Pinus Yunnanensis Forest Research Articles

Changes in plant carbon inputs alter soil phosphorus dynamics in a coniferous forest ecosystem in subtropical mountain area

Plant carbon input aboveground and underground influences soil biological and physicochemical processes in forest ecosystems. However, compared with the well-known interactions between plant carbon and soil carbon or nitrogen, the impact on soil phosphorus (P) dynamics remains unclear. Here we investigated soil P dynamics in aggregates and bulk soil after one-year of plant carbon removal treatments (RL, removing litter; TG, tree girdling; RLTG, combination of removing litter and tree girdling; CK, control) under subtropical Pinus yunnanensis forest in a soil P-enriched degraded mountain area. Compared to CK, tree girdling significantly decreased the macroaggregate proportion and increased the microaggregate proportion by changing soil total carbon (TC) and Fe concentration. With the decrease of soil TC, the increase of HCl-Pi (inorganic P extracted by HCl) in microaggregate and silt and clay was significantly higher than that in macroaggregate. In addition, easily-available P and non-available P at bulk soils significantly declined and increase under RL and TG treatments, respectively. Redundancy analysis revealed that Fe, TC, and acid phosphatase activity were the main factors affecting P fractions in bulk soils. Higher δ18OP (oxygen isotope composition of phosphate) values of HCl-Pi pool and its significantly negative relationship with soil TC across the all carbon removal treatments, suggesting the weakened ability for plant to unlock soil bioavailable inorganic P combined with minerals induced by different plant carbon removal treatments. Overall, our results highlight that the importance of litter and root carbon in regulating soil P fractions and dynamics by altering the proportion of soil aggregates and the physicochemical properties of bulk soil.

Response of Hydrothermal Conditions to the Saturation Values of Forest Aboveground Biomass Estimation by Remote Sensing in Yunnan Province, China

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research.

Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China

Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies.

Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R2 value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector Machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation.

Selective logging effects on plant functional traits depend on soil enzyme activity and nutrient cycling in a Pinus yunnanensis forest

Selective logging has a wide impact on the remaining trees and soils which in turn influences total ecosystem function. Lots of studies focus on effects of selective logging on changing of plant functional traits, but the mechanism underlying these effects is still unclear. Thus, we studied 6 plant functional traits including leaf N concentration (LNC), leaf P concentration (LPC), leaf N:P ratio (N:P), wood density (WD), specific leaf area (SLA), and leaf dry mass content (LDMC) typically associated with resource acquisition and allocation. We measured these traits across 13 tree species in 52 plots under 5 selective logging intensities in a Pinus yunnanensis forest and aimed to test how selective logging and soil factors affect community weighted mean (CWM) of plant functional traits. The results revealed significant positive correlations between LNC and LPC, LNC and SLA, LPC and SLA, N:P and LDMC. Significant negative correlations were found between LNC and LDMC, LPC and N:P, LPC and LDMC. Only soil β-glucosidase activity (SGA), soil phosphatase activity (SPA), soil total nitrogen (STN), soil total phosphorus (STP) and soil hydrolysable nitrogen (SHN) had a significant relationship with one or several of the 6 plant functional traits. Compared with the control group, there was little significant difference in all plant functional traits across the different selective logging intensities. However, for plant functional traits, selective logging significantly increased the LNC, LPC and SLA. For soil factors, selective logging significantly increased the SGA, soil urease activity (SUA), SPA, soil moisture (SM), STN, STP and SHN. Based on structural equation models, we found that although selective logging has no direct effect on plant functional traits, it can indirectly affect plant functional traits through soil factors, mainly soil enzyme activity and nutrient cycling. Meanwhile, the mechanism of selective logging affecting different functional traits may be varied. Our findings provide a theoretical basis for the scientific management of P. yunnanensis forest.

