Effects of Tree Species Mixing on Soil Microbial Community Structure in Karst Regions of Southwest China

Effects of Tree Species Mixing on Soil Microbial Community Structure in Karst Regions of Southwest China | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 September 2025 V1 Latest version Share on Effects of Tree Species Mixing on Soil Microbial Community Structure in Karst Regions of Southwest China Authors : Yan Wu 0000-0003-2335-3119 , Xumeng Tan , Yu Wu , and Jianfeng Li [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175914932.21109321/v1 200 views 133 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To investigate the effects of tree species mixing on soil microbial community structure in karst regions and its association with environmental variables, this study selected pure Cryptomeria fortunei , pure Liquidambar formosana , and mixed forests in the Zazuo Experimental Forest Farm of Guizhou Province, China, as research objects. We determined nutrient content of leaves, litter, and soil, as well as soil enzymatic activities. Using high-throughput sequencing technology, we analyzed the diversity and composition of the soil microbial community. Mantel tests and variance partitioning analyses (VPA) were used to elucidate the influence of environmental variables on changes in the soil microbial community. The results demonstrated that (1) compared with pure L. formosana forests, tree species mixing significantly increased the Smith-wilson index of soil bacteria, but decreased the Coverage index of soil fungi ( P < 0.05). Significant differences in soil fungal β-diversity were observed among different forest types ( P < 0.05). (2) The dominant bacterial phyla in all three forest types were Chloroflexi , Acidobacteriota , Proteobacteria , and Actinobacteriota , the dominant fungal phyla were Ascomycota , Mortierellomycota , and Basidiomycota . Compared with pure L. formosana and pure C. japonica forests, the relative abundances of Actinobacteria and Ascomycota in mixed forests significantly increased by 37.46% and 37.43%, respectively. In contrast, the relative abundance of Firmicutes in pure L. formosana forests significantly increased by 154.39% compared to that in mixed forests ( P <0.05). (3) Community composition at the phylum level was significantly correlated with leaf and soil nutrient contents, and soil enzymatic activity. The individual explanatory rates of organic carbon, soil nutrients, and soil enzymes on bacterial community variation were 10.08%, 11.14%, and a negative value, respectively, while synergistic effects between enzymes and organic carbon and between enzymes and soil nutrients explained 39.61% and 32.69%, respectively. For fungal communities, the individual explanatory rates of soil nutrients, soil enzymes, and leaf nutrients were 16.53%, 5.72%, and negative, respectively, with synergistic effects between enzymes and leaf nutrients accounting for 0.79%. In conclusion, tree species mixing altered the diversity and composition of soil microbial communities, with environmental variables individually or synergistically influencing microbial community structure. These findings provide a theoretical basis for enhancing ecosystem functions and sustainable management of karst forest ecosystems. Introduction Soil microbes constitute an essential part of soil ecosystems and are the main participants in soil nutrient cycling and energy flow and play a vital role in soil structure improvement, fertility maintenance, environmental pollutant purification, and plant growth(Wu et al., 2022). Changes in soil microbial community diversity and structure can alter soil ecosystem functions and reflect soil health status, thus serving as key indicators for evaluating soil quality and health(Peco et al., 2017). Extensive research has demonstrated that soil properties, including organic matter quality(Li et al., 2022), water content and salinity(Yan et al., 2015), mineral composition(Robert & Chenu, 2021), aggregate structure(Rovira & Greacen, 1957), carbon quality(Thuille et al., 2015), and available carbon and nitrogen(Yang et al., 2016), along with environmental variables, including land use patterns(Potthast et al., 2012), mineral fertilizers(Geisseler & Scow, 2014), topography(Tajik et al., 2020), and climate(Alkorta et al., 2017) (including mean annual precipitation(Chen et al., 2020), mean annual temperature(Ma et al., 2024), seasonal variations(Lin et al., 2023)), as well as forest characteristics, including plant diversity(Shang et al., 2021), stand age(Hu et al., 2024), forest density(Yang et al., 2022a; Zarafshar et al., 2024), community succession stage(Liu et al., 2020), and stand type(Guo & Gong, 2024; Li et al., 2023; Yang et al., 2022b), influence soil microbial communities. Among these, variation in stand type is considered the primary factor affecting soil microbial diversity and community structure. This was attributed to significant differences in nutrient composition, organic matter content, and root exudate characteristics among stand types, which regulate soil microbial communities through direct and indirect pathways(Guo & Gong, 2024). Furthermore, stand type differences alter microhabitat (for example light, temperature, and humidity), leading to significant divergence in soil microbial community characteristics within the same geographical region(Wang et al., 2025b). Stand-type variation, particularly tree species composition, constitutes a focal research area in ecology, particularly concerning its effects on soil microbial communities. The complex regulatory mechanisms exerted by higher litter species richness in mixed forests result in differential effects on soil microbial community structure and diversity(Guo et al., 2024). Positive effects were evidenced by significantly superior vertical spatial distribution and seasonal dynamics of soil microbial abundance following tree species mixing(Zou et al., 2000), effective alleviation of microbial diversity decline through native tree species mixing(Chu et al., 2019), and substantial enhancement of microbial community abundance via introduction of nitrogen-fixing tree species into monocultures by promoting soil microbe-plant root interactions(Dixon & Kahn, 2004). In addition, some studies reveal additional complexities of mixing effects: Li et al. (2021) reported significantly reduced Ascomycota abundance in rhizosphere soils of Pinus massoniana and Juniperus formosana after mixing with broadleaved species, with declining relative abundances of Saccharomycetes , Pezizomycetes , Leotiomycetes , and Xylonomycetes classes in P. massoniana rhizosphere; Yang et al. demonstrated lower Agaricomycetes relative abundance in litter but unchanged total fungal abundance in temperate Larix mixed forests of Northeast China(Yang et al., 2022b); Guo and Gong (2024)found altered microbial diversity and cross-domain network complexity in conifer-broadleaf mixed forests of Xinjiang’s Tianshan arid region, although their community structure resembled monocultures; Li et al. (2023) revealed no enhancement in soil bacterial abundance, diversity and fungal abundance in subtropical Cunninghamia lanceolata and Phoebe bournei mixtures. These findings indicate significant regional heterogeneity, taxon specificity, and tree species combination dependency in mixed forest effects on soil microorganisms(Guo & Gong, 2024), with driving mechanisms potentially linked to multidimensional variables, including litter properties, root exudates, and soil microhabitats(Li et al., 2023). Guizhou Province, which harbors the most extensive and intricately developed karst terrain in Southwest China exhibits pronounced ecosystem fragility dictated by its unique eco-geological setting. This fragility manifests as high ecological sensitivity, low environmental carrying capacity, diminished resistance to disturbance, and compromised stability(Zhu, 2003). To