{"paper_id":"12efd7c6-446e-41dd-9556-b20303b2aacf","body_text":"Response of soil micro-food web and nutrient transfer efficiency to reclamation strategies in mining area | 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 Response of soil micro-food web and nutrient transfer efficiency to reclamation strategies in mining area Peng Gao, Xiujuan Zhang, Hong Zhang, Junjian Li, Chao Su, Yong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5782088/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Plant and Soil → Version 1 posted 5 You are reading this latest preprint version Abstract Background & Aims: The soil micro-food web plays a crucial role in facilitating ecological restoration and maintaining ecosystem functionality in post-mining environments. However, the specific influence of reclamation patterns on the structure of soil micro-food web and their trophic transfer efficiency in mining soils remains unclear. Therefore, this study aimed to analyse the specific impacts of reclamation models on the soil micro-food web and elucidate the underlying mechanisms that restores ecosystem functions. Methods: We conducted a field experiment at 15 sites across three reclamation patterns—coniferous plantation (CP), broad-leaved plantation (BP), and mixed coniferous-broadleaved plantation (MP)—within the Pingshuo Open-pit Coal Mine in China. Using metagenomic sequencing, we analysed soil micro-food web structures and nutrient transfer efficiencies across various reclamation strategies. Results: MP exhibited greater microbial network complexity and higher nutrient transfer efficiency than those of CP and BP. Specifically, MP ecosystems demonstrated considerably enhanced nutrient transfer efficiency among higher trophic-level microorganisms such as protists and metazoans, indicating improved trophic energy flow and resource utilisation within the soil micro-food web. Moreover, reclamation patterns influenced soil nutrient transfer efficiency by modifying soil physicochemical properties, ultimately shaping soil carbon and nitrogen metabolic processes. Conclusion: The mixed coniferous-broadleaved plantation enhanced nutrient transfer efficiency within the soil micro-food web, thereby optimising trophic interactions and ecosystem nutrient cycling. Reclamation models can influence C/N metabolism processes via the soil microbial network. Our findings provide a comprehensive understanding of optimizing reclamation strategies and improving ecosystem functions in mining areas. opencast mining area reclamation pattern soil micro-food web trophic transfer efficiency Carbon/Nitrogen metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Open-pit mining, as a principal method for mineral extraction, has contributed considerably to rapid economic development (Zhao et al. 2013 ). In China, mining operations have impacted approximately 12 million hectares of land, leading to substantial degradation (Bai et al. 2018 ; Wang et al. 2017). The removal of topsoil and the alteration of soil profiles result in irreversible damage to biodiversity, severely compromising the health and function of Mining Area Ecosystem (Feng et al. 2019 ). China has made considerable efforts to combat the critical degradation of ecosystems through extensive reforestation programs (Cao et al. 2021; Guan et al. 2021 ). Soil microbial communities are vital for establishing plant communities in degraded ecosystems and play a crucial role in regulating the recovery of terrestrial environments (Coban et al. 2022; Schroeder et al. 2024 ). However, different afforestation strategies have vastly affected soil microbial ecosystems in mining areas (De Deyn et al. 2003 ; Shi et al. 2024 ). Xiao et al. ( 2021 ) demonstrated that distinct land restoration types markedly altered the distribution characteristics, diversity indices, and dominant populations of soil bacterial communities in these areas. Chang et al. ( 2022 ) found that various vegetation restoration models had prominently different impacts on the diversity of soil fungi and bacteria. Deng et al. (2020) further observed that planting different tree species could effectively improve the soil microbial community structure in mining areas, with Robinia pseudoacacia particularly effective in enhancing soil nutrients, boosting community diversity, and improving soil structure. These findings highlight the influence of different restoration strategies on the structure and multifunctionality of soil microbial communities, offering valuable scientific guidance for improving soil quality and restoring mining area ecosystems (Chen et al. 2019 ). However, current studies on restoration strategies mainly focused on changes in soil bacteria and fungi diversity, with less emphasis on the critical role of soil biotic communities and their food webs in restoring and maintaining ecosystem health. However, much of the current research on restoration strategies focused on the diversity of soil bacteria and fungi, less attention was paid to the roles of archaea, protists, and metazoans in the soil ecosystem. These groups of organisms, together with their interactions within the soil micro-food web, play crucial roles in nutrient cycling, energy flow, and the restoration of ecosystem health (Geisen et al. 2018 ).Delving deeper into the effect of different reclamation strategies on soil micro-food web can not only reveal specific mechanisms in ecosystem recovery but also provide more comprehensive scientific evidence for optimizing reclamation measures. The soil micro-food web, a complex network of soil organisms and their trophic interactions, is crucial for maintaining ecological processes through the regulation of energy and nutrient flows (Coleman et al. 2015; Frangoulis et al. 2005 ). In the micro-food web, lower trophic levels can impose bottom-up effects by controlling resource availability, thereby influencing the dynamics of higher trophic-level communities. Similarly, higher trophic levels can exert top-down effects shaping lower-level communities through predation and interference, influencing their structure and function (Bardgett et al. 2014). The diversity and abundance of these trophic relationships support essential ecosystem functions and primary productivity (Yuan et al. 2021 ). During vegetation restoration processes, increased species connectivity within the soil community enhances nutrient cycling efficiency and carbon sequestration (Zheng et al. 2018 ). However, limited evidence exists on the response of micro-food web complexity and stability to the revegetation of degraded ecosystems. (Ishii et al. 2021 ; Zhang et al. 2013 ). Addressing this knowledge gap requires a deeper understanding of the influence of different reclamation models on soil micro-food web structure and nutrient interactions. Investigating these processes can reveal the mechanisms through which ecosystems respond to reclamation-induced environmental changes and provide insights into the regulation of ecosystem multifunctionality. This study aims to fill this knowledge gap by analysing the specific impacts of reclamation models on the soil micro-food web and elucidating the underlying mechanisms restoring ecosystem functions. Therefore, in the current study, we selected the reclamation area of the Pingshuo Open-pit Coal Mine in China as our study region. Using metagenomic sequencing technology to compare the micro-food web structure and nutrient transfer efficiency under different reclamation patterns. The specific objectives of this study were: (1) Compare the differences in soil physicochemical properties, soil biotic community composition, and levels of diversity under various reclamation patterns; (2) Reveal the control by reclamation patterns on the structure of soil micro-food web and the efficiency of nutrient transfer; and (3) Clarify the mechanisms by which reclamation patterns influence the efficiency of nutrient transfer in soil micro-food web and the functions of soil ecosystems. This study will help enhance our understanding of the relationship between soil micro-food web and ecosystem functions and offer valuable insights for selecting appropriate reclamation patterns in coal mine ecological restoration. Materials and methods Study area and sample collection The Pingshuo mining area, located in northern Shanxi Province (112°10′–113°30′E, 39°23′–39°37′N), is one the most extensive open-pit mining sites of China. It features a semi-arid continental monsoon climate and is characterized by ecological vulnerability. Since 1990, the enterprise has implemented a series of reclamation measures, including the planting of monoculture (such as Pinus sylvestris, Picea asperata, and Larix gmelinii) and mixed (combinations such as Pinus tabuliformis with Robinia pseudoacacia , or Pinus tabuliformis with Caragana korshinskii and Ulmus pumila ) forests. These strategies aim to restore vegetation cover and soil quality in the mining area, thereby promoting overall ecosystem recovery (Guan et al. 2020 ; Zhou et al. 2022 ). In July 2022, a total of 15 sites were investigated across three reclamation patterns: coniferous (CP; 5 sites), broad-leaved (BP; 5 sites), and mixed coniferous–broadleaved (MP; 5 sites) plantations. Reclamation at all sites commenced simultaneously in 1990, and each site has undergone an identical 30-year restoration period. Detailed site characteristics are presented in Table S1 . At each site, a 400 m² plot (20 m × 20 m) was established, resulting in a total of 15 plots across all sites. Following the careful removal of surface vegetation and soil cover, bulk soil samples were then collected for analysis. Five soil samples were randomly extracted from the 0–10 cm soil layer within each plot, subsequently combined into a single composite sample, and sieved through a 2 mm mesh to eliminate large particles and debris. To ensure sample integrity and appropriate storage for various analyses, the composite sample was divided into three subsamples: (1) one subsample was stored at 4°C for the determination of soil enzyme activity, ammonium nitrogen (NH₄⁺-N), and nitrate nitrogen (NO₃⁻-N), with short-term storage (within one week) ensuring sample stability; (2) another was immediately frozen at -–80°C to preserve DNA integrity for metagenomic sequencing; and (3) the remaining portion was air-dried for soil physicochemical property analysis. Analysis of soil physiochemical and soil enzymatic parameters Soil pH was measured using a PHS-3C pH meter (INESA Instruments Inc., China) with a 1:2.5 soil-to-water ratio. The concentrations of NH 4 + -N and NO 3 − -N in the supernatant were determined through colorimetry using a Lachat auto analysis system (Zellweger Analytics, Milwaukee, WI). Soil organic carbon (SOC) was quantified using the H 2 SO 4 -K 2 Cr 2 O 7 oxidation method. Total nitrogen (TN) was analysed by elemental analysis-stable isotope mass spectrometry (EA-IRMS, Iso Prime100, Germany). Extracellular enzyme activities were measured using commercial assay kits and colorimetric methods. For enzymes related to the carbon (C) cycle, dehydrogenase, β-glucosidase, and cellulase were measured using the S-DHA activity assay kit, S-β-GC activity assay kit, and S-CL activity assay kit, respectively. For enzymes related to the nitrogen (N) cycle, urease, alkaline protease, and N-acetyl-β-D-glucosaminidase were measured using the S-UE, alkaline protease, and S-NAG activity assay kits, respectively. All the commercial assay kits were provided by Sangon Biotech Co., Ltd. (China). DNA extraction, metagenomic sequencing, and data processing DNA extraction from the soil samples was performed using the E.Z.N.A. Soil DNA Kit (Omega Bio-tek) following the manufacturer's instructions. To ensure the quality and quantity of the extracted DNA, the concentration was measured with a TBS-380 mini-fluorometer (Turner BioSystems), whereas the purity was assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific Inc.,). The integrity of the extracted DNA was further evaluated by 1% agarose gel electrophoresis, which was run at 5 V/cm for 20 min. Once the DNA samples were confirmed to be of high quality, they were sent to Shanghai Meiji Biomedical Technology Co., Ltd. (Shanghai, China) for metagenomic sequencing. Adapter sequences at the 3' and 5' ends of the reads were trimmed using Seqprep (version 0.20.0, https://github.com/OpenGene/fastp ), and low-quality reads (trimmed length < 50 bp, average base quality < 20, or containing N bases) were discarded. The filtered reads were assembled using MEGAHIT (version 1.1.2, https://github.com/voutcn/megahit ) based on the principle of succinct de Bruijn graphs. Open reading frames (ORFs) in the assembled contigs were identified using MetaGene ( http://metagene.cb.k.u-tokyo.ac.jp/ ), a widely employed tool for prokaryotic gene prediction. To mitigate the inherent limitations of MetaGene in predicting eukaryotic genes, we implemented stringent post-prediction filtering criteria. Specifically, genes with a nucleotide length of at least 100 base pairs (bp) were retained and translated to their corresponding amino acid sequences. To construct a non-redundant gene set, all predicted gene sequences from the samples were clustered using CD-HIT (version 4.6.1, http://www.bioinformatics.org/cd-hit/ ), applying a threshold of 90% sequence identity and 90% coverage. The longest gene sequence in each cluster was selected as the representative sequence. Subsequently, high-quality sequencing reads from each sample were aligned with this non-redundant gene set using SOAPaligner (version 2.21, http://soap.genomics.org.cn/ ) with a 95% identity threshold to obtain gene abundance information. This approach ensures high-confidence quantification, minimizes potential errors from incomplete gene prediction, and enhances the robustness of gene abundance estimates. Co-occurrence network analysis To explore microbial co-occurrence patterns, ecological networks were constructed based on OTU abundance data and analysed for topological parameters. The Molecular Ecological Network Analyses Pipeline (MENAP) ( http://ieg2.ou.edu/MENA/ ) was used to construct these networks (Deng et al. 2012 ; Zhou et al. 2010 , 2011). An RMT-threshold value of 0.84 was applied to reduce network complexity, retaining only OTUs present in more than 84% of samples. Spearman correlation analysis (psych package, R) identified significant correlations (|r| > 0.8, p < 0.05), which were then used to construct the networks. Topological parameters, including modularity, node degree, clustering coefficient, and average path length, were calculated using the igraph R package (Csardi and Nepusz 2006 ). Positive and negative edges were evaluated to assess network stability. Additional metrics, such as the number of nodes, edges, modularity index, Module numbers, and average degree, were also computed to evaluate network connectivity and structural complexity. The co-occurrence network was visualized using the Fruchterman-Reingold layout in Gephi 0.9.4. Soil micro-food web structure and trophic transfer efficiency Metagenomic sequences were classified into six ecological functional groups—viridiplantae, bacteria, fungi, archaea, protists, and metazoans—based on their similarity to entries in the NCBI-NR database. Sequences classified as Viridiplantae represent organic matter derived from plants (Long et al. 2025 ). Metazoan sequences primarily represent nematodes, rotifers, tardigrades, and arthropods, which play key roles in soil microbial interactions, decomposition, and nutrient cycling (Darby and Neher 2016 ; Potapov et al. 2022 ). The interaction structure and trophic transfer efficiency of different micro-food webs were analysed using these ecological functional groups (Dang et al. 2022 ; Yang et al. 2021 ). Correlations among the different ecological groups were examined to determine the types of interactions between them. A micro-food web was constructed based on the potential predator-prey relationships among various microbial taxa (Li et al. 2020 ; Wu et al. 2022 ; Zou et al. 2022 ). Trophic transfer efficiency was estimated by calculating the ratio of predator to prey biomass (log10GA: log10 calculation of gene numbers) across different trophic levels. Although gene abundance (log10GA) serves as an indirect proxy for metabolic activity with inherent limitations, it remains a practical and widely accepted approach in soil micro-food web studies (Glavatska et al. 2017 ; Heijboer et al. 2017 ; Trap et al. 2024 ). As an example for metazoan, the total nutrient transfer efficiency (TETE) of metazoan was calculated as: TETE metazoan =log 10 GA metazoan /(log 10 GA viridiplantae + log 10 GA bacteria + log 10 GA fungi + log 10 GA archaea + log 10 GA protists ) The trophic transfer efficiency from bacteria to metazoan was calculated as: Metazoan: Bacteria = TETE metazoan ×log 10 GA bacteria /(log 10 GA viridiplantae + log 10 GA bacteria + log 10 GA fungi + log 10 GA archaea + log 10 GA protists ) This usage was motivated by earlier studies (Bai et al. 2017 ; Delmont et al. 2015 ; Fierer et al. 2012 ; García et al. 2016; Jeppesen et al. 2003 ; Singh et al. 2019 ; Sun et al. 2024 ; Yvon et al. 2010). Statistical analysis The α-diversity of bacterial, fungal, archaeal, protist, and metazoan communities was calculated using the Shannon index with the 'Vegan' package in R. β-diversity was assessed using the Bray-Curtis distance matrix to evaluate species turnover and richness differences, and visualized through non-metric multidimensional scaling (NMDS). Statistical significance was evaluated using the permanova test (Adonis function, vegan package, R language (Oksanen et al. 2007 ). One-way analysis of variance (ANOVA) followed by Tukey’s HSD test was used to assess the significant effects of different reclamation patterns on microbial diversity, enzyme activity, and soil properties. Enzyme activities and soil properties were standardized using z-transformation, and the average of the standardized values was used for analysis (Delgado et al. 2016; Wagg et al. 2019 ). Structural equation models (SEMs) were constructed to explore the effects of reclamation patterns on ecosystem multifunctionality and micro-food web structures, utilizing the maximum-likelihood estimation method. Results Soil biotic community composition and diversity in different reclamation patterns The compositions of soil biotic communities differed among the different reclamation patterns (Fig. 1 a). As key components of the micro-ecosystem, bacteria, fungi, and archaea showed similar distribution patterns across the three reclamation patterns (Fig. 1 a). However, the proportions of protists and metazoans were lower in both BP and CP (Fig. 1 a). The dominant bacterial phyla include Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, and Gemmatimonadetes. Comparative analysis of the dominant bacterial compositions across different reclamation patterns revealed that five bacterial phyla—Chloroflexi, Firmicutes, Nitrospirae, Chlorobi, and Spirochaetes—were significantly more abundant in MP ( P < 0.05; Fig. 1 b, Table S2). The fungal community is dominated by Ascomycota, followed by Basidiomycota, Blastocladiomycota, and Chytridiomycota. For the archaeal community, the dominant phyla are Thaumarchaeota, Euryarchaeota, and Crenarchaeota, which were significantly more abundant in MP compared with those in CP and BP ( P < 0.05; Fig. 1 b, Table S2). Additionally, the metazoan groups Platyhelminthes and Porifera also displayed higher abundances in MP. The Shannon index for bacterial, fungal, archaeal, protist, and metazoan communities is shown in Fig. 2 . No significant differences in Shannon index values were observed among bacteria, fungi, protists, and metazoans. However, the Shannon index for archaea was significantly lower in BP compared with that in MP and CP ( P < 0.05; Fig. 2 a, c, f). The beta diversity decomposition analyses showed that the compositional dissimilarities among different reclamation patterns for bacterial, fungal, archaeal, metazoan, and protist communities, and total soil microbial communities, were dominated by species replacement processes (Repl > RichDiff; Fig. S1 ). Soil physiochemical and enzyme activities in different reclamation patterns Our results indicated that different reclamation patterns significantly affected soil physicochemical properties and enzyme activities. Except N-acetylglucosaminidase and protease, the activities of all other soil enzymes considered (β-glucosidase, cellulase, and urease) varied considerably among the different reclamation patterns (Fig. 3 a, b, c, d, e). Specifically, the activities of cellulase, urease and β-glucosidase were significantly lower in CP soils compared with those in BP and MP soils ( P < 0.05; Fig. 3 a, c, d). The concentrations of SOC in CP were significantly lower compared with those in BP and MP ( P < 0.05; Fig. 3 f). A similar pattern was observed for TN and AN ( P < 0.05; Fig. 3 g, h). Relationship between environmental variables and soil biotic communities The Mantel test results demonstrate different correlations of environmental variables with bacterial, fungal, archaeal, metazoan, protistan, and total soil microbial communities (Table 1 ).The bacterial community structure was predominantly influenced by parameters related to carbon metabolism, including β-glucosidase activity (r = 0.217, P = 0.04) and SOC (r = 0.252, P = 0.048). Table 1 Mantel test of bacteria, fungi, archaea, metazoan, and protists with soil properties and enzyme activities. Bacteria Fungi Archaea Metazoa Protists r P r P r P r P r P Enzyme activities Urease 0.069 0.278 0.117 0.163 0.378 0.006** 0.215 0.043* 0.275 0.025* Protease -0.076 0.678 0.081 0.264 0.199 0.074 -0.183 0.967 0.078 0.259 Beta-glucosidase 0.217 0.041* 0.051 0.321 0.518 0.002** -0.028 0.563 0.104 0.189 Cellulase 0.111 0.172 0.053 0.310 0.224 0.035* -0.123 0.898 0.102 0.187 N-acetylglucosaminidase 0.098 0.232 0.385 0.008** 0.252 0.046* 0.106 0.175 0.062 0.293 Soil properties SOC 0.252 0.048* 0.165 0.081 0.499 0.0009*** -0.071 0.717 0.087 0.229 TN -0.077 0.652 -0.029 0.530 -0.204 0.970 -0.108 0.792 -0.049 0.603 NN -0.139 0.881 0.077 0.243 -0.046 0.584 -0.139 0.930 0.137 0.137 AN -0.001 0.462 0.207 0.037* 0.241 0.020* -0.090 0.821 0.046 0.289 pH 0.257 0.029* -0.006 0.486 -0.047 0.612 -0.092 0.803 0.002 0.471 Values in bold indicate statistical significance. Significance levels are shown at * P < 0.05, ** P < 0.01 and *** P < 0.001. SOC, total soil organic carbon; TN, total nitrogen; AN, ammonium nitrogen; NN, nitrate nitrogen. Another major component of the micro-ecosystem, the fungal community, was primarily influenced by parameters related to nitrogen metabolism. Key factors included N-acetyl-β-D-glucosidase activity (r = 0.385, P = 0.0079) and AN (r = 0.2072, P = 0.037). Compared with those of bacteria and fungi, the archaeal community showed significant correlations with a broader range of environmental variables, including β-glucosidase, cellulase, SOC, urease, N-acetyl-β-D-glucosidase, and AN ( P < 0.05; Table 1 ). Protists and metazoan groups were correlated with urease activity ( P < 0.05; Table 1 ). Ecological networks and trophic transfer efficiency in different reclamation patterns To uncover the relationships within microbial communities, a co-occurrence network was constructed (Fig. 4 ). The co-occurrence network modularity index for different reclamation patterns ranged from 0.35–0.48, indicating a clear modular structure. The MP microbial communities comprised more functionally interrelated members compared with those in BP and CP plantations. Specifically, the MP network consisted of 10 modules, 122 nodes, and 1,557 links, while the BP network had 8 modules, 119 nodes, and 1,483 links, and the CP network had 5 modules, 89 nodes, and 605 links. Therefore, the network complexity, from highest to lowest, was MP, BP, and CP, respectively (Fig. 4 ; Table S3). Furthermore, the positive edges in the CP network (87.57% positive) were significantly lower in the topsoil compared with those in the BP (89.21% positive) and MP (90.75% positive) networks. Contrastingly, its negative edges were significantly higher than those of the MP network. Consequently, the network stability, ranked from highest to lowest, is MP, BP, and CP, respectively, which aligns with that of the observed network complexity. The micro-food web structure is linked to trophic cascade effects and energy transfer efficiency. The trophic structures in MP showed more extensive interactions among different ecological groups compared with those in CP and BP patterns (Fig. 5 ). Among these relationships, significant correlations were observed between detritus decomposers (i.e., bacteria, fungi, and archaea), metazoans, protists, and Viridiplantae. In the MP, more positive correlations between different groups were evident (Fig. 5 c). The detrital decomposers, such as metazoans and protists, were more abundant in the MP structure compared with those in the BP and CP structures (Fig. 5 ). Variations in micro-food web structures can result in changes in trophic transfer efficiency. In the MP, the transfer efficiency from the preceding trophic level to protists and metazoans was 9.36% and 17.19%, respectively (Fig. 5 c). Comparatively, the transfer efficiency to protists and metazoans was 8.89% and 16.45% in BP, and 9.05% and 16.82% in CP, respectively (Fig. 5 a, b). This suggests that the trophic structures in MP are more efficient at transferring biomass energy to higher trophic levels. Links between soil properties, trophic transfer efficiency, and C/N metabolism functions To further explore the effects of reclamation patterns on microbial trophic transfer efficiency, the feedback control among reclamation patterns, trophic transfer efficiency (top-down and bottom-up), Carbon/Nitrogen metabolism function were revealed using the structural equation model. According to the SEM results, in the top-down regulation of the food web, the soil physicochemical properties SOC and AN directly affect the predation nutrient transfer efficiency of metazoans on protists and the predation nutrient transfer efficiency of protists on bacteria and fungi, which in turn impacts soil carbon metabolism functions, particularly β-glucosidase and cellulase activities (Fig. 6 a). In the bottom-up regulation of the food web, the soil physicochemical properties SOC and AN significantly affect the trophic transfer efficiency of lower trophic levels obtaining nutrients from higher trophic levels through decomposition, thereby significantly impacting the nitrogen metabolism function of soil, particularly urease (Fig. 6 b). Overall, reclamation patterns can indirectly influence the microbial trophic transfer efficiency by altering soil properties, and eventually affect the C/N metabolism functions of soil ecosystems. Discussion Effect of reclamation patterns on soil physicochemical properties and soil biotic community We observed that soil nutrient concentrations varied among the three reclamation patterns. Specifically, the BP and MP exhibited higher SOC and TN levels than those by CP. The findings agreed well with those of the earlier studies (Vitali et al. 2016 ). This is likely because vegetation influences soil properties mainly through the amount and chemical composition of root exudates and litter (Bañeras et al. 2012 ; Bremer et al. 2007 ). BP and MP can enhance soil C and N content by facilitating herbaceous restoration and litter decomposition, provided that the density is within a range that offers sufficient shade (Zhang et al. 2019 ). Contrastingly, the dense coniferous litter on the forest floor in CP obstructs air circulation, leading to reduced nutrient accumulation. We found that β-glucosidase and cellulase activities were significantly lower in CP, which have lower SOC content. Wang et al. (2020) demonstrated that soil enzyme activities are significantly correlated with SOC fractions and microbial biomass carbon. SOC acts as a substrate stimulating enzyme release, thereby enhancing soil enzyme activities (Allison et al. 2006 ). Regarding bacterial communities, we observed an enrichment of specific phyla such as Chloroflexi and Firmicutes in the mixed coniferous-broad plantation. These bacteria are typically involved