Search Results (52)

Post-sunset supplemental light promotes Picea seedling stem elongation, but the underlying hormonal regulation mechanisms on interspecific differences in spruce growth response to photoperiod remain unclear. This study aimed to clarify the physiological mechanism underlying the response of two Picea species to different supplemental light durations. Three-year-old seedlings of P. abies and P. crassifolia were subjected to 0 (CK), 4, 8, and 12 h of post-sunset supplemental light treatments for two growing seasons, with growth characteristics and endogenous hormone contents analyzed. The results showed that species and the interaction between species and photoperiod were the principal factors driving phenotypic divergence in spruce growth traits. Supplemental light treatments significantly promoted sustained growth of P. abies, with 4 h treatment being optimal. This treatment also resulted in the highest levels of gibberellins (GAs) and zeatin riboside (ZR), as well as the highest ratios of ZR/GAs. For P. crassifolia, supplemental light treatment promoted dry matter accumulation (8 h treatment being optimal) but had no significant effect on other growth traits, most endogenous hormones (ZR, IAA), and their ratios across treatments. Correlation and causal inference mediation analysis suggest that ZR and the ZR/IAA ratio could be the main factors driving shoot elongation. Thus, the findings provide a valuable insight for optimizing species-specific supplemental light regimes for seedling production in nurseries. Full article

Aiming to clarify the spatial distribution characteristics of soil microbial assemblages and the environmental factors shaping them across a narrow altitudinal transect, this investigation concentrated on the surface soil layer within naturally occurring mixed forests of Picea crassifolia and Betula platyphylla, situated in the elevation band from 2400 to 2800 m along the southern flank of the Qilian Mountains. Leveraging the Illumina NextSeq 2000 high-throughput sequencing platform, integrated with α- and β-diversity analyses and redundancy analysis (RDA), we systematically characterized the composition and diversity traits of soil bacterial and fungal communities, as well as their associations with environmental factors. Notably, the bacterial communities were dominated by Pseudomonadota, Actinomycetota, and Acidobacteria with the abundance of Pseudomonadota decreasing with increasing altitude and that of Acidobacteria increasing with increasing altitude. Furthermore, Ascomycota and Basidiomycota were the dominant phyla in the fungal community. In contrast, bacterial α-diversity—as estimated by the Ace index—showed no significant variation across altitudes. Yet, the fungal alpha diversity metrics—namely Ace and Chao1—were markedly elevated at the 2800 m elevation relative to those observed at both intermediate and lower-altitude locations. Importantly, fungal diversity and community composition showed stronger altitudinal differentiation than bacterial communities in this dataset. Moreover, soil pH, total phosphorus, organic carbon, litter C:N:P stoichiometric ratios, and microbial biomass C:N:P stoichiometric ratios were strongly associated with soil microbial community variation along the altitude gradient, suggesting that they may act as important environmental filters. In conclusion, altitude-driven variations in litter characteristics and soil physicochemical properties jointly shape the assembly processes and spatial distribution patterns of soil microbial communities in this region. Full article

Forest canopies effectively remove airborne particles, reducing the frequency of atmospheric haze and improving air quality as well as playing a crucial role in maintaining human health. In this study, we examined the retention of particulate matter by Picea crassifolia Kom. (P. crassifolia) needles using an aerosol regenerator in two typical catchments, while the concentrations of dissolved trace elements (Na, Zn, Pb, and Cd) were determined only in the Tianlaochi catchment. The results showed that the retention of airborne particles was lower in the Tianlaochi catchment (e.g., total suspended particles (TSP): 0.0049 μg cm⁻² in summer) than in the Sancha catchment (e.g., TSP: 0.0145 μg cm⁻²) in summer and autumn, while the opposite trend was found in winter and spring, with Tianlaochi catchment reaching higher TSP levels (0.0230 μg cm⁻² in spring) compared to Sancha catchment (0.0205 μg cm⁻²). The big tree exhibited the highest particulate retention, with a maximum flux of 84.870 μg cm⁻², indicating it was the most effective at particle trapping. The highest Na, Zn, Cd, and Pb values absorbed by the needle samples were 1.794 mg L⁻¹, 11.345 μg L⁻¹, 0.081 μg L⁻¹, and 4.316 μg L⁻¹, respectively. P. crassifolia needles absorbed more Na, Zn, and Cd in July and August than in June. The absorption capacity of the needles decreased in the order Na > Zn > Pb > Cd. P. crassifolia forest can effectively reduce airborne particulate matter. Our study provides a theoretical foundation to examine the role of forest ecosystems in the retention of atmospheric particulate matter in the Qilian Mountains region. Full article

