increased and then decreased with the increase in diameter class; (3) Gradient-dependent divergence emerged in severe damage (stem failure/uprooting), whereas no significant gradient-related differences were observed for minor damage. By establishing quantitative relationships between wind damage gradients and these critical geographical parameters, we aim to develop climate-resilient management strategies that address the escalating threats of intensified typhoons under climate change. Similar content being viewed by others

Correction to: Journal of Forestry Research (2026) 37:49 https://doi.org/10.1007/s11676-026-01996-2 

In this article, the author’s name Andrés Bravo-Núñez was incorrectly written as Andrés Núñez-Bravo. 

The original article has been corrected.

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.

In the Mediterranean region, wind-driven crown fires are becoming more frequent, leading to increased atmospheric pollutant emissions. This study explored how the distribution of pre-fire fuels across the crown, shrub and litter layers varies among different fuel types, and how these variations were linked with fuel consumption and fire severity for each layer to quantify and compare atmospheric pollutant emissions (CO₂, CO, CH₄ and PM_2.5) in pine (Pinus halepensis) and oak (Quercus suber) forests. Our analysis was carried out in the Jonquera wildfire in Northeast Spain, which burned 10,264 ha. Pre-fire fuel loading among fuel types in pine and oak forests showed different vertical distributions despite, having similar fire-type patterns. Pine forests had a higher percentage of crown and shrub fuel loading for all fuel. In contrast, oak forests had more litter than pine forest. Fuel types characterized by large trees and low densities had the lowest fire severity in both forest types. Pine forests were more resistant to the effects of surface fires than oak forests due to their taller trees, which allowed them to withstand high-intensity surface fires with less tree damage. However, these fires have resulted in higher surface fuel consumption in pine forests. Fuel types with more vertical and horizontal continuity experienced higher fire severity and fuel consumption (72–85% of high severity). Fire severity rather than species or fuel type was the primary factor influencing pollutant emissions. Emissions of CO₂ and CH₄ were higher in pine than in oak forests especially at lower severities, while at intermediate and higher severity oak forests emitted more CO and PM_2.5. Although remote sensing technologies are useful for fuel loading and wildfire severity assessments, field data are essential for accurately quantifying fuel consumption across fuel types and layers.

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.

Understanding the drivers shaping biodiversity patterns, including the spatial distribution of ancient trees, is essential for effective conservation planning. Ancient trees are ecologically and culturally important, yet their persistence is increasingly threatened by urban expansion and other human activities. In this study, we compiled a comprehensive dataset of 18,581 records of living ancient trees across Hainan Province, China (70 families, 174 genera, 305 species). After excluding grids with missing environmental variables, 18,459 trees were retained for modeling. We analyzed species composition, age structure, and spatial distribution, and explored their drivers using Ordinary Least Squares (OLS) regression and Spatial Lag Models (SLM). The results showed high species diversity and a pyramidal age structure, indicating a relatively stable population with continuous recruitment into older age classes. Spatially, the trees exhibited significant clustering, with higher densities in western and northern regions and markedly lower concentrations in highly urbanized coastal zones. Habitat heterogeneity, particularly the difference between maximum and minimum annual precipitation (MAPR), emerged as the predominant predictor of species richness and abundance across most age classes. In contrast, the distribution of the youngest ancient trees (Grade Ⅲ) and overall tree density were primarily associated with socioeconomic factors, especially accessibility. Based on these results, our findings suggest that effective conservation of ancient trees requires not only the maintenance of environmental heterogeneity but also careful regulation of human accessibility. The regulation of human accessibility here should aim to strike a balance between effective conservation management and minimizing human interference, especially in rapidly urbanizing landscapes, to ensure their long-term persistence.