Effects of prescribed burning on understory Quercus species of Pinus yunnanensis forest

IntroductionPositioning studies on prescribed burning in Pinus yunnanensis forests have been conducted for several years, focusing on the effects of fire on the composition and structure, growth, regeneration, relative bark thickness, and bark density of understory oak species in Pinus yunnanensis forests.MethodsThe study was conducted on Zhaobi Mountain, Yi-Dai Autonomous County of Xinping, Yuxi City, Yunnan Province. In the prescribed burn after restoration of full 1 year of the area and did not implement the prescribed burn area were set up 10 m × 10 m sample plots 30 pairs of comparisons, and all the oak trees in the sample plots were recorded, each sample plot in the four apexes and the middle were set up five 2 m × 2 m small sample squares, the shrubs in the small sample squares for each plant survey, comparison, statistics and analysis of all data.ResultsThe study results showed that (1) prescribed burning significantly affected the species composition of the understorey of Pinus yunnanensis forests. In both tree and shrub layers, the important values of Quercus aliena, Quercus serrata, Quercus fabri, and Quercus variabilis were significantly reduced in the burned areas. In contrast, the important values of Quercus acutissima increased somewhat. (2) The under crown height of oak trees in the burned areas was significantly lower than in the burned areas, but the height of oak trees in the burned areas was not significantly different from that in the burned areas. In the shrub layer, the height and cover of oak plants in the prescribed burning areas were significantly lower than in the unprescribed burned areas, effectively reducing the vertical continuity of the forest surface combustible material and reducing the possibility of fire converting from surface to canopy fire along the “ladder fuel.” (3) The regeneration of oak plants in the burned area is mainly by sprout tillers, and very few young sprouts are regenerated by seed germination. Renewed young sprouts are difficult to survive the prescribed burn areas the following year due to their lack of fire tolerance. (4) The relative bark thickness and density of oak plants in prescribed burn areas were significantly higher than those in unprescribed burn areas due to the fire tolerance exhibited by oak plants in long-term prescribed burns.DiscussionPrescribed burning has profoundly altered the structural composition and growth of oak plants in the understory of Pinus yunnanensis forests, and oak plants have shown significant fire-adapted traits to resist fire under long-term fire disturbance. The study can provide a scientific basis for prescribed burning, forest fuels, and forest fire management.

Nest-Site Features and Breeding Ecology of Chestnut-Vented Nuthatch Sitta nagaensis in Southwestern China.

The breeding ecology of birds is the cornerstone of bird life-history theory, and breeding success directly affects the survival and development of populations. We studied the breeding ecology of a secondary cavity-nesting bird, the chestnut-vented nuthatch Sitta nagaensis, in southwestern China from March to June in 2020, 2021, and 2022. In total, 16 nests in nest boxes and 19 nests in natural cavities were studied. The nesting habitat was mainly Pinus yunnanensis forest (68.4%), and the nest trees were mainly P. yunnanensis and pear Pyrus spp. Cavities made by woodpeckers and knot holes were used as nest sites, and the nuthatches plastered the hole entrance with mud. The nesting material was mainly pine bark. The clutch size was 3.47 ± 0.56 (range 2-4, n = 30), with an incubation period of 16.06 ± 0.91 days (range 15-19 days, n = 18). The nestling period was 20.88 ± 1.90 days (range 18-23 days, n = 23), and both parents fed the nestlings.

Soil Moisture and Available Phosphorus as the Factors Driving Variation in Functional Characteristics across Different Restoration Communities in a Subtropical Mountain Ecosystem

