Structural variability in bulk soil and rhizosphere microbial communities at different restoration modes of open-pit coal mine

Structural variability in bulk soil and rhizosphere microbial communities at different restoration modes of open-pit coal mine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Structural variability in bulk soil and rhizosphere microbial communities at different restoration modes of open-pit coal mine Qingjun Meng, Mengke Ma, Shengnan Li, Xiaoyu Han, Tao Jin, Yang Jiao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7218394/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract methods herbaceous revegetation (O) and shrub (specifically Hippophae rhamnoides, S) revegetation. The aim was to elucidate the impact of different restoration measures on soil-microbe interactions. The results demonstrated that soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), total potassium (TK), and available potassium (AK) contents were significantly higher in the herbaceous restoration area (O) than in the seabuckthorn area (S), by 51.7%, 88.6%, 38.2%, 13.1%, and 4.7%, respectively. Compared to bulk soil, rhizosphere soil exhibited higher microbial community diversity and richness. Furthermore, seabuckthorn rhizosphere microbial diversity surpassed that of herbaceous rhizosphere. Different restoration areas (DRE) significantly (p < 0.05) influenced the relative abundances of Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria. The seabuckthorn area showed higher proportions of Proteobacteria (26.48 ~ 42.86%) and Actinobacteria (28.26 ~ 45.19%) compared to the herbaceous area. Functional gene prediction revealed that the seabuckthorn area expressed significantly higher abundances of core metabolic functional genes related to energy production and conversion (C), amino acid transport and metabolism (E), carbohydrate metabolism (G), and lipid metabolism (I) than the herbaceous area. Additionally, a symbiotic functional guild comprising animal pathogens, endophytes, lichen parasites, plant pathogens, and wood saprotrophs was formed in the seabuckthorn area. Redundancy analysis (RDA) indicated significant positive correlations (p < 0.05) between Acidobacteria, Chloroflexi, Actinobacteria, and Ascomycota and the contents of SOC, TN, and total phosphorus (TP). Bacterial networks formed with Actinobacteria as the core hub, comprising 300 edges connecting 50 nodes, while fungal networks were dominated by Ascomycota. Based on these findings, this study proposes a synergistic restoration strategy characterized by "herbaceous-induced short-term priming" coupled with "seabuckthorn-driven long-term stability." This strategy provides a theoretical foundation for the targeted microbial regulation of ecological restoration in mining areas. Open-pit coal mine Bacteria and fungi Rhizosphere microorganism Network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Open-pit mining induces multiple environmental challenges including vegetation destruction (Bi et al. 2021 ), soil and water erosion (Li et al. 2022 ), structural degradation of soil matrices (Lu et al. 2024 ), and diminished soil fertility (Tang et al. 2023 ). Consequently, artificial rehabilitation of abandoned open-pit coal mines becomes imperative. During mining operations, topsoil is typically stockpiled for subsequent soil remediation. However, insufficient topsoil reserves substantially compromise ecological restoration efficacy and soil quality improvement in these mining areas (Wang et al. 2020 ). Notably, diverse rehabilitation approaches exhibit significant differential impacts on soil nutrient dynamics and microbial community composition within waste dumps. As a critical ecological restoration strategy for degraded mining ecosystems, vegetation rehabilitation demonstrates economic feasibility and favorable environmental outcomes, effectively enhancing soil quality through systematic implementation (Liao et al. 2023 ). Microbial communities, serving as pivotal bioindicators of ecosystem health, exert substantial regulatory influences on restoration processes through structural and functional modifications in degraded ecosystems (Guo et al. 2024 ). Within soil ecosystems, microorganisms occupy central ecological niches through multifaceted biogeochemical mechanisms: these include but are not limited to driving biogeochemical cycling of essential nutrients (C, N, P) (Bastida et al. 2021 ), catalyzing organic matter degradation and humification processes (Pasche et al. 2025 ), mediating pathogen antagonism via metabolic byproducts, regulating soil aggregate formation and pore structure evolution (Rabbi et al. 2020 ), and enhancing plant nutrient utilization efficiency through rhizosphere interactions (Griffin et al. 2024). This multidimensional functional network not only sustains soil ecosystem multifunctionality but also provides critical biological drivers for vegetation reestablishment and productivity enhancement (Kiesewetter et al. 2025 ). Soil microbial communities are both regulated by and exert control over critical soil ecosystem functions, including nutrient decomposition and cycling, soil organic matter formation, and plant rhizosphere processes (Chen et al. 2023 ). Empirical studies have demonstrated distinct distribution patterns of bacterial communities and individual taxa in pre- and post-remediation mining soil ecosystems (Wang et al. 2023 ). Investigations into severely degraded tailings reveal that soil phosphorus (P) cycling exhibits significant enhancement following restoration interventions, primarily attributed to the pivotal role of gcd gene-driven phosphate-solubilizing bacteria in P cycling (Xu and Mao 2019 ).However, these studies primarily rely on bulk soil samples, whereas most soil-plant-microbe interactions occur in the rhizosphere, defined as the region within one millimeter of the root surface. Plants promote their growth by exuding specific chemicals into the rhizosphere soil environment (Ling et al. 2022 ).In open-pit coal mine ecosystems, plant-soil-microbe interactions critically influence plant growth, productivity, and soil quality parameters (Mukhopadhyay et al. 2019). Although a portion of the plant microbiome originates from vertically transmitted endophytes (López-Lozano et al. 2020 ), bulk soil and rhizosphere soil matrices constitute the primary reservoirs for root-associated microbial communities (Compant et al. 2019 ). Compared to bulk soil microbiota, rhizosphere soil microbial assemblages demonstrate heightened responsiveness to plant-derived litter inputs. This phenomenon stems from soil fungi's capacity to synthesize substantial quantities of cellulases, chitinases, and hemicellulases (Barbi et al. 2014 ), enzymes that catalyze litter decomposition (Yang et al. 2022 ). Comparative analyses of taxonomic composition and functional traits between rhizosphere and bulk soil microbial communities have elucidated mechanistic insights into rhizosphere selection processes (Yan et al. 2016 ). In this paper for the better understanding of the role of microorganisms in the ecological restoration process of open-pit coal mine, the bacteria and fungi in rhizosphere soil, and bulk soil under different restoration modes were studied. By defining the differences of soil chemistry and microbial communities under different remediation modes, we will understand the plant changes under different remediation measures and identify the microbial communities that are effective for the remediation. This will help us to understand the differences of different remediation methods, and the impact of herbs and shrubs on microorganisms in the soil damaged by mining. Therefore, the purpose of our study was to understand; (1) the effect of different repair methods on soil nutrients contents; (2) the influence of different repair methods on the diversity and structure of bulk soil and rhizosphere microbial communities in herb, and effects of different plants on root microorganisms under the same remediation measures; (3) define the relationship between nutrients and the diversity/structure of bulk soil and rhizosphere microbial communities. 2. Materials and methods 2.1. Description of trial site and soil sampling method This study was conducted at the Lingquan open-pit coal mine, in Dalai Nur District city, Inner Mongolia Province, Northeast China (117°43′00″~117°45′33″E,49°25′00″~49°26′30″N). The climate is mesotemperate, characterized by short warm summers and long cold winters. The annual rainfall ranges from 280 to 300mm, and the mean monthly temperature ranges from − 21 to 21℃, with an annual average of 0.8℃. The Lingquan open-pit coal mine, operational from 1960 to 2017, transitioned its remaining coal resources to underground extraction following closure. For ecological restoration of the mine dump, differentiated rehabilitation strategies were implemented based on comprehensive soil nutrient and environmental assessments. In herbaceous zones, slope grading and mulching were conducted prior to artificial seeding to accelerate vegetation recovery, while in sea buckthorn areas, targeted planting was employed to facilitate natural ecosystem regeneration. During May 2021 sampling, 5–8 randomized subsites were established per zone, with composite samples created through homogenization. Sampling protocols varied between restoration types: herbaceous zones provided 3 bulk soil and 3 rhizosphere soil samples, whereas sea buckthorn zones yielded 3 bulk soil, 3 herbaceous rhizosphere, and 3 sea buckthorn rhizosphere samples. Bulk soil collection avoided root zones by targeting areas 20–50 cm from plant bases to ensure edaphic representativeness. Rhizosphere sampling focused on healthy roots at 5–20 cm depth using sterile techniques, where intact roots were excavated and surface-adhered soil gently brushed into sterile containers. This methodology minimized microbial disturbance while capturing authentic soil-root interactions. Post-collection processing included sieving through a 2-mm mesh to remove macro-organisms and debris. A portion of each soil sample was placed in 50 mL sterile tubes, flash-frozen in liquid nitrogen for microbial analysis, while remaining samples were air-dried for physicochemical characterization after sieving. Bulk soils from herbaceous and sea buckthorn zones were designated as O and S, respectively. Rhizosphere soils were labeled OHR (herbaceous zone), with SHR and SR distinguishing herbaceous and sea buckthorn rhizospheres within the sea buckthorn zone. 2.2. Soil nutrients analyses For the determination of soil sample indexes, referred to the method of Baustein(Bao 2005), the potential method is used for the determination of soil pH value, and the potassium dichromate volumetric method is used for the determination of organic matter content. Semi micro Kjeldahl method was used for soil total nitrogen; Soil alkali hydrolyzable nitrogen was determined by diffusion method; Soil total phosphorus sodium hydroxide melting molybdenum antimony anti colorimetry; Soil available phosphorus was determined by Olsen method; Soil total potassium alkali melting flame photometer method soil available potassium was determined by ammonium acetate extraction flame photometry. 2.3. DNA extraction, sequencing, and data processing The total DNA extraction, PCR amplification, and sequencing of soil samples were performed by Shanghai Majorbio Bio-pharm Technology Co., Ltd (Davide et al. 2015 ). Bacterial 16S rRNA and fungal ITS genes were amplified using primer pairs 338F/806R (Adams et al. 2013 ) and ITS1F/ITS2R (Lee et al. 2012 ), respectively. These primer pairs serve as reliable tools for bacterial and fungal community analysis due to their high specificity, broad coverage, and technical compatibility, demonstrating particular advantages in complex environmental samples. Sequencing steps: (1) DNA extraction and PCR amplification: Total DNA extraction was performed according to E.Z.N.A.