Ectomycorrhizal exploration types mediate soil decomposition and nitrogen dynamics of sub-alpine forest

Ectomycorrhizal exploration types mediate soil decomposition and nitrogen dynamics of sub-alpine forest | 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 Ectomycorrhizal exploration types mediate soil decomposition and nitrogen dynamics of sub-alpine forest lixia wang, Haoying Gao, Shuangjia Fu, Huichao Li, Lin Xu, Li Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6883380/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Plant and Soil → Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aims Ectomycorrhizal (ECM) fungi interact with saprotrophic fungi and bacteria, thereby influencing soil decomposition and nitrogen (N) mineralization. However, how the functional composition of ECM communities (i.e., exploration types) affects these processes under varying levels of N availability remains unclear. Methods We conducted a soil trenching experiment to manipulate root-associated fungal communities in two forest types—natural forest (higher N availability, dominated by long- and medium-distance ECM types) and plantation (lower N availability, dominated by contact exploration types). We evaluated the effects of trenching on fungal biomass, community composition, soil enzyme activities, nitrogen mineralization, and root decomposition. Results Trenching significantly reduced ECM fungal biomass and the relative abundance of medium-distance exploration types, particularly in the natural forest. In contrast, saprotrophic fungal sequence read abundance increased more in the plantation. Enzyme activities (except β-glucosidase) and nitrification rates were more strongly affected by trenching in the natural forest, where nitrification was positively correlated with the activities of leucine aminopeptidase and N-acetyl-β-D-glucosaminidase, and negatively correlated with ECM fungal biomass. Root decomposition increased only in the plantation and was also negatively correlated with ECM fungal biomass. Conclusion ECM exploration types influence soil N cycling and decomposition through their effects on fungal biomass and enzyme activity, with these impacts modulated by soil N availability. In low-N soils dominated by contact exploration type-ECM fungi, ECM communities exert a suppressive effect on decomposition. These findings underscore the role of ECM functional traits in shaping belowground processes under changing forest conditions. ectomycorrhizal fungi exploration type nitrogen mineralization root litter decomposition saprophytic fungi soil enzyme activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Soil organic matter decomposition is a key process in the carbon (C) and nitrogen (N) cycle of forest soils, and affects the productivity and stability of forest ecosystems (Fernandez et al., 2020; Knops et al., 2002). Soil fungi are important participants in decomposition, and obtain growth-limiting resources by secreting enzymes to mobilize C or organically bound mineral nutrients (Baldrian, 2016; van der Heijden et al., 2015). Interactions or antagonisms between key microbial groups, such as saprophytic and symbiotic fungi, or between bacteria and fungi, play a significant role in driving microbial mechanisms that influence decomposition and mineralization. In addition, other microbial interactions and environmental factors also contribute to these processes (Fernandez and Kennedy, 2016; Romaní et al., 2006; Sterkenburg et al., 2018). The relative importance of these interactions, however, depends on factors such as environmental conditions, community composition, and resource availability. Ectomycorrhizal (ECM) fungal species have evolved from different lineages (Martin et al., 2016), and differ in appearance and morphology, particularly in the presence and abundance of extracellular mycelia and rhizomorphs (Agerer, 2001; Peay et al., 2011). Based on these structural traits, ectomycorrhizal fungi are classified into different exploration types (Agerer, 2001), which differ in function in terms of carbon storage efficiency, enzyme activity, and nutrient uptake and transport (Hobbie and Agerer, 2010; Tedersoo et al., 2012). Recently, Jörgensen et al. (2023) suggested that ectomycorrhizal species that have a high abundance of mycelium in soils (medium distance exploration types) have a longer lifespan and lower foraging ability than species that have less mycelium in soils (short distance exploration types). Soil enzymes, as mediators of decomposition and mineralization, are mainly released by microorganisms such as saprophytic and ectomycorrhizal fungi, and bacteria (Lindahl and Tunlid, 2015; Schneider et al., 2012; Whalen et al., 2021). The ability of ectomycorrhizal and saprophytic fungi to release enzymes may vary significantly depending on species/genus or specific morphological type (exploration type), soil nutrient availability, and their interactions (Courty et al., 2010; Otgonsuren et al., 2020; Tedersoo et al., 2012). It has been shown that some ectomycorrhizal genera, such as Hygrophorus sp , Russula sp , Continarius sp , and Tomentella sp , have the ability to secrete the enzymes involved in organic matter decomposition (Walker et al., 2014). In support of the role of ectomycorrhizas in organic matter decomposition, Cortinarius acutus was associated with lower soil C storage in a boreal forest (Lindahl et al., 2021). The activity of ectomycorrhizal fungi has been shown to lead to suppression of the activity of saprotrophic fungi, the so-called Gadgil-effect. In studies by Gadgil and Gadgil (1978), Inocybe spp. were assumed to suppress decomposition, and more recently, Tomentella species were related to a ‘Gadgil effect’ in Pinus stands (Fernandez et al., 2020). Compared with the contact and the short-distance exploration types, the potential enzyme activity on the root tip surface of ECM with long and medium-distance exploration types is relatively higher (Finlay, 2008; Tedersoo et al., 2012). It is precisely these different nutrient acquisition capabilities, which may lead to different degrees of inhibition of saprophytic fungi, and therefore, to different effects on ecological function in forest soils. However, the relative importance of dominant genera and exploration types in decomposition and mineralization is still unclear. Globally, the area of planted forests is expanding at a rate of 2% per year, of which about half is derived from conversion from primary and secondary forests (van Dijk and Keenan, 2007). The conversion of natural forests to plantations has a range of impacts on ecosystem functions and services, such as soil organic carbon loss and reduced nutrient availability (Foley et al., 2005; Guillaume et al., 2015). Our previous study (Wang et al., 2023) has shown that clearing natural forests and replanting as a plantation result in considerably lower amounts of N and soil organic carbon (SOC), and also lower rates of N mineralization. After conversion from natural forest to plantation, the ratio of ectomycorrhizal fungi to saprophytic fungi increased significantly, and ectomycorrhizal fungi dominated. The decrease in saprotrophic fungi was explained by the lower amounts of SOC (Wang et al., 2023). However, how differences in nutrient availability resulting from forest conversion affect the competitive relationship between ectomycorrhizal and saprophytic fungi and further soil function, remains largely unknown. Soil trenching is a common method used to alter belowground C inputs through roots and mycorrhizas to simulate forest disturbance (Fernandez and Kennedy, 2016). Soil trenching results in the death of ectomycorrhizas and fine roots as root stores of non-structural carbohydrates become depleted (Aubrey and Teskey, 2018; Bååth et al., 2004). In this study, we set up un-trenched and trenched plots in a natural forest and a plantation that differ in N availability (Fig. S1). The soil properties, soil fungal communities, and soil functions (enzyme activity, decomposition, and mineralization) of the un-trenched plots and trenched plots in two forest types were determined. We tested the following hypotheses, 1) we predict that trenching will have a greater effect on ectomycorrhizal medium- and long-distance exploration types than contact exploration types (Fig. 1, H1), and 2) we predict that within each forest type and across both forest types changes in other microorganisms (bacteria or saprophytic fungi), will be most strongly correlated with changes in the relative abundance of medium and long distance exploration type ectomycorrhizal fungi (as compared to other exploration types) (Fig. 1, H2). 3) We predict that within each forest type the effect of trenching on root litter decomposition will negatively correlate with N availability (as measured by NH 4 + and NO 3 - ) and will be greater in the low N availability forest than the higher N availability forest (Fig. 1, H3). Material and methods Experimental design and sample collection In September 2020, we selected two adjacent forest types with similar elevations in the Western Sichuan subalpine area near Miyaluo town, Sichuan Province (31°47′36" N, 102°42′16" E, 3400 m above sea level). One is a Picea asperata Mast (Dragon spruce) plantation with low N availability and the other is a natural forest with high N availability with Picea asperata Mast (Dragon spruce) as the dominant tree (85% of stem number) (Wang et al., 2023). The other tree species in the natural forest is Abies faxoniana . The main shrubs are Sorbus amurensis , Rosa spp. , Euonymus spp. , and Lonicera spp . The main herbs are Pedicularis sylvatica , Rubia cordifolia L. , Pternopetalum tanakai , Anemone cathayensis , and ferns. In contrast, the plantation consists entirely of Picea asperata , and the understory vegetation is sparse. The average tree height in natural forests is 19 m and the age varies, with the maximum age being 55 a. In contrast, the plantation was more evenly aged at 41 years old (in 2021) with a mean height of 20 m. For additional information on the origin and historical background of plantations and natural forests see Wang et al. (2023). In May 2021, three 20 × 20 m plots were established in each of the two forest types (natural forest and plantation). The plots within each forest type were spaced approximately 30 m apart, and the minimum distance between plots of different forest types was approximately 200 m (Fig. S1). Within each plot, a 1.5 × 1.5 m subplot was trenched using 0.4 m deep steel barriers to minimize inputs from roots and mycorrhizal fungi. The trenched subplots within each forest type were spaced at least 40 m apart to ensure independence of treatment effects. Root samples from Picea asperata trees were extracted using a spade from a different part of the site. After removing the soil, the fine roots (<2 mm) were cut from the root system and then dried to constant weight in a 50 ℃ oven, and used to fill root litter bags. Two grams of dried fine roots were put into mesh bags (5×10 cm, and 80 μm in pore size) to form a root litter decomposition bag. In September of 2021, after almost five months of equilibrium, three root litter bags were buried to a depth of 15 cm in the soil of each un-trenched and trenched plot. After seven months, root litter bags were collected. Nine soil cores (five cm in diameter, 15 cm in depth) per plot were collected in May 2022. Three soil cores were pooled within each plot and mixed by hand, resulting in three composite samples per plot (2 forests types×3 plots×3 replicates×2 treatments=36 samples). All