Functional characteristics are increasingly used to evaluate the success of different vegetation restoration. Community functional diversity (FD) and the community-weighted mean (CWM), as two main complementary components, are closely linked to site environment and ecosystem functions. However, the patterns and driving factors of functional characteristics are still not clear in different vegetation restoration types. Here, four community restoration types (secondary shrubland, SL; Pinus yunnanensis forest, PF; mixed needle-broad-leaved forest, MF; natural secondary forest, NSF) were selected to investigate species diversity, FD, CWM, and soil physicochemical properties. The relative effects of species diversity and soil abiotic features on variation in functional characteristics were then evaluated. We found that different restoration communities altered most community structures and functional properties in terms of species diversity, FD, and CWM. CWM values and FD in different communities presented different distribution patterns depending on certain traits and parameters. Significant correlations between functional traits were found at the species and community scales, suggesting a potential covariation between these selected traits in communities. The results of redundancy analysis and variation partitioning showed that most of the variation in functional characteristics, especially CWM, was explained by soil moisture and available phosphorus, indicating that habitat filters regulate the functional characteristics of plant communities mainly by changing the dominant species composition and functional traits of species. Therefore, the selection of restoration species adapted to low soil moisture and available phosphorus and the construction of communities based on selected species as the dominant species can effectively drive community assembly and ecosystem functions in the vegetation restoration process.

Regional estimation of dead fuel moisture content in southwest China based on a practical process-based model

Background Dead fuel moisture content (DFMC) is crucial for quantifying fire danger, fire behaviour, fuel consumption, and smoke production. Several previous studies estimating DFMC employed robust process-based models. However, these models can involve extensive computational time to process long time-series data with multiple iterations, and are not always practical at larger spatial scales. Aims Our aim was to provide a more time-efficient method to run a previously established process-based model and apply it to Pinus yunnanensis forests in southwest China. Methods We first determined the minimum processing time the process-based model required to estimate DFMC with a range of initial DFMC values. Then a long time series process was divided into parallel tasks. Finally, we estimated 1-h DFMC (verified with field-based observations) at regional scales using minimum required meteorological time-series data. Key results The results show that the calibration time and validation time of the model-in-parallel are 1.3 and 0.3% of the original model, respectively. The model-in-parallel can be generalised on regional scales, and its estimated 1-h DFMC agreed well with field-based measurements. Conclusions Our findings indicate that our model-in-parallel is time-efficient and its application in regional areas is promising. Implications Our practical model-in-parallel may contribute to improving wildfire risk assessment.

Forest foliage fuel load estimation from multi-sensor spatiotemporal features

• Spatiotemporal features of spaceborne SAR and optical data were combined first time. • The addition of spatiotemporal features greatly improves the estimation of FFL. • Temporal features seem more important than spatial features. • Both spatial and temporal changes in FFL can indicate forest fire risk. Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R 2 of 0.75 (relative Root Mean Squared Error (rRMSE) = 25.3 %), while when only SAR data was used the R 2 was 0.76 (rRMSE = 25.6 %). However, when optical and SAR data were combined, the R 2 increased to 0.81 (rRMSE = 23.2 %). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.

Enhancing Aboveground Biomass Estimation for Three Pinus Forests in Yunnan, SW China, Using Landsat 8

The estimation of forest aboveground biomass (AGB) using Landsat 8 operational land imagery (OLI) images has been extensively studied, but forest aboveground biomass (AGB) is often difficult to estimate accurately, in part due to the multi-level structure of forests, the heterogeneity of stands, and the diversity of tree species. In this study, a habitat dataset describing the distribution environment of forests, Landsat 8 OLI image data of spectral reflectance information, as well as a combination of the two datasets were employed to estimate the AGB of the three common pine forests (Pinus yunnanensis forests, Pinus densata forests, and Pinus kesiya forests) in Yunnan Province using a parametric model, stepwise linear regression model (SLR), and a non-parametric model, such as random forest (RF) and support vector machine (SVM). Based on the results, the following conclusions can be drawn. (1) As compared with the parametric model (SLR), the non-parametric models (RF and SVM) have a better fitting performance for estimating the AGB of the three pine forests, especially in the AGB segment of 40 to 200 Mg/ha. The non-parametric model is more sensitive to the number of data samples. In the case of the Pinus densata forest with a sample size greater than 100, RF fitting provides better fitting performance than SVM fitting, and the SVM fitting model is better suited to the AGB estimation of the Pinus yunnanensis forest with a sample size of less than 100. (2) Landsat 8 OLI images exhibit superior accuracy in estimating the AGB of the three pine forests using a single dataset. Variables, such as texture and vegetation index variables, which can reflect the comprehensive reflection information of ground objects, play a significant role in estimating AGBs, especially the texture variables. (3) By incorporating the combined dataset with characteristics of tree species distribution and ground object reflectance spectrum, the accuracy and stability of AGB estimation of the three pine forests can be improved. Moreover, the employment of a combined dataset is also effective in reducing the number of estimation errors in cases with AGB less than 100 Mg/ha or exceeding 150 Mg/ha.