® SOIL DNA Kit (Omega Bio-Tek, Norcross, GA, USA) instructions. DNA concentration and purity were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA).The quality of DNA extraction was detected by 1% agarose gel electrophoresis. Using the extracted soil microbial DNA as a template, 338F and 806R were used as primers for PCR amplification of v3-V4 variable region of bacteria, and ITS1F and ITS2R were used as primers for PCR amplification of ITS1 region of fungi. PCR products were recovered by conventional methods, purified, detected and quantified. (2) Illumina MiSeq sequencing: PCR products of the same origin were mixed and recovered using 2% agarose Gel. The recovered PCR products were purified using AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA).2% agarose gel electrophoresis and QuantusTM Fluorometer (Promega, USA) were used to detect and quantify the recovered products. Based on Illumina MiSeq platform (Illumina, SanDiego, USA) standard operating procedures, purified amplified segments were constructed into libraries. Sequencing was performed using Illumina's MiSeq PE300 platform (Shanghai Meggie Biomedical Technology Co. LTD.).Data processing: The original sequencing sequence was controlled by Trimmomatic; FLASH is used for splicing, a window of 50 bp is set, the sequences with a length less than 50 bp after quality control are removed, the sequences at both ends are splicing according to overlap base overlap, and the sequences are split to each sample according to barcode and primers at both ends of the sequence. UPARSE Version 7.1 ( http://drive5.com/uparse/ ) was used to perform OTU clustering according to 97% similarity and eliminate chimera to generate OTU tables. Each sequence was annotated for species classification by RDP classifier ( http://rdp.cme.msu.edu/ ) and compared with Silva database (SSU123), with a threshold of 70%.The taxonomic information and community composition of each sample at each classification level were obtained and visualized by graphics. OTU abundance and Alpha diversity were calculated by Usearch and Mothur respectively to obtain the species information of the samples. 2.4. Statistical analysis We calculated Shannon diversity, Chao1 richness and Heip’s evenness index with Meiji Cloud Platform, and richness represented the number of unique OTUs in each sample.A one-way analysis of variance (ANOVA) based on least significant difference (LSD) was used to test for significant differences in the alpha diversity and dominant bacterial and fungal taxa in communities among different groups (SPSS Statistics 20.0). A principal coordinates analysis (PCoA) based on Bray-Curtis dissimilarity of OTU level was conducted using Meiji Cloud Platform to analyze the distributions of bacterial and fungal communities. An analysis of similarity (ANOSIM) was conducted on both bacterial and fungal communities using the vegan package in Meiji Cloud Platform based on Bray-Curtis distances. Spearman’s correlations were conducted to determine the relationships between alpha diversity, dominant genera, dominant functional profiles, and vegetation and root traits(SPSS statistics20.0). Mantel tests and Spearman’s rank correlations with Bray-Curtis dissimilarities of bacterial and fungal communities on OTU level were conducted using the vegan package in Meiji Cloud Platform. Linear discriminant analysis effect size was used to reveal the differences in bacterial and fungal taxa among different sampling groups. The species abundance among different samples was analyzed using Network(Meiji Cloud Platform). 3. Results 3.1. Soil nutrients in seabuckthorn restoration areas are lower than in herbaceous restoration areas According to the test results, the herbaceous restoration area (O) exhibited significantly higher levels of Soil Organic Carbon (SOC) (P = 0.002), Total Nitrogen (TN) (P < 0.001), Available Nitrogen (AN) (P < 0.026), Total Potassium (TK) (P = 0.019), and Available Potassium (AK) (P = 0.007) compared to the sea buckthorn restoration area (S). Specifically, SOC, TN, AN, TK, and AK contents in the herbaceous area were 51.7%, 88.6%, 38.2%, 13.1%, and 4.7% higher than those in the sea buckthorn area, respectively. Additionally, the pH value was significantly lower in the O site than in the S site (P = 0.001), with the pH in the sea buckthorn area being 8.2% higher. In contrast, Total Phosphorus (TP) content (P = 0.052) and Available Phosphorus (AP) content (P = 0.460) did not differ significantly between the two sites (Fig. 1 ). 3.2. Significant Effects of Rhizosphere and Non-Rhizosphere Soil Conditions on Soil Microbial α-Diversity Indices in Different Restoration Zones After removing ambiguous, short, low-quality reads and singleton OTUs, a total of 1,629 unique fungal OTUs and 5,163 bacterial OTUs were obtained from 15 samples eligible for community analysis. Significant differences in α-diversity indices of bacterial and fungal communities were observed across different samples (Fig. 2 ). The bacterial Shannon diversity and Chao1 richness were significantly higher in OHR and S samples, indicating richer species composition and more even distributions within these bacterial communities. In contrast, O samples exhibited lower values in both metrics. In terms of Heip evenness, S samples demonstrated the highest evenness, while O samples showed the lowest. Fungal communities in bulk soil samples from the two restored areas showed higher Shannon diversity and Heip evenness but lower Chao1 richness. Conversely, herbaceous rhizosphere soils showed the opposite pattern, with both herbaceous rhizosphere samples exhibiting higher fungal Chao1 richness but lower Heip evenness. Importantly, sea-buckthorn rhizosphere (SR) samples exhibited higher Shannon diversity and Heip evenness compared to herbaceous rhizosphere samples. Table 1 Two-way ANOVA results for the effects of Different Restoration Zones (DRZ) and different ecological niches (DEN) on bacterial Alpha Indexes. Treatment df Bacterial community Alpha Indexes -- Shannon Chao Heip -- -- F-value P F-value P F-value P DRZ 1 0.134 0.724 0.592 0.464 0.006 0.984 DEN 1 4.021 0.080 64.778 < 0.001 0.078 0.797 DRZ×DEN 1 2.765 0.135 1.871 0.209 6.625 0.033 Treatment df Fungal community Alpha Indexes -- Shannon Chao Heip -- -- F-value P F-value P F-value P DRZ 1 3.21 0.111 1.096 0.329 6.447 0.035 DEN 1 292.11 < 0.001 200.135 < 0.001 30.068 < 0.001 DRZ×DEN 1 18.46 0.003 8.062 0.022 3.226 0.110 Bacterial communities across different ecological niches (DEN) exhibited significant differences in Chao1 richness (P < 0.001, Table 1 ), while no significant variations were observed in Shannon diversity or Heip evenness. In contrast, DEN demonstrated significant effects on fungal communities' Shannon diversity, Chao1 richness, and Heip evenness. No statistically significant differences were detected in bacterial alpha-diversity indices among different restoration Zones (DRZ). However, DRZ significantly influenced fungal Heip evenness (P = 0.035, Table 1 ). Furthermore, the DRZ × DEN interaction exerted significant effects on fungal Shannon diversity and Chao1 richness (P < 0.05, Table 1 ), as well as on bacterial Heip evenness (P = 0.033, Table 1 ). 3.3. Divergent Impacts of Rhizosphere versus Bulk Soil Niches on Phylum-Level Microbial Community Assembly Across Restoration Regions A total of 44 bacterial phyla and 13 fungal phyla were identified in the soil samples from the study area. Among these, 9 dominant bacterial phyla and 5 dominant fungal phyla (relative abundance > 1%) collectively accounted for 97.0% and 98.1% of the total sequences, respectively (Fig. 3 ). The bacterial communities were dominated by Proteobacteria (29.89%), Actinobacteria (27.84%), Chloroflexi (8.04%), Firmicutes (11.17%), and Cyanobacteria (7.54%), all exhibiting relative abundances exceeding 5.0% (Fig. 3 a). Notably, the relative abundances of Herbaspirillum (Firmicutes) and Rhizobiales (Proteobacteria) in herbaceous areas were significantly higher than those in sea-buckthorn areas (p < 0.05). Bulk soils generally showed higher Proteobacteria abundance compared to rhizosphere soils, whereas Actinobacteria demonstrated significant enrichment in sea-buckthorn rhizosphere soils (42.86%). Intriguingly, the sea-buckthorn rhizosphere microbiome was primarily composed of Proteobacteria (39.80%) and Actinobacteria (29.37%), with subdominant phyla including Chloroflexi (7.60%), Acidobacteria (7.22%), and Bacteroidetes (5.89%). Different restoration Zones (DRZ) exerted significant effects on Actinobacteria (P = 0.006), Proteobacteria (P = 0.024), Chloroflexi (P = 0.006), and Acidobacteria (P = 0.002). Distinct ecological niches (DEN) significantly influenced Actinobacteria (P = 0.035) and Proteobacteria (P = 0.045), while the DRZ × DEN interaction showed no significant impact on bacterial communities (Table 2 ). Regardless of sampling location or restoration mode, the predominant fungal phylum was Ascomycota, with a relative abundance of 82.8% (Fig. 3 b), and it was notably more prevalent in rhizosphere soil. Basidiomycota (7.34%) also emerged as a dominant phylum across diverse sampling sites. Notably, members of the phylum Rozellomycota were detected in bulk soil but were absent in rhizosphere soil microbial communities (Fig. 3 b). In the covering soil layer, the major fungal phyla with relative abundances exceeding 1% included Ascomycota (82.81%), Basidiomycota (2.83%), Glomeromycota (2.33%), Mortierellomycota (3.36%), unclassified fungi (7.24%), and Rozellomycota (3.33%). Similarly, in the subsoil layer, the dominant phyla with relative abundances > 1% were Ascomycota (95.91%) and Basidiomycota (2.96%). Both Ascomycota and Basidiomycota demonstrated higher abundances in bulk and rhizosphere soils. In Hippophae rhamnoides plantation soils, fungal phyla with relative abundances > 1% comprised Ascomycota (76.7%), Basidiomycota (9.8%), Glomeromycota (4.2%), Mortierellomycota (3.5%), and Chytridiomycota (1.3%). Correspondingly, in H. rhamnoides root zone soils, the dominant phyla with relative abundances > 1% were Ascomycota (78.2%), Basidiomycota (6.6%), Glomeromycota (2.2%), Mortierellomycota (1.9%), and Chytridiomycota (1.7%). However, in H. rhamnoides herbaceous root nodule soils, the predominant fungal phyla (> 1%) were exclusively Ascomycota (94.5%), Basidiomycota (2.1%), and Glomeromycota (1.2%). Notably, Mortierellomycota was undetectable in H. rhamnoides root nodule communities (Fig. 3 b). A significant interactive effect between DRZ and DEN was observed on root nodule communities (P = 0.041; Table 2 ). Table 2 Results of two-way ANOVA for the effects of the Different Restoration Zones(DRZ) and Different ecological niches (DEN)on Richness and Shannon Treatment Bacterial community Community Actinobacteria Proteobacteria Chloroflexi Acidobacteriota Bacteroidota -- F P F P F P F P F P DRZ 13.70 0.006 7.71 0.024 13.69 0.006 19.633 0.002 0.006 0.94 DEN 6.467 0.035 5.384 0.049 0.765 0.407 0.715 0.422 1.063 0.333 DRZⅹDEN 0.225 0.648 0.074 0.793 0.83 0.389 0.806 0.395 4.596 0.064 Treatment Fungal community Community Ascomycota Basidiomycetes Mortierellomycota unclassified Chytridiomycota -- F P F P F P F P F P DRZ 1.647 0.235 1.726 0.225 10.078 0.013 0.721 0.421 1.951 0.2 DEN 4.029 0.08 0.408 0.541 24.735 0.001 1.293 0.288 0.768 0.406 DRZⅹDEN 0.008 0.932 0.438 0.527 5.915 0.041 6.732 0.032 0.19 0.675 To investigate how microbial communities varied across sampling locations and restoration modes, a Principal Coordinate Analysis (PCoA) was performed based on calculated Bray-Curtis distances between sampling sites. Microbial communities from the five groups were distinctly clustered into separate positions within the PCoA ordination (Fig. 4 ). The entire bacterial community exhibited significant segregation across the five defined sampling locations (P = 0.001), while fungal communities also showed clear partitioning among the five sites (P = 0.001). For bacterial communities, the first two principal coordinates collectively accounted for 47.60% of the total variation. The first principal coordinate (29.64%) separated communities at varying distances from roots, while the second coordinate (17.96%) partitioned communities under different restoration modes. However, discrepancies between rhizosphere soils of different plant species were less pronounced compared to those under distinct restoration modes (Fig. 4 ). In the fungal community assemblage, the first two principal coordinates explained 49.21% of the total variation. The first coordinate (26.96%) distinguished communities at different root proximity levels, whereas the second coordinate (22.25%) differentiated communities subjected to contrasting restoration practices. Analogous to bacterial patterns, variations between rhizosphere soils of different plant species remained statistically indistinct relative to those influenced by restoration modes (Fig. 4 ). 