remediate degraded ecosystems, China has implemented comprehensive rocky desertification control initiatives across the southwestern karst region. Since the 1990s, successive national afforestation programs, including the Grain for Green Project, Natural Forest Conservation Program, Yangtze River Shelterbelt Initiative, and the Pearl River Shelterbelt Project, which have all been executed systematically. By 2023, the province’s forest cover had expanded to 11.1 million hectares, representing 63% of its land area, with planted forests constituting 26% of the total forested landscape (https://lyj.guizhou.gov.cn). Notably, C. fortunei and L. formosana are the principal afforestation species in this region, accounting for 2.2% and 1.8% of total forest cover, respectively(Zhang & Ding, 2019; Zhang & Guo, 2020). Guizhou Province successfully implemented a L. formosana-C. fortunei mixed afforestation model at the Zazuo Experimental Forest Farm, enhancing stand structural complexity. Research on stand type-soil relationships in this region has predominantly focused on water conservation capacity of soil layers(Zeng et al., 2023), soil physicochemical properties and nutrient dynamics(Ni et al., 2017), and soil enzyme activity(Wang et al., 2025b). However, systematic investigation into how stand type variation, particularly mixed forests, affects soil microbial community characteristics and their driving variables remains a critical knowledge gap. This study investigated the effects of tree species mixing on soil microbial community diversity and structure in pure C. fortunei forests, pure L. formosana forests, and their mixed stands at the Zazuo Experimental Forest Farm in Guizhou Province. This study employed Illumina MiSeq high-throughput sequencing technology to investigate the effects of tree species mixing on the diversity and structural characteristics of soil microbial communities and conducted comprehensive analyses of their relationships with key environmental drivers, including foliar nutrients, litter nutrients, soil nutrients, and soil enzymatic activities. The scientific hypotheses addresses were as follows: (1) tree species mixing enhances soil microbial community diversity and structural complexity; (2) tree species mixing modifies leaf nutrient composition, litter nutrient characteristics, soil nutrient content, and soil enzymatic activities; and (3) significant correlations exist between soil microbial community composition and environmental variables (leaf/litter/soil nutrients and soil enzymes). These findings will help elucidate stand type-microbe feedback mechanisms at regional scales and provide microbiology-informed strategies for sustainable management and soil fertility maintenance in native forest ecosystems. 1 Materials and Methods 1.1 Study Area Description The study area is situated within the state-owned Zazuo Experimental Forest Farm, Xiuwen County, Guiyang City, Guizhou Province (106°36′–107°3′ E, 26°2′–26°59′ N). This forested region features a hilly plateau topography and lies within a subtropical monsoon climate zone characterized by abundant precipitation. The area experiences a mean annual temperature of 15.3°C and mean annual precipitation of 1,129.5 mm, with predominantly acidic to slightly acidic yellow earth soils. The managed area spans 10,108.73 hectares, where dominant tree species include Pinus massoniana , Cryptomeria fortunei , Pinus armandii , Quercus spp. and Cunninghamia lanceolata . The principal shrubs are Serissa serissoides , Viburnum dilatatum , and Corylus heterophylla , whereas the herbaceous vegetation is dominated by Hypolepis punctata , Diplazium esculentum , and Ophiopogon bodinieri (Zeng et al., 2023). 1.2 Plot Establishment and Sample Collection We established three replicate plots for each stand type: pure C. fortunei forest, pure L. formosana forest, and mixed C. fortunei-L. formosana forest within the minimally disturbed areas devoid of significant anthropogenic interference or natural disasters. Replicate plots were spaced >100 m apart, whereas different stand types were separated by >1,000 m (site characteristics are detailed in Table 1). Each 20 m × 20 m (400 m²) main plot contained five 2 m × 2 m (4 m²) shrub subplots positioned in the southwest, northwest, northeast, southeast, and central locations. Five 1 × 1 m (1 m²) herbaceous quadrats were established in each shrub subplot. Representative trees were selected for mature leaf collection using pole pruners and secateurs. Litter samples were collected from herbaceous quadrats. All fresh leaves and litter were labeled, air-dried on newspaper, oven-dried to constant weight at 65°C, then ground and stored for C, N, P analysis. Soil samples from the 0-20 cm layer were collected by arranging 5 sampling points according to the ”S-shaped sampling pattern”; these samples were mixed into a composite sample. Visible plant and animal residues were removed from the composite sample, which was then passed through a 2-mm aperture soil sieve. The sieved soil was divided into two portions: one portion was stored at -80°C for high-throughput sequencing of soil microorganisms; the other was spread on newspaper, air-dried to a constant weight, ground, passed through a 0.149-mm aperture sieve, and subsequently used for determination of soil organic carbon (SOC), soil total nitrogen (STN), soil total phosphorus (STP), glomalin-related soil protein (GRSP) content, and soil enzyme activities. Additionally, undisturbed soil samples were collected and soil aggregate fractions were separated using the wet-sieving method, isolating macroaggregates (diameter > 0.25 mm), microaggregates (diameter 0.053–0.25 mm), and silt and clay fractions (diameter < 0.053 mm). These fractions were used to determine organic carbon, total nitrogen, total phosphorus, and glomalin-related soil protein contents within the soil aggregates. Table 1. Basic information of the sample plots Sample plot number Forest type Main tree species Stand age (years) Age class Elevation (m) Slope (°) Aspect Mean tree height (m) Mean DBH (cm) Mean height to the first live branch (m) Tree density (stems/ha) 1 pure Cryptomeria fortunei Cryptomeria fortunei 21 Middle-aged forest 1333 55° North 17.71 21.72 9.01 625 2 21 1349 45° Northwest 16.77 23.6 9.96 725 3 23 1323 54° North 16.54 23.94 9.17 850 4 pure Liquidambar formosana Liquidambar formosana 22 Middle-aged forest 1317 53° Northwest 18.14 26.81 12.94 650 5 21 1317 48° Southwest 15.97 27.98 11.62 725 6 21 1317 60.5° Northwest 16.62 28.25 9.02 750 7 mixed forests Cryptomeria fortunei and Liquidambar formosana 22 Middle-aged forest 1337 58.5° Northwest 19.32 26.58 9.95 550 8 21 1339 53.1° Northwest 15.59 30.97 9.69 475 9 22 1350 46° Northwest 16.06 22.71 8.61 525 1.3 Experimental Methods 1.3.1 Determination of Leaf, Litter, Soil Nutrient Contents, and Soil Enzyme Activities The organic carbon (OC), total nitrogen (TN), and total phosphorus (TP) contents in leaves, litter, and soil were determined using the potassium dichromate external heating method, semi-micro Kjeldahl method, and molybdenum-antimony anti-colorimetry method, respectively(Bao, 1999). Glomalin-related soil protein (GRSP) content was determined using the Bradford method(Wright et al., 1998). Phosphatase, urease, and catalase activities were determined by disodium phenyl phosphate colorimetry, active phenol-sodium hypochlorite colorimetry, and potassium permanganate titration, respectively(Guan, 1986). Data on leaf litter, soil nutrients, and soil enzyme activity in the study area are presented in Table 2. Table 2. Foliar Nutrients, Litter, Soil Nutrients, and Soil Enzyme Activities in Different Forest Stands in the Study Area LS FX HJ Leaf Nutrients LOC 518.35±4.60 504.64±12.21 497.34±1.55 LTN 16.07±0.74 15.62±0.95 11.43±0.28 LTP 1.74±0.05 1.32±0.02 1.43±0.02 Litter Nutrients LLOC 495.27±2.50 467.68±3.13 495.74±3.84 LLTN 11.34±0.43 12.05±0.58 