samples were stored at 4 ℃. After the 2 mm sieving, the soil sample was divided into two subsamples, one for the determination of soil water content (SWC), total dissolved nitrogen (TDN), soil dissolved organic carbon (DOC), nitrate nitrogen (NO 3 - ), ammonium nitrogen (NH 4 + ), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), microbial biomass phosphorus (MBP), soil enzymes, and soil microbial community. The other was used for the determination of pH, soil organic carbon (SOC), total nitrogen (TN), available phosphorus (AVP), and total phosphorus (TP). We completed those parameters measured with fresh soil (SWC, TDN, DOC, MBC, MBN, MBP, soil enzyme activity, microbial biomasses, and soil fungal community) in one week. Soil properties Soil pH was determined using a pH meter (Bante902 multi-parameter meters, Bante, China) in a 1:2.5 (w: v) water-to-soil suspension. Ten grams of fresh soil was taken and dried in an oven at 105 ℃ to constant weight, and the soil moisture content was calculated according to the moisture loss. SOC was determined by the potassium dichromate oxidation method. Specifically, soil organic matter was oxidized with 0.8 mol L -1 of 1/6 K 2 Cr 2 O 7 (170 ~ 180 ℃ oil bath for 5 min), and the remaining potassium dichromate solution was titrated with ferrous sulfate. The content of SOC was calculated from the amount of potassium dichromate consumed (Nelson and Sommers, 1996). TN was determined by the Kjeldahl nitrogen determination method. Specifically, after the soil has been digested with concentrated sulfuric acid, the ammonia produced by alkaline rectification is absorbed with boric acid, and the TN of the soil is determined by titration with a standard acid solution (Pruden et al., 1985). After the soil was digested with HClO 4 + H 2 SO 4 , the TP content was determined by ascorbate molybdate colorimetry. The absorbance was then determined using a spectrophotometer (Perkin Elmer HGA500, USA) at 750 nm (Murphy and Riley, 1962). AVP was extracted with 0.5 mol·L -1 NaHCO 3 and determined by molybdate ascorbic acid colorimetric method (UV-1601, Shimadzu Inc., Japan) at pH 8.5. Five grams of fresh soil were extracted with 50 ml (2 mol L -1 ) KCL, and the concentration of NO 3 - in extracts was measured photometrically at 275 nm (UV spectrophotometer (Perkin Elmer HGA500, USA) (Norman and Stucki, 1981), while the concentration of NH 4 + was determined by using the indophenol blue spectrophotometric method (UV spectrophotometer (Perkin Elmer HGA500, USA) (Rhine et al., 1998). DOC and TDN were determined using fresh soil extraction with 2 mol·L -1 KCl and then determined by TOC-TN analyzer (Liqui TOC II, Elementar, Germany). The contents of MBC, MBN, and MBP were obtained by calculating the difference between the contents of elements in the fumigated sample and the contents of elements in the unfumigated sample divided by the conversion coefficient KE (the proportion of C, N, and P extracted from the microorganisms killed by fumigation). The KE of MBC, MBN, and MBP are 0.45, 0.54, and 0.40, respectively (Brookes et al., 1985; Joergensen and Mueller, 1996; Vance et al., 1987). Soil fungal community DNA was extracted from 1 g of fresh soil using the DNA extraction kit (Power Soil, QIAGEN), and then purified using the BIOMICS DNA Microprep Kit (D4301, Zymo Research). 1% agar Gel electrophoresis was used to test the integrity of genomic DNA. NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) was used to detect DNA concentration and quality. The fungal 18S primers ITS3 (5’-GATGAAGAACGYAGYRAA-3’) and ITS4 (5‘-TCCTCCGCTTATTGATATGC-3’) were used for polymerase chain reaction (Polymerase Chain Reaction, PCR) amplification. The PCR procedure is consistent with the description in the previous publication (Wang et al., 2023). The quality of PCR products was detected by 2% agarose gel electrophoresis, and the kit AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) was used to purify the PCR products. The library was constructed and sequenced using the Illumina MiSeq platform (Meiji Biotechnology Co., Ltd., Shanghai). The Quantitative Insights into Microbial Ecology (QIIME) 1.7.0-dev pipeline (http://www.qiime.org) was used for assembly and quality control of the sequencing data, and low-quality sequences were eliminated. Sequences with more than 97% similarity were classified into a single taxon (Operational Taxon, OTU) and Ribosome Database Project (RDP) (http://rdp.cme.msu). edu/) to get the classification operation unit OTU. The species information was compared in the UNITE database (https://unite.ut.ee/) to obtain the grouping information of OTU sequences at the level of kingdom, phylum, class, order, family, genus, and species using a confidence threshold of 0.7-1. The trophic types and functional groups of fungi were classified using the FUNGuild v1.0 tool, and the OTU output information was converted using QIIME. The output results for analysis include taxon of species, taxon level of species, trophic mode, and functional information such as grouping "Guild" and credibility "Confidence". In order not to over-interpret the functional groups of fungi, only the confidence levels of "highly probable" and "probable" were used. The quality-controlled sequencing libraries yielded an average of 71,891 ± 2,599 reads per sample (range: 33,856 to 143,725). To standardize sampling effort across samples and minimize biases caused by uneven sequencing depth, we rarefied all samples to 33,856 reads, corresponding to the lowest read count observed. All subsequent diversity and community structure analyses were based on rarefied and proportionally normalized datasets. Based on previous literature, we classified ectomycorrhizal fungi into four types: contact exploration type, short-distance exploration type, medium-distance exploration type, and long-distance exploration type. Two genera of ectomycorrhizal fungi have been classified as contact exploration types, 21 genera have been classified as medium-distance exploration types, 10 genera have been classified as long-distance exploration types, and 25 genera have yet to be recorded and are classified as unknown types. The classification of exploration types of ectomycorrhizal fungi is listed in Table S1. The relative abundance of each ectomycorrhizal fungal exploration type or saprotrophic fungal functional group was calculated by dividing the read abundance of each ectomycorrhizal fungal exploration type or saprotrophic fungal group by the total read abundance of ectomycorrhizal fungi or saprotrophic fungi, respectively. Sequence data were deposited into the NBCI with the Project number PRJNA1010565. Soil fungal and bacterial biomasses The fungal biomass was determined using the microbial phospholipid fatty acids (PLFAs) method described in Frostegård et al. (1993). The soil samples were collected in May 2023, with the sampling depth and method identical to those used for the samples collected in May 2022. Specifically, the fatty acids were extracted from 8 g freeze-dried soil for 2 h using a single-phase extract of 23 mL chloroform: methanol: citrate buffer (volume ratio 1:2:0.8). Phospholipids were separated from neutral lipids and glycolipids on a silica gel column. Neutral lipids and glycolipids were eluted with chloroform and acetone, and polar lipids were eluted with methanol. The PLFAs was were analyzed on a gas chromatography-mass spectrometry (GC-MS). The PLFA 18:2 ω6,9c represents fungi. The sum of the following PLFAs was used a measure of the bacterial biomass: i14:0, i15:0, a15:0, 15:0, i16:0, 10Me16:0, i17:0, a17:0, cy17:0, 17:0, br18, 10Me17:0, 18:1ω7, 10Me18:0 and cy19:0 (Frostegård and Bååth, 1996; Frostegård et al., 1993). The PLFA biomass was expressed in nanomoles of PLFA per gram (dry weight) soil. Soil ectomycorrhizal fungal and saprophytic fungal biomasses After the soil sample was collected, 5 g of fresh soil was taken for the determination of ergosterol content, while another 5 g of soil was placed in a culture bottle and incubated in a 25℃ incubator for five months. Subsequently, the ergosterol content was determined. The difference between the ergosterol content before and after incubation was used to calculate the ectomycorrhizal fungal biomass (Bååth et al., 2004). The ergosterol content measured at the end of incubation was considered to represent saprophytic fungal biomass. Ergosterol, a fungal-specific biomarker, was extracted as described by Nylund and Wallander (1992). Specifically, 5 g of soil was extracted with 5 mL of 10% KOH in methanol. The soil solution was shaken for 15 minutes and then refluxed at 70 ℃ for one hour. After cooling, 1 mL of ultra-pure water was added before centrifuging for five minutes at 3000 rpm. The cyclohexane layer obtained from this process underwent further extraction with an additional volume of methanol using another portion (1.5 ml) of cyclohexane before being evaporated under N 2 gas and dissolved in 1ml methanol. Following ergosterol extraction, samples were analyzed by high performance liquid chromatography (HPLC). Soil enzyme activity Enzyme activities involving carbon ((β-glucosidase (βG), β-glucuronidase (βLU), phenoloxidase (PHE), and peroxidase (PRO)), nitrogen (N-acetyl-β-D-glucosaminidase (NAG) and leucine aminopeptidase (LAP)), and phosphorus cycles (acid phosphatase (AP)) were determined. Specifically, one gram of fresh soil was suspended in a 100 ml (2 mol L -1 ) sodium acetate solution at pH 5.5. Subsequently, we took 100 µL of the soil suspension to react with the respective substrates and then incubated it at 20 ℃ in the dark for two hours. After the incubation, the hydrolases except LAP were terminated with 10-μL NaOH (1 M). Values of fluorescence at 365 nm excitation and 460 nm emission were measured with a fluorometer (Multimode Plate Reader, EnSpire). When calculating, LAP was calibrated using the AMC (7-amino-4-methyl coumarin) standard curve, and other hydrolytic enzymes were calibrated using the MUF (4-methylumbelliferone) standard curve. For the measurement of oxidase, 900 µL of soil suspension was taken to react with L-3,4-dihydroxyphenylalanine (20 mM) to measure the activities of PHE and PRO. For the determination of PRO, an additional 10 µL of 0.3% hydrogen peroxide should be added to the soil mixture. The absorbance values were measured before and after 20 hours of incubation (20 ℃) using a spectrophotometer at 450 nm. The calculation formula for soil enzymes was given in a previous publication (Wang et al., 2017). Litter decomposition rate In May 2022, after one year incubation, we collected three root litter bags from each un-trenched and trenched plot. Root litter was cleaned, and weighed after drying to a constant mass at 65 ℃. The decomposition rate was calculated by dividing the weight lost by the number of decomposition days. Net nitrogen mineralization To determine net nitrogen mineralization, we took 5 grams of fresh soil into a 100ml polyethylene plastic bottle and incubated it at 25° C with 24h light/dark cycle for 7 days. After the incubation was completed, the soil was extracted and the contents of NH 4 + and NO 3 - were determined by the above methods. The ammonification rate (NH 4 + ), nitrification rate (NO 3 - ), and mineralization rate (NH 4 + + NO 3 - ) are calculated by dividing the difference in the content before and after incubation by the number of days of incubation, respectively. Statistical analyses Mixed effect models were used to assess the effects of forest type, trenching, and their interaction on various soil properties (pH, SWC, SOC, DOC, TDN, TN, NH 4 + , NO 3 - , TP, AVP, MBC, MBN, and MBP), the relative sequence read abundance of fungal genera and fungal groups, and soil functions (enzyme activity, root litter decomposition, ammonification, nitrification, and mineralization rates). In these models, fixed effects included forest type, trenching, and their interaction, while forest plots were included as a random intercept to account for plot-level variability. Significance of fixed effects was assessed using Type III tests. The mixed effect models were implemented using SPSS 27. Pairwise comparisons of main effects were conducted using post-hoc tests with Bonferroni adjustment. Independent samples t-tests were used to determine the difference between forest type or treatments. The effect size of trenching on soil property, soil microbial biomass, relative abundance of fungi, and soil function using Cohen's d formula in the SPSS 27. We considered d = 0.2 a ‘small’ effect size, d = 0.5 a ‘medium’ effect size, and d = 0.8 a ‘large’ effect size. The effect of forest type, trenching, and their interaction on soil saprophytic fungal and ectomycorrhizal communities using permutational multivariate analysis of variance (PERMANOVA) based on a Bray-Curtis dissimilarity matrix using the Adonis function in the vegan package (R v. 4.1.2, R Development Core Team, 2021). The contribution of different genera of ectomycorrhizal and saprophytic fungal communities to differences between natural forest and plantation or between un-trenched and trenched plots was obtained using a simper analysis (R v. 4.1.2, R Development Core Team, 2021, Vegan package). First, we used random forest regression to identify key predictors associated with soil function. In this analysis, we used (root litter mass loss, ammonification, nitrification, mineralization) as response variables, and (bacterial biomass, fungal biomass, ectomycorrhizal fungal biomass, saprophytic fungal biomass, dominant genera of ECM and SAP communities, ECM exploration types, SAP fungal groups) as predictor variables. The random forest model was implemented with 500 trees, and model performance was assessed using out-of-bag (OOB) error rates to ensure robustness. We initially utilized random forest regression to predict biomass or functional groups that are significantly associated with soil function. Then, to clarify the potential relationship between changes in nitrogen processes and soil properties, soil fungi, and soil enzymes after forest conversion, structural equation models were used. Trenching pathways of natural forest and plantation were used to elucidate the relationship among them. In the process of obtaining the best model, paths and variables are deleted or added according to their strong correlation or linear regression relationship. Therefore, the final model contains data such as inorganic nitrogen, DOC, the relative abundance of long- and medium-distance exploration types, SAP basidiomycetes, soil PER, soil NAG, ammonification rate, and nitrification rate. The P -values, maximum likelihood (χ 2 ), goodness-of-fit index (GFI), and root-mean-square error of approximation (RMSEA) were used to evaluate the fitness of the model. The structural equation model was constructed in Amos 24.0. Results Soil properties The natural forest had significantly higher inorganic nitrogen (IN) and NO 3 - contents compared to the plantation forest (Table 1). For microbial biomass fractions, the MBP content in the natural forest was significantly higher than that in the plantation forest, while the MBN content was significantly lower (Table 1). In contrast, there were no significant differences in DOC or MBC contents between the two forest types under un-trenched conditions (Table 1). Trenching significantly reduced DOC content by 21.2% compared to un-trenched plots across both forest types (Table 1). Trenching also significantly increased IN and NH 4 + concentrations, with a greater effect size observed in the plantation forest than in the natural forest (Table 1, Fig. S2). For microbial biomass, trenching significantly increased MBC by 26.9% across both forest types (Table 1). Similar to IN and NH 4 + , the effect size of trenching on MBC was greater in the plantation forest than in the natural forest (Fig. S2). However, trenching had no significant effect on MBP or MBN contents in either forest type ( P > 0.05). Details of other soil properties are provided in Table S2. Soil bacterial biomass, fungal biomass, and fungal community Soil saprophytic fungal (F = 5.6, P < 0.001) and ectomycorrhizal fungal (F = 5.6, P = 0.001) community compositions were significantly different between the natural forest and plantation (Fig. 2), but no difference was found between trenched and un-trenched plots (Fig. 2) . A total of 6923 OTUs were identified, of which 359 OTUs belonged to ECM fungi.The ECM fungi were the dominant community in any forest type or plot, having more than 50 % of the fungal sequence reads. The most abundant ectomycorrhizal fungal genera in the natural forest were Russula , Piloderma , Cortinarius , Amphinema , Inocybe , and Macowanites , whereas Macowanites , Thelephora , Hygrophorus , Inocybe, and Russula were the dominant ectomycorrhizal genera in the plantation (Fig. 3a). Among these dominant genera, Hygrophorus , Tomentella , Macowanites , and Amphinema were the main genera that caused the difference of ectomycorrhizal fungal communities between the forest types (Table S3). However, the genera that caused slight differences in ectomycorrhizal fungal communities between un-trenched and trenched plots were Piloderma , Cortinarius , Suillus , Laccaria , and Cenococcum , among others (Table S3). The dominant genera of saprophytic fungal community in the natural forest were Mortierella , Pleotrichocladium , Gymnostellatospora , Cladophialophora , and unclassified_f__Hyaloscyphaceae , while the dominant genera in the plantation were Mortierella , Archaeorhizomyces , Cladophialophora , Trichoderma, and Gymnostellatospora (Fig. 3b). The genera that caused the difference in saprophytic fungal communities between two forest types were Mortierella , Glarea , and unclassified_f__Clavariaceae , while the genera responsible for the differences between trenched and un-trenched plots were the two ascomycetes Glarea and Infundichalara (Table S3) . The plantation had higher reads of ectomycorrhizal community, ectomycorrhizal fungal biomass, and saprophytic fungal biomass, but lower reads of saprophytic fungal community and the ratio of bacterial and fungal biomass compared to the natural forest (Figs. 4a, 4d, 4e). Trenching significantly decreased the fungal biomass, saprophytic fungal biomass, ectomycorrhizal fungal biomass, and reads of the ectomycorrhizal fungal community (Figs. 4a, 4c, 4d). The effect size of trenching on ectomycorrhizal fungal biomass and ratio of bacterial and fungal biomass were greater in the natural forest than in the plantation (Fig. S3a, Fig. S3b). However, trenching significantly increased reads number of the saprophytic fungal community, and the increase was much higher in the plantation than in the natural forest (Fig. 4b; Fig. S3a, 3b). The result showed that the relative sequence read abundance of ectomycorrhizal fungi was significantly higher in the plantation than in the natural forest, but the relative sequence read abundance of saprophytic fungi was lower in the plantation than in the natural forest (Fig. 5). Trenching significantly reduced the relative sequence read abundance of ectomycorrhizal fungi in both forest types ( P <0.001) (Fig. 5), whereas it significantly increased the relative sequence read abundances of saprotrophs ( P <0.05) (Fig. 5, Table S4). We divided ectomycorrhizal fungi and saprophytic fungi into different functional groups according to ectomycorrhizal morphology or saprophytic fungal phylogeny. The results showed that the relative sequence read abundance of contact exploration type in the plantation was significantly higher than that in the natural forest ( P <0.01), but trenching had no effect on it (Fig. 6a). Trenching significantly reduced the relative sequence read abundance of the medium-distance exploration type relative to the total ectomycorrhizal fungal reads ( P = 0.045) (Fig. 6c) and its total read abundance (Fig. S4b), with this effect being greater in natural forests than in plantation forests (Figs. S3c and S3d). Furthermore, the relative sequence read abundances of short-distance and long-distance exploration types, calculated relative to the total ectomycorrhizal fungal reads, showed no significant differences across forest types and trenching treatments (Figs. 6b and 6d). For the saprophytic fungal groups, the relative sequence read abundances of saprophytic ascomycetes, basidiomycetes, and mortierellomycetes, calculated relative to the total saprotrophic fungal reads, were significantly higher in the natural forest than in the plantation. However, the relative sequence read abundance of mucoromycetes was significantly lower in the natural forest compared to the plantation. After trenching, the relative sequence read abundances of saprophytic ascomycetes and mortierellomycetes, expressed as proportions of the total saprotrophic fungal reads, increased significantly (Fig. 6f, Fig. 6h, and Fig. 6i). Soil function Our results showed that the natural forest had significantly higher activities of β-glucosidase (βG) and β-glucuronidase (βLU) than the plantation. However, trenching significantly decreased the activity of βG (Fig. 7a). The interaction between the forest type and trenching significantly influenced the activity of βLU ( P =0.009). Trenching significantly reduced the activity of βLU in the natural forest; however, it did not affect the activity of βLU in the plantation (Fig. 7b, Fig S5). For the nitrogen-cycling associated enzymes, the natural forest had a significantly higher activity of N-acetyl-β-D-glucosaminidase (NAG) than that of the plantation. Trenching significantly reduced the activity of NAG, but the effect size of trenching was greater in the natural forest (Fig. 7d, Fig. S5a, Fig. S5b). Similarly, the natural forest had a significantly higher leucine aminopeptidase (LAP) activity compared to the plantation, but was not affected by trenching (Fig. 7e). In contrast, we found that the natural forest had significantly lower activities of phenoloxidase (PHE) and peroxidase (PER) than the plantation, trenching increased the activity of PER in the natural forest (Fig. 7f and 7g), and the effect size of trenching on PER was greater in the natural forest than in the plantation. The activity of acid phosphatase (AP) was higher in the natural forest than in the plantation, but was not affected by trenching (Fig. 7c). In general, except for βG, the effect size of trenching on soil enzymes in natural forests was greater than that in plantation forests (Fig. S5). The ammonification rate of soil in the plantation was significantly higher than that in the natural forest, but the nitrification rate and mineralization rate were significantly lower than that in the natural forest. Trenching significantly reduced the soil ammonification rate, but increased the soil nitrification rate, and the effect size of trenching on the soil nitrification or nitrogen mineralization rates was greater in natural forests than in the plantation (Fig. 8, Fig. S5c, Fig. S5d). Additionally, we found that trenching significantly increased the root litter decomposition rate in the plantation ( P =0.028), but had no effect on the root litter decomposition rate