3.4. Differences in the levels of soil nutrients are correlated with changes in microbial community composition Bacterial COG functional analysis revealed that sea buckthorn planting and the rhizosphere effect significantly altered soil microbial functional characteristics. The sea buckthorn areas (S/SHR/SR) generally exhibited higher core metabolic functions compared to the herbaceous area (O/OHR): Energy production and conversion (C) increased by 19.0% in SHR (4,259,687) versus O (3,578,672); Amino acid transport and metabolism (E) increased by 27.1% in SHR (6,471,816) versus O (5,095,519). Carbohydrate metabolism (G) surged by 37.4% in SHR (4,051,699) versus S (2,949,237). Cell motility (N) increased by 30.9% in S (621,168) versus O (474,672). The rhizosphere effect manifested as a 25.7% increase in lipid metabolism (I) in SHR compared to O, and extracellular structure genes (W) in SR reached 3.3 times that of the O sample (74.41). These findings suggest that sea buckthorn introduction drives microbial functional restructuring by enhancing carbon and nitrogen metabolism (G/E), environmental adaptability (N), and rhizosphere interactions (W). Fungal guild analysis revealed significant differential abundance across treatments (O, OHR, S, SHR, SR). The Fungal Parasite-Plant Pathogen-Saprotroph guild demonstrated substantially higher relative abundance in SR (10,532.67; p < 0.01) compared to all other treatments. Similarly, the Animal Pathogen-Endophyte-Lichen Parasite-Plant Pathogen-Wood Saprotroph guild exhibited maximal abundance in SR (7,289.33; p < 0.001).The Animal/Plant Pathogen-Undefined Saprotroph guild peaked exclusively in OHR (6,894.00), exceeding abundances in other treatments by 5- to 24-fold (p < 0.001). Conversely, the Animal Pathogen-Soil Saprotroph guild was enriched in bulk soil treatments (S: 4,209.33; O: 3,278.33) but severely suppressed in rhizosphere treatments (OHR: 0.67; SHR: 32.67).Ericoid mycorrhizal symbionts showed exceptional treatment specificity, with abundance in S (6,107.67) exceeding other treatments by 2–3 orders of magnitude (p < 0.0001). The Animal Pathogen-Endophyte-Ericoid Mycorrhiza-Plant Pathogen-Wood Saprotroph composite guild dominated O treatment (15,508.33) but was nearly undetectable in OHR (6.67). 3.5. Differences in the levels of soil nutrients are correlated with changes in microbial community composition To better understand the correlations between soil nutrient content and microbial community composition, we performed a redundancy analyses (RDA). The top 5 species in the community are indicated in the Fig. 5 . For the overall bacterial communities, the first two axes explained 95.06% of the total variance between the bulk soil bacterial communities at the different restoration modes (Fig. 7 a). The RDA demonstrated that SOC, TN, and TP content were positively correlated with the phylum Firmicutes, Cyanobacteria, and inversely correlated with the phylum Chloroflexi and Proteobacteria, while pH and TK were opposite. Similarly, in the bulk soil, first two RDA axes explained 95.08% of the total variance between rhizosphere bacterial communities at the different restoration modes (Fig. 7 b). The SOC, TN, and TP contents were positively correlated to Acidobacteriota, Chloroflexi and Gemmatimonadota, while, inversely correlated with the phylum Proteobacteria, and Actinobacteriota. Though, unlike bulk soil, the correlation between TK and bacterial community was consistent with SOC, but opposite to pH. For the fungal communities, the first two axes explained 93.93% of the total variance between bulk soil bacterial communities at the different Restoration modes (Fig. 5 c). The RDA confirmed that SOC, TN, and, TP content were positively correlated with the phyla Rozellomycota, and unclassified-k-Fungi, and inversely correlated with the phylum Mortierellomycota (Fig. 5 c). Likewise, in bulk soil the first two RDA axes explained 99.55% of the total variance between rhizosphere fungal communities at the different restoration modes (Fig. 5 d). The SOC, TN, and TP content, and pH were positively correlated with the phyla Ascomycota. 3.6. Co-occurrence patterns of bacterial and fungal communities A network analysis was conducted to investigate the cooccurrence patterns of bacterial and fungal communities (Fig. 8 ). The exploration of co-occurrence networks is an effective tool to determine the occurrence of biological interactions in the microbial communities (Ma et al. 2015). Single factor network analysis builds a species correlation network by calculating the correlation between species. The nodes in the network diagram are the species nodes. When the correlation coefficient between species meets a certain threshold, there is a line develops between species. In this study, Pearson model was used to conduct univariate network analysis on the top 50 genera with relative abundance in microbial communities at different sampling points with absolute value of relative coefficient ≥ 0.5 and P value < 0.05. In this study, the single network factor of soil bacteria in different remediation methods was connected. The network diameter was 4, the average shortest path length between nodes was 1.980, and a total of 300 sides corresponding to 50 nodes. The OTUs of the top 50 species belong to Proteobacteria, Actinobacteriota, Chloroflexi, Gemmatimonadota, Acidobacteriota, Firmicutes, Cyanobacteriota, and Bacteroidota. The top 50 species OTUs belong to OTU 2761 of Actinobacteria, which with the highest network centrality value. The single factor network of soil fungi was unconnected, with 191 edges among nodes. The OTUs of the top 50 species belong to Ascomycota, Basidiomycota, Mortierellomycota, Rozellomycota, and unclassified-fungi. The top 50 species OTUs belong to Actinobacteria with the highest network centrality value. 4. Discussion Open-pit coal mining ecosystems are inherently unstable and highly prone to degradation, characterized by disrupted soil physical structures, reduced water and soil retention capabilities, and subsequent soil loss and vegetation destruction (Wang et al. 2020 ). Vegetation reconstruction is the primary step in restoring such ecosystems. Since the closure of the open-pit coal mine in the study area in 2017, ecological restoration has been carried out through comprehensive analysis of various factors, including soil nutrients and structural factors (soil parent material, slope, climate), and different restoration methods have been applied to different restoration zones. In the herbaceous restoration zone, due to low soil fertility and poor soil structure, restoration involves artificial soil covering followed by sowing of herbaceous plants. In contrast, the sea buckthorn restoration zone employs direct planting of sea buckthorn on the existing soil. Analyzing the shifts in microbial community composition under different restoration approaches provides insights into the role of microbes during the restoration process. This study examines the structure and diversity of microbial communities in bulk and rhizosphere soils across different restoration methods, analyzes the relationships between soil nutrients and microbes, and explores the potential functions of dominant microbes under various restoration approaches. Restoration approaches exerted substantial impacts on soil nutrients and pH. The herbaceous area exhibited mid-to-high levels across multiple nutrient indices compared to H. rhamnoides plots, with soil pH approaching neutrality. Herbaceous plants, characterized by short growth cycles and high biomass production (Zhao et al. 2021), enhance soil organic carbon (SOC) and total nitrogen (TN) through rapid decomposition of abundant litter (senescent leaves, root residues) (Wu et al. 2024). Concurrently, their root exudates (organic acids, carbohydrates) mobilize phosphorus and potassium (available phosphorus, available potassium) while promoting available nitrogen (AN) mineralization (Zhang et al. 2025), collectively improving soil organic matter and structural stability. In contrast, deep-rooted H. rhamnoides systems may exacerbate surface nutrient leaching, coupled with strong phosphorus fixation capacity, resulting in suppressed AP availability (Lu et al. 2024 ). The diversity and composition of microbial communities in rhizosphere soil are significantly different from those in bulk soil, which is consistent with previous studies demonstrating that plant metabolism can influence the growth environment of microorganisms, thereby causing differences from bulk soil (Chen et al. 2016). Both the diversity and abundance of bacteria and fungi in rhizosphere soil are higher than those in bulk soil. This is primarily because plant root exudates and signaling molecules recruit specific populations from the soil pool to colonize the rhizosphere, forming particular microbial communities, such as nitrate-reducing bacteria, denitrifying bacteria, mycorrhizal fungi, and rhizobia (Luo et al. 2021). Meanwhile, the uniform distribution of root exudates provides microorganisms with abundant carbon sources and nutrients. The enrichment effect of root exudates results in microbial biomass in rhizosphere soil being several to tens of times higher than that in non-rhizosphere soil (Zhu et al. 2022). However, the fungal community evenness in rhizosphere soil is lower than that in bulk soil. This may be because some fungal communities can efficiently utilize root exudates as carbon sources, thereby proliferating rapidly in the rhizosphere and gaining a dominant position, while other groups are inhibited due to resource competition or the presence of inhibitory metabolites (Wang et al. 2023 ). The dominant fungal phylum in the mining area is Ascomycota, while the dominant bacterial phyla are Proteobacteria and Actinobacteria. These findings align with the microbial community structure reported in open-pit coal mines in Inner Mongolia (Chen et al. 2020 ). The abundance of Actinobacteria in rhizosphere soil exceeds that in bulk soil, and Proteobacteria are more prevalent in sea buckthorn areas than in herbaceous plant areas. This discrepancy can be attributed to the microaerobic preference of Actinobacteria, which thrive in rhizosphere microenvironments shaped by plant respiration and water gradients (Wu et al. 2019).In the root nodule layer of sea buckthorn (Hippophae rhamnoides), Proteobacteria (39.80%) and Actinobacteria (29.37%) exhibit a co-dominance pattern. As a non-leguminous nitrogen-fixing plant, sea buckthorn recruits Proteobacteria through root-secreted flavonoids and achieves efficient nitrogen fixation via symbiosis between its root nodule bacteria (Proteobacteria) and Frankia (Actinobacteria) (Bi and Zhang 2014). In contrast, herbaceous plants exhibit weaker nitrogen-fixing capacities, resulting in narrower ecological niches for Proteobacteria (Song et al. 2024).The relative abundance of Ascomycota remains stable across different soil conditions, likely due to their high species diversity and rapid evolutionary adaptation to heterogeneous habitats (Wang et al. 2010). Bacterial phyla such as Acidobacteriota, Chloroflexi, and Gemmatimonadota show positive correlations with soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) content. Acidobacteriota, for instance, harbor diverse carbohydrate-active enzymes (CAZymes) capable of degrading high-molecular-weight organic compounds (cellulose and hemicellulose, key SOC sources) while assimilating organic nitrogen as a nutrient source. The rhizosphere soil of sea buckthorn (SR) exhibited significantly elevated gene abundances associated with bacterial amino acid