In the process of secondary forests restoration on the eastern Qinghai-Tibet Plateau, the regeneration of Minjiang fir (Abies fargesii var. faxoniana) is of critical importance. Key challenges in regeneration include securing a stable seed supply and ensuring seedling survival; however, research on the seed ecology of these species remains limited. This study collected cones and seeds of Minjiang fir from seven sites distributed along four elevations on the eastern Qinghai-Tibet Plateau. We compared the coefficient of variation and diversity, conducted multiple comparisons, examined correlations, and analyzed seed vigor and germination across elevations and crown directions. The results showed that: (1) Cones length and weight were 5.98 ± 1.36 cm and 29.06 ± 12.39 g. Seed size was 11.2 ± 1.9 mm, and the weight of 1,000 seeds was 8.86 ± 2.31 g. Seed vigor averaged 19.05% and germination rate was 17.61%. (2) With increasing elevation, the morphological traits of cones and seeds first increased, peaked at 3,300 − 3,600 m, and then decreased. Trait variations were less pronounced than at other elevations, and diversity analysis revealed weaker differentiation, with more stable seed trait expression. Furthermore, cone and seed traits near the timberline areas are significantly smaller than those in adjacent lower elevation zones. The 3,300–3,600 m elevation zone appears to be the most suitable habitat for Minjiang fir. (3) Seed vigor and germination rate followed unimodal distribution patterns. Seeds at 3,900 and 3,300 m germinated faster than 3600 m, whereas seeds at 3,000 m took the longest time to initiate germination. Seeds at higher elevations exhibited faster post-initiation germination but later initial germination and a lower final germination rate. (4) Seed vigor and germination rate are positively correlated with cone length, cone weight, seed length, seed weight, tree height, diameter at breast height, and crown direction, and negatively correlated with elevation and the number of seeds per cone. Cone weight had the highest contribution to seed vigor and germination (21.5%), followed by elevation (18.39%). Minjiang fir growing on the sunny side of the crown exhibited better cone and seed traits, including larger, plumper seeds. This study validates the mid-elevation peak pattern of Minjiang fir with increasing elevation and can help understand the regeneration of coniferous trees in high-mountain areas.

Schinus terebinthifolia (Raddi) is a tropical fast-growing broadleaf tree species in Brazil’s Atlantic Forest, a threatened biodiversity hotspot. The species has socioeconomic importance, is used in forest restoration programs and urban greening. In urban environments, plants are exposed to several abiotic stressors such as atmospheric pollution. Tropospheric ozone (O₃) is one of the main secondary pollutants that affect plant growth and survival. Therefore, determining the critical levels (CL) of phytotoxic O₃ is essential. Ozone risk assessment for S. terebinthifolia is unknown. Forty-five S. terebinthifolia seedlings were cultivated in pots and submitted to five ozone treatments for three months in an O₃-free-air controlled facility. Ozone risk assessment was based on environmental data, measurements of stomatal O₃ uptake and seedling biomass. To find the best model for predicting O₃-induced biomass loss, we tested the accumulated ozone exposure over a threshold of 40 ppb (AOT40) and the phytotoxic ozone dose above a threshold ‘y’ (POD_y) using linear and 116 nonlinear statistical models. POD₁₆ and POD₁₅ when applied with the four-parameter nonlinear logarithmic model “Bragg4” provided the best fit for assessing O₃ risk based on total biomass and leaf biomass, respectively. The species had a 4% biomass loss at 2.56 mmol O3 m⁻² POD16 and in leaf biomass at 3.37 mmol O₃ m⁻² POD₁₅. The results indicate that biomass accumulation was stimulated at low to moderate O₃ levels but reduced at higher levels. Overall, S. terebinthifolia demonstrated a high tolerance to tropospheric O₃.

Corection to: Journal of Forestry Research (2026) 37:75 https://doi.org/10.1007/s11676-026-01985-5

 In this article, the keyword ‘Taper Modeling’ was incorrectly written as ‘Aper Modeling’. 

The original article has been corrected.

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.