in the natural forest ( P =0.91) (Fig. 8d, Fig. S5c, Fig. S5d). Relationship between soil properties, soil microbe, and function First, we used random forest to identify the most important factors associated with soil function in terms of biomass, dominant genera, and functional groups (Table S5). Our results showed that soil ectomycorrhizal fungal biomass was the most important predictor for root litter mass loss in the plantation ( P =0.099), however, the relative sequence read abundance of ectomycorrhizal fungi, Cladophialophora , and fungal biomass were the most important predictors for root litter mass loss in the natural forest. The relative sequence read abundances of Ascomycota and ectomycorrhizal fungi were the most important predictor of ammonification rate in the plantation, but the most important predictor of ammonification rate in the natural forest were the relative sequence read abundances of ectomycorrhizal fungi, medium-distance exploration type, Russula , and Ascomycota. For nitrification rate, the ratio of bacterial biomass to fungal biomass-PLFAs, fungal biomass-PLFAs, ectomycorrhizal fungal biomass-ergosterol, and the relative sequence read abundance of saprophytic fungi were significantly associated with changes in nitrification rate in the natural forest. However, these factors were not identified as predictors for nitrification rate in the plantation (Table S5). Based on the above predictors identified by the random forest model, and combining changes in soil properties and soil enzymes, we constructed an SEM to examine the associations and potential pathways among these factors affecting root litter mass loss. In the natural forest, the final model explained 24% of the variation in root litter mass loss after trenching (CMIN/DF=1.032, CFI=0.997, RMSEA=0.04, X 2 =3.1, P =0.38, Fig. 9a). The SEM results indicate that trenching was not associated with direct or indirect changes in root litter mass loss. However, trenching was associated with changes in soil PER enzyme activity and ectomycorrhizal fungal biomass, potentially mediated by its association with inorganic nitrogen content. In the plantation, the final model explained 67% of the variation in root litter mass loss after trenching (CMIN/DF=1.015, CFI=0.999, RMSEA=0.03, X 2 =2.0, P =0.36; Fig. 9b). Trenching showed associations with root litter mass loss indirectly through NH 4 + ( P <0.001) and MBC ( P =0.016). Trenching was also linked to changes in ectomycorrhizal fungal biomass ( P =0.007) and fungal biomass ( P =0.01), which were further associated with root litter mass loss. Additionally, trenching was associated with changes in PER and PHE enzyme activities, mediated through NH 4 + and MBC contents. However, only PER activity showed a weak association with the rate of root litter mass loss ( P =0.089). An SEM was also constructed to explore the associations between these factors and soil nitrogen processes. In the natural forest, the final model explained 85% (CMIN/DF=0.37, CFI=1, RMSEA=0, X 2 =1.1, P =0.78, Fig. 9c) and 96% (CMIN/DF=0.64, CFI=1, RMSEA=0, X 2 =3.1, P =0.67, Fig. 9d) of the variation in ammonification and nitrification rates after trenching, respectively. The SEM results suggest that ectomycorrhizal fungal biomass was linked to nitrification rates through its association with NAG activity. Inorganic nitrogen was directly associated with ammonification and nitrification rates. In the plantation, the ammonification rate showed an association only with NH 4 + levels. Discussion Effects of forest type and trenching on saprophytic and ectomycorrhizal fungal communities Through the previous conversion of the natural forest to the plantation, the two sites differed in soil physical and chemical properties as well as the community structure of saprophytic fungi and ectomycorrhizal fungi (Wang et al., 2023). In the soil, this was particularly expressed in the lower levels of soil C and N in the plantation compared to the natural forest. In the ectomycorrhizal community, the difference between the natural forest and the plantation was mainly due to differences in the sequence read abundance of four dominant taxa, resulting in a greater sequence read abundance of contact exploration type in the plantation (Fig. S4). In both forest types, trenching reduced the number of ectomycorrhizal reads and ectomycorrhizal fungal biomass, particularly in the natural forest. After trenching, fine roots die within 1-2 years as non-structural carbon reserves are depleted (Fernandez et al., 2020; Whalen et al., 2021), which also results in the death of ectomycorrhizal root tips and the associated mycelium in the soil. The greater decrease of ectomycorrhizal reads and ectomycorrhizal fungal biomass in the soil of natural forests may be linked to the greater sequence read abundance of long and medium-distance exploration types in the community. The long and medium-distance taxa Piloderma , Cortinarius , Suillus , and Cenococcum , contribute most to the difference between the un-trenched and trenched plots in both forest types. The proportionally greater decrease in long- and medium-distance exploration types could be due to either a higher demand for carbohydrates to maintain the large hyphal biomass or a faster turnover rate. A faster turnover rate is contrary to the supposition of Jörgensen et al. (2023), who suggested that long-distance exploration types have the longest turnover times. In both forest types, soil fungal biomass decreased significantly after trenching. However, the decrease was less pronounced in the plantation, potentially due to the relative increase in saprophytic fungi sequence read abundance. Although the biomass of saprophytic fungi, as measured by ergosterol, did not show an increase after trenching, it is important to recognize the limitations of ergosterol as a biomarker. Not all saprophytic fungi contain ergosterol. For instance, studies have revealed that Mortierella species do not contain ergosterol but instead contain desmosterol (Olsson et al., 2003). Mortierella sp was the dominant genus in the two forest types, accounting for about 40% of the entire saprophytic fungal reads. Therefore, the saprophytic fungal biomass characterized by ergosterol is likely underestimated in our study. Moreover, our study showed that MBC increased significantly after trenching, with the proportion of increase being significantly higher in the plantation than in the natural forest. However, this increase in MBC is difficult to reconcile with the observed decrease in fungal and bacterial biomass. It is possible that the MBC increase reflects contributions from other microbial groups or a shift in microbial activity rather than absolute biomass changes. These results highlight the complexity of microbial responses to trenching and the need for complementary approaches to disentangle the underlying mechanisms. A potential limitation of our study arises from the use of the estimation technique by Bååth et al. (2004) in trenched vs. untrenched plots. Trenching inherently introduces disturbances such as the cessation of root carbon inputs and microbial community shifts, which overlap with the effects simulated during incubation. This overlap complicates the interpretation of microbial biomass and activity changes, as the observed effects may represent cumulative impacts of trenching and incubation rather than distinct incubation effects. While we focused on relative differences between treatments to mitigate this issue, these findings should be interpreted with caution. Future studies could address this limitation by incorporating additional controls, such as non-incubated trenched plots, or employing less invasive methods like in situ microbial activity measurements or isotopic tracing to disentangle these overlapping effects. Despite this caveat, our study provides valuable insights into microbial community dynamics and soil carbon processes under reduced carbon input conditions. From the analysis of fungal reads, it is evident that ectomycorrhizal fungi outnumber saprophytic fungi by several multiples, suggesting that ectomycorrhizal fungi constitute the majority of fungal biomass. The observed trend in the abundance of ectomycorrhizal fungal reads aligns with the changes in fungal biomass, providing further support for this inference. The higher biomass of fungi in the plantation was also due to the higher biomass of ectomycorrhizal fungi. Studies have shown that more ectomycorrhizal fungi are recruited in nutrient-poor soils (Corrales et al., 2018). Trenching significantly increased the relative sequence read abundance of the saprophytic fungi in both forest types. The most important changes in terms of sequence read abundance were shown for ascomycetes and mortierellomycetes (Fig. S4). An increase in ascomycetes, as shown in other studies after trenching (Mayer et al., 2021) and girdling (Mayer et al., 2023) in other forest types, is related to increased decomposition (Mayer et al., 2023). Community structure of ectomycorrhizal fungi was not significantly affected after one year of trenching. It may be due to the fact that the reduction of dissoved organic carbon in the early stage of treatment may have a more obvious effect on the species or genus with a relatively high demand for dissolved organic carbon, such as some medium- and long- distance exploration types, and these species or genus only account for a small proportion in the ectomycorrhizal fungal community. Therefore, one year of treatment did not lead to significant changes in ectomycorrhizal fungal community. Effect of forest type and trenching on soil enzyme activity There were clear differences between the forest types for most of the hydrolase and oxidase enzymes in soils. The clear differences in the activity of PER was not reflected in differences between the forest types in decomposition. Contrary to the results of Mayer et al. (2021) where the activity of peroxidase increased with soil fertility, the plantation with the lower levels of N had the highest peroxidase activity. In Swedish boreal forests, the activity of Mn-peroxidase was significantly correlated with the presence of Cortinarius . However, our results showed no significant difference in the relative abundance of Cortinarius between the natural forest and the plantation. Trenching and the subsequent loss of fine roots and mycorrhizal fungi resulted in lower activities of β-glucosidase and β-glucuronidase, N-acetyl-β-D-glucosaminidase, and acid phosphatase ( P =0.074). As similar loss activity of these enzymes was found in a clear cut Picea abies forest (Kohout et al., 2018). This pattern may indicate that these enzymes are secreted by fine roots or mycorrhizal fungi, or that their activity stimulates microorganisms to produce these enzymes. However, within a forest type, only in natural forests did trenching significantly affect soil enzyme levels, shown as a decrease in the activity of β-glucuronidase and an increase in the activity of peroxidase. In a boreal forest, a decrease in Mn-peroxidase after trenching is used as evidence to support the direct involvement of ectomycorrhizal fungi in decomposition (Sterkenburg et al., 2018). In the natural forest, the increase in peroxidase was associated with an increase in the relative abundance of Inocybe , a genus known for the ability to break down organic substrates (Bahram et al., 2018; Courty et al., 2005). While these findings provide preliminary insights into the functional shifts in microbial communities, the lack of replication at a larger scale limits