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

This study utilizes data from the eighth and ninth Chinese National Forest Inventories of Qinghai Province to establish a climate-sensitive transfer matrix growth model for natural forests in Qinghai Province. The model considers tree species diversity (S_d), size diversity (D_c), mean annual temperature (MAT), and mean annual precipitation (MAP) and their impacts on tree growth, mortality, and recruitment. Additionally, the forest stand growth and development were predicted under different climate scenarios (RCP2.6, RCP4.5, RCP8.5) for the next 50 years. The results show that the number of Qinghai spruce (Picea crassifolia Kom.) and White birch (Betula platyphylla Sukaczev) trees per hectare gradually decreases, but the stock volume continues to increase. The number of trees per hectare remains relatively stable (from 2235 to 855), with stock volume increasing annually for the first 30 years of the simulation and then stabilizing (from 76.96 to 798.02). Other tree species groups exhibit a continuous annual increase. Comparing the changes in stock volume and tree numbers under three different climate scenarios, there was no significant difference, and the overall trend remained similar. The finding fills a gap in the research on climate-sensitive transfer matrix growth models for natural forests in Qinghai Province. Compared to single-tree and whole-stand models, this model can predict forest stand growth more quickly and effectively, providing a reliable reference for future forest management. It helps formulate policies to address climate change and promote the sustainable development of forest health. This achievement will contribute to a better understanding of future forest stand growth trends, offering valuable insights for sustainable forest management. Full article

Soil microbial communities are pivotal in maintaining ecosystem functions, particularly in alpine regions with highly heterogeneous environmental conditions. However, the influence of vegetation type on soil microbial communities in high-elevation areas remains insufficiently understood. This study investigates the dynamics of soil microbial communities across grassland, shrubland, and forest ecosystems on the southern slope of the Qilian Mountains. Soil bacterial and fungal communities were examined using high-throughput 16S rRNA and ITS gene sequencing, and their potential ecological functions were inferred using the FAPROTAX and FUNGuild databases. Analysis of similarity (ANOSIM) based on Bray–Curtis distances revealed significant differences in bacterial and fungal community structures among vegetation types, with forest soils showing greater intra-group variability and more distinct microbial assemblages. Acidobacteriota and Proteobacteria were the dominant bacterial phyla, while Basidiomycota and Ascomycota predominated among fungi. Fungal communities in forest soils were dominated by ectomycorrhizal taxa, closely linked to coniferous forests dominated by Picea crassifolia. Overall, the structure and functional diversity of soil microbial communities were governed by soil physicochemical properties, particularly soil pH, which emerged as a key influencing factor. These findings deepen our understanding of microbial ecological processes in alpine environments and offer valuable insights for effective vegetation management and ecosystem conservation in mountainous regions. Full article

The watershed of the Yellow River is an important water conservation area in the Yellow River Basin. Its fragile ecological environment, climate change and unreasonable human activities have led to the continuous degradation of plant community structure in the watershed. This study only considers environmental factors, based on MaxEnt, Garp and other niche models and spatial-temporal analysis methods such as Mess and MoD analysis, to explore the suitable areas of Salix oritrepha Schneid. (First published in C.S.Sargent, Pl. Wilson. 3: 113 (1916)) and Picea crassifolia Kom. (First published in Bot. Mater. Gerb. Glavn. Bot. Sada R.S.F.S.R. 4: 177 (1923)) in the watershed of the Yellow River under different emission scenarios in the future. The results show that the MaxEnt model has a good simulation effect. In terms of spatial distribution, the suitable areas of the two species are mainly concentrated in the southeastern part of the Yellow River source area. Compared with the current period (1970–2000), by 2070, the suitable areas of the two species in each scenario showed a distribution of high in the east and low in the west, with an obvious expansion trend in the area and moving to high altitude and high latitude. According to the analysis of Mess and MoD, the annual average temperature (Bio_1) may be the most important variable affecting the future distribution of the two vegetation types. Full article