in key ecological processes, including organic matter decomposition and nitrogen cycling (Hartman et al. 2008 ; Kim et al. 2014 ; Llado et al. 2016 ; Lladó et al. 2017 ), suggesting their presence may have a positive impact on accelerating nutrient cycling and improving soil quality in reclaimed mine soils. Furthermore, the abundances of the archaeal phyla Thaumarchaeota, Euryarchaeota, and Crenarchaeota were significantly higher in the MP compared with those in BP and CP. Given the critical role of archaea in N transformation processes (Baldrian et al. 2012 ; Uroz et al. 2013 ), this finding implies that the MP may be more effective in sustaining or re-establishing N cycling in reclaimed mine soils (Kirk et al. 1987; Lindahl et al. 2010 ). The relative proportions of protists and metazoans were prominently lower in both BP and CP compared with those in the MP. This observation suggests that the MP may be more conducive to maintaining a complex soil micro-food web. These findings support our objectives that different reclamation practices not only alter the soil physicochemical properties in the soil but also influence the composition of the soil biotic communities. We found that, except for archaea, reclamation patterns had little effect on microbial α diversity. This may be due to the rapid response of microbes to environmental changes, and more disturbed soils could favour certain microbial communities, such as r-selected groups (Deng et al. 2019 ). Alternatively, anthropogenic disturbances and the resulting environmental changes may allow a broader diversity of microbes to coexist within smaller geospatial scales (Prober et al. 2015 ). Contrarily, β-diversity reflects the variation in ecological niches and is more strongly influenced by environmental factors, including soil pH and organic matter content (Lladó et al. 2018 ; Rivest et al. 2019 ). The differences in bacterial, fungal, archaeal, metazoan, protist, and total soil microbial community compositions among the different reclamation patterns were mainly attributable to underlying soil properties, these properties play a more significant role in shaping soil biotic assemblages compared with that of vegetation. (Heděnec et al. 2018 ). According to the Mantel test results, distinct correlations were observed between environmental variables and the communities of bacteria, fungi, archaea, metazoans, protists, and total soil microbes. It is well known that soil pH is a key driving factor for soil bacterial community development (Rousk et al. 2010 ). Simultaneously, Proteobacteria and Actinobacteria dominate the bacterial communities in soils across all three types of vegetation and exhibit a positive relationship with carbon mineralization (Fazi et al. 2005 ; Fierer et al. 2007 ). We deduced that this is the reason for the correlation of the bacterial community with carbon metabolism-related parameters (such as β-glucosidase and SOC) and pH. The fungal community showed a stronger correlation with parameters related to N metabolism, likely because fungi possess the capability to process N and phosphorus. (Guest et al. 2007). Compared with those of bacteria, fungi were reported to exhibit greater rates and higher production of NO 2 − -N and NO 3 − -N (Eylar et al. 1959; Kurakov et al. 1996). The archaea community showed significant correlations with both C metabolism-related and N metabolism-related parameters. Abundant archaeal ammonia oxidizers in soils can form nitrate through microbial activity (Leininger et al. 2006 ). Simultaneously, archaeal ammonia oxidation is coupled with carbon fixation (Santoro et al. 2019 ). Ammonia oxidation yields low energy, suggesting that a large quantity of ammonium metabolism by archaea is required to fix a given amount of CO 2 (Norman et al. 2015 ). Effect of reclamation patterns on soil micro-food web and microbial Trophic Transfer Efficiency We observed that the MP exhibited the highest network complexity followed by the BP and the CP, which agrees with that of earlier studies showing that plant diversity positively influences soil microbial diversity and network complexity, leading to more stable and resilient ecosystems (Yuan et al. 2021 ). This is likely attributed to the heterogeneity and resource richness created by the MP (Delgado et al. 2016). The MP typically provide a more diverse array of niches and resources, including a variety of organic matter inputs, pH levels, and microclimates (Jiao et al. 2022 ). This diversity supports a wider range of microbial interactions and functional roles, leading to a more complex and interconnected network (Wang et al. 2018 ; Zhang et al. 2013 ). Contrastingly, the more homogeneous environments of BP and CP, particularly the CP, may limit the diversity of available niches and resources, resulting in fewer and less interconnected soil biotic communities. Therefore, the MP consisted more stable soil biotic communities than those by the BP and CP. Furthermore, the higher positive cohesion in the MP and BP networks indicates a greater proportion of positive interactions (e.g., mutualism, commensalism) among the soil biotic communities, which is essential for maintaining a stable and resilient soil biotic community. Positive interactions, such as mutualistic relationships, can enhance the overall stability of the community by promoting cooperative behaviours and resource sharing (Krashevska et al. 2019 ; Schulz et al. 2019 ). Contrastingly, the lower positive and higher negative edges in the CP network suggest a higher prevalence of competitive or antagonistic interactions, which can destabilize the soil biotic communities and reduce their overall resilience (Kouzuma et al. 2015; Wang et al. 2021 ). Summarily, our results certify that the MP had higher stability in microbial communities than that by the BP and CP under opencast coal mining disturbance conditions. We found that the compositions of ecological functional groups varied among the different reclamation patterns. The higher proportions of protists and metazoans in the MP indicated that more consuming organisms were involved in the nutritional processes. In the MP, a greater number of positive correlations among different groups were observed indicating more mutually beneficial symbiotic relationships between different ecological groups in MP. Different groups obtain the nutrients they require through the decomposition of detrital organic matter or by predation (Steffan et al. 2017 ). The micro-food web structures in the MP contain more detrital nutrients (i.e., metazoans and protists), suggesting that the effect of predation relationships is weakened in the MP. Reclamation patterns can indirectly influence the multifunctionality of ecosystems by directly influencing micro-food web structures (Domingues et al. 2017 ). A well-structured micro-food web plays a crucial role in stabilizing ecological functions by promoting nutrient retention, organic matter decomposition, and microbial interactions. Additionally, different micro-food web structures alter trophic transfer efficiency, which is fundamental to ecosystem restoration (Palijan et al. 2018). The transfer efficiency from the previous trophic level to protists and metazoans was higher in the MP compared with that in the BP and CP. This result suggests that the trophic structures in the MP facilitate more efficient biomass energy transfer to higher trophic levels, indicating enhanced trophic transfer efficiency within the soil micro-food web. This improved energy flow may contribute to increased microbial activity, potentially accelerating nutrient cycling and organic matter decomposition (Sui et al. 2022 ). We acknowledge the limitations of using gene abundance as a proxy for microbial activity, as it does not directly measure metabolic processes, and the relationship between gene counts and microbial biomass remains complex and not fully characterized. Although gene abundance provides valuable insights into microbial functional potential, future studies incorporating direct metabolic measurements and biomass quantification would enhance the robustness of nutrient transfer models and offer a more comprehensive understanding of microbial interactions and nutrient cycling within soil ecosystems. Influence of Reclamation Patterns on C/N metabolism functions Building on the observed differences in trophic transfer efficiency among reclamation patterns, we investigated the mechanisms by which these patterns regulate nutrient transfer and C/N metabolism functions in soil ecosystems. Our results show that reclamation patterns can indirectly influence the microbial trophic transfer efficiency via altered soil properties, and eventually affect the C/N metabolism functions of soil ecosystems. This result indicates that in the top-down regulation of the food web, high levels of SOC provide abundant organic carbon sources for microorganisms, promoting their growth and activity. This increase in microbial abundance and activity enhances the food supply for protists, thereby increasing the predation efficiency of metazoans on protists. Additionally, SOC also increases the predation efficiency of protists on bacteria and fungi, as the numbers and activity of these microorganisms are elevated. High predation nutrient transfer efficiency implies that more organic carbon is decomposed and utilized, thereby increasing the activity of β-glucosidase (Fierer et al. 2003 ; Islam et al. 2022 ). β-glucosidase is involved in the decomposition of organic carbon in the soil, and its increased activity facilitates the acceleration of the mineralization process of organic carbon. However, cellulase activity shows a significant negative correlation, possibly due to slower cellulose decomposition under high SOC levels or reduced secretion from increased predation pressure. (Allen et al. 2015 ; Domeignoz et al. 2020). Although high levels of AN provide available N sources for microorganisms, excessively high AN levels may inhibit the growth of certain microorganisms, thereby reducing the efficiency of nutrient acquisition by lower from higher trophic levels. The impact of AN on β-glucosidase activity may be due to the inhibition of carbon metabolism enzymes under high nitrogen conditions (Liu et al. 2020 ; Mihelič et al. 2021 ). The negative correlation with cellulase activity may be because high AN levels reduce the ability of microorganisms to decompose cellulose. In the top-down regulation of the food web, higher trophic-level organisms, such as predators, regulate the abundance and activity of lower trophic level microorganisms. Contrastingly, in bottom-up regulation, high levels of SOC promote the growth and activity of these microorganisms. These microorganisms decompose organic matter from higher trophic levels, releasing more nutrients and thereby increasing the efficiency of nutrient transfer. Higher nutrient transfer efficiency implies that more organic nitrogen is decomposed and utilized, thus increasing urease activity (Lorenz et al. 2019; Zhang et al. 2014 ). Urease, which is involved in the mineralization of organic nitrogen in the soil, accelerates the transformation of organic N when its activity is increased (Li et al. 2022 ; Ouyang et al. 2017 ; Wang et al. 2013 ). Additionally, we further elucidate the mechanism by which MP exhibit higher nutrient transfer efficiency compared with that by CP and BP. MP create diverse microhabitats supporting complex microbial community structures, thereby enhancing nutrient transfer efficiency and ultimately influencing the C and N metabolism of the soil ecosystem. This finding underscores the importance of considering soil properties in the design of reclamation models. For example, increasing organic matter input not only enhances soil fertility but also improves soil structure and microbial diversity, thereby promoting the multifunctionality of the ecosystem. Conclusion We explored the influence of different reclamation models on soil micro-food web structure and nutrient transfer efficiency in mining areas. Our findings demonstrate that reclamation patterns not only modify soil physicochemical properties but also regulate microbial community interactions and trophic transfer processes. Compared with those of single-species reclamation patterns, the MP exhibited greater microbial network complexity and higher nutrient transfer efficiency, particularly at higher trophic levels, such as protists and metazoans. This suggests that MP enhances trophic energy flow and resource utilisation within the micro-food web, contributing to improved ecosystem functioning. Furthermore, our study highlights that reclamation models indirectly regulate microbial nutrient transfer efficiency by altering soil properties, ultimately influencing soil carbon and nitrogen metabolic functions. These findings underscore the importance of integrating soil organic matter management and mixed-species plantations into reclamation strategies to enhance microbial interactions and accelerate ecosystem recovery. Declarations Acknowledgments We would like to thank the research team at the Institute of Loess Plateau for their technical assistance. This work was supported by National Natural Science Foundation of China (U1910207 and 42107420) . Author contributions Conceptualization: Peng Gao, Xiujuan Zhang and Yong Liu; Methodology: Peng Gao; Formal analysis and investigation: Peng Gao, and Xiujuan Zhang; Writing-original draft preparation: Peng Gao; Writing - review and editing: Hong Zhang, Junjian Li and Yong Liu. All authors have read and approved the final manuscript. Funding Funding was provided by the National Natural Science Foundation of China (U1910207 and 42107420). Data availability The datasets generated during the current study are available from the corresponding author on reason able request. 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. 