Leaf Carbon, Nitrogen and Phosphorus Stoichiometry in a Pinus yunnanensis Forest in Southwest China

Pinus yunnanensis forest is a unique forest type in southwest China and one of the main forest types in Yunnan Province, which also has great ecological, economic and social significance. Understanding the changes in the stoichiometric characteristics is a key to study the nutrient cycling, limiting factors and stability mechanisms of the forest ecosystem. However, the stoichiometric characteristics, stability of the ecosystem of P. yunnanensis natural forests and whether they are limited by nutrients are still poorly understood. Based on a K-S test, ANOVA analysis and OLS regression analysis, we analyzed the concentrations of leaf C, N and P in 48 woody species of natural P. yunnanensis forests from 122 plots to explore the pattern of leaf C:N:P stoichiometry. Our results showed that the mean values of leaf C, N and P plus C:N, C:P and N:P for the 48 woody species were 451.12, 11.05 and 1.11 mg/g and 45.03, 496.98 and 11.27, respectively. The coefficients of variation of leaf C, N and P plus C:N, C:P and N:P were 5.29%, 36.75%, 51.53%, 29.63%, 43.46% and 41.68%, respectively. The geometric mean values of leaf N, P and N:P were 10.49 and 1.00 mg/g and 10.51, respectively. Leaf C and N, and C and P relationships showed significant negative correlations, but a significant positive correlation was observed between leaf N and P. There were significant differences in leaf N and C:N across functional groups. There were significant differences in leaf C and P between evergreen and deciduous, conifer and broadleaf trees. Significant differences in leaf C:P were only observed between evergreen and deciduous trees, and significant differences in leaf N:P were observed between conifer and broadleaf trees. The relatively low N:P in all sampled trees indicated that N was a limiting factor in the distribution of natural P. yunnanensis forests. However, the higher leaf C:N and C:P ratios indicated that the P. yunnanensis natural forest ecosystem was in a relatively stable state.

Effects of Prescribed Burning on Soil CO2 Emissions from Pinus yunnanensis Forestland in Central Yunnan, China

The effects of low-intensity and high-frequency prescribed burning on the soil CO2 emissions from Pinus yunnanensis forestland should be explored to achieve sustainable operation and management under fire disturbance. A Li-6400XT portable photosynthesis meter (equipped with a Li-6400-09 soil respiration chamber) and a TRIME®-PICO 64/32 soil temperature and moisture meter were used to measure the soil CO2 flux, soil temperature, and soil moisture at fixed observation sites in two treatments (i.e., unburned (UB) and after prescribed burning (AB)) in a Pinus yunnanensis forest of Zhaobi Mountain, Xinping County, Yunnan, China from March 2019 to February 2021. We also determined the relationships between the soil CO2 flux and soil hydrothermal factors. The results showed that (1) the soil CO2 flux in both UB and AB plots exhibited a significant unimodal trend of seasonal variations. In 2020, the highest soil CO2 fluxes occurred in September; they were 7.08 μmol CO2·m−2·s−1 in the morning and 7.63 μmol CO2·m−2·s−1 in the afternoon in the AB treatment, which was significantly lower than those in the UB treatment (p < 0.05). The AB and the UB treatment showed no significant differences in annual soil carbon flux (p > 0.05). (2) The relationship between the soil CO2 flux and moisture in the AB and UB plots was best fitted by a quadratic function, with a degree of fitting between 0.435 and 0.753. The soil CO2 flux and soil moisture showed an inverted U-shaped correlation in the UB plot (p < 0.05) but a positive correlation in the AB plot (p < 0.05). Soil moisture was the key factor affecting the soil CO2 flux (p < 0.05), while soil temperature showed no significant effect on soil CO2 flux in this area (p > 0.05). Therefore, the application of low-intensity prescribed burning for fire hazard reduction in this region achieved the objective without causing a persistent and drastic increase in the soil CO2 emissions. The results could provide important theoretical support for scientific implementation of prescribed burning, as well as scientific evaluation of ecological and environmental effects after prescribed burning.