Understanding long-term aboveground biomass density (AGBD) trends under a changing climate is essential for quantifying forest carbon dynamics. However, management activities such as harvesting and thinning can obscure climate-driven signals. In this study, we developed a national framework to estimate annual AGBD across Japan by aggregating Global Ecosystem Dynamics Investigation (GEDI) footprints into 1000 m grid cells and integrating them with multisource satellite and topographic predictors. We first trained a series of models using GEDI reference data aggregated under different minimum footprint requirements across grid cells. By applying the best performing model, which was obtained when each grid cell contained at least 28 GEDI footprints and showed the highest agreement with National Forest Inventory (NFI) data (r = 0.87, RMSE = 31.73 Mg ha⁻¹, RRMSE = 0.18, bias = 25.52 Mg ha⁻¹), we generated annual AGBD maps from 2009 to 2018. Using these estimates, we quantified a national mean AGBD increase of 0.21 Mg ha⁻¹ per year, with undisturbed forests showing a stronger increase of 0.43 Mg ha⁻¹ per year, while disturbed areas exhibited a decline of 0.20 Mg ha⁻¹ per year. By integrating GEDI observations, multisource remote sensing data, and annual forest disturbance maps, we successfully characterized long-term AGBD dynamics under different disturbance regimes and revealed distinct climate-driven and disturbance-driven trajectories.

We present a 966-year (1050–2015 CE) ring-width chronology of Pinus californiarum D.K. Bailey from Baja California, Mexico, representing the longest published dendrochronological record for any pine species in the country. Despite pronounced anatomical and ecological challenges, including frequent missing rings and growth irregularities, the final chronology exhibits strong signal strength and robust cross-dating, enabling the assessment of hydroclimatic variability across multiple temporal scales. Radial growth is primarily controlled by winter precipitation, with significant positive responses to both instrumental precipitation and precipitation associated with atmospheric river activity during November–March. Growth is negatively related to maximum temperatures, indicating enhanced drought stress under warmer conditions. Correlations with reconstructed drought indices confirm a strong sensitivity to regional moisture availability. At larger scales, P. californiarum growth is significantly influenced by Pacific climate variability. Positive correlations with the Multivariate ENSO Index and two independent ENSO reconstructions reveal a nonstationary ENSO–growth relationship over the last millennium, characterized by a prolonged interval of weakened teleconnections between approximately 1300 and 1600 CE, followed by strengthened coupling after ~ 1700 CE. Spectral and wavelet analyses further indicate intermittent expression of ENSO-scale variability and lower-frequency modulation consistent with Pacific decadal processes. Together, these results demonstrate that P. californiarum integrates both high-frequency hydroclimatic variability and long-term shifts in Pacific climate forcing, highlighting its value as a sensitive paleoclimatic proxy for the northeastern Pacific region and underscoring the importance of conserving old-growth stands in extreme water-limited environments.

Effective fuel management is a priority in Mediterranean Eucalyptus globulus plantations due to wildfire risk, but the short-term ecological effects of prescribed fire remain unclear. We hypothesized that PF would reduce post-harvest fuel loads without negatively affecting short-term soil properties or stump resprouting in E. globulus plantations. The main objectives were to determine the short-term effectiveness of PF in reducing fuel load and to evaluate its impacts on soil properties and on stump survival and resprouting height. A field experiment was carried out in two recently harvested blue gum stands under contrasting climates in northern and central Portugal during January 2023. Each site was divided into two plots (approximately 0.5 ha each): one prescribed fire treatment and one control, with three parallel transects per plot. Fuel loads, soil properties and stump resprouting were assessed pre-PF and 45–60 d after PF. Soil impacts were evaluated using temperature recordings at 0–2 cm depth and visual soil burn severity. Stump resprouting heights were measured post-fire in both regions. Statistical analyses were used to assess differences between treatments. PF significantly reduced fine fuel loads by 73.5% in Póvoa de Varzim and 60.3% in Nisa compared to the control (p < 0.05). Soil burn severity was low, with peak temperatures of 117.5 °C and 107.5 °C at a 2-cm depth in Póvoa de Varzim and Nisa, respectively. PF increased soil pH (from 4.0 to 5.0 in Póvoa and 5.7 to 7.1 in Nisa) and phosphorus content. Organic matter levels were unaffected in Nisa and slightly reduced in Póvoa. Stump survival rates were high (> 87%) in both sites, with PF showing no statistically significant difference from control. However, while similar in Póvoa (0.37 m), resprouting heights in Nisa were lower in PF plots (0.50 m) than in controls (0.91 m). Overall, this study provides field evidence that PF can be used to effectively reduce fire hazard without significant negative effects on soil chemical properties or on the short-term survival and sprouting of E. globulus stumps.

