Understanding the drivers of carbon sequestration in semi-arid shelterbelts is crucial for precision species selection and adaptive management. This study integrated multi-seasonal photosynthetic measurements of 22 species in Baotou, Inner Mongolia, with functional group classification and an explainable XGBoost-SHAP framework. Four distinct photosynthetic functional groups were identified, with XGBoost achieving high predictive skill for net photosynthetic rate (Pn) with a test set R2 of 0.897. SHAP interpretation revealed group-specific drivers: high Pn groups were primarily limited by photosynthetically active radiation (PAR), while others exhibited stronger stomatal conductance (gs) regulation. Notably, vapor pressure deficit (VPD) triggered an inhibitory transition near 2.1 kPa and a 95% confidence interval from 1.379 to 3.93 kPa. These findings provide a threshold-informed, functional group-based strategy for precision afforestation and enhancing carbon sequestration in semi-arid systems, supporting large-scale initiatives such as the Three-North Shelterbelt Program.

Introduction:Mycorrhizal fungi play a central role in nutrient cycling in forest ecosystems. The functional differences between arbuscular mycorrhizal (AM) and ectomycorrhizal (EcM) tree species significantly affect soil microbial community structure and patterns of microbial nutrient limitation, with substantial implications for ecosystem stability and biogeochemical cycling under global changes. However, the regulatory mechanisms of different dominant mycorrhizal tree species and their mixed mycorrhizal configurations on microbial nutrient limitation remain unclear. Methods:This study investigated a typical AM tree species (Ulmus pumila) and an EcM tree species (Pinus sylvestris var. mongolica) in the southern Horqin Sandy Land. We compared rhizosphere soil nutrient status, extracellular enzyme activities, and microbial community structure among pure U. pumila stands, pure P. sylvestris var. mongolica stands, and U. pumila-P. sylvestris mixed stands. Results:Results showed that AM pure forests exhibited extremely high C-acquiring enzyme activities but reduced soil nutrient content and microbial biomass, maintaining higher bacterial diversity. By contrast, the activities of N- and P-acquiring enzymes, soil nutrient contents, and fungal diversity in the EcM pure stands were significantly higher than those in the AM pure stands (p < 0.05). Mixed forests improved soil nutrient status through complementary mixed-mycorrhizal strategies, promoted microbial biomass accumulation, and modulated extracellular enzyme activities, thereby significantly alleviating microbial nutrient limitations. Moreover, their microbial networks exhibited greater complexity and stability than pure stands. Structural equation modeling further revealed that tree mycorrhizal dominance and microbial biomass were the primary factors alleviating microbial nutrient limitation: microbial biomass and mycorrhizal dominance showed significant negative effects on vector length (path coefficients -0.71 and 0.33, p < 0.01), whereas mycorrhizal dominance exerted a highly significant positive effect on vector angle (path coefficient 0.70, p < 0.001). Discussion:In conclusion, mixed mycorrhizal strategies alleviate microbial nutrient limitations by enhancing soil nutrient status, microbial community structure, and extracellular enzyme activities, providing theoretical support for ecosystem restoration and sustainable development in arid regions.

In arid ecosystems, environmental filtering is typically assumed to drive community assembly, yet the interplay between local microtopographic heterogeneity and landscape connectivity remains unclear. Here, we evaluated taxonomic, functional, and phylogenetic diversity and applied a functional β-diversity-based null model framework across windward slopes, interdune lowlands, and leeward slopes in the Mu Us Desert. We found that significant spatial variation in soil resources was accompanied by distinct patterns in multidimensional diversity. Nevertheless, stochastic processes consistently dominated community assembly (> 88%) across all microhabitats, primarily driven by homogenizing dispersal (56%–91%), which formed a shared backbone of compositional similarity across the landscape. Specifically, plants in interdune lowlands and leeward slopes were structured largely by dispersal processes, whereas windward slopes retained distinct deterministic signatures (11% homogeneous selection) and elevated contributions from undominated processes (29%). These patterns suggest that, even under environmentally stressful conditions, the observed stochastic dominance is likely supported by the combined influence of resource heterogeneity and landscape connectivity, rather than being driven solely by local filtering. Our results refine the stress-dominance hypothesis by implying that, in high-connectivity landscapes, mass effects can mask local selection pressures, providing ecological insights for integrating connectivity into dryland restoration strategies.