metabolism (E) and energy conversion (C). This enhancement primarily stems from the activation of metabolic pathways by the sea buckthorn-rhizobium symbiotic system, thereby directly or indirectly increasing rhizospheric nitrogen availability (Ke et al. 2022 ; Bai et al. 2022 ). Concurrently, the complementary root ecological niches between deep-rooted sea buckthorn and shallow-rooted herbaceous plants synergistically input diversified carbon and nitrogen substrates (Abdul et al., 2025). This drives markedly enhanced expression of carbohydrate metabolism genes in SHR/SR soils, promoting efficient integration of microbial metabolic networks (Wu et al., 2022 ).Under conditions of resource heterogeneity, bacteria in bulk soil (S) and herbaceous rhizospheres (O) upregulate motility gene (N) expression to optimize foraging strategies in response to patchy resource distribution (Song et al., 2025 ). Conversely, bacteria in the sea buckthorn rhizosphere (SR) rely on extracellular structural genes (W) mediating biofilm formation to strengthen colonization capacity, thereby resisting root-derived antimicrobial stress (Liu et al., 2023 ). These three strategies collectively constitute the core of bacterial functional group restructuring characterized by "high metabolic activity - strong environmental adaptation - deep rhizosphere colonization". Fungal community analysis further revealed that SR selectively enriches pathogenic/saprophytic fungi with high tolerance. The allelopathic compounds, such as tannins released by its lignified roots, provide chemotactic signals for colonization (Zhou et al., 2025 ), thereby activating a polyphagous saprotroph-pathogen functional complex. In contrast, phosphorus limitation and strigolactone signaling specifically induce Ericoid mycorrhizal (ErM) symbiosis in the S compartment(Chen et al., 2024 ). The significantly higher abundance of pathogenic and saprophytic fungi in non-rhizosphere soils (S/O) compared to rhizospheres is directly linked to the inhibitory effects of rhizosphere antibiotic secretion and the resultant migration of saprotrophic functions towards non-rhizospheric zones(Lin et al., 2023 ). These patterns collectively demonstrate that sea buckthorn directional shapes the rhizosphere microbial functional network through an ecological trade-off mechanism of "chemically mediated defense – obligate symbiosis." Single factor network analysis of bacteria and fungi showed that bacteria had better connectivity than fungi, with more nodes and edges. This indicates that the bacterial community network is more complex than bacteria and has good connectivity (Bello et al. 2020 ). Proteobacteria and Actinobacteria were the primary components of the bacterial network, whereas Ascomycota was the main component of the fungal network, thus these phyla may play dominant roles in structuring of the rhizosphere microbiomes (Chen et al. 2020 ). Our topology-based system approach has also suggested applicant keystone microbial species in co-occurrence networks. Keystone species in co-occurrence networks exert great effects on other community components (Ma et al. 2015). In their own modules and / or between different modules, a high number of generalists usually indicates good connections to different nodes (Zhou et al. 2011 ). In addition, highly connected network structures represent order with efficient material and energy fluxes (Olesen et al. 2007). 5. Conclusion This study compared the microbial community structures and nutrient characteristics of rhizosphere and bulk soils under two restoration modes: herbaceous revegetation vs. sea buckthorn restoration in alpine open-pit coal mine rehabilitation areas. The herbaceous restoration areas exhibited significantly higher soil organic carbon (SOC), total nitrogen (TN), and available nutrient contents compared to sea buckthorn areas. Rhizosphere microbial communities demonstrated significantly higher diversity than bulk soil, forming functional communities dominated by Actinobacteria, Proteobacteria, and Ascomycota. The lower fungal evenness in rhizosphere soils formed a loosely structured Ascomycota-dominated fungal network, suggesting reduced functional redundancy and reliance on unidirectional metabolic processes mediated by dominant taxa. Bacterial interaction networks exhibited high connectivity centered on Actinobacteria, supporting efficient material flows through SOC degradation and nitrogen-phosphorus activation. These differences indicate that herbaceous areas achieve short-term nutrient enhancement through rapid bacterial-driven cycling, whereas sea buckthorn areas maintain long-term nutrient restoration via fungal-bacterial functional complementarity and the establishment of dominant microbial consortia. Based on these findings, we propose a synergistic restoration strategy termed “short-term grass stimulation and long-term sea buckthorn stabilization,” providing a theoretical framework for microbial-targeted regulation in mining soil ecological restoration. While this study did not explore microbial genomics or metabolomics, future research could integrate metagenomics to elucidate functional gene expression, employ stable isotope probing (SIP) to trace carbon and nitrogen flow pathways, and assess community succession patterns under long-term restoration . Declarations Author Contribution Meng Qingjun: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Li Shengnan: Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Han Xiaoyu: Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jin Tao: Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Ma Mengke: Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Data curation. Jaio Yang: Visualization, Methodology, Investigation, Formal analysis, Data curation. Wang Liyan: Writing – review & editing, Supervision, Resources. Declaration of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Data will be made available on request. Acknowledge The authors would like to thank the financial support provided by the Project of Science and Technology from China Huaneng Group Co., LTD(HNMYKJ20-08). References Bi Y, Wang K, Du S, et al (2021) Shifts in arbuscular mycorrhizal fungal community composition and edaphic variables during reclamation chronosequence of an open-cast coal mining dump. Catena 203:105301. http://doi.org/10.1016/j.catena.2021.105301. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7218394","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496092109,"identity":"9fbbb44d-d7ff-416e-980a-90cd5325250f","order_by":0,"name":"Qingjun 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04:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7218394/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7218394/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88506807,"identity":"1034a46b-121b-4f6f-91ae-4a2b856d6048","added_by":"auto","created_at":"2025-08-07 07:35:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53456,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different restoration zones on soil nutrient content\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/a9eb1f5cbb9720d877722cb7.jpg"},{"id":88506842,"identity":"d1c3757a-6ca8-42e7-91e2-a8afe1e22c9a","added_by":"auto","created_at":"2025-08-07 07:35:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54084,"visible":true,"origin":"","legend":"\u003cp\u003eα-diversity indices of bacterial communities (a) and fungal communities (b) across different treatments\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/83a9483e831a22397822c1d8.jpg"},{"id":88506828,"identity":"81664d3d-20a3-4f5a-9fef-ecacf44721f3","added_by":"auto","created_at":"2025-08-07 07:35:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61139,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Microbial Community Structure in Bulk and Rhizosphere Soils Across Different Restoration Areas\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/fff0f3e9a652446d2365793d.jpg"},{"id":88508647,"identity":"9670b4e8-0d53-4e3a-87c4-ddc6ba6270ca","added_by":"auto","created_at":"2025-08-07 07:43:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40245,"visible":true,"origin":"","legend":"\u003cp\u003eBeta diversity (OTU level) of bacterial and fungal communities in different treatment groups. The beta diversities of the bacterial community (a) and fungal community (b) are shown using a principal coordinate analysis (PCoA). An analysis of similarities (ANOSIM) was calculated on an OTU level based on Bray-Curtis distance, indicating community similarity across sampling groups.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/6a86f212969c09f7390da453.jpg"},{"id":88506903,"identity":"4beaa0a0-0bb5-4cca-94d0-a584d0028bf9","added_by":"auto","created_at":"2025-08-07 07:35:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116329,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of bacterial functional genes in different samples\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/ad64ebb00551563f3bc41cd2.jpg"},{"id":88506822,"identity":"c82916e9-3bcf-47d3-9972-631b0ef5fe87","added_by":"auto","created_at":"2025-08-07 07:35:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":98826,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the abundance of fungal functional genes in different samples\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/77142a8e0be27c836bf38b6b.jpg"},{"id":88506855,"identity":"28a78cf3-5aa2-4e18-b926-276e6002e926","added_by":"auto","created_at":"2025-08-07 07:35:22","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":91487,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis(RDA) to estimate the relationship between soil nutrients (red arrows) and bulk bacterial community (a), bulk fungal community (b), rhizosphere bacterial community (c) and rhizosphere fungal community (d)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/2d27dcb4088753d3316b921e.jpg"},{"id":88506873,"identity":"450918a9-4225-4c9b-b8d8-afc3fb8670a1","added_by":"auto","created_at":"2025-08-07 07:35:25","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":87918,"visible":true,"origin":"","legend":"\u003cp\u003eThe co-occurrence networks and putative key connectors of bacterial and fungal communities at the OTU level. Networks of bacterial (a) and fungal communities (b) obtained using an RMT analysis of OTU profiles. Each node represents an OTU, and the size of each node is proportional to the number of connections (degree). The nodes in the networks are colored by phylum. A red line indicates a positive correlation and green line indicates a negative correlation.