Search Results (46)

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

Global warming is causing some regions to experience frequent and severe drought, with important impacts on montane forest vegetation. In this study, the Qilian Mountains is on the northeastern margin of the Tibetan Plateau which was divided into three study areas, the eastern (HaXi), middle (XiShui) and western (QiFeng) parts. This work focused on interannual trend comparison of tree-ring width (TRW) and enhanced vegetation index (EVI), their relationship characterization from 2000 to 2020, and spatial and temporal pattern portrayal of response to climate factors. The results showed that: (1) the overall interannual variation of TRW and EVI showed a stable increasing trend, and the trend of TRW and EVI gradually became consistent with the increase in drought stress (from the eastern region to the western region and from high elevation to low elevation) (p < 0.01); (2) a significant positive relation was observed between TRW and EVI at the same sampling sites, and the synchrony of the positive correlation gradually increased with the increase of drought stress (p < 0.01); and (3) compared to TRW, EVI is significantly more sensitive with climatic variations, and the dominant climate factors affecting both TRW and EVI dynamics are gradually identical with the increase of drought stress. Full article

The artificial young forest is an important component of ecosystems, and biomass models are important for estimating the carbon storage of ecosystems. However, research on biomass models of the young forest is lacking. In this study, biomass data of 96 saplings of three tree species from the southern foot of the Qilian Mountains were collected. These data, coupled with allometric growth equations and the nonlinear joint estimation method, were used to establish independent, component-additive, and total-control compatible models to estimate the biomass of artificial young wood of Picea crassifolia (Picea crassifolia Kom.), Sabina przewalskii (Sabina przewalskii Kom.), and Pinus tabulaeformis (Pinus tabuliformis Carr.). The distribution characteristics of the biomass components (branch, leaf, trunk, and root biomass) and the goodness of fit of the models were also analyzed. The results showed that (1) the multiple regression models with two independent variables (MRWTIV) were superior to the univariate models for all three tree species. Base diameter was the best-fitting variable of the univariate model for Picea crassifolia and Pinus tabulaeformis, and the addition of base diameter and crown diameter as variables to the MRWTIV can significantly improve model accuracy. Tree height was the best-fitting variable of the univariate model of Sabina przewalskii, and the addition of tree height and crown diameter to the MRWTIV can significantly improve model accuracy; (2) the two independent variable component-additive compatible model was the best-fitting biomass model. The compatible models constructed by the nonlinear joint estimation method were less accurate than the independent models. However, they maintained good compatibility among the biomass components and enabled more robust estimates of regional biomass; and (3) for the young wood of Picea crassifolia, Sabina przewalskii, and Pinus tabulaeformis, the aboveground biomass ratio of each component to total biomass was highest for leaf biomass (26%–68%), followed by branch (10%–46%) and trunk (11%–55%) biomass, and the aboveground biomass was higher than the underground biomass. In conclusion, the optimal biomass model of artificial young forest at the sampling site is a multivariate component-additive compatible biomass model. It can well estimate the biomass of young forest and provide a basis for future research. Full article

A new Choiromyces species was discovered at local wild mushroom markets in Songpan County, Sichuan, southwest China where it has been considered as a Chinese white truffle. Based on both morphological and phylogenetic analyses, the collection was described as Choiromyces sichuanensis sp. nov. This study confirms the occurrence of members of Choiromyces in China. In addition, the mycorrhizal synthesis via spore inoculation between C. sichuanensis and Pinus armandii or two Picea species of Pi. likiangensis and Pi. crassifolia was attempted in a greenhouse. Both morphoanatomical and molecular analyses evidenced well-developed mycorrhization between C. sichuanensis and P. armandii, but not in Picea seedlings. Our current study provides data about the species diversity and mycorrhizal research of this genus for further studies. In addition, a successful mycorrhization between C. sichuanensis and selected tree species, irrespective of Pinus genus or other plant species, would broaden the set of species for a successful mycorrhization in greenhouse conditions and potential outplanting for cultivation purposes. Full article