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.

Tropospheric ozone (O₃) pollution is increasingly recognized as a major stressor to forest ecosystems, particularly through its interference with plant nutrient cycling processes. However, the mechanistic basis by which O₃ influences the resorption of multiple essential nutrients remains inadequately characterized. In this study, we conducted a controlled open-top chamber experiment using hybrid poplar ‘107’ (Populus euramericana cv. ‘74/76’) to systematically assess the effects of elevated O₃ exposure (ranging from 19.2 to 99.0 ppb) on the resorption efficiency (RE) of six key macronutrients: nitrogen (N), phosphorus (P), sulfur (S), potassium (K), calcium (Ca), and magnesium (Mg). Elevated O₃ consistently enhanced the RE of N and S that are fundamental to core metabolic processes while reducing the RE of K, Mg, P, and Ca. Notably, K and Mg accumulated in senesced foliage at high O₃ doses, with critical thresholds observed at 46.0 and 41.7 ppm·h, respectively. These results suggest a trade-off in nutrient retrieval priorities, where O₃-induced oxidative stress favors the conservation of nutrients central to metabolic integrity (N, S), at the expense of those involved in osmotic balance (K) and photoprotection (Mg). Through a combination of power-law regression and structural equation modeling, we identified a triadic framework of coexisting nutrient resorption controls: (1) nutrient concentration control, influencing RE across N, P, S, K, and Mg; (2) nutrient limitation control, specifically modulating N and P based on their functional scarcity; and (3) stoichiometric control, preserving internal N:P ratios in senescing leaves. Elevated O₃ triggered a transition from phosphorus limitation to nitrogen limitation, revealing a disproportionate disruption in nitrogen-related metabolic pathways under oxidative stress. At the mechanistic level, the observed changes in RE were mediated through two interconnected pathways: direct physiological impairment of nutrient translocation processes (e.g., damage to membrane transport systems) and indirect modulation of soil–plant-microbe interactions, including shifts in soil pH and microbial community composition. Collectively, these findings offer new insights into how chronic O₃ exposure reshapes nutrient resorption dynamics and regulatory mechanisms in trees. They also provide a conceptual foundation for forecasting forest ecosystem responses to atmospheric oxidative stress. This study underscores the critical need for species-specific investigations and sustained monitoring of nutrient fluxes in O₃-impacted environments, with significant implications for adaptive forest management under changing air quality conditions.

The effect of forest structure affected productivity of harvester operator work in a continuous cover forest. In particular, the effects of the forests consisting of even- and uneven-age trees were compared. In addition to tree variables describing the structure of the forest, variables describing the time taken in each of the work phases were needed to accurately model the logging motions. Modelling also required single tree data from the harvester. The model precisely predicted the relationship between the variables and productivity in both the uneven-aged forest (R² = 0.96) and the even-aged forest (R² = 0.92). Both explanatory powers of models were statistically significant. Productivity was explained by the “volume of trunk”, the “length of the operating part of trunk”, the “moving time of the logging device to trunk”, the “felling time of tree” and the “processing time of trunk”. In the uneven-aged forest, the effective-hour productivity was 37.7 m³ E₀h⁻¹ and in the even-aged forest 43.5 m³ E₀h⁻¹. The work phases “moving the logging device to trunk” and “felling of tree” consumed more time in the uneven-aged forest. The results of the time and motion analysis justify the promotion of training both work phases to increase productivity of harvester operator work. This modelling approach can be recommended for studies on the development of selective logging method for continuous cover forestry.