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/fb24fefcc006208b49aab14c.jpg"},{"id":89062821,"identity":"f1661380-0cba-4b36-9cdd-2165cd5701e2","added_by":"auto","created_at":"2025-08-14 09:46:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1528084,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7218394/v1/a2fbdeea-e200-4f6a-b73d-c83b26cc91ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural variability in bulk soil and rhizosphere microbial communities at different restoration modes of open-pit coal mine","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOpen-pit mining induces multiple environmental challenges including vegetation destruction (Bi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), soil and water erosion (Li et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), structural degradation of soil matrices (Lu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and diminished soil fertility (Tang et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, artificial rehabilitation of abandoned open-pit coal mines becomes imperative. During mining operations, topsoil is typically stockpiled for subsequent soil remediation. However, insufficient topsoil reserves substantially compromise ecological restoration efficacy and soil quality improvement in these mining areas (Wang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, diverse rehabilitation approaches exhibit significant differential impacts on soil nutrient dynamics and microbial community composition within waste dumps. As a critical ecological restoration strategy for degraded mining ecosystems, vegetation rehabilitation demonstrates economic feasibility and favorable environmental outcomes, effectively enhancing soil quality through systematic implementation (Liao et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMicrobial communities, serving as pivotal bioindicators of ecosystem health, exert substantial regulatory influences on restoration processes through structural and functional modifications in degraded ecosystems (Guo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within soil ecosystems, microorganisms occupy central ecological niches through multifaceted biogeochemical mechanisms: these include but are not limited to driving biogeochemical cycling of essential nutrients (C, N, P) (Bastida et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), catalyzing organic matter degradation and humification processes (Pasche et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), mediating pathogen antagonism via metabolic byproducts, regulating soil aggregate formation and pore structure evolution (Rabbi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and enhancing plant nutrient utilization efficiency through rhizosphere interactions (Griffin et al. 2024). This multidimensional functional network not only sustains soil ecosystem multifunctionality but also provides critical biological drivers for vegetation reestablishment and productivity enhancement (Kiesewetter et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSoil microbial communities are both regulated by and exert control over critical soil ecosystem functions, including nutrient decomposition and cycling, soil organic matter formation, and plant rhizosphere processes (Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Empirical studies have demonstrated distinct distribution patterns of bacterial communities and individual taxa in pre- and post-remediation mining soil ecosystems (Wang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Investigations into severely degraded tailings reveal that soil phosphorus (P) cycling exhibits significant enhancement following restoration interventions, primarily attributed to the pivotal role of gcd gene-driven phosphate-solubilizing bacteria in P cycling (Xu and Mao \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).However, these studies primarily rely on bulk soil samples, whereas most soil-plant-microbe interactions occur in the rhizosphere, defined as the region within one millimeter of the root surface. Plants promote their growth by exuding specific chemicals into the rhizosphere soil environment (Ling et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).In open-pit coal mine ecosystems, plant-soil-microbe interactions critically influence plant growth, productivity, and soil quality parameters (Mukhopadhyay et al. 2019). Although a portion of the plant microbiome originates from vertically transmitted endophytes (L\u0026oacute;pez-Lozano et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), bulk soil and rhizosphere soil matrices constitute the primary reservoirs for root-associated microbial communities (Compant et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Compared to bulk soil microbiota, rhizosphere soil microbial assemblages demonstrate heightened responsiveness to plant-derived litter inputs. This phenomenon stems from soil fungi's capacity to synthesize substantial quantities of cellulases, chitinases, and hemicellulases (Barbi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), enzymes that catalyze litter decomposition (Yang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Comparative analyses of taxonomic composition and functional traits between rhizosphere and bulk soil microbial communities have elucidated mechanistic insights into rhizosphere selection processes (Yan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this paper for the better understanding of the role of microorganisms in the ecological restoration process of open-pit coal mine, the bacteria and fungi in rhizosphere soil, and bulk soil under different restoration modes were studied. By defining the differences of soil chemistry and microbial communities under different remediation modes, we will understand the plant changes under different remediation measures and identify the microbial communities that are effective for the remediation. This will help us to understand the differences of different remediation methods, and the impact of herbs and shrubs on microorganisms in the soil damaged by mining. Therefore, the purpose of our study was to understand; (1) the effect of different repair methods on soil nutrients contents; (2) the influence of different repair methods on the diversity and structure of bulk soil and rhizosphere microbial communities in herb, and effects of different plants on root microorganisms under the same remediation measures; (3) define the relationship between nutrients and the diversity/structure of bulk soil and rhizosphere microbial communities.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Description of trial site and soil sampling method\u003c/h2\u003e\u003cp\u003eThis study was conducted at the Lingquan open-pit coal mine, in Dalai Nur District city, Inner Mongolia Province, Northeast China (117\u0026deg;43\u0026prime;00\u0026Prime;~117\u0026deg;45\u0026prime;33\u0026Prime;E,49\u0026deg;25\u0026prime;00\u0026Prime;~49\u0026deg;26\u0026prime;30\u0026Prime;N). The climate is mesotemperate, characterized by short warm summers and long cold winters. The annual rainfall ranges from 280 to 300mm, and the mean monthly temperature ranges from \u0026minus;\u0026thinsp;21 to 21℃, with an annual average of 0.8℃. The Lingquan open-pit coal mine, operational from 1960 to 2017, transitioned its remaining coal resources to underground extraction following closure. For ecological restoration of the mine dump, differentiated rehabilitation strategies were implemented based on comprehensive soil nutrient and environmental assessments. In herbaceous zones, slope grading and mulching were conducted prior to artificial seeding to accelerate vegetation recovery, while in sea buckthorn areas, targeted planting was employed to facilitate natural ecosystem regeneration.\u003c/p\u003e\u003cp\u003eDuring May 2021 sampling, 5\u0026ndash;8 randomized subsites were established per zone, with composite samples created through homogenization. Sampling protocols varied between restoration types: herbaceous zones provided 3 bulk soil and 3 rhizosphere soil samples, whereas sea buckthorn zones yielded 3 bulk soil, 3 herbaceous rhizosphere, and 3 sea buckthorn rhizosphere samples. Bulk soil collection avoided root zones by targeting areas 20\u0026ndash;50 cm from plant bases to ensure edaphic representativeness. Rhizosphere sampling focused on healthy roots at 5\u0026ndash;20 cm depth using sterile techniques, where intact roots were excavated and surface-adhered soil gently brushed into sterile containers. This methodology minimized microbial disturbance while capturing authentic soil-root interactions.\u003c/p\u003e\u003cp\u003ePost-collection processing included sieving through a 2-mm mesh to remove macro-organisms and debris. A portion of each soil sample was placed in 50 mL sterile tubes, flash-frozen in liquid nitrogen for microbial analysis, while remaining samples were air-dried for physicochemical characterization after sieving. Bulk soils from herbaceous and sea buckthorn zones were designated as O and S, respectively. Rhizosphere soils were labeled OHR (herbaceous zone), with SHR and SR distinguishing herbaceous and sea buckthorn rhizospheres within the sea buckthorn zone.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Soil nutrients analyses\u003c/h2\u003e\u003cp\u003eFor the determination of soil sample indexes, referred to the method of Baustein(Bao 2005), the potential method is used for the determination of soil pH value, and the potassium dichromate volumetric method is used for the determination of organic matter content. Semi micro Kjeldahl method was used for soil total nitrogen; Soil alkali hydrolyzable nitrogen was determined by diffusion method; Soil total phosphorus sodium hydroxide melting molybdenum antimony anti colorimetry; Soil available phosphorus was determined by Olsen method; Soil total potassium alkali melting flame photometer method soil available potassium was determined by ammonium acetate extraction flame photometry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. DNA extraction, sequencing, and data processing\u003c/h2\u003e\u003cp\u003eThe total DNA extraction, PCR amplification, and sequencing of soil samples were performed by Shanghai Majorbio Bio-pharm Technology Co., Ltd (Davide et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Bacterial 16S rRNA and fungal ITS genes were amplified using primer pairs 338F/806R (Adams et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and ITS1F/ITS2R (Lee et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), respectively. These primer pairs serve as reliable tools for bacterial and fungal community analysis due to their high specificity, broad coverage, and technical compatibility, demonstrating particular advantages in complex environmental samples.\u003c/p\u003e\u003cp\u003eSequencing steps: (1) DNA extraction and PCR amplification: Total DNA extraction was performed according to E.Z.N.A.\u0026reg; SOIL DNA Kit (Omega Bio-Tek, Norcross, GA, USA) instructions. DNA concentration and purity were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA).The quality of DNA extraction was detected by 1% agarose gel electrophoresis. Using the extracted soil microbial DNA as a template, 338F and 806R were used as primers for PCR amplification of v3-V4 variable region of bacteria, and ITS1F and ITS2R were used as primers for PCR amplification of ITS1 region of fungi. PCR products were recovered by conventional methods, purified, detected and quantified. (2) Illumina MiSeq sequencing: PCR products of the same origin were mixed and recovered using 2% agarose Gel. The recovered PCR products were purified using AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA).2% agarose gel electrophoresis and QuantusTM Fluorometer (Promega, USA) were used to detect and quantify the recovered products. Based on Illumina MiSeq platform (Illumina, SanDiego, USA) standard operating procedures, purified amplified segments were constructed into libraries. Sequencing was performed using Illumina's MiSeq PE300 platform (Shanghai Meggie Biomedical Technology Co. LTD.).Data processing: The original sequencing sequence was controlled by Trimmomatic; FLASH is used for splicing, a window of 50 bp is set, the sequences with a length less than 50 bp after quality control are removed, the sequences at both ends are splicing according to overlap base overlap, and the sequences are split to each sample according to barcode and primers at both ends of the sequence. UPARSE Version 7.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/uparse/\u003c/span\u003e\u003cspan address=\"http://drive5.com/uparse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to perform OTU clustering according to 97% similarity and eliminate chimera to generate OTU tables. Each sequence was annotated for species classification by RDP classifier (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and compared with Silva database (SSU123), with a threshold of 70%.The taxonomic information and community composition of each sample at each classification level were obtained and visualized by graphics. OTU abundance and Alpha diversity were calculated by Usearch and Mothur respectively to obtain the species information of the samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e\u003cp\u003eWe calculated Shannon diversity, Chao1 richness and Heip\u0026rsquo;s evenness index with Meiji Cloud Platform, and richness represented the number of unique OTUs in each sample.A one-way analysis of variance (ANOVA) based on least significant difference (LSD) was used to test for significant differences in the alpha diversity and dominant bacterial and fungal taxa in communities among different groups (SPSS Statistics 20.0). A principal coordinates analysis (PCoA) based on Bray-Curtis dissimilarity of OTU level was conducted using Meiji Cloud Platform to analyze the distributions of bacterial and fungal communities. An analysis of similarity (ANOSIM) was conducted on both bacterial and fungal communities using the vegan package in Meiji Cloud Platform based on Bray-Curtis distances. Spearman\u0026rsquo;s correlations were conducted to determine the relationships between alpha diversity, dominant genera, dominant functional profiles, and vegetation and root traits(SPSS statistics20.0). Mantel tests and Spearman\u0026rsquo;s rank correlations with Bray-Curtis dissimilarities of bacterial and fungal communities on OTU level were conducted using the vegan package in Meiji Cloud Platform. Linear discriminant analysis effect size was used to reveal the differences in bacterial and fungal taxa among different sampling groups. The species abundance among different samples was analyzed using Network(Meiji Cloud Platform).