Understanding plant adaptation to extreme environments is crucial for conservation and evolutionary biology. Ammopiptanthus mongolicus, a drought-resistant evergreen shrub native to northwestern China, provides an excellent model for studying genetic and ecological responses to arid conditions. Climatic fluctuations, especially during the Quaternary, have shaped its distribution and genetic diversity, influencing its ability to survive in desert environments. However, the mechanisms underlying its adaptation remain insufficiently explored. We synthesize findings from previous genomic, ecological, and biogeographical studies to evaluate the adaptive mechanisms of A. mongolicus and assess the conservation implications for desert plant populations. Northwestern China encompasses vast arid regions characterized by extreme environmental conditions, including low precipitation, high evaporation rates, and significant temperature fluctuations. The uplift of the Qinghai-Tibet Plateau increased aridity by blocking moist air, leading to the transformation of humid forests into drought-resistant deserts. Ammopiptanthus mongolicus, a broad-leaved evergreen shrub, serves as a model for studying plant adaptation to arid environments. Genomic studies have identified several genes and pathways associated with drought and cold adaptation in this species. Core populations of A. mongolicus inhabit stable environments and exhibit high genetic diversity, whereas marginal populations endure extreme conditions and show strong local adaptations and distinct genetic traits. In this review, we hypothesize that the geographical distribution of core and peripheral populations may shift in response to future climate change, with peripheral populations potentially serving as sources of adaptive alleles for extreme climatic conditions. Marginal populations of A. mongolicus are essential reservoirs of adaptive traits, providing genetic resources for coping with environmental stressors such as drought and cold. However, they face a higher risk of local extinction due to genetic load and habitat fragmentation. Gene flow between core and marginal populations may be crucial for maintaining genetic diversity and adaptive potential. Conservation strategies should prioritize protecting marginal populations to reduce genetic load, enhance resilience, and preserve genetic diversity in response to intensifying climate change.

Understanding tree height (H)-diameter at breast height (D) allometric relationship is crucial for estimating biomass, carbon storage, and productivity. Environmental factors and species diversity strongly influence this relationship, yet remain understudied. Using data from 99 plots in Hulunbuir mixed forests, Inner Mongolia, we developed an environment-sensitive nonlinear mixed-effects model for five tree species. The model incorporates variables representing stand-level attributes, soil properties, climate factors, and species diversity. Variations at the sample plot and species levels were accounted for by introducing random components into the H-D model. Quadratic mean diameter, total basal area greater than the target D (BAL), Simpson's diversity index (SIM), mean annual precipitation (MAP), and soil organic carbon significantly affected the H-D relationship. Height growth increased with BAL and MAP but declined with higher SIM. Species traits and environmental factors jointly shaped H-D scaling. The proposed model supports adaptive forest management under changing environments.

Studying the biodiversity and multifunctionality relationships of the Hobq Desert shrub ecosystem and its response to environmental factors is crucial for ecological restoration in the region. In this study, we examined variations in biodiversity and ecosystem functioning along a precipitation gradient within the Hobq Desert shrub ecosystem. Using machine learning, we evaluated the predictive contributions of species richness and phylogenetic diversity to ecosystem multifunctionality (EMF) and applied structural equation modeling to analyze the direct and indirect impacts of biotic and environmental factors on multifunctionality. Our findings showed that species richness had a significant positive effect on EMF (p < 0.05), while phylogenetic diversity exhibited a relatively weaker influence, which was statistically non-significant (p = 0.257). Furthermore, species richness was identified as a stronger predictor of both individual ecosystem functions and EMF. Precipitation seasonality had a significant negative effect on EMF, indirectly influencing it through its impact on species richness. These findings highlight the essential role of species richness in maintaining ecosystem functioning within desert shrub ecosystems and emphasize the importance of effective biodiversity management, including both targeted conservation efforts and broad-scale ecological restoration, for preserving EMF under global climate change.