10.09±0.18 LLTP 0.79±0.02 0.81±0.01 0.75±0.01 Soil Nutrients SOC 26.43±3.01 23.27±1.56 30.27±1.48 STN 1.19±0.03 1.03±0.02 1.18±0.08 STP 0.44±0.01 0.40±0.02 0.41±0.02 SMSOC 18.59±1.67 22.93±2.91 28.92±4.79 SISOC 18.37±1.29 21.57±3.63 25.40±4.93 SCSOC 15.92±1.93 21.01±3.82 18.92±4.93 SMTP 0.86±0.08 0.60±0.05 0.89±0.02 SITP 0.76±0.11 0.73±0.04 0.70±0.05 SCTP 0.86±0.07 0.73±0.05 0.73±0.06 SMTN 0.19±0.02 0.18±0.02 0.27±0.02 SITN 0.24±0.03 0.23±0.00 0.14±0.03 SCTN 0.17±0.02 0.25±0.02 0.08±0.02 EEG 1.21±0.07 1.34±0.07 1.2±0.22 SMEEG 1.08±0.16 0.99±0.07 1.03±0.07 SIEEG 0.85±0.09 0.87±0.04 0.89±0.07 SCEEG 1.20±0.09 0.95±0.05 1.12±0.02 TG 5.73±0.47 6.07±0.68 5.77±0.80 SMTG 4.45±0.32 4.7±0.3 4.41±0.19 SITG 4.32±0.19 4.42±0.51 3.80±0.37 SCTG 4.73±0.15 4.69±0.49 4.30±0.30 Enzyme Activity ACP 1.22±0.06 1.17±0.1 0.81±0.28 URE 4.94±2.25 5.03±1.81 4.34±1.35 CAT 0.43±0.12 0.56±0.11 0.54±0.05 Notes: LS: pure Cryptomeria fortunei , FX: pure Liquidambar formosana , HJ: mixed forests. LOC: leaf organic carbon, LTN: leaf total nitrogen, LTP: leaf total phosphorus; LLOC: litter layer organic carbon, LLTN: litter layer total nitrogen, LLTP: litter layer total phosphorus; SOC: soil organic carbon, STN: soil total nitrogen, STP: soil total phosphorus; SMSOC: soil macroaggregate organic carbon, SISOC: soil intra-macroaggregate organic carbon, SCSOC: soil-clay organic carbon; SMTP: soil macroaggregate total phosphorus, SITP: soil intra-macroaggregate total phosphorus, SCTP: soil-clay total phosphorus; SMTN: soil macroaggregate total nitrogen, SITN: soil intra-macroaggregate total nitrogen, SCTN: soil-clay total nitrogen; EEG: soil easily-extractable glomalin-related soil protein, SMEEG: soil macroaggregate easily-extractable glomalin-related soil protein, SIEEG: soil intra-macroaggregate easily-extractable glomalin-related soil protein, SCEEG: soil-clay easily-extractable glomalin-related soil protein; TG: soil total glomalin-related soil protein, SMTG: soil macroaggregate total glomalin-related soil protein, SITG: soil intra-macroaggregate total glomalin-related soil protein, SCTG: soil-clay total glomalin-related soil protein; ACP: acid phosphatase, URE: urease, CAT: catalase. 1.3.2 Soil Microbial Illumina-MiSeq High-Throughput Sequencing Soil samples were sent to Shanghai Majorbio Bio-Pharm Technology Co., Ltd. Total genomic DNA of microbial communities was extracted using the DNeasy® PowerSoil ® Pro Kit, with DNA quality detected by 1% agarose gel electrophoresis and DNA concentration and purity measured by NanoDrop2000. Using the extracted DNA as a template, the V3-V4 hypervariable region of the bacterial 16S rRNA gene was PCR-amplified with the barcoded forward primer 338F and reverse primer 806R(Liu et al., 2016), and the ITS1 region of the fungal ITS gene was PCR-amplified with the barcoded forward primer ITS1 and reverse primer ITS2R(Adams et al., 2013). The amplification program was: 95°C pre-denaturation for 3 min; 35 cycles (bacteria) or 27 cycles (fungi) of 95°C denaturation for 30 s, 55°C annealing for 30 s, 72°C extension for 45 s; final extension at 72°C for 10 min; hold at 4°C (PCR instrument: ABI GeneAmp® 9700). Three replicates per sample were processed and PCR products from the same sample were pooled and recovered using 2% agarose gel electrophoresis. Purification was performed using the AxyPrep DNA Gel Extraction Kit, with recovered products verified by 2% agarose gel electrophoresis and quantified using the Quantus™ Fluorometer (Promega, USA). Purified PCR products were used to construct libraries using a NEXTFLEX Rapid DNA-Seq Kit. Sequencing was performed using the Illumina MiSeq PE300 platform (Shanghai Majorbio Bio-Pharm Technology Co., Ltd.). Raw data were deposited in the NCBI SRA database. Paired-end raw sequences were quality-controlled using fastp(Chen et al., 2018) (https://github.com/OpenGene/fastp, version 0.19.6), assembled using FLASH(Magoč & Salzberg, 2011) (http://www.cbcb.umd.edu/software/flash, version 1.2.11), and clustered into operational taxonomic units (OTUs) at a 97% similarity threshold, with chimeras removed using UPARSE(Edgar, 2013; Stackebrandt & Goebel, 1994) (http://drive5.com/uparse/, version 7.1). 1.3.3 Data Analyses The effects of different forest stand types on leaf, litter, and soil nutrients, and enzyme activities were examined using a one-way ANOVA in SPSS 27.0. Soil bacterial and fungal α-diversity was analyzed using Mothur 1.30.2, calculating community richness (Ace), community diversity (Simpson), community coverage (Coverage), and community evenness (Smith-wilson); intergroup differences were tested using one-way ANOVA. Non-metric multidimensional scaling (NMDS) analysis based on the binary Jaccard distance algorithm was performed to visualize differences in soil microbial community structure among forest stand types. Metastat analysis was used to evaluate the significance of differences in phylum-level microbial abundance between samples to identify taxa with significant variation. The Mantel test examined correlations between soil microbial community composition and environmental variables to quantify the influence of environmental variables and their relationships. Environmental variables significantly correlated with microbial community composition, and variables with high multicollinearity were excluded using Variance Inflation Factor (VIF) analysis. Variance Partitioning Analysis (VPA) was then applied to the categorized environmental variables to determine the relative contributions of the different factor categories to soil microbial community composition. 2 Results and Analysis 2.1 Effects of Stand Types on Microbial Community Diversity 2.1.1 Alpha Diversity Analysis of Bacterial and Fungal Communities The ranking of soil bacterial Smith-wilson and fungal Simpson indices across stand types were as follows: pure C. fortunei forest > conifer-broadleaf mixed forest > pure L. formosana forest. The bacterial Smith index differed significantly between mixed and pure L. formosana forests (1.04-fold greater, P < 0.05), whereas the fungal Simpson index showed significant differences between pure C. fortunei and pure L. formosana forests (2.04-fold greater, P pure C. fortunei forest > mixed forest, with significantly higher values in pure L. formosana than in mixed forests ( P 0.05). Both bacterial and fungal Coverage indices exceeded 0.99, confirming an adequate sequencing depth for community representation (Figures 1-2). Ace index Simpson index Coverage index Smith-wilson index Figure 1. Soil Bacterial Alpha Diversity in Different Forest Stand Types Notes: LS: pure Cryptomeria fortunei , FX: pure Liquidambar formosana , HJ: mixed forests. Ace index Simpson index Coverage index Smith-Wilson index Figure 2. Soil Fungal Alpha Diversity in Different Forest Stand Types Notes: LS: pure Cryptomeria fortunei , FX: pure Liquidambar formosana , HJ: mixed forests. 2.1.2 Beta Diversity Analysis of Bacterial and Fungal Communities NMDS ordination coupled with ANOSIM tests revealed no significant differences in soil bacterial community structure among stand types ( P > 0.05), whereas fungal communities exhibited significant structural divergence ( P < 0.05) (Figure 3). Both ordinations demonstrated meaningful configurations with stress values < 0.1, confirming an acceptable ordination quality. Figure 3. Soil Microbial Beta Diversity in Different Forest Stand Types (Top: Soil Bacteria; Bottom: Soil Fungi) Notes: LS: pure Cryptomeria fortunei , FX: pure Liquidambar formosana , HJ: mixed forests. 