Season-specific evapotranspiration partitioning using dual water isotopes in a Pinus yunnanensis ecosystem, southwest China

Quantifying the regional evapotranspiration is critical in terrestrial water balance and global water cycle, while partitioning of evapotranspiration is challenging but fundamental to predict the fate of terrestrial ecosystems under climate changes. Here, we performed in-situ measurements of water isotopes in atmospheric vapor, plant tissues and soil pools in a Pinus yunnanensis forest ecosystem in southwest China, aiming to partitioning ET by estimating the stable isotopic compositions of ET and that of its two components, i.e., plant transpiration and soil water evaporation. We used combined high-frequency laser spectroscopy and chamber methods, to constrain the estimates of T/ET. We first found that the estimated daily T/ET ratio ranges from 0.59 to 0.81, with an apparent increasing shift in the early growing season and maintaining a plateau level of over 0.75 during the peak growing season. This higher averaged T/ET of 0.73 ± 0.06 indicates that plant transpiration is the main component of evapotranspiration. The estimated δE and δT are in agreement with result from customized chamber method. We also found that in monsoon season (in June-September), soil water content is the main control of T/ET variations, with leaf area index playing only a partial role. Our study confirms the critical impact of soil water on the seasonal change of T/ET in Pinus yunnanensis ecosystem such as the SW China. However, we are also aware the sensitivity of controls on estimated T/ET at different time scale interested. Our results here provide insight into the regional hydrological cycle in alpine forest ecosystem and potentially benefits many applications from forest ecosystem protection to paleo isotope archives.

Changes of soil bacterial community structure at the secondary successional stages in the Pinus yunnanensis forest.

Soil bacterial diversity is a key factor for the maintanence of forest ecosystem function. Soil bacterial community would change along forest succession. We analyzed the variations of soil bacterial diversity and community composition at different successional stages in the Pinus yunnanensis forest, which would help understand the mechanism underlying forest restoration. We investigated soil bacterial diversity, community composition, and effect factors at different successional stages (including coniferous forest, mixed coniferous and broadleaf forest, and evergreen broadleaf forest) using Illumina Hiseq platform. The results showed that OTUs, Chao1 index, Ace index, and Shannon index of soil bacterial community decreased with the process of secondary succession. The highest soil bacterial total OTUs, richness, and complexity appeared at early successional stage. Soil bacterial community composition varied across different stages, with the mixed coniferous and broadleaf forest showing largest variation. Proteobacteria and Acidobacteria were common dominant phyla at secondary successional stages. Actinobacteria, Chloroflexi, and Patescibacteria were dominant phyla at the early successional stage, the abundance of which decreased with successional process in the P. yunnanensis forest. Proteobacteria and WPS-2 increased with the succession. Soil pH and tree species richness were key factors in driving soil bacterial community structure. The soil bacterial diversity decreased with forest succession, while the variations of soil bacterial community composition became larger.

滇中高原云南松林枯落物输入对土壤碳氮储量及其分布格局的影响

滇中高原云南松林枯落物输入对土壤碳氮储量及其分布格局的影响

Selective logging enhances ecosystem multifunctionality via increase of functional diversity in a Pinus yunnanensis forest in Southwest China