The factors determining litter decomposition incorporated into C and nutrient cycles were examined as part of a broader study investigating the biogeochemical cycle in forest ecosystems. Litter was collected from five altitudes of Qinghai spruce (Picea crassifolia) woodland stands in the Qilian Mountains and placed in litterbags. These litterbags were installed at the crown center (CC) and crown edge (CE) at different altitudes in Qinghai spruce forests during the growing season to study the effect of litter substrate quality on litter decomposition. Results indicate that at varying altitudes in the growing season, the initial mass loss rate and initial decomposition rate of Qinghai spruce litter showed a nonlinear relationship with altitude. The Olson exponential regression equation showed that the decomposition coefficient (k) was the largest at 3050 m (k = 0.709), and the decomposition coefficient (k) was the smallest at 3250 m (k = 0.476). Meanwhile, the initial decomposition rate was highly correlated with initial litter Ca and Mn concentrations. At the CC and CE at different altitudes in the growing season, the initial mass loss rate of CE was significantly higher than that of CC (p < 0.01), and the initial decomposition rate of CE was markedly faster than that of CC (p < 0.01). The Olson exponential regression equation showed that CE’s decomposition coefficients (k) were larger than those of CC. The initial decomposition rate of CE was highly correlated with initial litter C and Mn concentrations. However, the initial decomposition rate at CC was independent of the litter substrate quality. Finally, we realize that litter decomposition in the early stages is not ultimately determined by a single common factor, but rather the result of multiple factors working together in different orders and strengths. The results lay a foundation for understanding the process and mechanism of litter decomposition in the alpine mountain forest ecosystem and further understanding the structure and function of the ecosystem. Full article

Transpiration is fundamental to the understanding of the ecophysiology of planted forests in arid ecosystems, and it is one of the most uncertain components in the ecosystem water balance. The objective of this study was to quantify differences in whole-tree transpiration estimates obtained with a heat ratio probe in a secondary Qinghai spruce (Picea crassifolia) forest. To do this, we analyzed the sap flux density values obtained with sensors installed in (1) holes drilled in the preceding growing season (treatment) and (2) holes drilled in the current year (control). The study was conducted in a catchment in the Qilian Mountains of western China. The results showed that an incomplete diameter at breast height (DBH) range contributed to 28.5% of the overestimation of the sapwood area when the DBH > 10 cm and 22.6% of the underestimation of the sapwood area when the DBH < 5 cm. At daily scales, there were significant differences in both the quantity and magnitude of the sap flux density between the treatment and control groups. Furthermore, a linear regression function (R² = 0.96, p < 0.001), which was almost parallel to the 1:1 reference line, was obtained for the sap flux density correction for the treatment group, and the daily sap flux density and whole-tree transpiration were underestimated by 36.8 and 37.5%, respectively, at the half-hour scale. This study illustrates uncertainties and a correction function for sap flow estimations in young Qinghai spruce trees when using heat ratio sensors with minimal damage over multiple growing seasons. Full article

Forests in alpine mountainous regions are sensitive to global climate change. Accurate measurement of tree height is essential for forest aboveground biomass estimation. Unmanned aerial vehicle light detection and ranging (UAV-LiDAR) in tree height estimation has been extensively used in forestry inventories. This study investigated the influence of varying flight heights and point cloud densities on the extraction of tree height, and four flight heights (i.e., 85, 115, 145, and 175 m) were set in three Picea crassifolia plots in the Qilian Mountains. After point cloud data were classified, tree height was extracted from a canopy height model (CHM) on the basis of the individual tree segmentation. Through comparison with ground measurements, the tree height estimations of different flight heights and point cloud densities were analyzed. The results indicated that (1) with a flight height of 85 m, the tree height estimation achieved the highest accuracy (R² = 0.75, RMSE = 2.65), and the lowest accuracy occurred at a height of 175 m (R² = 0.65, RMSE = 3.00). (2) The accuracy of the tree height estimation decreased as the point cloud density decreased. The accuracies of tree height estimation from low-point cloud density (R² = 0.70, RMSE = 2.75) and medium density (R² = 0.69, RMSE = 2.80) were comparable. (3) Tree height was slightly underestimated in most cases when CHM-based segmentation methods were used. Consequently, a flight height of 145 m was more applicable for maintaining tree height estimation accuracy and assuring the safety of UAVs flying in alpine mountain regions. A point cloud density of 125–185 pts/m² can guarantee tree height estimation accuracy. The results of this study could potentially improve tree height estimation and provide available UAV-LiDAR flight parameters in alpine mountainous regions in Northwest China. Full article