their generalizability. N mineralization and the ectomycorrhizal fungal community Nitrogen mineralization from organic N to NO 3 - is a series of linked reactions in which the first step, depolymerization, is mediated by fungi, and the subsequent steps to NO 3 - aremediated by bacteria (Levy-Booth et al., 2014). The incubation of both the natural forest and the plantation soils showed negative net ammonification rates, characteristics of microbial immobilization, or high rates of nitrification (Turner et al., 2007). In the incubated soil taken from the natural forest after trenching, the net ammonification rate became more negative and the nitrification rate increased compared to soil from the un-trenched plots. The relationship between the ammonification and nitrification rates suggests removal of NH 4 + through nitrification is the primary cause of the negative net ammonification rates. As a consequence trenching increased the net N mineralization rate in the natural forest, but not in the plantation. However, in situ, trenching resulted in an increase in both the soil solution levels of NH 4 + and NO 3 - in the natural forest, and NH 4 + in the plantation. This suggests that either the increase in saprotrophic fungi increases the rates of organic N depolymerization, or that the ectomycorrhizas were actively involved in the removal of N (Hobbie and Agerer 2010). However, the significant correlation between the ectomycorrhizal fungal biomass (ergosterol) and soil NAG enzymes supports the idea that these fungi are involved in depolymerization (Fig. 9c). The effect of trenching on nitrification rate of natural forest is greater than that of plantation forest. This may be due to the increase in the ratio of bacterial to fungal biomass caused by the substantial reduction of ectomycorrhizal fungal biomass in natural forests, thus affecting enzyme activity related to nitrogen conversion (NAG). This view is supported by our random forest analysis, which found that the ratio of bacterial to fungal biomass is the most important predictor of nitrification rate in natural forests (Table S5). Despite all of the changes in the ectomycorrhizal and saprophytic fungal communities, only in the plantation did trenching result in higher rates of decomposition of root material. This would suggest that under conditions of low N availability, even ectomycorrhizal communities with a dominance of contact exploration types can affect root litter decomposition. However, the limited sample size restricts our ability to fully elucidate the complex interactions among saprophytic fungi, ectomycorrhizal fungi, and bacteria in driving N cycling and decomposition processes. These results should be regarded as preliminary observations, providing a foundation for future studies. To address the limitations of this study and build on these findings, future research should aim to increase sample sizes and field replicates to enhance the statistical power and robustness of results, while also conducting multi-site studies to assess the generalizability of these patterns across diverse forest ecosystems. Conclusion The initial ectomycorrhizal fungal communities of the two forest types were different. The natural forest was dominated by the medium and long-distance exploration types, while the plantation was dominated by the contact exploration type. The ectomycorrhizal fungal communities of the natural forest were more sensitive to trenching, showing a sharp decline in ectomycorrhizal fungal biomass. Moreover, Trenching had greater effects on soil enzyme activity and nitrogen mineralization in the natural forest. The changes of NAG enzyme activity related to nitrogen conversion in natural forest soil after trenching were correlated with the changes of ectomycorrhizal fungal biomass, further affecting the soil nitrogen process. Under conditions of low N availability, even ectomycorrhizal communities with a dominance of contact exploration types can affect root litter decompostion. Declarations Funding This work was supported by the National Natural Science Foundation of China (No. 32271849, 31870602), the Program of Sichuan Applied Basic Research Foundation (2023NSFSC0120 and 2024NSFSC0354), the Chinese Postdoctoral Science Foundation (2022M722296 and 2023M732500). DLG was supported by the project EXCELLENTIA under the Horizon Europe research and innovation programme, grant agreement N°101087262. Competing interests The authors declare no competing interests. Author contributions Lixia Wang, Douglas Godbold and Zhenfeng Xu contributed to the field experiment conception and design. Field sampling and Lab work were performed by Haoying Gao, Shuangjia Fu, and Huichao Li. Data collection and analyses were conducted by Lin Xu, Li Zhang, and Han Li. Chengming You, Sining Liu, Hongwei Xu, Jiao Li, and Bo Tan provided valuable input through discussions and support for the statistical analyses, and all authors contributed to the manuscript writing and revisions. All authors read and approved the final manuscript. Data availability The raw sequencing data presented in this study are openly available in in the NCBI Sequence Read Archive and included in the BioProject with accession number PRJNA1010565. All other data presented in this study are available in this article and the respective supplementary materials. References Agerer R (2001) Exploration types of ectomycorrhizae. Mycorrhiza 11: 107-114. https://doi.org/10.1007/s005720100108 Aubrey DP, Teskey RO (2018) Stored root carbohydrates can maintain root respiration for extended periods. 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Plant Soil 411: 467-481. https://doi.org/10.1007/s11104-016-3047-2 Whalen ED, Lounsbury N, Geyer K, Anthony M, Morrison E, van Diepen LTA, Le Moine J, Nadelhoffer K, vanden Enden L, Simpson MJ, Frey SD (2021) Root control of fungal communities and soil carbon stocks in a temperate forest. Soil Biol. Biochem. 161: 108390. https://doi.org/10.1016/j.soilbio.2021.108390 Table Table 1 Soil property (Means±SE) of un-trenched and trenched plots in the natural forest and plantation (n = 3). Natural forest Plantation Forest type Trenching F×T Un-trenched Trenched Un-trenched Trenched F P F P F P DOC (mg kg -1 ) 323±28 275±24 411±97 214±22 0.03 0.83 5.3 0.028* 1.98 0.17 IN 32.1±2.9 48.5±6.8 15.5±1.2b 32.3±3.8a 14.0 <0.001** 14.2 <0.001*** 0.002 0.96 NH 4 + (mg kg -1 ) 15.4±2.2 24.8±4.5 6.9±1.2b 21.5±2.8a 4.1 0.052 16.8 <0.001*** 0.76 0.39 NO 3 - (mg kg -1 ) 16.8±2.8 23.7±3.3 8.6±0.4 10.8±1.2 21.9 <0.001*** 4.0 0.053 1.1 0.30 MBC (mg kg -1 ) 1847±214 2127±118 1767±313 2871±354 1.6 0.22 6.8 0.014* 2.4 0.13 MBN (mg kg -1 ) 92±18 91±7 125±22 154±21 6.9 0.013* 0.60 0.44 0.64 0.43 MBP (mg kg -1 ) 2947±526 2730±495 781±228 1719±375 14.0 0.02* 0.73 0.40 1.9 0.18 Abbreviations: DOC: soil dissolved organic carbon; NH 4 + : soil ammonium nitrogen; NO 3 - : soil nitrate-nitrogen; MBN: soil microbial biomass nitrogen; MBC: soil microbial biomass carbon; MBP: soil microbial biomass phosphorus The results of mixed-effect analysis of variance (ANOVA) with forest type, trenching, and their interaction as fixed factors and sampling plot as a random factor are shown (*, P <0.05; **, P <0.01; ***, P <0.001). Values suggesting significant effects are given in bold. Supplementary Files Copyofsupplementarymaterial2025.xlsx Cite Share Download PDF Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Major revisions 12 Sep, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers invited by journal 17 Jun, 2025 Editor invited by journal 13 Jun, 2025 Editor assigned by journal 12 Jun, 2025 First submitted to journal 12 Jun, 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-6883380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472801828,"identity":"0deb1f14-f2cd-4b24-8286-413cad9c7100","order_by":0,"name":"lixia 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Fu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shuangjia","middleName":"","lastName":"Fu","suffix":""},{"id":472801831,"identity":"14c43dd5-0d8e-4c28-a30a-ba6fd5546fdd","order_by":3,"name":"Huichao Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Huichao","middleName":"","lastName":"Li","suffix":""},{"id":472801832,"identity":"c55d522d-3f5c-4b52-9e3d-7fec99d30932","order_by":4,"name":"Lin Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xu","suffix":""},{"id":472801833,"identity":"89e4c894-f345-4054-87ad-3b13be873ee8","order_by":5,"name":"Li Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":472801834,"identity":"98c6947a-fd91-49fa-a43f-5aaf984cd1cf","order_by":6,"name":"Han Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Li","suffix":""},{"id":472801835,"identity":"81c20219-919c-4ab0-80df-5c4994312db1","order_by":7,"name":"Chengming You","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chengming","middleName":"","lastName":"You","suffix":""},{"id":472801836,"identity":"d0b486fa-1154-44d7-9bf5-1b092b1054a1","order_by":8,"name":"Sining Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sining","middleName":"","lastName":"Liu","suffix":""},{"id":472801837,"identity":"f898f81a-fca2-4078-b333-b55c16230213","order_by":9,"name":"Hongwei Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Xu","suffix":""},{"id":472801838,"identity":"4267c644-705c-4cbd-bdb8-8d3448f85b79","order_by":10,"name":"Jiao Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Li","suffix":""},{"id":472801839,"identity":"e00ed105-4d28-4a78-97d5-3002ba6849b8","order_by":11,"name":"Bo Tan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Tan","suffix":""},{"id":472801840,"identity":"adc8448f-4a9b-4c0e-83ae-93ae4cb8ebc6","order_by":12,"name":"Zhenfeng Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhenfeng","middleName":"","lastName":"Xu","suffix":""},{"id":472801841,"identity":"6d08c62a-a4cf-4b5d-91cd-55187a09f5a1","order_by":13,"name":"Douglas Godbold","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Douglas","middleName":"","lastName":"Godbold","suffix":""}],"badges":[],"createdAt":"2025-06-12 22:44:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6883380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6883380/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-025-08097-9","type":"published","date":"2025-11-20T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84999551,"identity":"6eaba6b2-5b68-418b-9536-408bd1550675","added_by":"auto","created_at":"2025-06-19 17:13:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":365401,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model of root litter decomposition and microbial interactions in natural forests and plantations under differing nitrogen availability.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/ac262e67bcf527e72a2a8fab.jpg"},{"id":84999167,"identity":"e745437a-c043-475b-bfff-429b45ea5d98","added_by":"auto","created_at":"2025-06-19 17:05:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":419135,"visible":true,"origin":"","legend":"\u003cp\u003eNonmetric multidimensional scaling (NMDS) of soil saprophytic fungal (a) and ectomycorrhizal fungal (b) communities in the un-trenched and trenched plots in the natural forest and plantation. NMDS was plotted based on Bray–Curtis (n = 3).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/cdece871ced310ef52e3d307.jpg"},{"id":84999552,"identity":"e2c64b69-58d8-478c-b796-d3cc6d47bf88","added_by":"auto","created_at":"2025-06-19 17:13:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":467662,"visible":true,"origin":"","legend":"\u003cp\u003eRelative sequence read abundance of the top 15 most abundant ectomycorrhizal (ECM) (a) and saprophytic (SAP)(b) fungal genera in un-trenched (red) and trenched (blue) plots in the natural forest and plantation (n = 3).