2.2 Effects of Stand Types on Microbial Community Composition Circos plots at the phylum level visually displayed the abundance of microbial phyla across stand types (Figure 4). The top 10 bacterial phyla in the pure C. fortunei forests, pure L. formosana , and mixed forests were Chloroflexi , Acidobacteriota , Proteobacteria , Actinobacteriota , WPS-2, Verrucomicrobiota , Planctomycetota , RCP2-54, GAL15, and Firmicutes . The dominant phyla were Chloroflexi , Acidobacteriota , Proteobacteria , and Actinobacteriota (Figure 4). Metastatic analysis (Table 3) showed: Actinobacteriota relative abundance in conifer-broadleaf mixed forests was 37.46% higher than that in pure L. formosana forests ( P < 0.05), Firmicutes relative abundance in pure L. formosana forests was 158.93% higher than that in pure C. fortunei forests and 154.39% higher than that in mixed forests ( P < 0.05). The top ten fungal phyla across all three stand types were Ascomycota , Mortierellomycota , Basidiomycota , unclassified_k__Fungi, Rozellomycota, Mucoromycota , Chytridiomycota , Kickxellomycota , Zoopagomycota , and Olpidiomycota , and the dominant phyla were Ascomycota , Mortierellomycota , and Basidiomycota (Figure 4). Metastats analysis (Table 3) showed that Ascomycota relative abundance in conifer-broadleaf mixed forests was approximately 1.37 times higher than that in pure C. fortunei forests ( P < 0.05). Figure 4. Phylum-Level Circos Plot of Soil Microbial Communities in Different Forest Stand Types (Top: Soil Bacteria; Bottom: Soil Fungi) Notes: LS: pure Cryptomeria fortunei , FX: pure Liquidambar formosana , HJ: mixed forests. Table 3. Relative Abundance of Soil Microbial Communities at the Phylum Level Phylum LS FX HJ P _value （FX-LS) P _value (FX-HJ) P _value (LS-HJ) Soil Bacteria Chloroflexi 37.14% 39.90% 37.11% P >0.05 P >0.05 P >0.05 Acidobacteriota 18.73% 19.24% 19.49% P >0.05 P >0.05 P >0.05 Proteobacteria 16.66% 18.79% 16.68% P >0.05 P >0.05 P >0.05 Actinobacteriota 13.79% 9.29% 12.77% P >0.05 P 0.05 WPS-2 3.40% 3.09% 3.19% P >0.05 P >0.05 P >0.05 Verrucomicrobiota 1.56% 2.18% 2.44% P >0.05 P >0.05 P >0.05 Planctomycetota 1.44% 1.53% 1.77% P >0.05 P >0.05 P >0.05 RCP2-54 1.01% 1.25% 1.34% P >0.05 P >0.05 P >0.05 GAL15 1.58% 0.97% 0.67% P >0.05 P >0.05 P >0.05 Firmicutes 0.56% 1.45% 0.57% P <0.05 P 0.05 others 4.14% 5.31% 4.17% Soil Fungi Ascomycota 26.74% 39.69% 36.75% P >0.05 P >0.05 P 0.05 P >0.05 P >0.05 Basidiomycota 38.44% 10.20% 17.48% P >0.05 P >0.05 P >0.05 unclassified_k__Fungi 5.16% 9.93% 8.68% P >0.05 P >0.05 P >0.05 Rozellomycota 3.00% 4.87% 2.17% P >0.05 P >0.05 P >0.05 Mucoromycota 0.56% 0.23% 0.70% P >0.05 P >0.05 P >0.05 Chytridiomycota 0.28% 0.33% 0.35% P >0.05 P >0.05 P >0.05 Kickxellomycota 0.12% 0.37% 0.35% P >0.05 P >0.05 P >0.05 Zoopagomycota 0.09% 0.16% 0.10% P >0.05 P >0.05 P >0.05 Olpidiomycota 0.09% 0.06% 0.08% P >0.05 P >0.05 P >0.05 others 0.01% 0.12% 0.03% Notes: LS: pure Cryptomeria fortunei , FX: pure Liquidambar formosana , HJ: mixed forests. 2.3 Heatmap Network Analysis of Soil Microbial Community Composition and Environmental Variables The Mantel test was performed between environmental variables and the top five phyla in terms of relative abundance, and the results were visualized using heatmap networks. For soil bacteria (Figure 5): Acidobacteriota correlated significantly with soil organic carbon and soil macroaggregate total glomalin-related soil protein ( P < 0.05); Proteobacteria correlated significantly with acid soil phosphatase activity ( P < 0.05); Actinobacteriota correlated significantly with soil macroaggregate total phosphorus and silt-clay total particle ( P < 0.05); Chloroflexi correlated extremely significantly with urease activity ( P < 0.01); Actinobacteriota correlated extremely significantly with soil -clay easily-extractable glomalin-related soil protein, ( P < 0.01); WPS-2 correlated extremely significantly with leaf organic carbon ( P < 0.01). For soil fungi (Figure 6), Basidiomycota and unclassified_k__fungi correlated significantly with leaf total phosphorus, Rozellomycota with total glomalin-related soil protein ( P < 0.05), Basidiomycota correlated extremely significantly with soil macroaggregate total phosphorus ( P < 0.01), Ascomycota correlated extremely significantly with leaf organic carbon ( P < 0.001), Mortierellomycota correlated extremely significantly with catalase activity ( P < 0.001). Significant correlations existed among environmental variables: soil easily- extractable glomalin-related soil protein, soil-clay total nitrogen, and catalase exhibited significant correlations with soil intra-microaggregate total glomalin-related soil protein; phosphatase activity with leaf total nitrogen; soil-clay total glomalin-related soil protein, with soil total nitrogen; soil total glomalin-related soil protein with soil macroaggregate total phosphorus; soil easily-extractable glomalin-related soil protein with soil macroaggregate organic carbon; soil-clay total nitrogen with leaf total nitrogen, and litter layer total nitrogen; litter layer organic carbon and soil-clay total phosphorus with soil macroaggregate total phosphorus; soil macroaggregate organic carbon with soil intra-macroaggregate organic carbon; and soil total phosphorus, with soil total nitrogen, ( P < 0.05). Soil clay easily-extractable glomalin-related soil protein exhibited an extremely significant positive correlation with soil microaggregate easily-extractable glomalin-related soil protein ( P < 0.01), whereas litter layer total nitrogen showed a highly significant positive correlation with litter total nitrogen ( P < 0.001). Conversely, significant negative correlations were observed between soil total glomalin-related soil protein and soil intra-microaggregate total glomalin-related soil protein, soil macroaggregate total nitrogen and litter layer organic carbon, and soil clay total phosphorus and soil macroaggregate total phosphorus, litter total nitrogen, and litter layer total nitrogen ( P < 0.05). * P < 0.05, ** P < 0.01, *** P < 0.001. Figure 5. Mantel Test Network Heatmap of Phylum-Level Soil Bacterial Community and Environmental Factors P < 0.05, ** P < 0.01, *** P < 0.001. Figure 6. Mantel Test Network Heatmap of Phylum-Level Soil Fungal Community and Environmental Factors 2.4 Variance Partitioning Analysis (VPA) Variance Partitioning Analysis (VPA) was conducted using environmental variables that were significantly correlated with soil microbial communities (identified using Mantel tests), which were first screened using Variance Inflation Factor (VIF) analysis to retain variables with minimal multicollinearity. The VPA quantified the contribution of environmental variables to bacterial and fungal community structures. For bacterial communities, the variables significantly associated with composition were grouped as follows: organic carbon (Group A: LOC, SOC), soil nutrients (Group B: SCTP, SMEEG), and enzyme activities (Group C: ACP, URE). Collectively, these groups explained 55.7% of total variation in phylum-level bacterial communities (Figure 7), with negative joint effects. Group A individually contributed 10.08%, Group B contributed 11.14%, and Group C exhibited a negative explanatory rate. Synergistic effects between enzyme activities and organic carbon accounted for 39.61% of the variation, whereas enzymes and soil nutrients jointly explained 32.69% of variation. Consequently, the synergistic effect between enzyme activity and organic carbon exerted the strongest influence on bacterial communities at phylum level, surpassing the impact of individual variables. Environmental variables that significantly correlated with fungal community composition were grouped into three categories: soil nutrients (Group A: SITP and TG), enzyme activities (Group B: CAT), and leaf nutrients (Group C: LOC and LTP). At phylum level, these three environmental categories collectively explained 3.90% of fungal community variation (Figure 7). Group A contributed 16.53%, Group B contributed 5.72%, and Group C exhibited a negative explanatory rate. The synergistic effect between groups B and C accounted for 0.79% of variation. Suppressive effects were observed between Group A and both Groups B and C. Consequently, soil nutrients exerted the strongest individual influence on