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article

Soil microbes are crucial for regulating biogeochemical cycles and maintaining forest ecosystem sustainability; however, the understanding of microbial communities and enzyme activity under natural and plantation forests in plateau regions remains limited. Using soil samples from 15-, 30-, and 50-year-old Picea crassifolia plantations and a natural forest (NF) in eastern Qinghai, China, this study assessed physicochemical properties, microbial communities, and enzyme activity across three soil layers. Microbial composition was characterized using the phospholipid fatty acid (PLFA) method, which is sensitive to structural changes. The PLFAs of bacteria, fungi, and actinomycetes accounted for 58.31%–74.20%, 8.91%–16.83%, and 3.41%–10.41% of the total PLFAs in all forests, respectively. There were significant differences between the NF and plantations, with the NF exhibiting higher PLFA abundance and enzyme activities than plantations, except for fungal PLFAs. PLFAs in plantations increased with the plantation age. However, the fungi-to-bacteria ratio was lower in the NF than in plantations. Finally, a redundancy analysis revealed that soil properties influence microbial composition and enzyme functionality significantly. These findings highlight the influence of stand age on microbial communities and structure, offering valuable insights for forest management practices aimed at conserving natural forests. Full article

This study compared hierarchical Bayesian, mixed-effects Gaussian process regression, and random forest models for predicting height to crown base (HCB) in Qinghai spruce (Picea crassifolia Kom.) forests using LiDAR-derived data. Both modeling approaches were applied to a dataset of 510 trees from 16 plots in northern China. The models incorporated tree-level variables (height, diameter at breast height, crown projection area) and plot-level spatial competition indices. Model performance was evaluated using leave-one-plot-out cross-validation. The Gaussian mixed-effects process model (with an RMSE of 1.59 and MAE of 1.25) slightly outperformed the hierarchical Bayesian model and the random forest model. Both models identified LiDAR-derived tree height, DBH, and LiDAR-derived crown projection area as primary factors influencing HCB. The spatial competition index (SCI) emerged as the most effective random effect, with the lowest AIC and BIC values, highlighting the importance of local competition dynamics in HCB formation. Uncertainty analysis revealed consistent patterns across the predicted values, with an average relative uncertainty of 33.89% for the Gaussian process model. These findings provide valuable insights for forest management and suggest that incorporating spatial competition indices can enhance HCB predictions. Full article

The biodiversity and activity of microorganisms are crucial for litter decomposition, but how litter traits at different stages of decomposition drive changes in microbial communities has yet to be thoroughly explored. In the typical alpine hilly area of the Qinghai–Tibet Plateau, three types of litter at different decomposition stages were selected under a natural Picea crassifolia (Picea crassifolia Kom.) forest: undecomposed (A-1), partially decomposed (A-2), and fully decomposed (A-3). By measuring physicochemical indicators, microbial diversity, and the composition of the litter at different decomposition stages, this study investigates the community changes and responses of bacteria to litter characteristic changes at different decomposition levels. The results show that with the increase in decomposition level, bacterial diversity increases, community structure changes, and network complexity gradually increases, while the changes in fungal communities are insignificant. Structural equation modeling indicates that the first principal component (PC1) of litter properties is significantly negatively correlated with bacterial diversity and positively correlated with bacterial community composition. There is no significant correlation between fungal diversity and community composition, indicating a closer relationship between bacteria and litter characteristics than fungi. In summary, with an increase in litter decomposition level, the diversity and network complexity of bacterial and fungal communities will significantly increase, which is related to the changes in various litter characteristics. This study provides a scientific basis for the regulatory mechanism of litter decomposition and turnover in the alpine hilly area of the Qinghai–Tibet Plateau, specifically in Picea crassifolia forests. Full article

This study examines the relationship between needle age and rust resistance in Picea crassifolia, focusing on the needle morphology, including size, shape, and physiological traits. One-year-old spruce needles are more susceptible to rust, while two-year-old needles show effective resistance. Using RNA-seq on the Illumina HiSeq500 platform, we analyzed both healthy and diseased one-year-old needles (N and B), as well as healthy one-year-old and two-year-old needles (N and L). We applied a fold change (FC) threshold of ≥2 and a false discovery rate (FDR) of <0.01, alongside GO annotation and KEGG pathway enrichment, to identify differentially expressed genes (DEGs). In N vs. B, DEGs were significantly enriched in processes such as metabolism, cellular function, catalysis, binding, ribosomal function, plant-pathogen interactions, endoplasmic reticulum protein processing, and signal transduction, revealing a polygenic network regulating the rust response. Similarly, in N vs. L, electron microscopy highlighted morphological differences in the wax layers of needles, with subsequent transcriptome sequencing uncovering genes involved in the development of one-year-old and two-year-old needles. DEGs were primarily found in pathways related to cutin, suberin, wax biosynthesis, fatty acid metabolism, photosynthesis, and phenylalanine synthesis. Two-year-old needles displayed reduced stomatal density, higher lignin content, and a thicker wax layer compared to one-year-old needles. Validation of the RNA-seq data through RT-qPCR on 10 DEGs confirmed the consistency of gene expression trends, enhancing our understanding of Picea crassifolia’s genetic response to rust and supporting future research into its disease resistance. Full article