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Nat Commun 10:4841. https://doi.org/10.1038/s41467-019-12798-y Wang, Q.K., Xiao, F.M., He, T.X (2013) Responses of labile soil organic carbon and enzyme activity in mineral soils to forest conversion in the subtropics. Ann Forest Sci 70:579–587. https://doi.org/10.1007/s13595-013-0294-8 Wang, Q.K., Xiao, F.M., Zhang, F.Y., Wang, S.L (2013) Labile soil organic carbon and microbial activity in three subtropical plantations. Forestry 86:569-574. https://doi.org/10.1093/forestry/cpt024 Wang, S., Wang, X., Han, X., Deng, Y (2018) Higher precipitation strengthens the microbial interactions in semi‐arid grassland soils. Global Ecol Biogeogr 27: 570-580. https://doi.org/10.1111/geb.12718 Wang, Y., Chen, L., Xiang, W., Ouyang, S., Zhang, T., Zhang, X., Zeng, Y., Hu, Y., Luo, G., Kuzyakov, Y (2021) Forest conversion to plantations: A meta-analysis of consequences for soil and microbial properties and functions. Glob Chang Biol 27:5643–5656. https://doi.org/10.1111/gcb.15835 Wu, J.Y., Ding, F.G., Shen, Z.W., Hua, Z.L., Gu, L (2022) Linking microbiomes with per-and poly-fluoroalkyl substances (PFASs) in soil ecosystems: Microbial community assembly, stability, and trophic phylosymbiosis. Chemosphere 305:135403. https://doi.org/10.1016/j.chemosphere.2022.135403 Xiao, E.Z., Wang, Y.Q., Xiao, T.F., Sun, W.M., Deng, J.M., Jiang, S.M., Fan, W.J., Tang,J.F., Ning, Z.P (2021) Microbial community responses to land-use types and its ecological roles in mining area . Sci Total Environ,775:145753.https://doi.org/10.1016/j.scitotenv.2021.145753. Yang, N., Zhang, C., Wang, L., Li, Y., Zhang, W., Niu, L.H.,Zhang, H.J., Wang L.F (2021) Nitrogen cycling processes and the role of multi-trophic microbiota in dam-induced river-reservoir systems. Water Res 206:117730. https://doi.org/10.1016/j.watres.2021.117730 Yuan, M.M., Guo, X., Wu, L., Zhang, Y., Xiao, N., Ning, D., Shi, Z., Zhou, X., Wu, L., Yang, Y (2021) Climate warming enhances microbial network complexity and stability. Nat Clim Chang 11: 343–348. https://doi.org/10.1038/s41558-021-00989-9 Yvon-Durocher, G., Montoya, J., Trimmer, M., Woodward, G (2010) Warming alters the size spectrum and shifts the distribution of biomass in aquatic ecosystems. Glob Change Biol 17:1681-1694. https://doi.org/10.1111/j.1365-2486.2010.02321.x Zhang, B., Wang, H., Yao, S., Bi, L (2013) Litter quantity confers soil functional resilience through mediating soil biophysical habitat and microbial community structure on an eroded bare land restored with mono Pinus massoniana. Soil Biol Biochem 57:556–567. https://doi.org/10.1016/j.soilbio.2012.07.024 Zhang, J.M., Jia, G.D., Liu, Z.Q., Wang, D.D., Yu, X.X (2019) Populus simonii Carr. Reduces Wind Erosion and Improves Soil Properties in Northern China. Forests 10:315. https://doi.org/10.3390/f10040315 Zhang, X., Xu, S.J., Li, C.M., Zhao, L.,Feng, H.Y., Yue, G.Y., Ren, Z.W.,Cheng, G.D (2014) The soil carbon/nitrogen ratio and moisture affect microbial community structures in alkaline permafrost-affected soils with different vegetation types on the Tibetan plateau. Res Microbiol 165:128–139. https://doi.org/10.1016/j.resmic.2014.01.002 Zhao, Z., Shahrour, I., Bai, Z., Fan, W., Feng, L., Li, H (2013) Soils development in opencast coal mine spoils reclaimed for 1–13 years in the West-Northern Loess Plateau of China. Eur J Soil Biol 55:40-46. https://doi.org/10.1016/j.ejsobi.2012.08.006 Zheng, W., Zhao, Z., Gong, Q., Zhai, B., Li, Z (2018) Effects of cover crop in an apple orchard on microbial community composition, networks, and potential genes involved with degradation of crop residues in soil. Biol Fertil Soils 54:743–759. https://doi.org/10.1007/s00374-018-1298-1 Zhou, J.Z., Deng, Y., Luo, F., He, Z.L., Tu, Q.C., Zhi, X.Y. (2010). Functional molecular ecological networks. mBio , 1, e00169-10. https://doi.org/10.1128/mBio.00169-10 Zhou, J.Z., Deng, Y., Luo, F., He, Z.L., Yang, Y.F. (2010). Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. mBio , 1(4), e00122-11. https://doi.org/10.1128/mBio.00122-11 Zhou, W.X., Qian, M.J., Wang, S.F., Li, S.P., Cao, Y.G (2022) Spatial Distribution and Regulating Factors of Soil Nutrient Stocks in Afforested Dump of Pingshuo Opencast Coalmine, China. Forests 13:345. https://doi.org/10.3390/f13020345 Zou, G.H., Niu, L.H., Li, Y., Zhang, W.L., Wang, L.Q., Li, Y.Y., Zhang, H.J.,Wang, L.F., G, Y (2022) Depth induced assembly discrepancy of multitrophic microbial communities affect microbial nitrogen transformation processes in river cross-sections. Environ Res 214:113913. https://doi.org/10.1016/j.envres.2022.113913 Supplemental Data Supplemental figures and tables are not available with this version. Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Plant and Soil → Version 1 posted Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 27 Mar, 2025 First submitted to journal 26 Mar, 2025 Editorial decision: Major revisions 06 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5782088\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":435082971,\"identity\":\"26a090d5-602c-4e97-ab27-14a7c5b72df5\",\"order_by\":0,\"name\":\"Peng Gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Peng\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"},{\"id\":435082972,\"identity\":\"7992591f-e963-4be7-9cf6-7b993ed2ace2\",\"order_by\":1,\"name\":\"Xiujuan Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiujuan\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":435082973,\"identity\":\"ad05b69b-45f7-4946-9d62-d3dddeda5249\",\"order_by\":2,\"name\":\"Hong Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hong\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":435082974,\"identity\":\"04ef5c76-a436-4c0b-80ab-1578266c5616\",\"order_by\":3,\"name\":\"Junjian Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Junjian\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":435082975,\"identity\":\"44ea60c2-6744-4e51-b5df-26faf46bdbfb\",\"order_by\":4,\"name\":\"Chao Su\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chao\",\"middleName\":\"\",\"lastName\":\"Su\",\"suffix\":\"\"},{\"id\":435082976,\"identity\":\"2aa10745-ab14-4f4f-a1ee-aeaf19ff8704\",\"order_by\":5,\"name\":\"Yong Liu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACPmYILQeh2IjQwgbVYkyCFiid2EC8FnYeM4mPO2rT58/IMWD4UHaYgX92AyGH8ZhJzjxzPHfDmTMGjDPOHWaQuHOAsBZp3rZjuRvYewyYedsOMxhIJBCnJV2+mceA+S8JWmoSGI4DbWEkTgtbseXMtgOGG84cKzjYcy6dR+IGAS38/Ic33vjYVicvPyN544MfZdZy/DMIaAECFgkGhsNg1gEg5iGoHgiYPzAw1BGjcBSMglEwCkYqAAChozoiBMKVzAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Shanxi University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Yong\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-01-07 14:14:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5782088/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5782088/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s11104-025-07664-4\",\"type\":\"published\",\"date\":\"2025-07-07T15:57:28+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":79736306,\"identity\":\"3cb73ef1-f152-48ab-895e-bc036df1ad45\",\"added_by\":\"auto\",\"created_at\":\"2025-04-02 07:09:16\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":344418,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(a) Ecological groups structural composition. CP, Coniferous plantation, BP, Broad-leaved plantation, MP, Mixed coniferous-broad plantation. To emphasize distribution patterns, statistical significance is not explicitly tested in this figure. (b) Composition of bacteria, fungi, archaea, metazoan, and protists, with relative abundance data log10-transformed per thousand to reduce absolute differences and standardized using Z-score normalization:χ\\u003csub\\u003estandardized\\u003c/sub\\u003e=χ-μ/δ.Displayed taxonomic groups represent the most abundant detected taxa.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5782088/v1/cc32d0651de34ae4150bd4ef.png\"},{\"id\":79736304,\"identity\":\"8db8235d-6a00-4ecb-a33a-6328fc03c072\",\"added_by\":\"auto\",\"created_at\":\"2025-04-02 07:09:16\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":90025,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eShannon index of (a) bacteria, (b) fungi, (c) archaea, (d) metazoan, and (e) protists communitie\\u003cstrong\\u003es.\\u003c/strong\\u003e CP, Coniferous plantation, BP, Broad-leaved plantation, MP, Mixed coniferous-broad plantation. Different letters indicate significant differences (Turkey's test, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5782088/v1/8497cbc8ec5241cfe240ad77.png\"},{\"id\":79735727,\"identity\":\"bf0944c5-b8e8-4d4b-9b17-6e34ff29465c\",\"added_by\":\"auto\",\"created_at\":\"2025-04-02 07:01:16\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":166381,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBox plots of soil properties and enzyme activities. CP, Coniferous plantation, BP, Broad-leaved plantation, MP, Mixed coniferous-broad plantation. Different letters indicate significant differences (Turkey's test, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05). SOC, total soil organic carbon; TN, total nitrogen; AN, ammonium nitrogen; NN, nitrate nitrogen.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5782088/v1/a60c2b3d4606d4dd9cd22a08.png\"},{\"id\":79736308,\"identity\":\"a9ba1bc4-6c21-4e32-bc85-e7558ca9ae1d\",\"added_by\":\"auto\",\"created_at\":\"2025-04-02 07:09:16\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":665971,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCo-occurrence network of bacteria, fungi, archaea, metazoan, and protists in different reclamation patterns. The size of each node is proportional to a degree and colored according to specific ecological groups. Color represents a positive correlation (red) or negative correlation (blue; Spearman’s |r| \\u0026gt; 0.8;\\u003cem\\u003e P \\u003c/em\\u003e\\u0026lt; 0.05). CP, Coniferous plantation, BP, Broad-leaved plantation, MP, Mixed coniferous-broad plantation\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5782088/v1/443ba769fd27d6d84f9e6db9.png\"},{\"id\":79735726,\"identity\":\"23698a3c-322b-4115-ae76-0b64b3f077ef\",\"added_by\":\"auto\",\"created_at\":\"2025-04-02 07:01:16\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":420900,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMicro-food web structure and trophic transfer efficiency. Blue and orange lines represent negative and positive correlations, respectively, with line thickness proportional to the absolute value of the correlation coefficient. P \\u0026lt; 0.05, P \\u0026lt; 0.005, and P \\u0026lt; 0.001. Green and red directional arrows indicate trophic interactions, where green and red represent nutrient assimilation and decomposition, respectively. The thickness of the arrows reflects the magnitude of trophic transfer efficiency, while the numbers within different groups represent the total trophic transfer efficiency from the previous trophic level to the corresponding trophic level. CP, Coniferous plantation; BP, Broad-leaved plantation; MP, Mixed coniferous-broad plantation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5782088/v1/887c92ded1abaa04678d0f4c.png\"},{\"id\":79737007,\"identity\":\"d487f189-df95-4c6d-a754-ca963a4cd3d3\",\"added_by\":\"auto\",\"created_at\":\"2025-04-02 07:17:16\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":434699,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStructural equation model (SEM) demonstrates the effects of soil properties on trophic transfer efficiencies, as well as the influences of trophic transfer efficiency on carbon and nitrogen functions. Blue arrows indicate negative correlations and red arrows indicate positive correlations. Numbers adjacent to arrows are path coefficients, and the width of the arrows is proportional to the P-value. Significance levels are indicated: *\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05, **\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.01, and ***\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001. SOC, total soil organic carbon; TN, total nitrogen; AN, ammonium nitrogen; NN, nitrate nitrogen. Trophic transfer efficiency from A to B was represented as “B_A”, for example, trophic transfer efficiency from protists to metazoan was represented as Metazoan_Protists.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5782088/v1/5b625314b301d9edc56eb65c.png\"},{\"id\":86699491,\"identity\":\"291fac67-490b-4956-87ab-b4081214fa3f\",\"added_by\":\"auto\",\"created_at\":\"2025-07-14 16:10:31\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3243256,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5782088/v1/8fc8d56c-f1f1-409c-8e45-59eab6f8dd75.