fungal communities, with their solitary effects surpassing the combined variables’ impacts. Figure 7. Explanatory Power of Environmental Factors on Phylum-Level Communities (Top: Soil Bacteria; Bottom: Soil Fungi) 3 Discussion 3.1 Tree Species Mixing Alters Soil Microbial Community Diversity and Composition Soil microorganisms constitute the most diverse component of terrestrial biodiversity and play vital roles in ecosystem processes, including carbon-nitrogen cycling and pedogenesis(Guo & Gong, 2024). Our α-diversity analysis revealed significantly higher bacterial Smith-Wilson indices in mixed forests than pure L. formosana stands ( P < 0.05, Figure 1), while fungal Coverage indices were significantly lower in mixed forests ( P < 0.05, Figure 2). Previous research in Xinjiang, Fujian, Shandong, and Hunan and other regions have found that soil bacterial α-diversity in conifer-broadleaf mixed forests, such as C. lanceolata and Schima superba was higher than in pure forests, while soil fungal α-diversity in mixed forests was lower than in pure forests, supporting our conclusions(Dong et al., 2021; Guo & Gong, 2024). However, studies on Phyllostachys edulis, C. lanceolata mixed forests reported significantly decreased bacterial α-diversity and increased fungal diversity relative to pure stands, contradicting our findings(Jiang et al., 2023). Different stand types can drive the directional reshaping of soil microbial communities through multiple pathways, such as regulating litter input characteristics and root exudate composition(Zhang, 2018). Multiple pathways, such as stand structure and soil factor changes, will have an impact on soil microbial community structure(Dong et al., 2021). Owing to differences in soil environmental variables and dominant tree species across the study regions, research outcomes ultimately diverged(Guo et al., 2024). Our study also revealed significant differences in soil fungal β-diversity among the three stand types (Figure 3). This phenomenon may occur because coniferous tree rhizosphere soils in mixed forests exhibit higher organic carbon content and fine root biomass, leading to an altered fungal community structure(Li et al., 2021). Mixing of tree species facilitates the formation of dense root systems and enhances nutrient release, thereby increasing soil fungal activity, further complicating fungal community structure and strengthening their adaptability to environmental changes(Guo & Gong, 2024). Research on soil microbial community composition enables an understanding of soil ecosystem health, assessment of soil quality, and provides a basis for ecological restoration(Guo & Gong, 2024). In this study, Actinobacteriota relative abundance in conifer-broadleaf mixed forests was significantly higher (37.46 %) than that in pure L . formosana forests, whereas Firmicutes relative abundance in pure L. formosana forests was significantly higher (154.39 %) than that in mixed forests. Ascomycota relative abundance in mixed forests was approximately 1.37 times that in pure C. fortunei forests ( P < 0.05, Table 3), consistent with previous findings. Dong et al. (2021) observed lower relative abundances of Actinobacteriota and Proteobacteria in mixed forests than in pure European beech forests. Conversely, some studies have documented reduced Ascomycota but increased Basidiomycota or Glomeromycota abundance in mixed forests relative to monocultures(Li et al., 2021). Yang et al. (2022b) found no significant differences in fungal abundance between monoculture and mixed communities in Heilongjiang black soils, which diverged from our results. Distinct plant community assemblages formed by different species exert significantly varying effects on soil microbial communities and their associated processes(Gillespie, 2020). Owing to differences in tree species composition, mixed forests alter litter quality, quantity, root biomass, and root exudate chemistry, thereby modifying local microhabitat conditions and consequently influencing soil microbial community composition(Wen et al., 2014). 3.2 Synergistic Effects of Organic Carbon and Enzymatic Activities Drive Soil Bacterial Community Variation Soil bacteria play important roles in biogeochemical cycling, maintenance of soil fertility, plant health protection, and regulation of ecological balance(Li et al., 2024). Our study demonstrated that synergistic interactions between organic carbon and enzymatic activities explained 39.61% of bacterial community structural variation (VPA analysis, Figure 7). Previous research confirms that organic carbon content is a key driver of phylum-level bacterial community structure(Wang et al., 2025a). As a critical carbon and energy source, organic carbon not only influences bacterial community assembly(Yang et al., 2025) and metabolic functionality(Liu et al., 2025), but also provides essential substrates for bacterial growth(Xu et al., 2018). When soil organic carbon is sufficient, bacteria acquire adequate carbon and energy to sustain their vital activities. Notably, bacteria enhance microbial network complexity to increase soil multifunctionality and exert positive feedbacks on organic carbon accumulation and stabilization(He et al., 2024). Soil enzymes serve as critical indicators for assessing soil microbial activity and functionality(Tiwari et al., 2025), participate in bacterial metabolic processes and nutrient cycling, and function as sensitive proxies for bacterial nutrient demands and soil quality(Trasar-Cepeda et al., 2000; Xu et al., 2017), significantly influencing soil bacterial communities. Concurrently, enzymatic activity was significantly correlated with soil organic carbon content(Sun et al., 2023). Soil enzymes play crucial roles in nutrient cycling, with their activities intrinsically linked to soil carbon dynamics(Luo et al., 2017). Changes in enzymatic activities reflect nutrient transformations(Moitinho et al., 2021), whereas soil organic carbon improves soil environments(Wang et al., 2025a), providing optimal conditions for enzymatic functions to indirectly regulate their activity. Thus, both soil organic carbon content and enzymatic activity significantly regulated soil bacterial communities through multiple pathways, demonstrating close interconnections. Current research focuses on individual effects of enzymes or soil organic carbon on bacterial community structure(Bhattacharyya et al., 2022; Liu et al., 2025), and studies have revealed substantial synergistic contributions of organic matter and nitrate-nitrogen to bacterial community variation(Gao et al., 2024). In the present study, synergistic interactions between SOC and enzymatic activities explained 39.61% of bacterial structural variation, supplementing existing research on soil organic carbon and enzyme-driven bacterial community dynamics. 