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/4f3e24eab75b9ede3ea8dc52.jpg"},{"id":84999554,"identity":"ed919b46-cf63-4a2f-bbde-e90652c8955d","added_by":"auto","created_at":"2025-06-19 17:13:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":653959,"visible":true,"origin":"","legend":"\u003cp\u003eSequence reads\u003cstrong\u003e \u003c/strong\u003eof ectomycorrhizal fungi and saprophytic fungi, soil fungal biomass, ectomycorrhizal fungal biomass, saprophytic fungal biomass, bacterial biomass, and ratio of bacterial biomass and fungal biomass in untrenched and trenched plots in the natural forest and plantation. The results of mixed-effect analysis of variance (ANOVA) with forest type (F), trenching (T), and their interaction (F×T) as fixed factors and sampling plot as a random factor are shown.The red arrows and numbers show the percentage increase or decrease after trenching compared to the untrenched plot (n=3).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/b5ec7db3dc0eab303d67ad8d.jpg"},{"id":84999173,"identity":"ee36b1c3-f9a1-4ffb-a95f-6fb4170465de","added_by":"auto","created_at":"2025-06-19 17:05:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":360375,"visible":true,"origin":"","legend":"\u003cp\u003eRelative sequence read abundances of the fungal guilts in un-trenched (red) and trenched (blue) plots in the natural forest and plantation (n = 3).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/0ec5642284babdc5564f236f.jpg"},{"id":84999556,"identity":"d1af7ff7-c33a-432d-a20e-928cbbaee215","added_by":"auto","created_at":"2025-06-19 17:13:59","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":926228,"visible":true,"origin":"","legend":"\u003cp\u003eRelative sequence read abundance of exploration types and saprophytic groups in un-trenched (red) and trenched (blue) plots in natural forests and plantations. Relative sequence read abundance were calculated by dividing the sequence read abundance of each ectomycorrhizal fungal exploration type or saprotrophic fungal group by the total sequence reads of ectomycorrhizal fungi or saprotrophic fungi, respectively. Results of mixed-effect analysis of variance (ANOVA) with forest type (F), trenching (T), and their interaction (F×T) as fixed factors and sampling plot as a random factor are shown in each sub-figure. The bars represent standard errors of the means, n = 3.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/db7ff8a096827e1049d03f66.jpg"},{"id":84999555,"identity":"08c90985-adb2-45c0-aa1c-86c5240d6123","added_by":"auto","created_at":"2025-06-19 17:13:59","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":708226,"visible":true,"origin":"","legend":"\u003cp\u003eThe enzyme activities of β-glucosidase (a), β-glucuronidase (b), N-acetyl-β-D-glucosaminidase (c), leucine aminopeptidase (d), and acid phosphatase (e) at the ectomycorrhizal community-level were determined in the soil of un-trenched and trenched plots in plantation and natural forest. The results of mixed-effect analysis of variance (ANOVA) with forest type (F), trenching (T), and their interaction (F×T) as fixed factors and sampling plot as a random factor are shown in each sub-figure. The bars represent standard errors of the means, n = 3.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/cc5d522c8a699824ec97b034.jpg"},{"id":84999175,"identity":"d2952c4b-e1a3-4669-bc76-bebdafeb945f","added_by":"auto","created_at":"2025-06-19 17:05:59","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":566655,"visible":true,"origin":"","legend":"\u003cp\u003eSoil ammonification (a), nitrification (b), mineralization (c), and root litter decomposition rates (d) in the soil of un-trenched and trenched plots in plantation and natural forest. The results of mixed-effect analysis of variance (ANOVA) with forest type (F), trenching (T), and their interaction (F×T) as fixed factors and sampling plot as a random factor are shown in each sub-figure. The bars represent standard errors of the means, n = 3.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/7b759be5592d14cfc521d635.jpg"},{"id":84999182,"identity":"e145c755-56b6-4f6e-8a47-605580e7aef4","added_by":"auto","created_at":"2025-06-19 17:05:59","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2741188,"visible":true,"origin":"","legend":"\u003cp\u003eStructural equation model (SEM) analysis of the effects of trenching on the root litter mass loss and nitrogen process in the natural forest (a, b) and plantation (c, d). Black and red solid bold arrows indicate significant positive and negative effects at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 level, respectively, and red and black dashed lines indicate significant effects at 0.05\u0026lt;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.1 level. Gray dashed lines indicate insignificant effect \u003cem\u003eP\u003c/em\u003e\u0026gt;0.1. R\u003csup\u003e2\u003c/sup\u003e values indicate the proportion of variation explained by the corresponding variables. Values associated with the solid arrows indicate standardized regression weights. * \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.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/247531ca4a2c8d28d961395d.jpg"},{"id":96650127,"identity":"a89d7c67-3c4f-49af-9558-598c850da4b3","added_by":"auto","created_at":"2025-11-24 16:08:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7894580,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/5d996e2c-16e0-4b40-bda0-97bca20a0f8d.pdf"},{"id":84999190,"identity":"23604894-cae2-48c3-869f-5c31f63be112","added_by":"auto","created_at":"2025-06-19 17:05:59","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":8914404,"visible":true,"origin":"","legend":"","description":"","filename":"Copyofsupplementarymaterial2025.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6883380/v1/1e33d2492963a643f7026958.xlsx"}],"financialInterests":"","formattedTitle":"Ectomycorrhizal exploration types mediate soil decomposition and nitrogen dynamics of sub-alpine forest","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil organic matter decomposition is a key process in the carbon (C) and nitrogen (N) cycle of forest soils, and affects the productivity and stability of forest ecosystems (Fernandez et al., 2020; Knops et al., 2002). Soil fungi are important participants in decomposition, and obtain growth-limiting resources by secreting enzymes to mobilize C or organically bound mineral nutrients (Baldrian, 2016; van der Heijden et al., 2015). Interactions or antagonisms between key microbial groups, such as saprophytic and symbiotic fungi, or between bacteria and fungi, play a significant role in driving microbial mechanisms that influence decomposition and mineralization. In addition, other microbial interactions and environmental factors also contribute to these processes (Fernandez and Kennedy, 2016; Romaní et al., 2006; Sterkenburg et al., 2018). The relative importance of these interactions, however, depends on factors such as environmental conditions, community composition, and resource availability. Ectomycorrhizal (ECM) fungal species have evolved from different lineages (Martin et al., 2016), and differ in appearance and morphology, particularly in the presence and abundance of extracellular mycelia and rhizomorphs (Agerer, 2001; Peay et al., 2011). Based on these structural traits, ectomycorrhizal fungi are classified into different exploration types (Agerer, 2001), which differ in function in terms of carbon storage efficiency, enzyme activity, and nutrient uptake and transport (Hobbie and Agerer, 2010; Tedersoo et al., 2012). Recently, Jörgensen et al. (2023) suggested that ectomycorrhizal species that have a high abundance of mycelium in soils (medium distance exploration types) have a longer lifespan and lower foraging ability than species that have less mycelium in soils (short distance exploration types).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil enzymes, as mediators of decomposition and mineralization, are mainly released by microorganisms such as saprophytic and ectomycorrhizal fungi, and bacteria (Lindahl and Tunlid, 2015; Schneider et al., 2012; Whalen et al., 2021). The ability of ectomycorrhizal and saprophytic fungi to release enzymes may vary significantly depending on species/genus or specific morphological type (exploration type), soil nutrient availability, and their interactions (Courty et al., 2010; Otgonsuren et al., 2020; Tedersoo et al., 2012). It has been shown that some ectomycorrhizal genera, such as \u003cem\u003eHygrophorus sp\u003c/em\u003e, \u003cem\u003eRussula sp\u003c/em\u003e, \u003cem\u003eContinarius sp\u003c/em\u003e,\u003cem\u003e\u0026nbsp;and Tomentella sp\u003c/em\u003e, have the ability to secrete the enzymes involved in organic matter decomposition (Walker et al., 2014). In support of the role of ectomycorrhizas in organic matter decomposition, \u003cem\u003eCortinarius acutus\u0026nbsp;\u003c/em\u003ewas associated with lower soil C storage in a boreal forest (Lindahl et al., 2021). The activity of ectomycorrhizal fungi has been shown to lead to suppression of the activity of saprotrophic fungi, the so-called Gadgil-effect. In studies by Gadgil and Gadgil (1978), \u003cem\u003eInocybe\u003c/em\u003e \u003cem\u003espp.\u003c/em\u003e were assumed to suppress decomposition, and more recently, \u003cem\u003eTomentella\u003c/em\u003e species were related to a ‘Gadgil effect’ in \u003cem\u003ePinus\u003c/em\u003e stands (Fernandez et al., 2020). Compared with the contact and the short-distance exploration types, the potential enzyme activity on the root tip surface of ECM with long and medium-distance exploration types is relatively higher (Finlay, 2008; Tedersoo et al., 2012). It is precisely these different nutrient acquisition capabilities, which may lead to different degrees of inhibition of saprophytic fungi, and therefore, to different effects on ecological function in forest soils. However, the relative importance of dominant genera and exploration types in decomposition and mineralization is still unclear.\u003c/p\u003e\n\u003cp\u003eGlobally, the area of planted forests is expanding at a rate of 2% per year, of which about half is derived from conversion from primary and secondary forests (van Dijk and Keenan, 2007). The conversion of natural forests to plantations has a range of impacts on ecosystem functions and services, such as soil organic carbon loss and reduced nutrient availability (Foley et al., 2005; Guillaume et al., 2015). Our previous study (Wang et al., 2023) has shown that clearing natural forests and replanting as a plantation result in considerably lower amounts of N and soil organic carbon (SOC), and also lower rates of N mineralization. After conversion from natural forest to plantation, the ratio of ectomycorrhizal fungi to saprophytic fungi increased significantly, and ectomycorrhizal fungi dominated. The decrease in saprotrophic fungi was explained by the lower amounts of SOC (Wang et al., 2023). However, how differences in nutrient availability resulting from forest conversion affect the competitive relationship between ectomycorrhizal and saprophytic fungi and further soil function, remains largely unknown.