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Response of soil micro-food web and nutrient transfer efficiency to reclamation strategies in mining area\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eOpen-pit mining, as a principal method for mineral extraction, has contributed considerably to rapid economic development (Zhao et al. \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). In China, mining operations have impacted approximately 12\\u0026nbsp;million hectares of land, leading to substantial degradation (Bai et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Wang et al. 2017). The removal of topsoil and the alteration of soil profiles result in irreversible damage to biodiversity, severely compromising the health and function of Mining Area Ecosystem (Feng et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). China has made considerable efforts to combat the critical degradation of ecosystems through extensive reforestation programs (Cao et al. 2021; Guan et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSoil microbial communities are vital for establishing plant communities in degraded ecosystems and play a crucial role in regulating the recovery of terrestrial environments (Coban et al. 2022; Schroeder et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). However, different afforestation strategies have vastly affected soil microbial ecosystems in mining areas (De Deyn et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Shi et al. \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Xiao et al. (\\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) demonstrated that distinct land restoration types markedly altered the distribution characteristics, diversity indices, and dominant populations of soil bacterial communities in these areas. Chang et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) found that various vegetation restoration models had prominently different impacts on the diversity of soil fungi and bacteria. Deng et al. (2020) further observed that planting different tree species could effectively improve the soil microbial community structure in mining areas, with \\u003cem\\u003eRobinia pseudoacacia\\u003c/em\\u003e particularly effective in enhancing soil nutrients, boosting community diversity, and improving soil structure. These findings highlight the influence of different restoration strategies on the structure and multifunctionality of soil microbial communities, offering valuable scientific guidance for improving soil quality and restoring mining area ecosystems (Chen et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). However, current studies on restoration strategies mainly focused on changes in soil bacteria and fungi diversity, with less emphasis on the critical role of soil biotic communities and their food webs in restoring and maintaining ecosystem health. However, much of the current research on restoration strategies focused on the diversity of soil bacteria and fungi, less attention was paid to the roles of archaea, protists, and metazoans in the soil ecosystem. These groups of organisms, together with their interactions within the soil micro-food web, play crucial roles in nutrient cycling, energy flow, and the restoration of ecosystem health (Geisen et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).Delving deeper into the effect of different reclamation strategies on soil micro-food web can not only reveal specific mechanisms in ecosystem recovery but also provide more comprehensive scientific evidence for optimizing reclamation measures.\\u003c/p\\u003e \\u003cp\\u003eThe soil micro-food web, a complex network of soil organisms and their trophic interactions, is crucial for maintaining ecological processes through the regulation of energy and nutrient flows (Coleman et al. 2015; Frangoulis et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). In the micro-food web, lower trophic levels can impose bottom-up effects by controlling resource availability, thereby influencing the dynamics of higher trophic-level communities. Similarly, higher trophic levels can exert top-down effects shaping lower-level communities through predation and interference, influencing their structure and function (Bardgett et al. 2014). The diversity and abundance of these trophic relationships support essential ecosystem functions and primary productivity (Yuan et al. \\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). During vegetation restoration processes, increased species connectivity within the soil community enhances nutrient cycling efficiency and carbon sequestration (Zheng et al. \\u003cspan citationid=\\\"CR94\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, limited evidence exists on the response of micro-food web complexity and stability to the revegetation of degraded ecosystems. (Ishii et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Addressing this knowledge gap requires a deeper understanding of the influence of different reclamation models on soil micro-food web structure and nutrient interactions. Investigating these processes can reveal the mechanisms through which ecosystems respond to reclamation-induced environmental changes and provide insights into the regulation of ecosystem multifunctionality.\\u003c/p\\u003e \\u003cp\\u003eThis study aims to fill this knowledge gap by analysing the specific impacts of reclamation models on the soil micro-food web and elucidating the underlying mechanisms restoring ecosystem functions. Therefore, in the current study, we selected the reclamation area of the Pingshuo Open-pit Coal Mine in China as our study region. Using metagenomic sequencing technology to compare the micro-food web structure and nutrient transfer efficiency under different reclamation patterns. The specific objectives of this study were: (1) Compare the differences in soil physicochemical properties, soil biotic community composition, and levels of diversity under various reclamation patterns; (2) Reveal the control by reclamation patterns on the structure of soil micro-food web and the efficiency of nutrient transfer; and (3) Clarify the mechanisms by which reclamation patterns influence the efficiency of nutrient transfer in soil micro-food web and the functions of soil ecosystems. This study will help enhance our understanding of the relationship between soil micro-food web and ecosystem functions and offer valuable insights for selecting appropriate reclamation patterns in coal mine ecological restoration.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy area and sample collection\\u003c/h2\\u003e \\u003cp\\u003eThe Pingshuo mining area, located in northern Shanxi Province (112\\u0026deg;10\\u0026prime;\\u0026ndash;113\\u0026deg;30\\u0026prime;E, 39\\u0026deg;23\\u0026prime;\\u0026ndash;39\\u0026deg;37\\u0026prime;N), is one the most extensive open-pit mining sites of China. It features a semi-arid continental monsoon climate and is characterized by ecological vulnerability. Since 1990, the enterprise has implemented a series of reclamation measures, including the planting of monoculture (such as Pinus sylvestris, Picea asperata, and Larix gmelinii) and mixed (combinations such as \\u003cem\\u003ePinus tabuliformis\\u003c/em\\u003e with \\u003cem\\u003eRobinia pseudoacacia\\u003c/em\\u003e, or \\u003cem\\u003ePinus tabuliformis\\u003c/em\\u003e with \\u003cem\\u003eCaragana korshinskii\\u003c/em\\u003e and \\u003cem\\u003eUlmus pumila\\u003c/em\\u003e) forests. These strategies aim to restore vegetation cover and soil quality in the mining area, thereby promoting overall ecosystem recovery (Guan et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zhou et al. \\u003cspan citationid=\\\"CR97\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn July 2022, a total of 15 sites were investigated across three reclamation patterns: coniferous (CP; 5 sites), broad-leaved (BP; 5 sites), and mixed coniferous\\u0026ndash;broadleaved (MP; 5 sites) plantations. Reclamation at all sites commenced simultaneously in 1990, and each site has undergone an identical 30-year restoration period. Detailed site characteristics are presented in Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e. At each site, a 400 m\\u0026sup2; plot (20 m \\u0026times; 20 m) was established, resulting in a total of 15 plots across all sites. Following the careful removal of surface vegetation and soil cover, bulk soil samples were then collected for analysis. Five soil samples were randomly extracted from the 0\\u0026ndash;10 cm soil layer within each plot, subsequently combined into a single composite sample, and sieved through a 2 mm mesh to eliminate large particles and debris. To ensure sample integrity and appropriate storage for various analyses, the composite sample was divided into three subsamples: (1) one subsample was stored at 4\\u0026deg;C for the determination of soil enzyme activity, ammonium nitrogen (NH₄⁺-N), and nitrate nitrogen (NO₃⁻-N), with short-term storage (within one week) ensuring sample stability; (2) another was immediately frozen at -\\u0026ndash;80\\u0026deg;C to preserve DNA integrity for metagenomic sequencing; and (3) the remaining portion was air-dried for soil physicochemical property analysis.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eAnalysis of soil physiochemical and soil enzymatic parameters\\u003c/h3\\u003e\\n\\u003cp\\u003eSoil pH was measured using a PHS-3C pH meter (INESA Instruments Inc., China) with a 1:2.5 soil-to-water ratio. The concentrations of NH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e-N and NO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e-N in the supernatant were determined through colorimetry using a Lachat auto analysis system (Zellweger Analytics, Milwaukee, WI). Soil organic carbon (SOC) was quantified using the H\\u003csub\\u003e2\\u003c/sub\\u003eSO\\u003csub\\u003e4\\u003c/sub\\u003e-K\\u003csub\\u003e2\\u003c/sub\\u003eCr\\u003csub\\u003e2\\u003c/sub\\u003eO\\u003csub\\u003e7\\u003c/sub\\u003e oxidation method. Total nitrogen (TN) was analysed by elemental analysis-stable isotope mass spectrometry (EA-IRMS, Iso Prime100, Germany).\\u003c/p\\u003e \\u003cp\\u003eExtracellular enzyme activities were measured using commercial assay kits and colorimetric methods. For enzymes related to the carbon (C) cycle, dehydrogenase, β-glucosidase, and cellulase were measured using the S-DHA activity assay kit, S-β-GC activity assay kit, and S-CL activity assay kit, respectively. For enzymes related to the nitrogen (N) cycle, urease, alkaline protease, and N-acetyl-β-D-glucosaminidase were measured using the S-UE, alkaline protease, and S-NAG activity assay kits, respectively. All the commercial assay kits were provided by Sangon Biotech Co., Ltd. (China).\\u003c/p\\u003e\\n\\u003ch3\\u003eDNA extraction, metagenomic sequencing, and data processing\\u003c/h3\\u003e\\n\\u003cp\\u003eDNA extraction from the soil samples was performed using the E.Z.N.A. Soil DNA Kit (Omega Bio-tek) following the manufacturer's instructions. To ensure the quality and quantity of the extracted DNA, the concentration was measured with a TBS-380 mini-fluorometer (Turner BioSystems), whereas the purity was assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific Inc.,). The integrity of the extracted DNA was further evaluated by 1% agarose gel electrophoresis, which was run at 5 V/cm for 20 min. Once the DNA samples were confirmed to be of high quality, they were sent to Shanghai Meiji Biomedical Technology Co., Ltd. (Shanghai, China) for metagenomic sequencing.\\u003c/p\\u003e \\u003cp\\u003eAdapter sequences at the 3' and 5' ends of the reads were trimmed using Seqprep (version 0.20.0, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://github.com/OpenGene/fastp\\u003c/span\\u003e\\u003cspan address=\\\"https://github.com/OpenGene/fastp\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), and low-quality reads (trimmed length\\u0026thinsp;\\u0026lt;\\u0026thinsp;50 bp, average base quality\\u0026thinsp;\\u0026lt;\\u0026thinsp;20, or containing N bases) were discarded. The filtered reads were assembled using MEGAHIT (version 1.1.2, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://github.com/voutcn/megahit\\u003c/span\\u003e\\u003cspan address=\\\"https://github.com/voutcn/megahit\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) based on the principle of succinct de Bruijn graphs. Open reading frames (ORFs) in the assembled contigs were identified using MetaGene (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://metagene.cb.k.u-tokyo.ac.jp/\\u003c/span\\u003e\\u003cspan address=\\\"http://metagene.cb.k.u-tokyo.ac.jp/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), a widely employed tool for prokaryotic gene prediction. To mitigate the inherent limitations of MetaGene in predicting eukaryotic genes, we implemented stringent post-prediction filtering criteria. Specifically, genes with a nucleotide length of at least 100 base pairs (bp) were retained and translated to their corresponding amino acid sequences. To construct a non-redundant gene set, all predicted gene sequences from the samples were clustered using CD-HIT (version 4.6.1, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.bioinformatics.org/cd-hit/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.bioinformatics.org/cd-hit/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), applying a threshold of 90% sequence identity and 90% coverage. The longest gene sequence in each cluster was selected as the representative sequence. Subsequently, high-quality sequencing reads from each sample were aligned with this non-redundant gene set using SOAPaligner (version 2.21, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://soap.genomics.org.cn/\\u003c/span\\u003e\\u003cspan address=\\\"http://soap.genomics.org.cn/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) with a 95% identity threshold to obtain gene abundance information. This approach ensures high-confidence quantification, minimizes potential errors from incomplete gene prediction, and enhances the robustness of gene abundance estimates.\\u003c/p\\u003e\\n\\u003ch3\\u003eCo-occurrence network analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eTo explore microbial co-occurrence patterns, ecological networks were constructed based on OTU abundance data and analysed for topological parameters. The Molecular Ecological Network Analyses Pipeline (MENAP) (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://ieg2.ou.edu/MENA/\\u003c/span\\u003e\\u003cspan address=\\\"http://ieg2.ou.edu/MENA/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) was used to construct these networks (Deng et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Zhou et al. \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e, 2011). An RMT-threshold value of 0.84 was applied to reduce network complexity, retaining only OTUs present in more than 84% of samples. Spearman correlation analysis (psych package, R) identified significant correlations (|r| \\u0026gt; 0.8, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), which were then used to construct the networks. Topological parameters, including modularity, node degree, clustering coefficient, and average path length, were calculated using the igraph R package (Csardi and Nepusz \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). Positive and negative edges were evaluated to assess network stability. Additional metrics, such as the number of nodes, edges, modularity index, Module numbers, and average degree, were also computed to evaluate network connectivity and structural complexity. The co-occurrence network was visualized using the Fruchterman-Reingold layout in Gephi 0.9.4.