Relative humidity (RH) plays a crucial role in maintaining both forest and human health. However, the historical characteristics and drivers of RH in the southeastern Chinese Loess Plateau (SECLP) remain poorly understood. Here, a high-resolution tree-ring-width record from the SECLP reveals that the radial growth of Pinus tabuliformis is limited by RH from April to mid-August (RH_c10–23), which coincides with the period from the Chinese solar term Pure Brightness to End of Heat. The 180-year RH_c10–23 reconstruction explains 45.40% of instrumental variance (1956–2020 CE) and reveals a long-term drying trend since 1844 CE, especially evident since 1956 CE. Three iconic Northern China droughts—the ~ 1900, late-1920s, and 1940–1943 events—are clearly resolved, together with an unprecedented 1997–2023 drought. Conversely, wet periods occurred during 1850–1890, 1910–1916, and 1951–1966 CE. The reconstruction mirrors broad Chinese Loess Plateau hydroclimatic patterns while retaining local signals. Over the past two centuries, SECLP hydroclimatic variations have been anti-correlated with regional temperature and modulated by the Asian summer monsoon, Atlantic Multidecadal Oscillation/Atlantic Multidecadal Variability, El Niño-Southern Oscillation, and Indian Ocean Dipole. This study enhances our understanding of regional hydroclimatic evolution, highlights human impacts on natural systems, and provides scientific support for climate change adaptation and water resource management.

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.

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.

As a key factor affecting both ecosystem processes and human health, the long-term dynamics of the diurnal temperature range (DTR) and its underlying drivers remain poorly constrained, largely due to the scarcity of longterm observational data. Here, we developed the first tree-ring cellulose oxygen isotope (δ¹⁸O)-based reconstruction of May–October DTR for China's Sichuan Basin, spanning the period 1673–2022. The reconstruction reveals a significant increasing trend in DTR since approximately 1952, which contrasts with the general global pattern and highlights distinct regional heterogeneity. This observed DTR increase is primarily driven by an asymmetric warming pattern dominated by rising maximum temperature (TMAX), and is closely associated with local environmental factors including reduced cloud cover and decreased atmospheric humidity. Correlation and running correlation analyses demonstrate significant modulation of the reconstructed DTR by sea surface temperature anomalies in key ocean basins. We find that the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) jointly modulate DTR variability through their influences on monsoon circulation and cloud–radiation processes, with the Atlantic Multidecadal Oscillation (AMO) playing a secondary role. Future projections based on CMIP6 models indicate continued DTR increases under high-emission scenarios, underscoring the combined effects of internal climate variability and anthropogenic forcing on regional climate risks.

Monitoring forest ecosystems is essential for understanding ecological processes, assessing biodiversity, and guiding sustainable forest management. Chlorophyll fluorescence (ChlF) parameters provide valuable, physiologically relevant insights into the state of the photosynthetic apparatus and serve as indicators in forest monitoring. However, effectively describing the spatial distribution of ChlF parameters within the canopy and developing convenient methods to track their spatial variations remains challenging for scale extension and remote sensing applications. This study focused on several typical temperate deciduous forest species, for which the ChlF parameters, spectral reflectance, and canopy leaf coordinates were synchronously recorded. The results indicated that there were significant spatial variations in ChlF parameters within the canopy. The development of different machine learning (ML) algorithms for estimating ChlF parameters using different combinations of spectral reflectance, species, and leaf coordinates demonstrated that the random forest and extreme gradient boosting (XGBoost) models using leaf spectral reflectance have considerable potential for capturing spatial variation in ChlF parameters. Furthermore, the inclusion of additional information, such as species and leaf coordinates, significantly improved the performance of these models (explaining over 85% of the variance for all ChlF parameters) as well. This study supports the assessment of spatial patterns of ChlF parameters and demonstrates the potential of ML to characterize their variation across forest canopies. By directly modeling ChlF parameters at high spatial resolution, this approach provides new insights into forest physiological processes and advances biodiversity and environmental research.