Determining management units (MUs) for conservation typically focuses on target organisms' demography dependencies, often neglecting adaptive diversity and evolutionary processes. This study examines the genetic variation of Ammopiptanthus mongolicus, a xeric shrub from eastern Central Asia, to identify genetic traits linked to climate adaptation and delineate seed and breeding zones based on environmental factors. We analyzed RAD-seq data from 217 samples across 19 populations, integrating ecological niche modeling (ENM) and GradientForest (GF) to pinpoint adaptive genetic variations. ENM identified the central-east and west-north regions as optimal habitats, while GF analysis revealed seed and breeding zones based on 1,637 climate-adaptive SNPs, indicating significant genetic differentiation linked to temperature and precipitation. Population structure analysis revealed both congruences and discrepancies between genetic and climatic clustering, particularly in peripheral regions, suggesting that adaptive genes may not follow isolation-by-distance rules like neutral genes. Genomic turnover analysis showed significant allelic changes along climatic gradients, highlighting local adaptation to temperature fluctuations. Peripheral populations exhibited higher genetic loads of loss-of-function alleles, indicating rapid adaptation. The study emphasizes the necessity of incorporating adaptive diversity in conservation strategies and recommends establishing MUs that align with adaptive genotypes and climatic conditions. Integrating genomic insights into breeding programs can further enhance the resilience and sustainable management of this vulnerable species.

Hippophae rhamnoides (family Elaeagnaceae) is a deciduous shrub that has become a uniquely advantageous species in the arsenic sandstone area of Inner Mongolia due to its well-developed root system and strong tillering ability. This study, by taking 10-year-old H. rhamnoides in the arsenic sandstone area as the research object and analyzing the morphological traits of their fine roots and their coordination within soil under different stubble heights (0, 10, 15, and 20 cm) and non-stubble treatment, aims to select the optimal stubble height that is most conducive to the rejuvenation of H. rhamnoides and thus improve the decline in the productivity of H. rhamnoides in this region. The results reveal significant differences in fine root and soil properties under different stubble heights (p < 0.05). Among different traits, fine root area density shows the highest total coefficient of variation, making it the most sensitive trait. Principal component analysis results indicate that after stubble treatment, the traits of H. rhamnoides fine roots center on high specific surface area (0.316) + high specific root length (0.312), shifting toward a resource-acquisition ecological strategy with the best foraging efficiency observed under a stubble height of 15 cm. Soil N:P and C:P can explain 66% and 61% of the root morphological traits strategies deployed during stubble treatment, respectively. Fine roots exhibit high adaptability to the breaking of phosphorus limitation and fixation of carbon and nitrogen.

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R2), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R2 = 0.75 and RMSE = 8.16 m3/hm2, highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R2 = 0.71 and RMSE = 6.96 m3/hm2; for Quercus mongolica, R2 = 0.74 and RMSE = 6.90 m3/hm2; and for Populus davidiana, R2 = 0.51 and RMSE = 9.29 m3/hm2. The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m3/hm2. (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R2 of 0.86, lowest RMSE of 9.69 m3/hm2, and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems.

Accurate estimation of forest aboveground biomass (AGB) is vital for understanding ecosystem productivity and carbon dynamics, especially in complex mixed forests. This study analyzed data from 99 natural mixed forest plots in Hulunbuir, Inner Mongolia, using machine learning models (random forest, support vector machine, and boosted regression tree) to predict AGB based on forest structure, species diversity, and environmental factors. Models explained 50%∼84% of AGB variation, with basal area and dominant diameter as key predictors. Species diversity is crucial for the accurate estimation of AGB. Climatic factors play a significant role in both Random Forest (RF) and boosted Regression Tree (BRT) models, while soil properties, particularly pH, were important in the support vector machine model. The study found that stand structure and the Simpson diversity index (SIM) are the primary determinants of biomass accumulation, indicating that forest management should focus on optimizing stand density, preserving structural diversity, and strengthening climate adaptability.