\\u003c/p\\u003e\\n\\u003ch3\\u003eSoil micro-food web structure and trophic transfer efficiency\\u003c/h3\\u003e\\n\\u003cp\\u003eMetagenomic sequences were classified into six ecological functional groups\\u0026mdash;viridiplantae, bacteria, fungi, archaea, protists, and metazoans\\u0026mdash;based on their similarity to entries in the NCBI-NR database. Sequences classified as Viridiplantae represent organic matter derived from plants (Long et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Metazoan sequences primarily represent nematodes, rotifers, tardigrades, and arthropods, which play key roles in soil microbial interactions, decomposition, and nutrient cycling (Darby and Neher \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Potapov et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The interaction structure and trophic transfer efficiency of different micro-food webs were analysed using these ecological functional groups (Dang et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Yang et al. \\u003cspan citationid=\\\"CR87\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Correlations among the different ecological groups were examined to determine the types of interactions between them. A micro-food web was constructed based on the potential predator-prey relationships among various microbial taxa (Li et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Wu et al. \\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Zou et al. \\u003cspan citationid=\\\"CR98\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Trophic transfer efficiency was estimated by calculating the ratio of predator to prey biomass (log10GA: log10 calculation of gene numbers) across different trophic levels. Although gene abundance (log10GA) serves as an indirect proxy for metabolic activity with inherent limitations, it remains a practical and widely accepted approach in soil micro-food web studies (Glavatska et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Heijboer et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Trap et al. \\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAs an example for metazoan, the total nutrient transfer efficiency (TETE) of metazoan was calculated as:\\u003c/p\\u003e \\u003cp\\u003eTETE\\u003csub\\u003emetazoan\\u003c/sub\\u003e=log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003emetazoan\\u003c/sub\\u003e/(log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003eviridiplantae\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003ebacteria\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003efungi\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003earchaea\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003eprotists\\u003c/sub\\u003e)\\u003c/p\\u003e \\u003cp\\u003eThe trophic transfer efficiency from bacteria to metazoan was calculated as:\\u003c/p\\u003e \\u003cp\\u003eMetazoan: Bacteria\\u0026thinsp;=\\u0026thinsp;TETE\\u003csub\\u003emetazoan\\u003c/sub\\u003e\\u0026times;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003ebacteria\\u003c/sub\\u003e /(log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003eviridiplantae\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003ebacteria\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003efungi\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003earchaea\\u003c/sub\\u003e\\u0026thinsp;+\\u0026thinsp;log\\u003csub\\u003e10\\u003c/sub\\u003eGA\\u003csub\\u003eprotists\\u003c/sub\\u003e)\\u003c/p\\u003e \\u003cp\\u003eThis usage was motivated by earlier studies (Bai et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Delmont et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Fierer et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Garc\\u0026iacute;a et al. 2016; Jeppesen et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Singh et al. \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Sun et al. \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Yvon et al. 2010).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eThe α-diversity of bacterial, fungal, archaeal, protist, and metazoan communities was calculated using the Shannon index with the 'Vegan' package in R. β-diversity was assessed using the Bray-Curtis distance matrix to evaluate species turnover and richness differences, and visualized through non-metric multidimensional scaling (NMDS). Statistical significance was evaluated using the permanova test (Adonis function, vegan package, R language (Oksanen et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). One-way analysis of variance (ANOVA) followed by Tukey\\u0026rsquo;s HSD test was used to assess the significant effects of different reclamation patterns on microbial diversity, enzyme activity, and soil properties. Enzyme activities and soil properties were standardized using z-transformation, and the average of the standardized values was used for analysis (Delgado et al. 2016; Wagg et al. \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Structural equation models (SEMs) were constructed to explore the effects of reclamation patterns on ecosystem multifunctionality and micro-food web structures, utilizing the maximum-likelihood estimation method.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSoil biotic community composition and diversity in different reclamation patterns\\u003c/h2\\u003e \\u003cp\\u003eThe compositions of soil biotic communities differed among the different reclamation patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). As key components of the micro-ecosystem, bacteria, fungi, and archaea showed similar distribution patterns across the three reclamation patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). However, the proportions of protists and metazoans were lower in both BP and CP (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe dominant bacterial phyla include Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, and Gemmatimonadetes. Comparative analysis of the dominant bacterial compositions across different reclamation patterns revealed that five bacterial phyla\\u0026mdash;Chloroflexi, Firmicutes, Nitrospirae, Chlorobi, and Spirochaetes\\u0026mdash;were significantly more abundant in MP (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb, Table S2). The fungal community is dominated by Ascomycota, followed by Basidiomycota, Blastocladiomycota, and Chytridiomycota. For the archaeal community, the dominant phyla are Thaumarchaeota, Euryarchaeota, and Crenarchaeota, which were significantly more abundant in MP compared with those in CP and BP (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb, Table S2). Additionally, the metazoan groups Platyhelminthes and Porifera also displayed higher abundances in MP.\\u003c/p\\u003e \\u003cp\\u003eThe Shannon index for bacterial, fungal, archaeal, protist, and metazoan communities is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. No significant differences in Shannon index values were observed among bacteria, fungi, protists, and metazoans. However, the Shannon index for archaea was significantly lower in BP compared with that in MP and CP (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea, c, f). The beta diversity decomposition analyses showed that the compositional dissimilarities among different reclamation patterns for bacterial, fungal, archaeal, metazoan, and protist communities, and total soil microbial communities, were dominated by species replacement processes (Repl\\u0026thinsp;\\u0026gt;\\u0026thinsp;RichDiff; Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSoil physiochemical and enzyme activities in different reclamation patterns\\u003c/h2\\u003e \\u003cp\\u003eOur results indicated that different reclamation patterns significantly affected soil physicochemical properties and enzyme activities. Except N-acetylglucosaminidase and protease, the activities of all other soil enzymes considered (β-glucosidase, cellulase, and urease) varied considerably among the different reclamation patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea, b, c, d, e). Specifically, the activities of cellulase, urease and β-glucosidase were significantly lower in CP soils compared with those in BP and MP soils (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea, c, d). The concentrations of SOC in CP were significantly lower compared with those in BP and MP (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ef). A similar pattern was observed for TN and AN (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eg, h).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRelationship between environmental variables and soil biotic communities\\u003c/h2\\u003e \\u003cp\\u003eThe Mantel test results demonstrate different correlations of environmental variables with bacterial, fungal, archaeal, metazoan, protistan, and total soil microbial communities (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).The bacterial community structure was predominantly influenced by parameters related to carbon metabolism, including β-glucosidase activity (r\\u0026thinsp;=\\u0026thinsp;0.217, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.04) and SOC (r\\u0026thinsp;=\\u0026thinsp;0.252, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMantel test of bacteria, fungi, archaea, metazoan, and protists with soil properties and enzyme activities.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"12\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eBacteria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eFungi\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eArchaea\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eMetazoa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eProtists\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eEnzyme activities\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUrease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.069\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.278\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.117\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.378\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.006**\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.215\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.043*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.275\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.025*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProtease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.076\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.678\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.081\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.264\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.199\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.074\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.967\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.078\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.259\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBeta-glucosidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.217\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.041*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.321\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.518\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002**\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.028\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.563\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.104\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.189\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCellulase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.111\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.172\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.053\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.310\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.224\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.035*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.123\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.898\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.187\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN-acetylglucosaminidase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.098\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.232\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.385\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.008**\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.252\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.046*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.175\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.062\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.293\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eSoil properties\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSOC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.252\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.048*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.165\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.081\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.499\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0009***\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.071\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.717\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.087\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.229\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.077\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.652\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.029\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.530\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.204\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.970\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.108\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.792\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.603\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.881\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.077\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.243\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.584\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.930\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.137\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.462\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.207\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.037*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.241\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.020*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.090\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.821\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.289\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003epH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.257\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.029*\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.486\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.612\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.092\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.803\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.471\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"12\\\"\\u003eValues in bold indicate statistical significance. Significance levels are shown at *\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, **\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01 and ***\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. SOC, total soil organic carbon; TN, total nitrogen; AN, ammonium nitrogen; NN, nitrate nitrogen.