Qinghai spruce (Picea crassifolia Kom.) is an ecologically important species in the forest ecosystem of the Qilian Mountains region in China. Natural regeneration of this species is critical to maintaining forest ecosystem function. Here, we analyzed several biological indicators among naturally regenerating Qinghai spruce across several elevations in the Pailugou watershed. Specifically, seedling density, basal diameter (BD), and plant height were measured, as were soil physicochemical parameters, at 2700 m, 3000 m, and 3300 m above sea level. Differences in the regeneration indicators and correlations between the indicators and soil parameters were then assessed across elevations. The results showed that soil stoichiometry was more sensitive to changes in elevation than seedling indicators were. Furthermore, seedling density was positively correlated with soil pH, whereas BD was positively correlated with the carbon-to-nitrogen ratio (C/N), the carbon-to-phosphorus ratio (C/P), and soil organic carbon (SOC) contents. None of the analyzed soil stoichiometry parameters had a significant impact on elevation-specific differences in seedling density. However, soil pH, SOC, and C/N significantly affected variations in seedling basal diameter at different elevations. Finally, soil pH, SOC, C/N, and the carbon-to-phosphorus ratio significantly affected variations in seedlings’ heights at different elevations. This study provides a strong theoretical basis for further understanding of the mechanisms associated with Qinghai spruce regeneration, ultimately contributing to rational protection and management strategies for this important natural resource. Full article

Due to the lower canopy height at the maximum crown width at the bottom of young Picea crassifolia trees, they are mixed with undergrowth. This makes it challenging to accurately determine crown size using CHM data or point cloud data. UAV imagery, on the other hand, incorporates rich color information and, after processing, can effectively distinguish between spruce trees and ground vegetation. In this study, the experimental site was an artificial young forest of Picea crassifolia in Shangshan Village, Qinghai Province, China. UAV images were used to obtain normalized saturation data for the sample plots. A marker-controlled watershed segmentation algorithm was employed to extract tree parameters, and the results were compared with those obtained via point cloud clustering segmentation and the marker-controlled watershed segmentation algorithm based on Canopy Height Model (CHM) images. The research results showed that the single tree recognition capabilities of the three types of data were similar, with F-measures of 0.96, 0.95, and 0.987 for the CHM image, UAV imagery, and point cloud data, respectively. The mean square errors of crown width information extracted from the UAV imagery using the marker-controlled watershed segmentation algorithm were 0.043, 0.125, and 0.046 for the three sample plots, which were better than the values of 0.103, 0.182, and 0.074 obtained from CHM data, as well as the values of 0.36, 0.461, and 0.4 obtained from the point cloud data. The point cloud data exhibited better fitting results for tree height extraction compared to the CHM images. This result indicates that UAV-acquired optical imagery has applicability in extracting individual tree feature parameters and can compensate for the deficiencies of CHM and point cloud data. Full article

Soil bacterial and fungal communities play different roles in maintaining the ecosystem structure and functions. However, these differences, which are related to soil depths, remain unclear and are the subject of this study. We selected six sample plots (20 m × 50 m) in a natural Picea crassifolia forest in an alpine meadow to determine the vertical patterns (0~10 cm, 10~20 cm, 20~30 cm, and 30~50 cm) of soil bacterial and fungal communities, and to predict their potential functions. The phyla Verrucomicrobia, Acidobacteria, and Proteobacteria dominated the soil bacteria, with more than 50% of the relative abundance, while the fungi Basidiomycota and Ascomycota dominated the soil fungi. The potential functions of bacteria, including metabolism and transcription, increased with soil depth, and corresponded to specific bacterial taxa. The functional guilds of fungi, including endophytes, arbuscular mycorrhiza, and ectomycorrhiza, did not change with soil depth. The structural equation modeling analysis revealed that soil organic carbon (SOC) and pH were the key drivers shaping the soil bacterial communities and potential functions in the 0–50 cm soil layer. SOC was also a key driver of soil fungal α diversity. The sample plot, namely, its geographic locations, was another key driver shaping soil fungal β diversity and potential functions, but soil depth was not. Our results differentiate the importance of SOC and geographic location in shaping soil bacterial and fungal communities, respectively, and indicate that examining soil microbial composition and corresponding functions concomitantly is important for the maintenance and management of forest ecosystem functions. Full article