BackgroundThe impacts of selective logging on ecosystem multifunctionality (EMF) remain largely unexplored. In this study, we analyzed the response of nine variables related to four ecosystem functions (i.e. nutrient cycling, soil carbon stocks, decomposition, and wood production) to five selective logging intensities in a Pinus yunnanensis-dominated forest. We included a control group with no harvest to evaluate the potential shifts in EMF of the P. yunnanensis forests. We also assessed the relationship between above- and belowground biodiversity and EMF under these different selective logging intensities. Additionally, we evaluated the effects of biotic and abiotic factors on EMF using a structural equation modeling (SEM) approach.ResultsIndividual ecosystem functions (EFs) all had a significant positive correlation with selective logging intensity. Different EFs showed different patterns with the increase of selective logging intensity. We found that EMF tended to increase with logging intensity, and that EMF significantly improved when the stand was harvested at least twice. Both functional diversity and soil moisture had a significant positive correlation with EMF, but soil fungal operational taxonomic units (OTUs) had a significant negative correlation with EMF. Based on SEM, we found that selective logging improved EMF mainly by increasing functional diversity.ConclusionOur study demonstrates that selective logging is a good management technique from an EMF perspective, and thus provide us with potential guidelines to improve forest management in P. yunnanensis forests in this region. The functional diversity is maximized through reasonable selective logging measures, so as to enhance EMF.

Effects of plant diversity and soil properties on soil fungal community structure with secondary succession in the Pinus yunnanensis forest

Forest secondary succession alters plant diversity and soil properties in the forest ecosystems; however, effects of this process on the soil fungal community structure are poorly understood. We used Illumina high-throughput sequencing to identify soil fungal community during forest secondary succession (including 30-, 45-, 60-, and 80-year-old stands) in the Pinus yunnanensis forest. The aims were to explore how plant diversity and soil properties drive soil fungal community structure and elucidate the role of fungal guilds in integrating the linkages of plant diversity and soil properties with forest secondary succession. Our study found that forest secondary succession changed woody species composition, richness, and soil properties, furthermore, this also affected significantly soil fungal diversity, community composition, and the abundances of the different functional guilds along the forest secondary succession. Soil fungal diversity initially increased up to the 60-year-old stand, and then decreased with forest secondary succession. The main phyla included Agaricomycetes, Leotiomycetes, Mortierellomycetes, and Eurotiomycetes, and indicator phyla varied differently with forest secondary succession. Soil fungal community could be clearly divided into the four successional stages. Forest secondary succession affected significantly soil fungal diversity via altering woody species richness and soil properties. Mycorrhizal, saprotrophic, and endophytic fungi interacted differently with forest secondary succession. Ectomycorrhizal fungi (ECM) played a vital role in driving forest secondary succession, and greater ECM fungal abundance could drive the forest secondary succession process through changes in the ECM plant composition. ECM plant community composition displayed different effects on ECM fungal abundance. As a result, ECM fungal abundance increased with a higher abundance of Castanopsis orthacantha, and decreased with a higher abundance of P. yunnanensis. Ericoid mycorrhizal fungal abundance reached its maximum in the 45-year-old stand and then decreased, which could provide valuable information for understory restoration. Undefined saprotrophic fungal abundance decreased with increasing forest secondary succession, accompanied by an increase of ECM fungal abundance, while endophytic fungi increased with forest secondary succession. Our findings suggested that plant-fungal symbionts and environmental selection posed an important constraint in assembly of soil fungal community and functional guild abundances. Altogether, these results provided new insights for predicting shifts in plant and soil fungal community strategies with the forest secondary succession in the P. yunnanensis forest.