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAnother major component of the micro-ecosystem, the fungal community, was primarily influenced by parameters related to nitrogen metabolism. Key factors included N-acetyl-β-D-glucosidase activity (r\\u0026thinsp;=\\u0026thinsp;0.385, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0079) and AN (r\\u0026thinsp;=\\u0026thinsp;0.2072, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.037). Compared with those of bacteria and fungi, the archaeal community showed significant correlations with a broader range of environmental variables, including β-glucosidase, cellulase, SOC, urease, N-acetyl-β-D-glucosidase, and AN (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Protists and metazoan groups were correlated with urease activity (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEcological networks and trophic transfer efficiency in different reclamation patterns\\u003c/h2\\u003e \\u003cp\\u003eTo uncover the relationships within microbial communities, a co-occurrence network was constructed (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The co-occurrence network modularity index for different reclamation patterns ranged from 0.35\\u0026ndash;0.48, indicating a clear modular structure. The MP microbial communities comprised more functionally interrelated members compared with those in BP and CP plantations. Specifically, the MP network consisted of 10 modules, 122 nodes, and 1,557 links, while the BP network had 8 modules, 119 nodes, and 1,483 links, and the CP network had 5 modules, 89 nodes, and 605 links. Therefore, the network complexity, from highest to lowest, was MP, BP, and CP, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e; Table S3). Furthermore, the positive edges in the CP network (87.57% positive) were significantly lower in the topsoil compared with those in the BP (89.21% positive) and MP (90.75% positive) networks. Contrastingly, its negative edges were significantly higher than those of the MP network. Consequently, the network stability, ranked from highest to lowest, is MP, BP, and CP, respectively, which aligns with that of the observed network complexity.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe micro-food web structure is linked to trophic cascade effects and energy transfer efficiency. The trophic structures in MP showed more extensive interactions among different ecological groups compared with those in CP and BP patterns (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Among these relationships, significant correlations were observed between detritus decomposers (i.e., bacteria, fungi, and archaea), metazoans, protists, and Viridiplantae. In the MP, more positive correlations between different groups were evident (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ec). The detrital decomposers, such as metazoans and protists, were more abundant in the MP structure compared with those in the BP and CP structures (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Variations in micro-food web structures can result in changes in trophic transfer efficiency. In the MP, the transfer efficiency from the preceding trophic level to protists and metazoans was 9.36% and 17.19%, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ec). Comparatively, the transfer efficiency to protists and metazoans was 8.89% and 16.45% in BP, and 9.05% and 16.82% in CP, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea, b). This suggests that the trophic structures in MP are more efficient at transferring biomass energy to higher trophic levels.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLinks between soil properties, trophic transfer efficiency, and C/N metabolism functions\\u003c/h2\\u003e \\u003cp\\u003eTo further explore the effects of reclamation patterns on microbial trophic transfer efficiency, the feedback control among reclamation patterns, trophic transfer efficiency (top-down and bottom-up), Carbon/Nitrogen metabolism function were revealed using the structural equation model. According to the SEM results, in the top-down regulation of the food web, the soil physicochemical properties SOC and AN directly affect the predation nutrient transfer efficiency of metazoans on protists and the predation nutrient transfer efficiency of protists on bacteria and fungi, which in turn impacts soil carbon metabolism functions, particularly β-glucosidase and cellulase activities (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ea). In the bottom-up regulation of the food web, the soil physicochemical properties SOC and AN significantly affect the trophic transfer efficiency of lower trophic levels obtaining nutrients from higher trophic levels through decomposition, thereby significantly impacting the nitrogen metabolism function of soil, particularly urease (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb). Overall, reclamation patterns can indirectly influence the microbial trophic transfer efficiency by altering soil properties, and eventually affect the C/N metabolism functions of soil ecosystems.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEffect of reclamation patterns on soil physicochemical properties and soil biotic community\\u003c/h2\\u003e \\u003cp\\u003eWe observed that soil nutrient concentrations varied among the three reclamation patterns. Specifically, the BP and MP exhibited higher SOC and TN levels than those by CP. The findings agreed well with those of the earlier studies (Vitali et al. \\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). This is likely because vegetation influences soil properties mainly through the amount and chemical composition of root exudates and litter (Ba\\u0026ntilde;eras et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Bremer et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). BP and MP can enhance soil C and N content by facilitating herbaceous restoration and litter decomposition, provided that the density is within a range that offers sufficient shade (Zhang et al. \\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Contrastingly, the dense coniferous litter on the forest floor in CP obstructs air circulation, leading to reduced nutrient accumulation. We found that β-glucosidase and cellulase activities were significantly lower in CP, which have lower SOC content. Wang et al. (2020) demonstrated that soil enzyme activities are significantly correlated with SOC fractions and microbial biomass carbon. SOC acts as a substrate stimulating enzyme release, thereby enhancing soil enzyme activities (Allison et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). Regarding bacterial communities, we observed an enrichment of specific phyla such as Chloroflexi and Firmicutes in the mixed coniferous-broad plantation. These bacteria are typically involved in key ecological processes, including organic matter decomposition and nitrogen cycling (Hartman et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Kim et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Llado et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Llad\\u0026oacute; et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), suggesting their presence may have a positive impact on accelerating nutrient cycling and improving soil quality in reclaimed mine soils. Furthermore, the abundances of the archaeal phyla Thaumarchaeota, Euryarchaeota, and Crenarchaeota were significantly higher in the MP compared with those in BP and CP. Given the critical role of archaea in N transformation processes (Baldrian et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Uroz et al. \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), this finding implies that the MP may be more effective in sustaining or re-establishing N cycling in reclaimed mine soils (Kirk et al. 1987; Lindahl et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). The relative proportions of protists and metazoans were prominently lower in both BP and CP compared with those in the MP. This observation suggests that the MP may be more conducive to maintaining a complex soil micro-food web. These findings support our objectives that different reclamation practices not only alter the soil physicochemical properties in the soil but also influence the composition of the soil biotic communities.\\u003c/p\\u003e \\u003cp\\u003eWe found that, except for archaea, reclamation patterns had little effect on microbial α diversity. This may be due to the rapid response of microbes to environmental changes, and more disturbed soils could favour certain microbial communities, such as r-selected groups (Deng et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Alternatively, anthropogenic disturbances and the resulting environmental changes may allow a broader diversity of microbes to coexist within smaller geospatial scales (Prober et al. \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Contrarily, β-diversity reflects the variation in ecological niches and is more strongly influenced by environmental factors, including soil pH and organic matter content (Llad\\u0026oacute; et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Rivest et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The differences in bacterial, fungal, archaeal, metazoan, protist, and total soil microbial community compositions among the different reclamation patterns were mainly attributable to underlying soil properties, these properties play a more significant role in shaping soil biotic assemblages compared with that of vegetation. (Heděnec et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAccording to the Mantel test results, distinct correlations were observed between environmental variables and the communities of bacteria, fungi, archaea, metazoans, protists, and total soil microbes. It is well known that soil pH is a key driving factor for soil bacterial community development (Rousk et al. \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Simultaneously, Proteobacteria and Actinobacteria dominate the bacterial communities in soils across all three types of vegetation and exhibit a positive relationship with carbon mineralization (Fazi et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Fierer et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). We deduced that this is the reason for the correlation of the bacterial community with carbon metabolism-related parameters (such as β-glucosidase and SOC) and pH. The fungal community showed a stronger correlation with parameters related to N metabolism, likely because fungi possess the capability to process N and phosphorus. (Guest et al. 2007). Compared with those of bacteria, fungi were reported to exhibit greater rates and higher production of NO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e-N and NO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e-N (Eylar et al. 1959; Kurakov et al. 1996). The archaea community showed significant correlations with both C metabolism-related and N metabolism-related parameters. Abundant archaeal ammonia oxidizers in soils can form nitrate through microbial activity (Leininger et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). Simultaneously, archaeal ammonia oxidation is coupled with carbon fixation (Santoro et al. \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Ammonia oxidation yields low energy, suggesting that a large quantity of ammonium metabolism by archaea is required to fix a given amount of CO\\u003csub\\u003e2\\u003c/sub\\u003e (Norman et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEffect of reclamation patterns on soil micro-food web and microbial Trophic Transfer Efficiency\\u003c/h2\\u003e \\u003cp\\u003eWe observed that the MP exhibited the highest network complexity followed by the BP and the CP, which agrees with that of earlier studies showing that plant diversity positively influences soil microbial diversity and network complexity, leading to more stable and resilient ecosystems (Yuan et al. \\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This is likely attributed to the heterogeneity and resource richness created by the MP (Delgado et al. 2016). The MP typically provide a more diverse array of niches and resources, including a variety of organic matter inputs, pH levels, and microclimates (Jiao et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). This diversity supports a wider range of microbial interactions and functional roles, leading to a more complex and interconnected network (Wang et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Contrastingly, the more homogeneous environments of BP and CP, particularly the CP, may limit the diversity of available niches and resources, resulting in fewer and less interconnected soil biotic communities. Therefore, the MP consisted more stable soil biotic communities than those by the BP and CP. Furthermore, the higher positive cohesion in the MP and BP networks indicates a greater proportion of positive interactions (e.g., mutualism, commensalism) among the soil biotic communities, which is essential for maintaining a stable and resilient soil biotic community. Positive interactions, such as mutualistic relationships, can enhance the overall stability of the community by promoting cooperative behaviours and resource sharing (Krashevska et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Schulz et al. \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Contrastingly, the lower positive and higher negative edges in the CP network suggest a higher prevalence of competitive or antagonistic interactions, which can destabilize the soil biotic communities and reduce their overall resilience (Kouzuma et al. 2015; Wang et al. \\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Summarily, our results certify that the MP had higher stability in microbial communities than that by the BP and CP under opencast coal mining disturbance conditions.