3.3 Soil Nutrients as Primary Drivers of Fungal Community Composition Soil fungi mediate organic matter decomposition, promote nutrient acquisition, and establish symbiotic associations with plants, thereby fulfilling critical functions in ecosystem stability and phytobiont development(Li et al., 2024). Relative fungal abundance exhibits significant responsiveness to soil nutrient gradients(Fan & Tulli, 2016), which concurrently regulate fungal taxonomic diversity and population dynamics(Zhang et al., 2024), while fungi also play an important role in soil nutrient cycling(Zong et al., 2024), which can improve soil structure and promote soil nutrient cycling(Wu et al., 2025). Liu et al. (2024) demonstrated that soil heavy metals, physicochemical properties, and enzymatic activities explained 16.94%, 8.63%, and 7.27% of fungal community variation, respectively, indicating that soil physical and chemical properties, soil heavy metals and soil enzyme activities jointly affected the structure of fungal communities, and soil heavy metals played a leading role. In contrast, our study revealed a higher explanatory power of soil nutrients (total glomalin-related soil protein and microaggregate total phosphorus) over enzymatic activity and foliar nutrients for fungal structural changes (Figure 7). Glomalin-related soil proteins generated by arbuscular mycorrhizal fungi stabilize soil aggregates through cementation, creating optimal microhabitats for microbial survival and proliferation(Zhang et al., 2025). Furthermore, the decomposition of GRSP can release nutrients that can be used by soil microorganisms, thereby affecting the metabolic activity and diversity of the microbial community(Wu et al., 2024). Phosphorus, which is essential for microbial growth and metabolism, directly modulates microbial activity and community structure through its concentration and bioavailability(Pei et al., 2024). Discrepancies between our VPA outcomes and prior findings stem from variations in research focus, study regions, and selected environmental variables. Consequently, VPA analyses of phylum-level microbial communities across the three stand types in Guizhou’s fragile ecosystems warrant further investigation, incorporating broader categories of variables. 4 Conclusions This study investigated the effects of tree species mixing on soil microbial community diversity and composition in three stand types, namely pure C. fortunei , pure L. formosana , and mixed C. fortunei-L. formosana forests, within a typical karst region in Southwest China. We further analyzed the influence of leaf, litter, soil nutrients, and enzymatic activities on microbial community assembly. The key findings were as follows: (1) tree species mixing significantly restructured microbial communities. Compared to pure L. formosana forests, mixing significantly increased the soil bacterial Smith-Wilson index but decreased the fungal coverage index ( P < 0.05). Significant divergence in fungal β-diversity occurred among stand types ( P < 0.05). Mixed forests exhibited 37.46% and 37.43% higher relative abundances of Actinobacteriota and Ascomycota , respectively, than pure stands. Pure L. formosana forests showed 154.39% greater Firmicutes abundance than mixed stands ( P < 0.05). (2) Leaf, soil, and enzymatic activities regulate soil microbial community composition through synergistic and individual effects. Phylum-level microbial composition was significantly correlated with leaf nutrients, soil nutrients, and enzymatic activity. Synergistic effects between organic carbon (soil and leaves) and enzyme activities (soil phosphatase and urease) dominated community variation. For fungi, soil nutrients (total GRSP and total microaggregate phosphorus) had the strongest individual influence. Acknowledgments: We would like to express our sincere gratitude to all individuals who contributed to the completion of this work. We sincerely thank Yan Wu for conceiving the research idea, designing the overall study framework, supervising the entire research process. Xumeng Tan was responsible for in-depth data interpretation and statistical analysis. Yu Wu made substantial contributions to data collection, experimental operations, and preliminary data analysis. Jianfeng Li participated in critical revision of the manuscript, provided valuable academic insights, and ensured the accuracy of the research conclusions. The author gratefully acknowledge the financial support provided by Guizhou Province Science and Technology Plan Project (ZK [2023] General 282); and Guizhou Province Science and Technology Support Program Project [Qiankehe Support (2023) Y223]. Futhermore, we would like to thank Editage (www.editage.cn) for English language editing. References Adams, R. I., Miletto, M., Taylor, J. W., & Bruns, T. D. (2013). Dispersal in microbes: fungi in indoor air are dominated by outdoor air and show dispersal limitation at short distances. The ISME journal , 7 (7), 1262-1273. https://doi.org/https://doi.org/10.1038/ismej.2013.28 Alkorta, I., Epelde, L., & Garbisu, C. (2017). Environmental parameters altered by climate change affect the activity of soil microorganisms involved in bioremediation. Fems Microbiology Letters , 364 (19), fnx200. https://doi.org/https://doi.org/10.1093/femsle/fnx200 Bao, S. D. (1999). Soil agrochemical analysis methods . China Agricultural Science and Technology Press. Bhattacharyya, S. S., Ros, G. H., Furtak, K. L., & Iqbal, H. M. N. (2022). Soil carbon sequestration – An interplay between soil microbial community and soil organic matter dynamics. Science of The Total Environment , 815 , 152928. https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.152928 Chen, Q. Y., Niu, B., Hu, Y. L., Luo, T. X., & Zhang, G. X. (2020). Warming and increased precipitation indirectly affect the composition and turnover of labile-fraction soil organic matter by directly affecting vegetation and microorganisms. Science of The Total Environment , 714 , 136787. https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.136787 Chen, S., Zhou, Y. Q., Chen, Y. R., & Gu, J. (2018). Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics , 34 (17), i884-i890. https://doi.org/https://doi.org/10.1093/bioinformatics/bty560 Chu, S. S., Ouyang, J. H., Liao, D. D., Zhou, Y. D., Liu, S. S., Shen, D. C., Wei, X. H., & Zeng, S. C. (2019). Effects of enriched planting of native tree species on surface water flow, sediment, and nutrient losses in a Eucalyptus plantation forest in southern China. Science of The Total Environment , 675 , 224-234. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.04.214 Dixon, R., & Kahn, D. (2004). Genetic regulation of biological nitrogen fixation. Nature Reviews Microbiology , 2 (8), 621-631. https://doi.org/https://doi.org/10.1038/nrmicro954 Dong, X. D., Gao, P., Zhou, R., Li, C., Dun, X. J., & Niu, X. (2021). Changing characteristics and influencing factors of the soil microbial community during litter decomposition in a mixed Quercus acutissima Carruth. and Robinia pseudoacacia L. forest in Northern China. Catena , 196 , 104811. https://doi.org/https://doi.org/10.1016/j.catena.2020.104811 Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature methods , 10 (10), 996-998. https://doi.org/https://doi.org/10.1038/NMETH.2604 Fan, Y. G., & Tulli, G. E. (2016). Taxonomic study of the genus Umbrella Umbrella in China. Mycologist research , 14 (03), 129-132+141. https://doi.org/https://doi.org/10.13341/j.jfr.2014.1124. Gao, Z. F., Bao, T. X., Xu, S., Li, X. W., Yang, S. L., Yu, J., Yao, J. J., Mao, P. L., Ye, P., Yang, Z. B., & Lu, F. (2024). Effects of tobacco black leg disease on soil chemical properties and bacterial communities of different types of tobacco planting. Jiangsu Agricultural Sciences , 52 (15), 255-261. https://doi.org/https://doi.org/10.15889/j.issn.1002-1302.2024.15.033. Geisseler, D., & Scow, K. M. (2014). Long-term effects of mineral fertilizers on soil microorganisms – A review. Soil Biology & Biochemistry , 75 , 54-63. https://doi.org/https://doi.org/10.1016/j.soilbio.2014.03.023 Gillespie, L. (2020). Impact of tree diversity and climate change on soil microbial functioning in European forests along a latitudinal gradient Guan, S. Y. (1986). Soil enzymes and their research methods . Agricultural press. Guo, Q., & Gong, L. (2024). Compared with pure forest, mixed forest alters microbial diversity and increases the complexity of interdomain networks in arid areas. Microbiology Spectrum , 12 (1), e02642-02623. https://doi.org/https://doi.org/10.1128/spectrum.02642-23 Guo, X. W., Zhang, Y. X., You, Y. M., & Sun, J. X. (2024). Research progress on the effects of litter input on the conversion and stability of organic carbon in forest soils. Chinese Journal of Applied Ecology , 35 (09), 2352-2361. https://doi.org/https://doi.org/10.13287/j.1001-9332.202409.033. He, Y. Q., Jiang, C. Y., Fan, R. Y., Lan, Y. H., Zhang, H., Cui, Y. H., Li, L. X., Wu, H., & Ye, S. M. (2024). Mixed Eucalyptus plantations enhance soil