滇中亚高山森林植物叶-凋落叶-土壤生态化学计量特征

PDF HTML阅读 XML下载 导出引用 引用提醒 滇中亚高山森林植物叶-凋落叶-土壤生态化学计量特征 DOI: 10.5846/stxb201908301806 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金地区项目（31860212）；云南省科技厅"三区"人才支持计划；云南省高校优势特色重点学科（生态学）建设项目；国家林草局林业科技创新平台运行项目"云南玉溪森林生态系统国家定位观测研究站"（2019132161） C, N and P stoichiometric characteristics of leaf, litter and soil for subalpine forests in central Yunnan, China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了深入认识滇中亚高山区域5种典型森林生态系统养分循环规律和系统稳定机质，通过测定植物叶、凋落叶和土壤C、N、P含量，掌握该区域典型森林植物叶-凋落叶-土壤生态化学计量特征。结果显示：（1）5种森林类型C、N、P含量差异显著，其中各林型植物叶和凋落叶C、N、P含量均高于土壤。（2）5种森林类型植物N、P再吸收率均为华山松林 > 云南松林 > 常绿阔叶林 > 高山栎林 > 滇油杉林；5种森林类型的再吸收率均为P（均值为61.20%）高于N（均值为36.48%），表明了该区域土壤P的相对匮乏。（3）5种森林类型C/N、C/P、N/P均表现为凋落叶 > 植物叶 > 土壤；各林型植物叶N/P范围为10.17-15.31。（4）5种森林类型植物叶与凋落叶C、N、P含量、C/N和C/P均呈极显著或显著正相关；凋落叶与土壤的C、N含量及N/P呈显著正相关；5种森林类型植物叶N与P含量呈显著正相关关系；土壤N与P含量呈显著负相关关系。本文探究养分元素在"植物叶-凋落叶-土壤"之间的化学计量特征，为了解该区域森林生态系统的养分状况和揭示生物地球化学循环过程提供了理论数据。 Abstract:In order to understand the nutrient cycling and system stability mechanism of five typical forest ecosystems in subalpine of central Yunnan province, the characteristics of the typical forest leaf-litter-soil ecological stoichiometry were studied by measuring the contents of leaf, litter and soil C, N and P. The results showed that:(1) C, N and P contents of five forests were significantly different, among which the contents of C, N and P of leaf and litter were all higher than soil in each forest type. (2) The N and P resorption rates of five forest types were ranked as Pinus armandii forest > Pinus yunnanensis forest > Evergreen broad-leaf forest > Quercus semicarpifolia forest > Keteleeria evelyniana forest. The P (mean value of 61.20%) resorption rates of all forest types were higher than N (mean value of 36.48%), indicating the relative shortage of soil P in this region. (3) The C/N, C/P and N/P of five forest types presented in the order of litter > leaf > soil, and the N/P were ranged from 10.17 to 15.31. (4) The contents of C, N, P, C/N and C/P in the leaf of five forest types were significantly positively correlated with that of litter. There was a significantly positively correlation between litter and soil C, N contents and N/P. The contents of leaf N and P were significantly positively correlated, but the contents of soil N and P were significantly negatively correlated. This study explored the stoichiometric characteristics of nutrient elements between "leaf-litter-soil", which provided theoretical data for understanding the nutrient status of forest ecosystem in this region and revealing the biogeochemical cycling. 参考文献 相似文献 引证文献

Effects of the invasive herb Ageratina adenophora on understory plant communities and tree seedling growth in Pinus yunnanensis forests in Yunnan, China

ABSTRACTUnderstanding the influence of invasive species on understory communities is necessary to maintain ecosystem functions. Here, we describe the effects of different abundances of the invasive species Ageratina adenophora on the composition and structure of understory communities and on Pinus yunnanensis seedling growth under pine stands in mid-Yunnan, China. Our data show that the different abundances of A. adenophora altered species and functional diversity by changing the species composition and abundance in the understory community because of its higher specific leaf area, leaf nitrogen concentration (LNC), and leaf phosphorus concentration compared with native species. Meanwhile, the presence of A. adenophora led to changes in the patterns of species co-occurrence, with a shift from random to segregated patterns as the invasion intensity increased. Furthermore, the order of species loss under invasion was nonrandom. The LNC could be an indicative functional trait predicting the order of species loss, suggesting that species with a lower LNC were lost first with increasing invasion intensity. In addition, a high abundance of the invader A. adenophora suppressed the growth of the seedlings of native canopy trees. Our results indicate that the effect of the invasive herb A. adenophora on the understory community is associated with the nonrandom loss of species with different LNCs and that invasion also suppresses the growth of native canopy dominants.