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.

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 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

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.

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.

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.

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.

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.

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.

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

Understanding 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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.