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Soil nutrients in seabuckthorn restoration areas are lower than in herbaceous restoration areas\u003c/h2\u003e\n \u003cp\u003eAccording to the test results, the herbaceous restoration area (O) exhibited significantly higher levels of Soil Organic Carbon (SOC) (P\u0026thinsp;=\u0026thinsp;0.002), Total Nitrogen (TN) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Available Nitrogen (AN) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.026), Total Potassium (TK) (P\u0026thinsp;=\u0026thinsp;0.019), and Available Potassium (AK) (P\u0026thinsp;=\u0026thinsp;0.007) compared to the sea buckthorn restoration area (S). Specifically, SOC, TN, AN, TK, and AK contents in the herbaceous area were 51.7%, 88.6%, 38.2%, 13.1%, and 4.7% higher than those in the sea buckthorn area, respectively. Additionally, the pH value was significantly lower in the O site than in the S site (P\u0026thinsp;=\u0026thinsp;0.001), with the pH in the sea buckthorn area being 8.2% higher. In contrast, Total Phosphorus (TP) content (P\u0026thinsp;=\u0026thinsp;0.052) and Available Phosphorus (AP) content (P\u0026thinsp;=\u0026thinsp;0.460) did not differ significantly between the two sites (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2. Significant Effects of Rhizosphere and Non-Rhizosphere Soil Conditions on Soil Microbial \u0026alpha;-Diversity Indices in Different Restoration Zones\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eAfter removing ambiguous, short, low-quality reads and singleton OTUs, a total of 1,629 unique fungal OTUs and 5,163 bacterial OTUs were obtained from 15 samples eligible for community analysis. Significant differences in \u0026alpha;-diversity indices of bacterial and fungal communities were observed across different samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The bacterial Shannon diversity and Chao1 richness were significantly higher in OHR and S samples, indicating richer species composition and more even distributions within these bacterial communities. In contrast, O samples exhibited lower values in both metrics. In terms of Heip evenness, S samples demonstrated the highest evenness, while O samples showed the lowest. Fungal communities in bulk soil samples from the two restored areas showed higher Shannon diversity and Heip evenness but lower Chao1 richness. Conversely, herbaceous rhizosphere soils showed the opposite pattern, with both herbaceous rhizosphere samples exhibiting higher fungal Chao1 richness but lower Heip evenness. Importantly, sea-buckthorn rhizosphere (SR) samples exhibited higher Shannon diversity and Heip evenness compared to herbaceous rhizosphere samples.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTwo-way ANOVA results for the effects of Different Restoration Zones (DRZ) and different ecological niches (DEN) on bacterial Alpha Indexes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eBacterial community\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlpha Indexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHeip\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZ\u0026times;DEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eFungal community\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlpha Indexes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHeip\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZ\u0026times;DEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBacterial communities across different ecological niches (DEN) exhibited significant differences in Chao1 richness (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), while no significant variations were observed in Shannon diversity or Heip evenness. In contrast, DEN demonstrated significant effects on fungal communities\u0026apos; Shannon diversity, Chao1 richness, and Heip evenness. No statistically significant differences were detected in bacterial alpha-diversity indices among different restoration Zones (DRZ). However, DRZ significantly influenced fungal Heip evenness (P\u0026thinsp;=\u0026thinsp;0.035, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the DRZ \u0026times; DEN interaction exerted significant effects on fungal Shannon diversity and Chao1 richness (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), as well as on bacterial Heip evenness (P\u0026thinsp;=\u0026thinsp;0.033, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.3. Divergent Impacts of Rhizosphere versus Bulk Soil Niches on Phylum-Level Microbial Community Assembly Across Restoration Regions\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eA total of 44 bacterial phyla and 13 fungal phyla were identified in the soil samples from the study area. Among these, 9 dominant bacterial phyla and 5 dominant fungal phyla (relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;1%) collectively accounted for 97.0% and 98.1% of the total sequences, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The bacterial communities were dominated by Proteobacteria (29.89%), Actinobacteria (27.84%), Chloroflexi (8.04%), Firmicutes (11.17%), and Cyanobacteria (7.54%), all exhibiting relative abundances exceeding 5.0% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). Notably, the relative abundances of Herbaspirillum (Firmicutes) and Rhizobiales (Proteobacteria) in herbaceous areas were significantly higher than those in sea-buckthorn areas (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Bulk soils generally showed higher Proteobacteria abundance compared to rhizosphere soils, whereas Actinobacteria demonstrated significant enrichment in sea-buckthorn rhizosphere soils (42.86%). Intriguingly, the sea-buckthorn rhizosphere microbiome was primarily composed of Proteobacteria (39.80%) and Actinobacteria (29.37%), with subdominant phyla including Chloroflexi (7.60%), Acidobacteria (7.22%), and Bacteroidetes (5.89%). Different restoration Zones (DRZ) exerted significant effects on Actinobacteria (P\u0026thinsp;=\u0026thinsp;0.006), Proteobacteria (P\u0026thinsp;=\u0026thinsp;0.024), Chloroflexi (P\u0026thinsp;=\u0026thinsp;0.006), and Acidobacteria (P\u0026thinsp;=\u0026thinsp;0.002). Distinct ecological niches (DEN) significantly influenced Actinobacteria (P\u0026thinsp;=\u0026thinsp;0.035) and Proteobacteria (P\u0026thinsp;=\u0026thinsp;0.045), while the DRZ \u0026times; DEN interaction showed no significant impact on bacterial communities (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRegardless of sampling location or restoration mode, the predominant fungal phylum was Ascomycota, with a relative abundance of 82.8% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb), and it was notably more prevalent in rhizosphere soil. Basidiomycota (7.34%) also emerged as a dominant phylum across diverse sampling sites. Notably, members of the phylum Rozellomycota were detected in bulk soil but were absent in rhizosphere soil microbial communities (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). In the covering soil layer, the major fungal phyla with relative abundances exceeding 1% included Ascomycota (82.81%), Basidiomycota (2.83%), Glomeromycota (2.33%), Mortierellomycota (3.36%), unclassified fungi (7.24%), and Rozellomycota (3.33%). Similarly, in the subsoil layer, the dominant phyla with relative abundances\u0026thinsp;\u0026gt;\u0026thinsp;1% were Ascomycota (95.91%) and Basidiomycota (2.96%). Both Ascomycota and Basidiomycota demonstrated higher abundances in bulk and rhizosphere soils.\u003c/p\u003e\n \u003cp\u003eIn Hippophae rhamnoides plantation soils, fungal phyla with relative abundances\u0026thinsp;\u0026gt;\u0026thinsp;1% comprised Ascomycota (76.7%), Basidiomycota (9.8%), Glomeromycota (4.2%), Mortierellomycota (3.5%), and Chytridiomycota (1.3%). Correspondingly, in H. rhamnoides root zone soils, the dominant phyla with relative abundances\u0026thinsp;\u0026gt;\u0026thinsp;1% were Ascomycota (78.2%), Basidiomycota (6.6%), Glomeromycota (2.2%), Mortierellomycota (1.9%), and Chytridiomycota (1.7%). However, in H. rhamnoides herbaceous root nodule soils, the predominant fungal phyla (\u0026gt;\u0026thinsp;1%) were exclusively Ascomycota (94.5%), Basidiomycota (2.1%), and Glomeromycota (1.2%). Notably, Mortierellomycota was undetectable in H. rhamnoides root nodule communities (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). A significant interactive effect between DRZ and DEN was observed on root nodule communities (P\u0026thinsp;=\u0026thinsp;0.041; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of two-way ANOVA for the effects of the Different Restoration Zones(DRZ) and Different ecological niches (DEN)on Richness and Shannon\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"15\"\u003e\n \u003cp\u003eBacterial community\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eActinobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eProteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChloroflexi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAcidobacteriota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBacteroidota\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZⅹDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\n \u003cp\u003eFungal community\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAscomycota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eBasidiomycetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMortierellomycota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eunclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChytridiomycota\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRZⅹDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo investigate how microbial communities varied across sampling locations and restoration modes, a Principal Coordinate Analysis (PCoA) was performed based on calculated Bray-Curtis distances between sampling sites. Microbial communities from the five groups were distinctly clustered into separate positions within the PCoA ordination (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The entire bacterial community exhibited significant segregation across the five defined sampling locations (P\u0026thinsp;=\u0026thinsp;0.001), while fungal communities also showed clear partitioning among the five sites (P\u0026thinsp;=\u0026thinsp;0.001). For bacterial communities, the first two principal coordinates collectively accounted for 47.60% of the total variation. The first principal coordinate (29.64%) separated communities at varying distances from roots, while the second coordinate (17.96%) partitioned communities under different restoration modes. However, discrepancies between rhizosphere soils of different plant species were less pronounced compared to those under distinct restoration modes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the fungal community assemblage, the first two principal coordinates explained 49.21% of the total variation. The first coordinate (26.96%) distinguished communities at different root proximity levels, whereas the second coordinate (22.25%) differentiated communities subjected to contrasting restoration practices. Analogous to bacterial patterns, variations between rhizosphere soils of different plant species remained statistically indistinct relative to those influenced by restoration modes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Differences in the levels of soil nutrients are correlated with changes in microbial community composition\u003c/h2\u003e\n \u003cp\u003eBacterial COG functional analysis revealed that sea buckthorn planting and the rhizosphere effect significantly altered soil microbial functional characteristics. The sea buckthorn areas (S/SHR/SR) generally exhibited higher core metabolic functions compared to the herbaceous area (O/OHR): Energy production and conversion (C) increased by 19.0% in SHR (4,259,687) versus O (3,578,672); Amino acid transport and metabolism (E) increased by 27.1% in SHR (6,471,816) versus O (5,095,519). Carbohydrate metabolism (G) surged by 37.4% in SHR (4,051,699) versus S (2,949,237). Cell motility (N) increased by 30.9% in S (621,168) versus O (474,672). The rhizosphere effect manifested as a 25.7% increase in lipid metabolism (I) in SHR compared to O, and extracellular structure genes (W) in SR reached 3.3 times that of the O sample (74.41). These findings suggest that sea buckthorn introduction drives microbial functional restructuring by enhancing carbon and nitrogen metabolism (G/E), environmental adaptability (N), and rhizosphere interactions (W).