Modification of forest structure and species diversity is an effective strategy for enhancing forest carbon sequestration. However, the relative contributions of tree spatial arrangement, tree size, and species diversity to soil organic carbon (SOC) and its fractions remain poorly understood. To address this, we established 720 plots in Northeast China to investigate the influences of these structural characteristics on soil mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and total SOC. We found that: (1) tree spatial arrangement and size produced more effects on SOC, POC, and MAOC dynamics than species diversity. Plots dominated by larger trees (with greater diameter and height) had 10.8–26.2% lower SOC, POC, and MAOC compared to plots with smaller trees. In contrast, plots with more neighborhood species mixing, spatial clumping arrangement, and more even species distribution had 1.11–1.31 folds higher SOC, POC, and MAOC than those with lower mingling, clustering, and evenness trees, especially more favoring the POC accrual. Compared with tree size and spatial arrangement parameters, species diversity showed marginal effects on these carbon parameters. (2) Soil properties were important mediators of the plant-soil interactions. Both redundancy analysis and structural equation model showed that soil pH and available P counteracted the negative effect of tree size on MAOC, while soil nitrogen (N) directly increased POC by over sevenfold higher than the influence of spatial arrangement (tree mixing and clustering distribution). (3) Microbes were closely associated with variations in POC and MAOC, and our data identified the most probable functional genes and dominant phyla. Our findings underscore the critical roles of soil properties and microbial communities in SOC dynamics and highlight the potential for optimizing forest spatial structure and tree size aimed at coping with climate change.

In the context of accelerating climate change, this review comprehensively explores genetic innovations in forest tree breeding emphasizing their potential for forest restoration, nematode resistance and climate resilience. Rising global temperatures, extreme weather and pollution are degrading forest ecosystems, limiting biodiversity, carbon sequestration, and ecological stability. While traditional breeding has delivered a valuable genetic foundation but its long cycles and limited precision necessitate integration with advanced tools. Modern approaches such as CRISPR/Cas genome editing, genomic selection (GS), marker-assisted selection (MAS) and epigenetic breeding now enable targeted improvement of traits including drought tolerance, pest disease resistance, wood quality and climate adaptability. Recent advances highlight polygenic nematode resistance mechanisms, transcription factor–mediated defense regulation and secondary metabolite–based anti-pathogen strategies. Assisted Gene Flow (AGF) including pollen-based assisted migration, offers complementary pathways to enhance genetic diversity and adapt populations to future climates. Epigenetic mechanisms such as DNA methylation, histone modifications, small RNAs and epi-miRNAs are increasingly recognized for their role in stress memory, hybrid vigor and rapid heritable adaptation without altering DNA sequence. Integrating multi-omics platforms with these breeding strategies improves trait prediction, elucidates gene–environment interactions and accelerates genetic gain. The review also addresses key challenges such as high implementation costs, regeneration bottlenecks in woody species, ecological trade-offs, and the stability of induced modifications. A coordinated approach among scientists, policymakers and forest managers is essential to deploy these innovations, restore forest health, mitigate climate change and nematode threats, and safeguard ecosystem services for future generations.

Understanding how forest conversion and mycorrhizal disruption shape microbial phosphorus (P) cycling is essential for managing nutrient sustainability in subalpine forest ecosystems. We assessed the influence of long-term land-use change and short-term root trenching (ectomycorrhizal inputs reduction) on phospholipid fatty acid (PLFA)-derived microbial biomass pools, P-related functional genes, enzyme stoichiometry, root litter P mass remaining, and P fractions in paired natural and plantation Picea asperata forests on the eastern Tibetan Plateau. Natural forests exhibited more modular P-related gene networks, higher resin-Pi concentrations, and lower root litter P retention than plantations. Plantation soils had higher abundance of P-mineralization genes than natural forest soils but lower network connectivity and partial fragmentation following root trenching. Piecewise structural equation models revealed that forest type had stronger statistical associations with microbial gene composition, enzyme stoichiometry, and soil P partitioning than root trenching. Root trenching was statistically associated with PLFA-derived microbial biomass pools (PLFA-PC1) shifts that covaried with soil P fractions in natural forests but not in plantations. These findings highlight forest-type-related differences in microbial associations with soil phosphorus fractions and in gene co-occurrence network structure following root trenching. Microbial networks in natural forests were more modular and more strongly associated with soil P fractions, whereas plantations exhibited reduced network connectivity and limited covariation. Recognizing these belowground structural patterns, particularly the potential role of intact root–mycorrhizal networks, may help inform forest management strategies aimed at sustaining microbial-soil P linkages under land-use change. This study provides a data-driven foundation for future hypothesis testing on microbial indicators of biogeochemical variation in forest systems.