organic carbon accumulation and chemical stability through soil microbial community and multifunctionality. Catena , 245 , 108315. https://doi.org/https://doi.org/10.1016/j.catena.2024.108315 Hu, Y. X., Zhang, X. Q., Chen, H. Y., Jiang, Y. H., & Zhang, J. G. (2024). Effects of forest age and season on soil microbial communities in Chinese fir plantations. Microbiology Spectrum , 12 (8), e04075-04023. https://doi.org/https://doi.org/10.1128/spectrum.04075-23 Jiang, S. Y., Cheng, X. F., Zhang, J. C., Tang, Y. Z., Nie, H., Wang, Y. H., & Liu, J. (2023). Effects of invasion of Basinensis on fungal diversity and community structure in the surface soil of Chinese fir plantation. Journal of Northeast Forestry University , 51 (2), 91-96. https://doi.org/https://doi.org/10.13759/j.cnki.dlxb.2023.02.007. Li, J. W., Sun, X. Q., Li, M., Zou, J. Y., & Bian, H. F. (2022). Effects of stand age and soil organic matter quality on soil bacterial and fungal community composition in Larix gmelinii plantations, Northeast China. Land Degradation and Development , 33 (8), 1249-1259. https://doi.org/https://doi.org/10.1002/ldr.4219 Li, T., Wang, S. C., Liu, C. E., Yu, Y. D., Zong, M. M., & Duan, C. Q. (2024). Soil microbial communities’ contributions to soil ecosystem multifunctionality in the natural restoration of abandoned metal mines. Journal of Environmental Management , 353 , 120244. https://doi.org/https://doi.org/10.1016/j.jenvman.2024.120244 Li, W. Q., Huang, Y. X., Chen, F. S., Liu, Y. Q., Lin, X. F., Zong, Y. Y., Wu, G. Y., Yu, Z. R., & Fang, X. M. (2021). Mixing with broad-leaved trees shapes the rhizosphere soil fungal communities of coniferous tree species in subtropical forests. Forest Ecology and Management , 480 , 118664. https://doi.org/https://doi.org/10.1016/j.foreco.2020.118664 Li, W. Y., Sun, H. M., Cao, M. M., Wang, L. Y., Fang, X. H., & Jiang, J. (2023). Diversity and structure of soil microbial communities in Chinese fir plantations and Cunninghamia lanceolata–Phoebe bournei mixed forests at different successional stages. Forests , 14 (10), 1977. https://doi.org/https://doi.org/10.3390/f14101977 Lin, Y. B., Yang, L., Chen, Z. T., Gao, Y. Q., Kong, J. J., He, Q., Su, Y., Li, J. Y., & Qiu, Q. (2023). Seasonal variations of soil bacterial and fungal communities in a subtropical Eucalyptus plantation and their responses to throughfall reduction. Frontiers in Microbiology , 14 , 1113616. https://doi.org/https://doi.org/10.3389/fmicb.2023.1113616 Liu, C. S., Zhao, D. F., Ma, W. J., Guo, Y. D., & Lee, D. J. (2016). Denitrifying sulfide removal process on high-salinity wastewaters in the presence of Halomonas sp. Applied Microbiology and Biotechnology , 100 (3), 1421-1426. https://doi.org/https://doi.org/10.1007/s00253-015-7039-6 Liu, D., Su, C., Xie, R., & Liu, Y. J. (2024). Effects of heavy metal pollution on soil fungal community characteristics in coal industrial parks. Journal of Ecology , 43 (12), 3537-3544. https://doi.org/https://doi.org/10.13292/j.1000-4890.202412.043. Liu, J., Jia, X. Y., Yan, W. M., Zhong, Y. Q. W., & Shangguan, Z. P. (2020). Changes in soil microbial community structure during long‐term secondary succession. Land Degradation & Development , 31 (9), 1151-1166. https://doi.org/https://doi.org/10.1002/ldr.3505 Liu, Y. J., Li, J. Q., Liu, F. Y., Yang, S. Y., Yan, H. Y., & Zhao, S. Q. (2025). Effects of litter input changes on soil microbial community structure and function in Yunnan pine forest. Guangxi plant , 1-16. https://doi.org/https://doi.org/10.27034/d.cnki.ggxiu.2024.002736. Luo, L., Meng, H., & Gu, J. D. (2017). Microbial extracellular enzymes in biogeochemical cycling of ecosystems. Journal of Environmental Management , 197 , 539-549. https://doi.org/https://doi.org/10.1016/j.jenvman.2017.04.023 Ma, Y., Tian, L. L., Lu, J., Liu, P., Zhang, X., Li, E. Y., & Zhang, Q. H. (2024). Study on soil microbial community and influencing factors of spruce forest on the northern slope of the Tianshan Mountains. Journal of Ecology and Environment , 33 (1), 1-11. https://doi.org/https://doi.org/10.16258/j.cnki.1674-5906.2024.01.001 Magoč, T., & Salzberg, S. L. (2011). FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics , 27 (21), 2957-2963. https://doi.org/https://doi.org/10.1093/bioinformatics/btr507 Moitinho, M. R., Teixeira, D. D. B., Bicalho, E. d. S., Panosso, A. R., Ferraudo, A. S., Pereira, G. T., Tsai, S. M., Borges, B. M. F., & La Scala Jr, N. (2021). Soil CO2 emission and soil attributes associated with the microbiota of a sugarcane area in southern Brazil. Scientific Reports , 11 (1), 8325. https://doi.org/https://doi.org/10.1038/s41598-021-87479-2 Ni, X. W., Ning, C., Yan, W. D., Liu, Z. Z., Chen, Y., & Ning, X. B. (2017). Soil nutrient distribution characteristics of P. massoniana wetland plantation in Longli Forest Farm, Guizhou. Journal of Central South University of Forestry and Technology , 37 (09), 49-56. https://doi.org/https://doi.org/10.14067/j.cnki.1673-923x.2017.09.009. Peco, B., Navarro, E., Carmona, C. P., Medina, N. G., & Marques, M. J. (2017). Effects of grazing abandonment on soil multifunctionality: The role of plant functional traits. Agriculture, Ecosystems & Environment , 249 , 215-225. https://doi.org/https://doi.org/10.1016/j.agee.2017.08.013 Pei, L., Ye, S., Xie, L., Zhou, P., He, L., Yang, S., Ding, X., Yuan, H., Dai, T., & Laws, E. A. (2024). Differential effects of warming on the complexity and stability of the microbial network in Phragmites australis and Spartina alterniflora wetlands in Yancheng, Jiangsu Province, China. Frontiers in Microbiology , 15 , 1347821. https://doi.org/https://doi.org/10.3389/fmicb.2024.1347821 Potthast, K., Hamer, U., & Makeschin, F. (2012). Land-use change in a tropical mountain rainforest region of southern Ecuador affects soil microorganisms and nutrient cycling. Biogeochemistry , 111 (1), 151-167. https://doi.org/https://doi.org/10.1007/s10533-011-9626-7 Robert, M., & Chenu, C. (2021). Interactions Between Soil Minerals and Microorganisms . CRC Press. Rovira, A. D., & Greacen, E. L. (1957). The effect of aggregate disruption on the activity of microorganisms in the soil. Crop & Pasture Science , 8 (6), 659-673. https://doi.org/https://doi.org/10.1071/AR9570659 Shang, R. G., Li, S. F., Huang, X. B., Liu, W. D., Lang, X. D., & Su, J. R. (2021). Effects of Soil Properties and Plant Diversity on Soil Microbial Community Composition and Diversity during Secondary Succession. Forests , 12 (6), 805. https://doi.org/https://doi.org/10.3390/f12060805 Stackebrandt, E., & Goebel, B. M. (1994). Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. International journal of systematic and evolutionary microbiology , 44 (4), 846-849. https://doi.org/https://doi.org/10.1099/00207713-44-4-846 Sun, C. C., Wang, Z. Q., Pan, C., Song, Y. Q., & Yu, Y. C. (2023). Effects of Fir wood-lotus mixing on soil nutrients and enzyme activities. Journal of Jiangxi Agricultural University , 45 (03), 517-525. https://doi.org/https://doi.org/10.27662/d.cnki.gznlc.2025.000718. Tajik, S., Ayoubi, S., & Lorenz, N. (2020). Soil microbial communities affected by vegetation, topography and soil properties in a forest ecosystem. Applied Soil Ecology , 149 , 103514. https://doi.org/https://doi.org/10.1016/j.apsoil.2020.103514 Thuille, A., Laufer, J., Höhl, C., & Gleixner, G. (2015). Carbon quality affects the nitrogen partitioning between plants and soil microorganisms. Soil Biology and Biochemistry , 81 , 266-274. https://doi.org/https://doi.org/10.1016/j.soilbio.2014.11.024 Tiwari, S., Sonali, A., & Archana, M. (2025). Factors Influencing the Activities of Soil Enzymes Involved in Nutrient Cycling in Terrestrial Ecosystems. Environmental Science , 139. https://doi.org/https://doi.org/10.5281/ZENODO.14878492 Trasar-Cepeda, C., Leirós, M. C., Seoane, S., & Gil-Sotres, F. (2000). Limitations of soil enzymes as indicators of soil pollution. Soil Biology & Biochemistry , 32 (13), 1867-1875. https://doi.org/https://doi.org/10.1016/S0038-0717(00)00160-7 Wang, Q., Wang, Y. Y., Li, Y., Tian, X., Li, X. Y., & Wen, Y. J. (2025a). Effects of organic fertilizer and straw mulching on soil microbial community structure and function in mountain orchards in the Loess Plateau. Journal of Applied and Environmental Biology , 31 (06), 963-974. https://doi.org/https://doi.org/10.19675/j.cnki.1006-687x.2024.11043. Wang, Y. X., Ding, F. J., Zhou, H., Wu, P., Yuan, C. J., & Liu, N. (2025b). Soil enzyme activities and microbial biomass characteristics of three stand types in high altitude areas. Journal of Forests and Environment , 45 (1), 11-19. https://doi.org/https://doi.org/10.13324/j.cnki.jfcf.202406016. Wen, L., Lei, P. F., Xiang, W. H., Yan, W. D., & Liu, S. G. (2014). Soil microbial biomass carbon and nitrogen in pure and mixed stands of Pinus massoniana and Cinnamomum camphora differing in stand age. Forest Ecology & Management , 328 , 150-158. https://doi.org/https://doi.org/10.1016/j.foreco.2014.05.037 Wright, S. F., Upadhyaya, A., & Buyer, J. S. (1998). Comparison of N-linked oligosaccharides of glomalin from arbuscular mycorrhizal fungi and soils by capillary electrophoresis. Soil Biology and Biochemistry , 30 (13), 1853–1857. https://doi.org/https://doi.org/10.1016/S0038-0717(98)00047-9 Wu, G., Yu, F., Yuan, M., Wang, J., Liu, C., He, W., Ge, Z., Sun, Y., & Liu, Y. (2022). Responses of rhizosphere bacterial and fungal communities to the long-term continuous monoculture of water oat. Microorganisms , 10 (11), 2174. https://doi.org/https://doi.org/10.3390/microorganisms10112174 Wu, M. L., Zhang, S. C., Gu, X. J., He, Z. H., Liu, Y., & Mo, Q. F. (2024). Accumulation of Glomalin-Related Soil Protein Regulated by Plantation Types and Vertical Distribution of Soil Characteristics in Southern China. Forests , 15 (8), 1479. https://doi.org/https://doi.org/10.3390/f15081479 Wu, X. Q., He, W. W., Wu, D., Wang, D. Y., Wang, L. P., & Zhu, W. M. (2025). Deep soil fungus Aspergillus sp. GZWMJZ-1617 secondary metabolite study. Chinese Journal of Antibiotics , 50 (04), 401-407. https://doi.org/https://doi.org/10.13461/j.cnki.cja.007883. Xu, L. Y., Wang, M. Y., Shi, X. Z., Yu, Q. B., Shi, Y. J., Xu, S. X., & Sun, W. X. (2018). Effect of long-term organic fertilization on the soil pore characteristics of greenhouse vegetable fields converted from rice-wheat rotation fields. Science of The Total Environment , 631 , 1243-1250. https://doi.org/https://doi.org/10.1016/j.scitotenv.2018.03.070 Xu, Z. W., Yu, G. R., Zhang, X. Y., He, N. P., Wang, Q. F., Wang, S. Z., Wang, R. L., Zhao, N., Jia, Y. L., & Wang, C. Y. (2017). Soil enzyme activity and stoichiometry in forest ecosystems along the North-South Transect in eastern China (NSTEC). Soil Biology & Biochemistry , 104 , 152-163. https://doi.org/https://doi.org/10.1016/j.soilbio.2016.10.020 Yan, N., Marschner, P., Cao, W. H., Zuo, C. Q., & Qin, W. (2015). Influence of salinity and water content on soil microorganisms. International Soil and Water Conservation Research , 3 (4), 312-323. https://doi.org/https://doi.org/10.1016/j.iswcr.2015.11.003 Yang, J., Blondeel, H., Meeussen, C., Govaert, S., Vangansbeke, P., Boeckx, P., Lenoir, J., Orczewska, A., Ponette, Q., & Hedwall, P. (2022a). Forest density and edge effects on soil microbial communities in deciduous forests across Europe. Applied Soil Ecology , 179 , 104586. https://doi.org/https://doi.org/10.1016/j.apsoil.2022.104586 Yang, K., Zhu, J. J., Zhang, W. W., Gu, J. C., Wang, Z. Q., & Xu, S. (2022b). Comparison of soil chemical and microbial properties in monoculture larch and mixed plantations in a temperate forest ecosystem in Northeast China. Ecological Processes , 11 (1), 12. https://doi.org/https://doi.org/10.1186/s13717-022-00358-0 Yang, L. J., Zhang, L. L., Geisseler, D., Wu, Z. J., Gong, P., Xue, Y., Yu, C. X., Juan, Y. H., & Horwath, W. R. (2016). Available C and N affect the utilization of glycine by soil microorganisms. Geoderma , 283 , 32-38. https://doi.org/https://doi.org/10.1016/j.geoderma.2016.07.022 Yang, M. M., Shen, J. H., Zhou, J. Y., & Wang, R. (2025). Characteristics of interfoliar bacterial community of silver hazel and its influencing mechanism. Journal of Ecology and Environment , 1-14. https://doi.org/https://link.cnki.net/urlid/21.1148.Q.20250429.1119.004. Zarafshar, M., Vincent, G., Korboulewsky, N., & Bazot, S. (2024). The impact of stand composition and tree density on topsoil characteristics and soil microbial activities. Catena , 234 , 107541. https://doi.org/https://doi.org/10.1016/j.catena.2023.107541 Zeng, C. Y., Wu, Y., Ding, B., Fu, Y. H., Zhang, H. C., Xu, Y. M., & Song, X. H. (2023). Research on soil layer water conservation capacity of three forest restoration models in Guizhou fragile ecological region. Anhui Agricultural Bulletin , 29 (9), 95-99. https://doi.org/https://doi.org/10.16377/j.cnki.issn1007-7731.2023.09.036. Zhang, H., Hu, Y., Sun, X., Wang, Y., Zhang, B., Liu, C., Rafiq, A., Wang, B., An, S., & Zhu, Z. (2025). Glomalin-related soil proteins in particulate and mineral-associated organic carbon pools in alpine grasslands with different degradation degrees. Applied Soil Ecology , 210 , 106068. Zhang, J., & Ding, G. (2019). Research on the individual tree volume model of artificial Cunninghamia lanceolata in Guizhou. Forest resource management (06), 62-68. https://doi.org/https://doi.org/10.13466/j.cnki.lyzygl.2019.06.012 Zhang, J., & Guo, Y. (2020). Development of a volume model for natural redwood standing timber in Guizhou. Guizhou Forestry Technology , 48 (04), 6-14. https://doi.org/https://doi.org/10.16709/j.cnki.gzlykj.2020.04.002 Zhang, Q., Li, R., Lin, Y., Zhao, W., Lin, Q., Ouyang, L., Pang, S., & Zeng, H. (2024). Dynamics of Physiological Properties and Endophytic Fungal Communities in the Xylem of Aquilaria sinensis (Lour.) with Different Induction Times. Journal of Fungi , 10 (8), 562-581. https://doi.org/https://doi.org/10.3390/jof10080562 Zhang, X., Liu, S, Huang, Y, et al. (2018). Tree species mixtures inhibit soil organic carbon mineralization with a reduction in R-selective bacteria. Plant Soil , 431 , 203-216. Zhu, S. Q. (2003). Karst forest ecology research . Guizhou Science and Technology Press. https://doi.org/https://doi.org/10.3390/jof10080562 Zong, D., Zhou, Y., Zhou, J., Zhao, Y., Hu, X., & Wang, T. (2024). Soil microbial community composition by crop type under rotation diversification. BMC microbiology , 24 (1), 435. https://doi.org/https://doi.org/10.1186/s12866-024-03580-2 Zou, L., Chen, Y. L., & Yan, T. Z. (2000). Ecological distribution and seasonal change of soil microorganisms in pure and mixed plantations. Journal of Forestry Research , 11 (2), 106-108. https://doi.org/https://doi.org/10.1007/BF02856684 Information & Authors Information Version history V1 Version 1 29 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords forest soil forest soil bacterial and fungal communities karst mixed tree species soil microbial community Authors Affiliations Yan Wu 0000-0003-2335-3119 Guizhou Education University View all articles by this author Xumeng Tan Guizhou Education University View all articles by this author Yu Wu Guizhou Education University View all articles by this author Jianfeng Li [email protected] Guizhou Education University View all articles by this author Metrics & Citations Metrics Article Usage 200 views 133 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yan Wu, Xumeng Tan, Yu Wu, et al. Effects of Tree Species Mixing on Soil Microbial Community Structure in Karst Regions of Southwest China. Authorea . 29 September 2025. DOI: https://doi.org/10.22541/au.175914932.21109321/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175914932.21109321/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffe1c740ff04193',t:'MTc3OTQ3NjkyMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();