This study aims to explore the spatiotemporal heterogeneity patterns and driving factors of the net primary productivity （NPP） of vegetation in the Greater Xing'an Mountains of Inner Mongolia from 2000 to 2022, which is of great significance for optimizing ecological protection and restoring the ecosystem in the ecological hotspot area. Based on the annual average NPP data of MOD17A3 and topographic, meteorological, and human activity data, this study used trend analysis, the Hurst index, and geographic detector to construct a Bayesian network； identified the interaction of driving variables； analyzed the spatiotemporal change patterns of the NPP of different vegetation types and the key variable influence； and depicted the ecological restoration optimization area in spatial pattern. The results show that： ① From 2000 to 2022, the NPP of vegetation in the Greater Xing'an Mountains of Inner Mongolia increased annually, with an average value of 341.14 g·（m2 a）-1. The overall growth rate from 2000 to 2008 was higher than that from 2009 to 2022. Spatially, it presented a north-south gradient with 60.89% and 12.02% of vegetation showing improvement and degradation trends, respectively. ② The future change characteristics of NPP were marked by a clear countertrend, with the explanatory power of the dominant factors in descending order being： normalized difference vegetation index （NDVI）, annual precipitation （PRE）, evapotranspiration （ET）, plant available water content （PAWC）, rainfall erosion force （RE）, and annual mean temperature （TEM）. The dominant interactive factors were： TEM and ET, TEM and PAWC, and PRE and RE. ③ The sensitivity analysis showed that the annual NPP was greatly influenced by the key subsets of PRE, RE, and PAWC, and different states of conditions could divide the ecological pattern optimization space. The spatiotemporal heterogeneity patterns of NPP of vegetation in the Greater Xing'an Mountains of Inner Mongolia were obvious.

Wetlands in the Yellow River Watershed of Inner Mongolia face significant reductions under future climate and land use scenarios, threatening vital ecosystem services and water security. This study employs high-resolution projections from NASA’s Global Daily Downscaled Projections (GDDP) and the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6), combined with a machine learning and Cellular Automata–Markov (CA–Markov) framework to forecast the land cover transitions to 2040. Statistically downscaled temperature and precipitation data for two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) are integrated with satellite-based land cover (Landsat, Sentinel-1) from 2007 and 2023, achieving a high classification accuracy (over 85% overall, Kappa > 0.8). A Maximum Entropy (MaxEnt) analysis indicates that rising temperatures, increased precipitation variability, and urban–agricultural expansion will exacerbate hydrological stress, driving substantial wetland contraction. Although certain areas may retain or slightly expand their wetlands, the dominant trend underscores the urgency of spatially targeted conservation. By synthesizing downscaled climate data, multi-temporal land cover transitions, and ecological modeling, this study provides high-resolution insights for adaptive water resource planning and wetland management in ecologically sensitive regions.

Understanding how ecotypic divergence persists under extensive gene flow is critical for predicting adaptive responses in long-lived conifers. Mongolian Scots pine (Pinus sylvestris var. mongolica) occupies contrasting mountain and sandy-dune habitats in northeastern China, forming two ecotypes with distinct environmental adaptations. Using reference-free Specific Locus Amplified Fragment sequencing (SLAF-seq) data and genome-wide SNPs, we explored patterns of genomic differentiation and genotype-environment associations to determine whether adaptive divergence is driven by few large-effect loci or polygenic shifts. Despite weak population structure, we identified 3

The Greater Khingan Range in northern China is undergoing significant climate-induced transformations that are jeopardizing nutrient cycling and the stability of its forests. We quantified carbon (C), nitrogen (N), and phosphorus (P) in leaves, branches, trunks, roots, and soils of Larix gmelinii, Betula platyphylla, and Pinus sylvestris to compare organ-level stoichiometry, N:P ratios, and nutrient resorption strategies. Because boreal forests are major carbon sinks, insights from this region can inform global climate models and management strategies. Elemental concentrations were measured for each organ and soil depth; stoichiometric ratios, resorption efficiencies, PCA, and RDA were used to test species-specific nutrient strategies. B. platyphylla had higher tissue N and P and greater N and P resorption efficiencies than the conifers, indicating a more acquisitive, soil-dependent strategy. L. gmelinii and P. sylvestris showed lower tissue nutrient concentrations and greater reliance on internal nutrient recycling. Leaf N:P in B. platyphylla suggested P limitation. These interspecific differences provide insights into the adaptive mechanisms of boreal forest species in response to nutrient limitations, offering valuable guidance for reforestation efforts and predictions of forest ecosystem dynamics under climate change.