\u003c/p\u003e\n\u003cp\u003eSoil trenching is a common method used to alter belowground C inputs through roots and mycorrhizas to simulate forest disturbance (Fernandez and Kennedy, 2016). Soil trenching results in the death of ectomycorrhizas and fine roots as root stores of non-structural carbohydrates become depleted (Aubrey and Teskey, 2018; Bååth et al., 2004). In this study, we set up un-trenched and trenched plots in a natural forest and a plantation that differ in N availability (Fig. S1). The soil properties, soil fungal communities, and soil functions (enzyme activity, decomposition, and mineralization) of the un-trenched plots and trenched plots in two forest types were determined. We tested the following hypotheses, 1) we predict that trenching will have a greater effect on ectomycorrhizal medium- and long-distance exploration types than contact exploration types (Fig. 1, H1), and 2) we predict that within each forest type and across both forest types changes in other microorganisms (bacteria or saprophytic fungi), will be most strongly correlated with changes in the relative abundance of medium and long distance exploration type ectomycorrhizal fungi (as compared to other exploration types) (Fig. 1, H2). 3) We predict that within each forest type the effect of trenching on root litter decomposition will negatively correlate with N availability (as measured by NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e) and will be greater in the low N availability forest than the higher N availability forest (Fig. 1, H3).\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eExperimental design and sample collection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn September 2020, we selected two adjacent forest types with similar elevations in the Western Sichuan subalpine area near Miyaluo town, Sichuan Province (31°47′36\" N, 102°42′16\" E, 3400 m above sea level). One is a \u003cem\u003ePicea asperata Mast\u003c/em\u003e (Dragon spruce) plantation with low N availability and the other is a natural forest with high N availability with \u003cem\u003ePicea asperata Mast\u003c/em\u003e (Dragon spruce) as the dominant tree (85% of stem number) (Wang et al., 2023). The other tree species in the natural forest is \u003cem\u003eAbies faxoniana\u003c/em\u003e. The main shrubs are \u003cem\u003eSorbus amurensis\u003c/em\u003e, \u003cem\u003eRosa spp.\u003c/em\u003e, \u003cem\u003eEuonymus spp.\u003c/em\u003e, and \u003cem\u003eLonicera spp\u003c/em\u003e. The main herbs are \u003cem\u003ePedicularis sylvatica\u003c/em\u003e, \u003cem\u003eRubia cordifolia\u003c/em\u003e \u003cem\u003eL.\u003c/em\u003e, \u003cem\u003ePternopetalum tanakai\u003c/em\u003e, \u003cem\u003eAnemone cathayensis\u003c/em\u003e, and ferns. In contrast, the plantation consists entirely of \u003cem\u003ePicea asperata\u003c/em\u003e, and the understory vegetation is sparse. The average tree height in natural forests is 19 m and the age varies, with the maximum age being 55 a. In contrast, the plantation was more evenly aged at 41 years old (in 2021) with a mean height of 20 m. For additional information on the origin and historical background of plantations and natural forests see Wang et al. (2023).\u003c/p\u003e\n\u003cp\u003eIn May 2021, three 20 × 20 m plots were established in each of the two forest types (natural forest and plantation). The plots within each forest type were spaced approximately 30 m apart, and the minimum distance between plots of different forest types was approximately 200 m (Fig. S1). Within each plot, a 1.5 × 1.5 m subplot was trenched using 0.4 m deep steel barriers to minimize inputs from roots and mycorrhizal fungi. The trenched subplots within each forest type were spaced at least 40 m apart to ensure independence of treatment effects.\u003c/p\u003e\n\u003cp\u003eRoot samples from \u003cem\u003ePicea asperata\u003c/em\u003e trees were extracted using a spade from a different part of the site. After removing the soil, the fine roots (\u0026lt;2 mm) were cut from the root system and then dried to constant weight in a 50\u0026nbsp;℃ oven, and used to fill root litter bags. Two grams of dried fine roots were put into mesh bags (5×10 cm, and 80 μm in pore size) to form a root litter decomposition bag.\u003c/p\u003e\n\u003cp\u003eIn September of 2021, after almost five months of equilibrium, three root litter bags were buried to a depth of 15 cm in the soil of each un-trenched and trenched plot. After seven months, root litter bags were collected. Nine soil cores (five cm in diameter, 15 cm in depth) per plot were collected in May 2022. Three soil cores were pooled within each plot and mixed by hand, resulting in three composite samples per plot (2 forests types×3 plots×3 replicates×2 treatments=36 samples). All samples were stored at 4 ℃. After the 2 mm sieving, the soil sample was divided into two subsamples, one for the determination of soil water content (SWC), total dissolved nitrogen (TDN), soil dissolved organic carbon (DOC), nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), ammonium nitrogen (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), microbial biomass phosphorus (MBP), soil enzymes, and soil microbial community. The other was used for the determination of pH, soil organic carbon (SOC), total nitrogen (TN), available phosphorus (AVP), and total phosphorus (TP). We completed those parameters measured with fresh soil (SWC, TDN, DOC, MBC, MBN, MBP, soil enzyme activity, microbial biomasses, and soil fungal community) in one week.\u003c/p\u003e\n\u003cp\u003eSoil properties\u003c/p\u003e\n\u003cp\u003eSoil pH was determined using a pH meter (Bante902 multi-parameter meters, Bante, China) in a 1:2.5 (w: v) water-to-soil suspension. Ten grams of fresh soil was taken and dried in an oven at 105 ℃ to constant weight, and the soil moisture content was calculated according to the moisture loss. SOC was determined by the potassium dichromate oxidation method. Specifically, soil organic matter was oxidized with 0.8 mol L\u003csup\u003e-1\u003c/sup\u003e of 1/6 K\u003csub\u003e2\u003c/sub\u003eCr\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e (170 ~ 180 ℃ oil bath for 5 min), and the remaining potassium dichromate solution was titrated with ferrous sulfate. The content of SOC was calculated from the amount of potassium dichromate consumed (Nelson and Sommers, 1996). TN was determined by the Kjeldahl nitrogen determination method. Specifically, after the soil has been digested with concentrated sulfuric acid, the ammonia produced by alkaline rectification is absorbed with boric acid, and the TN of the soil is determined by titration with a standard acid solution (Pruden et al., 1985). After the soil was digested with HClO\u003csub\u003e4\u003c/sub\u003e + H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, the TP content was determined by ascorbate molybdate colorimetry. The absorbance was then determined using a spectrophotometer (Perkin Elmer HGA500, USA) at 750 nm (Murphy and Riley, 1962). AVP was extracted with 0.5 mol·L\u003csup\u003e-1\u003c/sup\u003e NaHCO\u003csub\u003e3\u003c/sub\u003e and determined by molybdate ascorbic acid colorimetric method (UV-1601, Shimadzu Inc., Japan) at pH 8.5. Five grams of fresh soil were extracted with 50 ml (2 mol L\u003csup\u003e-1\u003c/sup\u003e) KCL, and the concentration of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e in extracts was measured photometrically at 275 nm (UV spectrophotometer (Perkin Elmer HGA500, USA) (Norman and Stucki, 1981), while the concentration of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e was determined by using the indophenol blue spectrophotometric method (UV spectrophotometer (Perkin Elmer HGA500, USA) (Rhine et al., 1998).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDOC and TDN were determined using fresh soil extraction with 2 mol·L\u003csup\u003e-1\u003c/sup\u003e KCl and then determined by TOC-TN analyzer (Liqui TOC II, Elementar, Germany). The contents of MBC, MBN, and MBP were obtained by calculating the difference between the contents of elements in the fumigated sample and the contents of elements in the unfumigated sample divided by the conversion coefficient KE (the proportion of C, N, and P extracted from the microorganisms killed by fumigation). The KE of MBC, MBN, and MBP are 0.45, 0.54, and 0.40, respectively (Brookes et al., 1985; Joergensen and Mueller, 1996; Vance et al., 1987).\u003c/p\u003e\n\u003cp\u003eSoil fungal community\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDNA was extracted from 1 g of fresh soil using the DNA extraction kit (Power Soil, QIAGEN), and then purified using the BIOMICS DNA Microprep Kit (D4301, Zymo Research). 1% agar Gel electrophoresis was used to test the integrity of genomic DNA. NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) was used to detect DNA concentration and quality. The fungal 18S primers ITS3 (5’-GATGAAGAACGYAGYRAA-3’) and ITS4 (5‘-TCCTCCGCTTATTGATATGC-3’) were used for polymerase chain reaction (Polymerase Chain Reaction, PCR) amplification. The PCR procedure is consistent with the description in the previous publication (Wang et al., 2023). The quality of PCR products was detected by 2% agarose gel electrophoresis, and the kit AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) was used to purify the PCR products. The library was constructed and sequenced using the Illumina MiSeq platform (Meiji Biotechnology Co., Ltd., Shanghai).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Quantitative Insights into Microbial Ecology (QIIME) 1.7.0-dev pipeline (http://www.qiime.org) was used for assembly and quality control of the sequencing data, and low-quality sequences were eliminated. Sequences with more than 97% similarity were classified into a single taxon (Operational Taxon, OTU) and Ribosome Database Project (RDP) (http://rdp.cme.msu). edu/) to get the classification operation unit OTU. The species information was compared in the UNITE database (https://unite.ut.ee/) to obtain the grouping information of OTU sequences at the level of kingdom, phylum, class, order, family, genus, and species using a confidence threshold of 0.7-1.\u003c/p\u003e\n\u003cp\u003eThe trophic types and functional groups of fungi were classified using the FUNGuild v1.0 tool, and the OTU output information was converted using QIIME. The output results for analysis include taxon of species, taxon level of species, trophic mode, and functional information such as grouping \"Guild\" and credibility \"Confidence\". In order not to over-interpret the functional groups of fungi, only the confidence levels of \"highly probable\" and \"probable\" were used. The quality-controlled sequencing libraries yielded an average of 71,891 ± 2,599 reads per sample (range: 33,856 to 143,725). To standardize sampling effort across samples and minimize biases caused by uneven sequencing depth, we rarefied all samples to 33,856 reads, corresponding to the lowest read count observed. All subsequent diversity and community structure analyses were based on rarefied and proportionally normalized datasets. Based on previous literature, we classified ectomycorrhizal fungi into four types: contact exploration type, short-distance exploration type, medium-distance exploration type, and long-distance exploration type. Two genera of ectomycorrhizal fungi have been classified as contact exploration types, 21 genera have been classified as medium-distance exploration types, 10 genera have been classified as long-distance exploration types, and 25 genera have yet to be recorded and are classified as unknown types. The classification of exploration types of ectomycorrhizal fungi is listed in Table S1. The relative abundance of each ectomycorrhizal fungal exploration type or saprotrophic fungal functional group was calculated by dividing the read abundance of each ectomycorrhizal fungal exploration type or saprotrophic fungal group by the total read abundance of ectomycorrhizal fungi or saprotrophic fungi, respectively. Sequence data were deposited into the NBCI with the Project number PRJNA1010565.