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.

Bark beetle infestations can cause rapid mortality in Norway spruce, yet the sequence of physiological changes leading to death is not fully understood. The aim of this research was to disentangle the earliest tree physiological signs of spruce bark beetle (Ips typographus L.) induced stress in Norway spruce (Picea abies L. Karst). Phloem girdling was used to attract bark beetles in an alpine forest and monitor tree physiological changes from the very beginning of the infestation, when symptoms are not yet visible in the crowns. Girdled–infested trees were compared with girdled–only and healthy control trees. Sap flux density, tree stem growth, and canopy optical properties were measured as indicative physiological parameters of tree stress response. Cessation of stem growth was the first stress signal after bark beetle infestation, while sap flux density declines > 40% occurred weeks later. Additionally, girdled–bark beetle–infested trees showed an anticipated decrease in sap flux density with increasing vapour pressure deficit and declines in canopy reflectance. Girdled–only trees did not show statistically significant differences in sap flux density or stem growth compared to the control, and have not yet experienced mortality since the start of the experiment. Mechanical phloem disruption alone does not cause rapid Norway spruce mortality, indicating that additional factors, such as fungal sapwood infection, might strongly contribute to bark beetle–induced death. Mortality appears to be preceded by a shift of resources from growth to defence, followed by hydraulic failure. Radial growth monitoring thus provides a valuable tool for early detection and modelling of beetle–driven spruce decline.

Public health crises have presented evolving challenges and opportunities for tourism management. By integrating ground survey data with social media big data using Bayesian Network modeling, we analyzed how the pandemic changes in visitor engagement with forest environments in national park from 2019 to 2023 based on 45,007 unique records. Results show increased sensitivity of nature and forest-based attributes and decreased sensitivity of infrastructure to overall satisfaction in 2020 and 2021. Educational level, income, and age were key demographic factors associated with satisfaction. Four scenario analyses explored outcomes of hypothetical visitor shifts and management interventions, while backpropagation analyses identified efficient pathways to optimal satisfaction, with park infrastructure and hospitality yielding the greatest marginal benefits. This study supports data-driven strategies to increase park ecological and operational resilience, enhance visitor experience and loyalty, and inform adaptive park management for the new normal and future public health crises.

Studies in Quebec have reported contrasting trends in conifer composition, with some documenting long-term declines while others suggest increases. This study examined changes in conifer basal area percentage (CBAP) from 1985 to 2021 using 1796 permanent forest inventory plots across deciduous, mixed, and boreal forest zones and evaluates Landsat based Cubist regression models from two time periods (1992/1993—2016/2017) to monitor these shifts. Field data showed a consistent increase in CBAP over time, with nearly 50% of all plots showing increases, especially in mixed forests where balsam fir (Abies balsamea) accounted for much of the observed change. This trend may reflect successional processes in stands recovering from the last major spruce budworm outbreak (1972–1986) and forest tent caterpillar outbreaks in the province. Cubist models predicting CBAP and trained on Landsat-8 imagery from 2016/2017 had the best performance overall, with an average R² of 0.679, correlation of 0.82, and lowest average error (14.1%). To improve comparability across sensors and years, we selected a combined model (M24) trained on data from both time periods to generate spatial predictions. The model achieved an R² of 0.633 and used only four untransformed spectral bands within a single rule. This model effectively reproduced the median CBAP values through time at the regional scale, predicting medians of 38.3% (observed: 39.3%) in 1992/1993 and 62.4% (observed: 60.0%) in 2016/2017, though it underestimated values at the extremes. At the plot level, predicted changes in CBAP were only moderately aligned with observed changes (R² = 0.241, MAE = 17.31, RMSE = 22.18). Overall, our findings demonstrate that satellite-based models can reliably detect broad trends in forest composition, and integrating CBAP into remote sensing workflows provides a scalable, interpretable approach for long-term ecological monitoring and forest management.