Vegetation construction is a key process for restoring and rehabilitating degraded ecosystems. However, the spatial pattern and process of native plants colonized by different vegetation restoration methods in semi-arid sandy land are poorly understood. In this study, two artificial vegetation restoration patterns (P1: row belt restoration pattern of Salix matsudana with low coverage; P2: a living sand barrier pattern of Caryopteris mongolica with low coverage) were selected to analyze the spatial distribution pattern and interspecific association of the colonizing native shrubs. The effects of the two restoration models on the spatial patterns of the main native semi-shrubs of the colonies (i.e., Artemisia ordosica and Corethrodendron lignosum var. leave) were studied using single variable and bivariate transformation point pattern analysis based on Ripley’s L function. Our results showed that two restoration patterns significantly facilitated the establishment of A. ordosica and C. lignosum var. leave, with their coverage reaching 17.04% and 22.62%, respectively. In P1, the spatial distribution pattern of colonial shrubs tended to be a random distribution, and there was no spatial correlation between the species. In P2, the colonial shrub aggregation distribution was more dominant, and with the increase in scale, the aggregation distribution changed to a random distribution, whereas the interspecific association was negatively correlated. The differences in the spatial distribution patterns of colonized native semi-shrubs in these two restoration patterns could be related to the life form of planted plants, configuration methods, biological characteristics of colonized plants, and intra- and interspecific relationships of plants. Our results demonstrated that the nurse effect of artificially planted vegetation in the early stage of sand ecological restoration effectively facilitated the near-natural succession of communities. These findings have important implications for ecological restoration of degraded sandy land in the semi-arid region of northern China.

Introduction Understanding how human activities affect biodiversity is needed to inform systemic policies and targets for achieving sustainable development goals. Shallow tillage to remove Artemisia ordosica is commonly conducted in the Mu Us Desert. However, the impacts of shallow tillage on plant community species diversity, phylogenetic structure, and community assembly processes remain poorly understood.Methods This study explores the effects of shallow tillage on species diversity including three a-diversity and two b-diversity indicators, as well as phylogenetic structure [phylogenetic diversity (PD), net relatedness index (NRI), and nearest taxon index (NTI)]. Additionally, this research analyzes the effects of shallow tillage on the community assembly process.Results and discussion The results showed that the a-diversity index, b-diversity index, and PD of the shallow tillage (ST) communities were significantly higher than those of the non-shallow tillage (NT) communities, and the phylogenetic structures of both the ST and NT communities tended to be differentiated, with competitive exclusion being the main mechanism of plant assembly. However, shallow tillage increased the relative importance of the stochastic processes dominated by dispersal limitation, mitigating plant competition in the communities. This conclusion was supported by the Raup-Crick difference index-based analysis.Conclusion Therefore, for the ecological restoration of the Mu Us Desert, species with adaptability and low niche overlap should be selected to increase the utilization efficiency of the environmental resources. The results of this study provide a foundation for policy development for ecosystem management and restoration in the Mu Us Desert.