\u003c/p\u003e\n \u003cp\u003eFungal guild analysis revealed significant differential abundance across treatments (O, OHR, S, SHR, SR). The Fungal Parasite-Plant Pathogen-Saprotroph guild demonstrated substantially higher relative abundance in SR (10,532.67; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to all other treatments. Similarly, the Animal Pathogen-Endophyte-Lichen Parasite-Plant Pathogen-Wood Saprotroph guild exhibited maximal abundance in SR (7,289.33; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).The Animal/Plant Pathogen-Undefined Saprotroph guild peaked exclusively in OHR (6,894.00), exceeding abundances in other treatments by 5- to 24-fold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the Animal Pathogen-Soil Saprotroph guild was enriched in bulk soil treatments (S: 4,209.33; O: 3,278.33) but severely suppressed in rhizosphere treatments (OHR: 0.67; SHR: 32.67).Ericoid mycorrhizal symbionts showed exceptional treatment specificity, with abundance in S (6,107.67) exceeding other treatments by 2\u0026ndash;3 orders of magnitude (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The Animal Pathogen-Endophyte-Ericoid Mycorrhiza-Plant Pathogen-Wood Saprotroph composite guild dominated O treatment (15,508.33) but was nearly undetectable in OHR (6.67).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Differences in the levels of soil nutrients are correlated with changes in microbial community composition\u003c/h2\u003e\n \u003cp\u003eTo better understand the correlations between soil nutrient content and microbial community composition, we performed a redundancy analyses (RDA). The top 5 species in the community are indicated in the Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. For the overall bacterial communities, the first two axes explained 95.06% of the total variance between the bulk soil bacterial communities at the different restoration modes (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). The RDA demonstrated that SOC, TN, and TP content were positively correlated with the phylum Firmicutes, Cyanobacteria, and inversely correlated with the phylum Chloroflexi and Proteobacteria, while pH and TK were opposite. Similarly, in the bulk soil, first two RDA axes explained 95.08% of the total variance between rhizosphere bacterial communities at the different restoration modes (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb). The SOC, TN, and TP contents were positively correlated to Acidobacteriota, Chloroflexi and Gemmatimonadota, while, inversely correlated with the phylum Proteobacteria, and Actinobacteriota. Though, unlike bulk soil, the correlation between TK and bacterial community was consistent with SOC, but opposite to pH.\u003c/p\u003e\n \u003cp\u003eFor the fungal communities, the first two axes explained 93.93% of the total variance between bulk soil bacterial communities at the different Restoration modes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). The RDA confirmed that SOC, TN, and, TP content were positively correlated with the phyla Rozellomycota, and unclassified-k-Fungi, and inversely correlated with the phylum Mortierellomycota (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). Likewise, in bulk soil the first two RDA axes explained 99.55% of the total variance between rhizosphere fungal communities at the different restoration modes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed). The SOC, TN, and TP content, and pH were positively correlated with the phyla Ascomycota.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Co-occurrence patterns of bacterial and fungal communities\u003c/h2\u003e\n \u003cp\u003eA network analysis was conducted to investigate the cooccurrence patterns of bacterial and fungal communities (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The exploration of co-occurrence networks is an effective tool to determine the occurrence of biological interactions in the microbial communities (Ma et al. 2015). Single factor network analysis builds a species correlation network by calculating the correlation between species. The nodes in the network diagram are the species nodes. When the correlation coefficient between species meets a certain threshold, there is a line develops between species. In this study, Pearson model was used to conduct univariate network analysis on the top 50 genera with relative abundance in microbial communities at different sampling points with absolute value of relative coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.5 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In this study, the single network factor of soil bacteria in different remediation methods was connected. The network diameter was 4, the average shortest path length between nodes was 1.980, and a total of 300 sides corresponding to 50 nodes. The OTUs of the top 50 species belong to Proteobacteria, Actinobacteriota, Chloroflexi, Gemmatimonadota, Acidobacteriota, Firmicutes, Cyanobacteriota, and Bacteroidota. The top 50 species OTUs belong to OTU 2761 of Actinobacteria, which with the highest network centrality value. The single factor network of soil fungi was unconnected, with 191 edges among nodes. The OTUs of the top 50 species belong to Ascomycota, Basidiomycota, Mortierellomycota, Rozellomycota, and unclassified-fungi. The top 50 species OTUs belong to Actinobacteria with the highest network centrality value.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOpen-pit coal mining ecosystems are inherently unstable and highly prone to degradation, characterized by disrupted soil physical structures, reduced water and soil retention capabilities, and subsequent soil loss and vegetation destruction (Wang et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Vegetation reconstruction is the primary step in restoring such ecosystems. Since the closure of the open-pit coal mine in the study area in 2017, ecological restoration has been carried out through comprehensive analysis of various factors, including soil nutrients and structural factors (soil parent material, slope, climate), and different restoration methods have been applied to different restoration zones. In the herbaceous restoration zone, due to low soil fertility and poor soil structure, restoration involves artificial soil covering followed by sowing of herbaceous plants. In contrast, the sea buckthorn restoration zone employs direct planting of sea buckthorn on the existing soil. Analyzing the shifts in microbial community composition under different restoration approaches provides insights into the role of microbes during the restoration process. This study examines the structure and diversity of microbial communities in bulk and rhizosphere soils across different restoration methods, analyzes the relationships between soil nutrients and microbes, and explores the potential functions of dominant microbes under various restoration approaches.\u003c/p\u003e\u003cp\u003eRestoration approaches exerted substantial impacts on soil nutrients and pH. The herbaceous area exhibited mid-to-high levels across multiple nutrient indices compared to H. rhamnoides plots, with soil pH approaching neutrality. Herbaceous plants, characterized by short growth cycles and high biomass production (Zhao et al. 2021), enhance soil organic carbon (SOC) and total nitrogen (TN) through rapid decomposition of abundant litter (senescent leaves, root residues) (Wu et al. 2024). Concurrently, their root exudates (organic acids, carbohydrates) mobilize phosphorus and potassium (available phosphorus, available potassium) while promoting available nitrogen (AN) mineralization (Zhang et al. 2025), collectively improving soil organic matter and structural stability. In contrast, deep-rooted H. rhamnoides systems may exacerbate surface nutrient leaching, coupled with strong phosphorus fixation capacity, resulting in suppressed AP availability (Lu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe diversity and composition of microbial communities in rhizosphere soil are significantly different from those in bulk soil, which is consistent with previous studies demonstrating that plant metabolism can influence the growth environment of microorganisms, thereby causing differences from bulk soil (Chen et al. 2016). Both the diversity and abundance of bacteria and fungi in rhizosphere soil are higher than those in bulk soil. This is primarily because plant root exudates and signaling molecules recruit specific populations from the soil pool to colonize the rhizosphere, forming particular microbial communities, such as nitrate-reducing bacteria, denitrifying bacteria, mycorrhizal fungi, and rhizobia (Luo et al. 2021). Meanwhile, the uniform distribution of root exudates provides microorganisms with abundant carbon sources and nutrients. The enrichment effect of root exudates results in microbial biomass in rhizosphere soil being several to tens of times higher than that in non-rhizosphere soil (Zhu et al. 2022). However, the fungal community evenness in rhizosphere soil is lower than that in bulk soil. This may be because some fungal communities can efficiently utilize root exudates as carbon sources, thereby proliferating rapidly in the rhizosphere and gaining a dominant position, while other groups are inhibited due to resource competition or the presence of inhibitory metabolites (Wang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe dominant fungal phylum in the mining area is Ascomycota, while the dominant bacterial phyla are Proteobacteria and Actinobacteria. These findings align with the microbial community structure reported in open-pit coal mines in Inner Mongolia (Chen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The abundance of Actinobacteria in rhizosphere soil exceeds that in bulk soil, and Proteobacteria are more prevalent in sea buckthorn areas than in herbaceous plant areas. This discrepancy can be attributed to the microaerobic preference of Actinobacteria, which thrive in rhizosphere microenvironments shaped by plant respiration and water gradients (Wu et al. 2019).In the root nodule layer of sea buckthorn (Hippophae rhamnoides), Proteobacteria (39.80%) and Actinobacteria (29.37%) exhibit a co-dominance pattern. As a non-leguminous nitrogen-fixing plant, sea buckthorn recruits Proteobacteria through root-secreted flavonoids and achieves efficient nitrogen fixation via symbiosis between its root nodule bacteria (Proteobacteria) and Frankia (Actinobacteria) (Bi and Zhang 2014). In contrast, herbaceous plants exhibit weaker nitrogen-fixing capacities, resulting in narrower ecological niches for Proteobacteria (Song et al. 2024).The relative abundance of Ascomycota remains stable across different soil conditions, likely due to their high species diversity and rapid evolutionary adaptation to heterogeneous habitats (Wang et al. 2010). Bacterial phyla such as Acidobacteriota, Chloroflexi, and Gemmatimonadota show positive correlations with soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) content. Acidobacteriota, for instance, harbor diverse carbohydrate-active enzymes (CAZymes) capable of degrading high-molecular-weight organic compounds (cellulose and hemicellulose, key SOC sources) while assimilating organic nitrogen as a nutrient source.