\\u003c/p\\u003e \\u003cp\\u003eWe found that the compositions of ecological functional groups varied among the different reclamation patterns. The higher proportions of protists and metazoans in the MP indicated that more consuming organisms were involved in the nutritional processes. In the MP, a greater number of positive correlations among different groups were observed indicating more mutually beneficial symbiotic relationships between different ecological groups in MP. Different groups obtain the nutrients they require through the decomposition of detrital organic matter or by predation (Steffan et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The micro-food web structures in the MP contain more detrital nutrients (i.e., metazoans and protists), suggesting that the effect of predation relationships is weakened in the MP. Reclamation patterns can indirectly influence the multifunctionality of ecosystems by directly influencing micro-food web structures (Domingues et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). A well-structured micro-food web plays a crucial role in stabilizing ecological functions by promoting nutrient retention, organic matter decomposition, and microbial interactions. Additionally, different micro-food web structures alter trophic transfer efficiency, which is fundamental to ecosystem restoration (Palijan et al. 2018). The transfer efficiency from the previous trophic level to protists and metazoans was higher in the MP compared with that in the BP and CP. This result suggests that the trophic structures in the MP facilitate more efficient biomass energy transfer to higher trophic levels, indicating enhanced trophic transfer efficiency within the soil micro-food web. This improved energy flow may contribute to increased microbial activity, potentially accelerating nutrient cycling and organic matter decomposition (Sui et al. \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWe acknowledge the limitations of using gene abundance as a proxy for microbial activity, as it does not directly measure metabolic processes, and the relationship between gene counts and microbial biomass remains complex and not fully characterized. Although gene abundance provides valuable insights into microbial functional potential, future studies incorporating direct metabolic measurements and biomass quantification would enhance the robustness of nutrient transfer models and offer a more comprehensive understanding of microbial interactions and nutrient cycling within soil ecosystems.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eInfluence of Reclamation Patterns on C/N metabolism functions\\u003c/h2\\u003e \\u003cp\\u003eBuilding on the observed differences in trophic transfer efficiency among reclamation patterns, we investigated the mechanisms by which these patterns regulate nutrient transfer and C/N metabolism functions in soil ecosystems. Our results show that reclamation patterns can indirectly influence the microbial trophic transfer efficiency via altered soil properties, and eventually affect the C/N metabolism functions of soil ecosystems. This result indicates that in the top-down regulation of the food web, high levels of SOC provide abundant organic carbon sources for microorganisms, promoting their growth and activity. This increase in microbial abundance and activity enhances the food supply for protists, thereby increasing the predation efficiency of metazoans on protists. Additionally, SOC also increases the predation efficiency of protists on bacteria and fungi, as the numbers and activity of these microorganisms are elevated. High predation nutrient transfer efficiency implies that more organic carbon is decomposed and utilized, thereby increasing the activity of β-glucosidase (Fierer et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Islam et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). β-glucosidase is involved in the decomposition of organic carbon in the soil, and its increased activity facilitates the acceleration of the mineralization process of organic carbon. However, cellulase activity shows a significant negative correlation, possibly due to slower cellulose decomposition under high SOC levels or reduced secretion from increased predation pressure. (Allen et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Domeignoz et al. 2020). Although high levels of AN provide available N sources for microorganisms, excessively high AN levels may inhibit the growth of certain microorganisms, thereby reducing the efficiency of nutrient acquisition by lower from higher trophic levels. The impact of AN on β-glucosidase activity may be due to the inhibition of carbon metabolism enzymes under high nitrogen conditions (Liu et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Mihelič et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The negative correlation with cellulase activity may be because high AN levels reduce the ability of microorganisms to decompose cellulose.\\u003c/p\\u003e \\u003cp\\u003eIn the top-down regulation of the food web, higher trophic-level organisms, such as predators, regulate the abundance and activity of lower trophic level microorganisms. Contrastingly, in bottom-up regulation, high levels of SOC promote the growth and activity of these microorganisms. These microorganisms decompose organic matter from higher trophic levels, releasing more nutrients and thereby increasing the efficiency of nutrient transfer. Higher nutrient transfer efficiency implies that more organic nitrogen is decomposed and utilized, thus increasing urease activity (Lorenz et al. 2019; Zhang et al. \\u003cspan citationid=\\\"CR92\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Urease, which is involved in the mineralization of organic nitrogen in the soil, accelerates the transformation of organic N when its activity is increased (Li et al. \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Ouyang et al. \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAdditionally, we further elucidate the mechanism by which MP exhibit higher nutrient transfer efficiency compared with that by CP and BP. MP create diverse microhabitats supporting complex microbial community structures, thereby enhancing nutrient transfer efficiency and ultimately influencing the C and N metabolism of the soil ecosystem. This finding underscores the importance of considering soil properties in the design of reclamation models. For example, increasing organic matter input not only enhances soil fertility but also improves soil structure and microbial diversity, thereby promoting the multifunctionality of the ecosystem.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eWe explored the influence of different reclamation models on soil micro-food web structure and nutrient transfer efficiency in mining areas. Our findings demonstrate that reclamation patterns not only modify soil physicochemical properties but also regulate microbial community interactions and trophic transfer processes. Compared with those of single-species reclamation patterns, the MP exhibited greater microbial network complexity and higher nutrient transfer efficiency, particularly at higher trophic levels, such as protists and metazoans. This suggests that MP enhances trophic energy flow and resource utilisation within the micro-food web, contributing to improved ecosystem functioning. Furthermore, our study highlights that reclamation models indirectly regulate microbial nutrient transfer efficiency by altering soil properties, ultimately influencing soil carbon and nitrogen metabolic functions. These findings underscore the importance of integrating soil organic matter management and mixed-species plantations into reclamation strategies to enhance microbial interactions and accelerate ecosystem recovery.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank the research team at the Institute of Loess Plateau for their technical assistance. This work was supported by National Natural Science Foundation of China (U1910207 and 42107420) .\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization: Peng Gao, Xiujuan Zhang and Yong Liu; Methodology: Peng Gao; Formal analysis and investigation: Peng Gao, and Xiujuan Zhang; Writing-original draft preparation: Peng Gao; Writing - review and editing: Hong Zhang, Junjian Li and Yong Liu. All authors have read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFunding was provided by the National Natural Science Foundation of China (U1910207 and 42107420).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated during the current study are available from the corresponding author on reason able request.\\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\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAllen, K., Corre, M.D., Tjoa, A., Veldkamp, E (2015) Soil nitrogen-cycling responses to conversion of lowland forests to oil palm and rubber plantations in Sumatra, Indonesia.PLoS One 10:e0133325. https://doi.org/10.1371/journal.pone.0133325\\u003c/li\\u003e\\n\\u003cli\\u003eAllison, S.D., Nielsen, C., Hughes, R.F (2006) Elevated enzyme activities in soils under the invasive nitrogen-fixing tree Falcataria moluccana. 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However, the specific influence of reclamation patterns on the structure of soil micro-food web and their trophic transfer efficiency in mining soils remains unclear. Therefore, this study aimed to analyse the specific impacts of reclamation models on the soil micro-food web and elucidate the underlying mechanisms that restores ecosystem functions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe conducted a field experiment at 15 sites across three reclamation patterns—coniferous plantation (CP), broad-leaved plantation (BP), and mixed coniferous-broadleaved plantation (MP)—within the Pingshuo Open-pit Coal Mine in China. Using metagenomic sequencing, we analysed soil micro-food web structures and nutrient transfer efficiencies across various reclamation strategies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMP exhibited greater microbial network complexity and higher nutrient transfer efficiency than those of CP and BP. Specifically, MP ecosystems demonstrated considerably enhanced nutrient transfer efficiency among higher trophic-level microorganisms such as protists and metazoans, indicating improved trophic energy flow and resource utilisation within the soil micro-food web. Moreover, reclamation patterns influenced soil nutrient transfer efficiency by modifying soil physicochemical properties, ultimately shaping soil carbon and nitrogen metabolic processes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe mixed coniferous-broadleaved plantation enhanced nutrient transfer efficiency within the soil micro-food web, thereby optimising trophic interactions and ecosystem nutrient cycling. Reclamation models can influence C/N metabolism processes via the soil microbial network. Our findings provide a comprehensive understanding of optimizing reclamation strategies and improving ecosystem functions in mining areas.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Response of soil micro-food web and nutrient transfer efficiency to reclamation strategies in mining area\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-04-02 07:01:11\",\"doi\":\"10.21203/rs.3.rs-5782088/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2025-03-28T07:32:51+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-03-28T01:46:07+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-03-27T06:03:58+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Plant and Soil\",\"date\":\"2025-03-26T09:47:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"decision\",\"content\":\"Major revisions\",\"date\":\"2025-02-07T02:56:20+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"plant-and-soil\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"plso\",\"sideBox\":\"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)\",\"snPcode\":\"11104\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11104/3\",\"title\":\"Plant and Soil\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"c4208cdd-9a52-44a0-aab0-56220f339b4f\",\"owner\":[],\"postedDate\":\"April 2nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-14T16:05:33+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5782088\",\"link\":\"https://doi.org/10.1007/s11104-025-07664-4\",\"journal\":{\"identity\":\"plant-and-soil\",\"isVorOnly\":false,\"title\":\"Plant and Soil\"},\"publishedOn\":\"2025-07-07 15:57:28\",\"publishedOnDateReadable\":\"July 7th, 2025\"},\"versionCreatedAt\":\"2025-04-02 07:01:11\",\"video\":\"\",\"vorDoi\":\"10.1007/s11104-025-07664-4\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11104-025-07664-4\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5782088\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5782088\",\"identity\":\"rs-5782088\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}