The western conifer seed bug, Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae), is an invasive pest that severely threatens the health and production of conifer orchards since its arrival to Europe about two decades ago. Despite its economic and ecological relevance, little is known about the potential of native entomopathogenic fungi (EF) for its biological control. In this study, we sampled native fungal isolates from L. occidentalis and surrounding soils in coniferous forests. Pathogenicity bioassays were conducted to assess mortality rates and median lethal time (TL₅₀) across different fungal species and environmental conditions. We specifically examined the effects of water availability, insect sex and potential host defense responses. Beauveria bassiana was found naturally infecting L. occidentalis, with an 18% incidence in quarantined individuals. All tested fungal isolates induced high mortality. The lowest TL₅₀ was observed with Cordyceps farinosa (5.9 d, 100% mortality) under dry conditions, and with Metarhizium anisopliae (8 d, 91.6% mortality) when a moisture source was present. Native B. bassiana strains exhibited pathogenicity comparable to commercial formulation. Mortality accelerated under water stress, except in treatments with M. anisopliae, where mortality and TL₅₀ were reduced. Sex differences in susceptibility were detected with females showing higher resistance. Limb loss was observed during infection and postmortem metallic grey discoloration of compound eyes was detected exclusively in insects treated with M. anisopliae. These findings suggest that native fungal isolates are promising candidates for integrated control strategies against L. occidentalis in forest systems.

Mycorrhizal associations are critical to forest productivity and biomass carbon stock. Ectomycorrhiza (EcM) and arbuscular mycorrhiza (AM) associations, the dominant types in forest soils, differ in their nutrient acquisition and soil feedback mechanisms. However, previous studies mainly focused on single-species stands or experimental forests, leaving the influence and underlying mechanism of mycorrhizal associations on forest carbon stocks at regional scales unclear. Based on data from 64,303 trees across 1226 natural forest plots in the tropical/subtropical regions of southwest China, we found that aboveground carbon stock levels peaked at intermediate proportions (53.8%) of EcM trees, suggesting that mixed mycorrhizal strategies, characterized by the coexistence of EcM and AM trees, are more effective in enhancing carbon stocks than single-mycorrhizal type. Mechanistically, this effect may be primarily attributed to the contrasting impacts of mycorrhizal associations on species diversity and stand structure, suggesting that mycorrhizal-mediated niche complementary in spatial resource allocation and nutrient acquisition drives forest aboveground carbon accumulation. In addition, in EcM-dominated forests, the negative impact of increasing EcM tree proportions on carbon stocks diminishes as precipitation rises. These findings highlight the importance of simultaneously incorporating mycorrhizal associations and forest attributes in carbon sequestration strategies and ecosystem management practices.

Lingonberry (Vaccinium vitis-idaea L.) is an understory shrub esteemed for its unique ecological functions and economic value within the forests of northern China. Soil microbial consortia can improve soil quality and increase plant growth through synergistic interactions among microbial species. However, the tripartite interactions among soil microbial communities, lingonberry, and soil remain poorly understood. This study analyzed the structure and characteristics of rhizospheric fungal communities in lingonberry, isolated and verified mycorrhizal fungi that can promote lingonberry growth, and evaluated the effects of their combined application. The results revealed that soil pH, organic carbon (SOC), water content, and altitude significantly influenced fungal diversity. A beneficial fungal consortium consisting of the strains Arachnopeziza aurata and Oidiodendron maius significantly increased plant height, plant weight, SOC, soil NH₄⁺-N, and soil NO₃⁻-N and the expression of growth-related genes (VvmatK and VvPsbA) in lingonberries. In vitro interaction assays demonstrated that the interaction between A. aurata and O. maius improved mycelial growth quality and increased extracellular enzyme activity. Overall, this study demonstrated that a beneficial fungal consortium can activate positive plant–microbe feedback that concomitantly enhances soil fertility and lingonberry productivity, thereby providing a microbe-assisted strategy for understory vegetation management and soil health restoration.