\u003c/p\u003e\u003cp\u003eThe rhizosphere soil of sea buckthorn (SR) exhibited significantly elevated gene abundances associated with bacterial amino acid metabolism (E) and energy conversion (C). This enhancement primarily stems from the activation of metabolic pathways by the sea buckthorn-rhizobium symbiotic system, thereby directly or indirectly increasing rhizospheric nitrogen availability (Ke et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bai et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Concurrently, the complementary root ecological niches between deep-rooted sea buckthorn and shallow-rooted herbaceous plants synergistically input diversified carbon and nitrogen substrates (Abdul et al., 2025). This drives markedly enhanced expression of carbohydrate metabolism genes in SHR/SR soils, promoting efficient integration of microbial metabolic networks (Wu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Under conditions of resource heterogeneity, bacteria in bulk soil (S) and herbaceous rhizospheres (O) upregulate motility gene (N) expression to optimize foraging strategies in response to patchy resource distribution (Song et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conversely, bacteria in the sea buckthorn rhizosphere (SR) rely on extracellular structural genes (W) mediating biofilm formation to strengthen colonization capacity, thereby resisting root-derived antimicrobial stress (Liu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These three strategies collectively constitute the core of bacterial functional group restructuring characterized by \"high metabolic activity - strong environmental adaptation - deep rhizosphere colonization\". Fungal community analysis further revealed that SR selectively enriches pathogenic/saprophytic fungi with high tolerance. The allelopathic compounds, such as tannins released by its lignified roots, provide chemotactic signals for colonization (Zhou et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), thereby activating a polyphagous saprotroph-pathogen functional complex. In contrast, phosphorus limitation and strigolactone signaling specifically induce Ericoid mycorrhizal (ErM) symbiosis in the S compartment(Chen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The significantly higher abundance of pathogenic and saprophytic fungi in non-rhizosphere soils (S/O) compared to rhizospheres is directly linked to the inhibitory effects of rhizosphere antibiotic secretion and the resultant migration of saprotrophic functions towards non-rhizospheric zones(Lin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These patterns collectively demonstrate that sea buckthorn directional shapes the rhizosphere microbial functional network through an ecological trade-off mechanism of \"chemically mediated defense \u0026ndash; obligate symbiosis.\"\u003c/p\u003e\u003cp\u003eSingle factor network analysis of bacteria and fungi showed that bacteria had better connectivity than fungi, with more nodes and edges. This indicates that the bacterial community network is more complex than bacteria and has good connectivity (Bello et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Proteobacteria and Actinobacteria were the primary components of the bacterial network, whereas Ascomycota was the main component of the fungal network, thus these phyla may play dominant roles in structuring of the rhizosphere microbiomes (Chen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our topology-based system approach has also suggested applicant keystone microbial species in co-occurrence networks. Keystone species in co-occurrence networks exert great effects on other community components (Ma et al. 2015). In their own modules and / or between different modules, a high number of generalists usually indicates good connections to different nodes (Zhou et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, highly connected network structures represent order with efficient material and energy fluxes (Olesen et al. 2007).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study compared the microbial community structures and nutrient characteristics of rhizosphere and bulk soils under two restoration modes: herbaceous revegetation vs. sea buckthorn restoration in alpine open-pit coal mine rehabilitation areas. The herbaceous restoration areas exhibited significantly higher soil organic carbon (SOC), total nitrogen (TN), and available nutrient contents compared to sea buckthorn areas. Rhizosphere microbial communities demonstrated significantly higher diversity than bulk soil, forming functional communities dominated by Actinobacteria, Proteobacteria, and Ascomycota. The lower fungal evenness in rhizosphere soils formed a loosely structured Ascomycota-dominated fungal network, suggesting reduced functional redundancy and reliance on unidirectional metabolic processes mediated by dominant taxa. Bacterial interaction networks exhibited high connectivity centered on Actinobacteria, supporting efficient material flows through SOC degradation and nitrogen-phosphorus activation. These differences indicate that herbaceous areas achieve short-term nutrient enhancement through rapid bacterial-driven cycling, whereas sea buckthorn areas maintain long-term nutrient restoration via fungal-bacterial functional complementarity and the establishment of dominant microbial consortia. Based on these findings, we propose a synergistic restoration strategy termed “short-term grass stimulation and long-term sea buckthorn stabilization,” providing a theoretical framework for microbial-targeted regulation in mining soil ecological restoration. While this study did not explore microbial genomics or metabolomics, future research could integrate metagenomics to elucidate functional gene expression, employ stable isotope probing (SIP) to trace carbon and nitrogen flow pathways, and assess community succession patterns under long-term restoration .\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMeng Qingjun: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Li Shengnan: Writing \u0026ndash; review \u0026amp; editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Han Xiaoyu: Writing \u0026ndash; review \u0026amp; editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jin Tao: Writing \u0026ndash; review \u0026amp; editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Ma Mengke: Writing \u0026ndash; review \u0026amp; editing, Visualization, Methodology, Investigation, Formal analysis, Data curation. Jaio Yang: Visualization, Methodology, Investigation, Formal analysis, Data curation. Wang Liyan: Writing \u0026ndash; review \u0026amp; editing, Supervision, Resources.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the financial support provided by the Project of Science and Technology from China Huaneng Group Co., LTD(HNMYKJ20-08).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBi Y, Wang K, Du S, et al (2021) Shifts in arbuscular mycorrhizal fungal community composition and edaphic variables during reclamation chronosequence of an open-cast coal mining dump. 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MBIO 2(4): e00122-11\u0026ndash;e00122-11. http://doi.org/10.1128/mbio.00122-11.\u003c/li\u003e\n\u003cli\u003eWan XL, Gao Q, Zhao JS, et al (2020) Biogeographic patterns of microbial association networks in paddy soil within Eastern China, Soil Biology and Biochemistry, Volume 142, 107696, ISSN 0038-0717,https://doi.org/10.1016/j.soilbio.2019.107696.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Open-pit coal mine, Bacteria and fungi, Rhizosphere microorganism, Network","lastPublishedDoi":"10.21203/rs.3.rs-7218394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7218394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003emethods\u003c/h2\u003e\u003cp\u003eherbaceous revegetation (O) and shrub (specifically Hippophae rhamnoides, S) revegetation. The aim was to elucidate the impact of different restoration measures on soil-microbe interactions. The results demonstrated that soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), total potassium (TK), and available potassium (AK) contents were significantly higher in the herbaceous restoration area (O) than in the seabuckthorn area (S), by 51.7%, 88.6%, 38.2%, 13.1%, and 4.7%, respectively. Compared to bulk soil, rhizosphere soil exhibited higher microbial community diversity and richness. Furthermore, seabuckthorn rhizosphere microbial diversity surpassed that of herbaceous rhizosphere. Different restoration areas (DRE) significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) influenced the relative abundances of Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria. The seabuckthorn area showed higher proportions of Proteobacteria (26.48\u0026thinsp;~\u0026thinsp;42.86%) and Actinobacteria (28.26\u0026thinsp;~\u0026thinsp;45.19%) compared to the herbaceous area. Functional gene prediction revealed that the seabuckthorn area expressed significantly higher abundances of core metabolic functional genes related to energy production and conversion (C), amino acid transport and metabolism (E), carbohydrate metabolism (G), and lipid metabolism (I) than the herbaceous area. Additionally, a symbiotic functional guild comprising animal pathogens, endophytes, lichen parasites, plant pathogens, and wood saprotrophs was formed in the seabuckthorn area. Redundancy analysis (RDA) indicated significant positive correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between Acidobacteria, Chloroflexi, Actinobacteria, and Ascomycota and the contents of SOC, TN, and total phosphorus (TP). Bacterial networks formed with Actinobacteria as the core hub, comprising 300 edges connecting 50 nodes, while fungal networks were dominated by Ascomycota. Based on these findings, this study proposes a synergistic restoration strategy characterized by \"herbaceous-induced short-term priming\" coupled with \"seabuckthorn-driven long-term stability.\" This strategy provides a theoretical foundation for the targeted microbial regulation of ecological restoration in mining areas.\u003c/p\u003e","manuscriptTitle":"Structural variability in bulk soil and rhizosphere microbial communities at different restoration modes of open-pit coal mine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 07:08:05","doi":"10.21203/rs.3.rs-7218394/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T06:29:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T04:53:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T00:52:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318475167307844206292707327909842412821","date":"2025-10-29T21:37:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318458170132205295941849703448395991385","date":"2025-10-29T13:13:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55622215341193018320002111280022856075","date":"2025-08-21T16:02:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T23:12:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T02:36:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T11:59:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Management","date":"2025-07-26T04:22:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7c150116-c7ff-4be8-b27f-c9f2988a2f98","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-27T02:10:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 07:08:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7218394","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7218394","identity":"rs-7218394","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}