This study examines climate change impacts on evapotranspiration in Inner Mongolia, analyzing potential (PET) and actual (AET) evapotranspiration shifts across diverse land-use classes using the SEBAL model and SSP2-4.5 and SSP5-8.5 projections (2030–2050) relative to a 1985–2015 baseline. Our findings reveal substantial PET increases across all LULC types, with Non-Vegetated Lands consistently showing the highest absolute PET values across scenarios (931.19 mm under baseline, increasing to 975.65 mm under SSP5-8.5) due to limited vegetation cover and shading effects, while forests, croplands, and savannas exhibit the most pronounced relative increases under SSP5-8.5, driven by heightened atmospheric demand and vegetation-induced transpiration. Monthly analyses show pronounced PET increases, particularly in the warmer months (June–August), with projected SSP5-8.5 PET levels reaching peaks of over 500 mm, indicating significant future water demand. AET increases are largest in densely vegetated classes, such as forests (+242.41 mm for Evergreen Needleleaf Forests under SSP5-8.5), while croplands and grasslands exhibit more moderate gains (+249.59 mm and +167.75 mm, respectively). The widening PET-AET gap highlights a growing vulnerability to moisture deficits, particularly in croplands and grasslands. Forested areas, while resilient, face rising water demands, necessitating conservation measures, whereas croplands and grasslands in low-precipitation areas risk soil moisture deficits and productivity declines due to limited adaptive capacity. Non-Vegetated Lands and built-up areas exhibit minimal AET responses (+16.37 mm for Non-Vegetated Lands under SSP5-8.5), emphasizing their limited water cycling contributions despite high PET. This research enhances the understanding of climate-induced changes in water demands across semi-arid regions, providing critical insights into effective and region-specific water resource management strategies.

沙冬青(Ammopiptanthus mongolicus),又名蒙古黄花木、蒙古沙冬青,为国家二级保护植物.其为第三纪孑遗种,也是亚洲中部特有物种和中国荒漠地区中唯一的超旱生常绿阔叶灌木.沙冬青是我国阿拉善荒漠主要的植被类型之一,在生态环境保护、园林绿化、药用开发、种质科研等方面具有重要的利用价值.

[目的]研究影响白榆(Ulmus pumila)在我国分布的气候条件、分布范围及未来变化,为白榆的科学保护和合理开发提供理论依据.[方法]采用MaxEnt和ArcGIS软件对当代、2050s和2070s(RCP2.6和RCP6.0)3个时期气候情景下白榆的潜在分布区进行预测.[结果]MaxEnt模型对白榆潜在分布区的预测具有很高的准确度,其训练集和测试集的AUC值分别为0.921和0.911.其中温度季节性变化(Bio4)、年降水量(Bio12)和降水量季节性变化(Bio15)对白榆的影响贡献率累计高达88.4％.依托气候环境变量,对白榆当前时期潜在分布区进行预测,中适生区主要集中在我国新疆、内蒙古中部、甘肃等西北地区,吉林、辽宁和内蒙古呼伦贝尔市等东北地区有少量分布;高适生区主要集中在我国河北、陕西、山西、山东等华北地区.未来2050s和2070s时期RCP2.6和RCP6.0气候情景下,白榆高适生区面积减小,中低适生区面积增大且会出现新的潜在适生区.[结论]以年为单位的温度和降水是白榆分布的主要因子,当前白榆的适生区主要集中在我国华北、西北和东北地区,未来其分布有向高纬度、高海拔区域迁移的趋势.

Aims Soil quality is undergoing severe degradation under anthropogenic effects. Different methods of land management have been implemented for soil reclamation, such as turfing. Although widely accepted to improve soil quality, turfing in specific environments may also culminate in soil deterioration. We aim to know how turfing impacts soils by changing mycobiomes. Methods and results The soil physicochemical properties and ITS metabarcoding were used to investigate mycobiome diversity and eco-function differences between the eudicot Dianthus plumarius and the monocot Poa pratensis in dry, cold, and high-alkali soil. The effects of plantation and the rhizosphere (e.g. root exudates) were tested. We showed that the change in soil mycobiomes in different planted bulk soils and rhizospheres could mainly be attributed to species turnover, with minor nestedness. Unexpectedly, the soil deteriorates more following turfing. The increasing saprotrophs in planted bulk soil were more marked in the monocot than in the eudicot, even the rhizosphere effect alleviated saprotrophic risks in the rhizosphere. Conclusions Turfing deteriorates the health of high-alkali soil by reducing nitrification, and upshift the soil saprotrophs in a dry and cold environment.