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil fungal and bacterial biomasses\u003c/p\u003e\n\u003cp\u003eThe fungal biomass was determined using the microbial phospholipid fatty acids (PLFAs) method described in Frostegård et al. (1993). The soil samples were collected in May 2023, with the sampling depth and method identical to those used for the samples collected in May 2022. Specifically, the fatty acids were extracted from 8 g freeze-dried soil for 2 h using a single-phase extract of 23 mL chloroform: methanol: citrate buffer (volume ratio 1:2:0.8). Phospholipids were separated from neutral lipids and glycolipids on a silica gel column. Neutral lipids and glycolipids were eluted with chloroform and acetone, and polar lipids were eluted with methanol. The PLFAs was were analyzed on a gas chromatography-mass spectrometry (GC-MS). The PLFA 18:2 ω6,9c\u0026nbsp;represents fungi. The sum of the following PLFAs was used a measure of the bacterial biomass: i14:0, i15:0, a15:0, 15:0, i16:0, 10Me16:0, i17:0, a17:0, cy17:0, 17:0, br18, 10Me17:0, 18:1ω7, 10Me18:0 and cy19:0 (Frostegård and Bååth, 1996; Frostegård et al., 1993). The PLFA biomass was expressed in nanomoles of PLFA per gram (dry weight) soil.\u003c/p\u003e\n\u003cp\u003eSoil ectomycorrhizal fungal and saprophytic fungal biomasses\u003c/p\u003e\n\u003cp\u003eAfter the soil sample was collected, 5 g of fresh soil was taken for the determination of ergosterol content, while another 5 g of soil was placed in a culture bottle and incubated in a 25℃ incubator for five months. Subsequently, the ergosterol content was determined. The difference between the ergosterol content before and after incubation was used to calculate the ectomycorrhizal fungal biomass (Bååth et al., 2004). The ergosterol content measured at the end of incubation was considered to represent saprophytic fungal biomass. Ergosterol, a fungal-specific biomarker, was extracted as described by Nylund and Wallander (1992). Specifically, 5 g of soil was extracted with 5 mL of 10% KOH in methanol. The soil solution was shaken for 15 minutes and then refluxed at 70 ℃ for one hour. After cooling, 1 mL of ultra-pure water was added before centrifuging for five minutes at 3000 rpm. The cyclohexane layer obtained from this process underwent further extraction with an additional volume of methanol using another portion (1.5 ml) of cyclohexane before being evaporated under N\u003csub\u003e2\u003c/sub\u003e gas and dissolved in 1ml methanol. Following ergosterol extraction, samples were analyzed by high performance liquid chromatography (HPLC).\u003c/p\u003e\n\u003cp\u003eSoil enzyme activity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnzyme activities involving carbon ((β-glucosidase (βG), β-glucuronidase (βLU), phenoloxidase (PHE), and peroxidase (PRO)), nitrogen (N-acetyl-β-D-glucosaminidase (NAG) and leucine aminopeptidase (LAP)), and phosphorus cycles (acid phosphatase (AP)) were determined. Specifically, one gram of fresh soil was suspended in a 100 ml (2 mol L\u003csup\u003e-1\u003c/sup\u003e) sodium acetate solution at pH 5.5. Subsequently, we took 100 µL of the soil suspension to react with the respective substrates and then incubated it at 20\u0026nbsp;℃\u0026nbsp;in the dark for two hours. After the incubation, the hydrolases except LAP were terminated with 10-μL NaOH (1 M). Values of fluorescence at 365 nm excitation and 460 nm emission were measured with a fluorometer (Multimode Plate Reader, EnSpire). When calculating, LAP was calibrated using the AMC (7-amino-4-methyl coumarin) standard curve, and other hydrolytic enzymes were calibrated using the MUF (4-methylumbelliferone) standard curve. For the measurement of oxidase, 900 µL of soil suspension was taken to react with L-3,4-dihydroxyphenylalanine (20 mM) to measure the activities of PHE and PRO. For the determination of PRO, an additional 10 µL of 0.3% hydrogen peroxide should be added to the soil mixture. The absorbance values were measured before and after 20 hours of incubation (20\u0026nbsp;℃) using a spectrophotometer at 450 nm. The calculation formula for soil enzymes was given in a previous publication (Wang et al., 2017).\u003c/p\u003e\n\u003cp\u003eLitter decomposition rate\u003c/p\u003e\n\u003cp\u003eIn May 2022, after one year incubation, we collected three root litter bags from each un-trenched and trenched plot. Root litter was cleaned, and weighed after drying to a constant mass at 65\u0026nbsp;℃. The decomposition rate was calculated by dividing the weight lost by the number of decomposition days.\u003c/p\u003e\n\u003cp\u003eNet nitrogen mineralization\u003c/p\u003e\n\u003cp\u003eTo determine net nitrogen mineralization, we took 5 grams of fresh soil into a 100ml polyethylene plastic bottle and incubated it at 25° C with 24h light/dark cycle for 7 days. After the incubation was completed, the soil was extracted and the contents of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e were determined by the above methods. The ammonification rate (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), nitrification rate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), and mineralization rate (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e+ NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e) are calculated by dividing the difference in the content before and after incubation by the number of days of incubation, respectively.\u003c/p\u003e\n\u003cp\u003eStatistical analyses\u003c/p\u003e\n\u003cp\u003eMixed effect models were used to assess the effects of forest type, trenching, and their interaction on various soil properties (pH, SWC, SOC, DOC, TDN, TN, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, TP, AVP, MBC, MBN, and MBP), the relative sequence read abundance of fungal genera and fungal groups, and soil functions (enzyme activity, root litter decomposition, ammonification, nitrification, and mineralization rates). In these models, fixed effects included forest type, trenching, and their interaction, while forest plots were included as a random intercept to account for plot-level variability. Significance of fixed effects was assessed using Type III tests. The mixed effect models were implemented using SPSS 27. Pairwise comparisons of main effects were conducted using post-hoc tests with Bonferroni adjustment. Independent samples t-tests were used to determine the difference between forest type or treatments. The effect size of trenching on soil property, soil microbial biomass, relative abundance of fungi, and soil function using Cohen's d formula in the SPSS 27. We considered d = 0.2 a ‘small’ effect size, d = 0.5 a ‘medium’ effect size, and d = 0.8 a ‘large’ effect size. The effect of forest type, trenching, and their interaction on soil saprophytic fungal and ectomycorrhizal communities using permutational multivariate analysis of variance (PERMANOVA) based on a Bray-Curtis dissimilarity matrix using the Adonis function in the vegan package (R v. 4.1.2, R Development Core Team, 2021). The contribution of different genera of ectomycorrhizal and saprophytic fungal communities to differences between natural forest and plantation or between un-trenched and trenched plots was obtained using a simper analysis (R v. 4.1.2, R Development Core Team, 2021, Vegan package).\u003c/p\u003e\n\u003cp\u003eFirst, we used random forest regression to identify key predictors associated with soil function. In this analysis, we used (root litter mass loss, ammonification, nitrification, mineralization) as response variables, and (bacterial biomass, fungal biomass, ectomycorrhizal fungal biomass, saprophytic fungal biomass, dominant genera of ECM and SAP communities, ECM exploration types, SAP fungal groups) as predictor variables. The random forest model was implemented with 500 trees, and model performance was assessed using out-of-bag (OOB) error rates to ensure robustness. We initially utilized random forest regression to predict biomass or functional groups that are significantly associated with soil function. Then, to clarify the potential relationship between changes in nitrogen processes and soil properties, soil fungi, and soil enzymes after forest conversion, structural equation models were used. Trenching pathways of natural forest and plantation were used to elucidate the relationship among\u0026nbsp;them. In the process of obtaining the best model, paths and variables are deleted or added according to their strong correlation or linear regression relationship. Therefore, the final model contains data such as inorganic nitrogen, DOC, the relative abundance of long- and medium-distance exploration types, SAP basidiomycetes, soil PER, soil NAG, ammonification rate, and nitrification rate. The \u003cem\u003eP\u003c/em\u003e-values, maximum likelihood (χ\u003csup\u003e2\u003c/sup\u003e), goodness-of-fit index (GFI), and root-mean-square error of approximation (RMSEA) were used to evaluate the fitness of the model. The structural equation model was constructed in Amos 24.0.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003eSoil properties\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe natural forest had significantly higher inorganic nitrogen (IN) and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003econtents compared to the plantation forest (Table 1). For microbial biomass fractions, the MBP content in the natural forest was significantly higher than that in the plantation forest, while the MBN content was significantly lower (Table 1). In contrast, there were no significant differences in DOC or MBC contents between the two forest types under un-trenched conditions (Table 1).\u003c/p\u003e\n\u003cp\u003eTrenching significantly reduced DOC content by 21.2% compared to un-trenched plots across both forest types (Table 1). Trenching also significantly increased IN and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e concentrations, with a greater effect size observed in the plantation forest than in the natural forest (Table 1, Fig. S2). For microbial biomass, trenching significantly increased MBC by 26.9% across both forest types (Table 1). Similar to IN and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, the effect size of trenching on MBC was greater in the plantation forest than in the natural forest (Fig. S2). However, trenching had no significant effect on MBP or MBN contents in either forest type (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Details of other soil properties are provided in Table S2.\u003c/p\u003e\n\u003cp\u003eSoil bacterial biomass, fungal biomass, and fungal community\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil saprophytic fungal (F = 5.6, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and ectomycorrhizal fungal (F = 5.6, \u003cem\u003eP\u003c/em\u003e = 0.001) community compositions were significantly different between the natural forest and plantation (Fig. 2), but no difference was found between trenched and un-trenched plots (Fig. 2)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eA total of 6923 OTUs were identified, of which 359 OTUs belonged to ECM fungi.The ECM fungi were the dominant community in any forest type or plot, having more than 50 % of the fungal sequence reads. The most abundant ectomycorrhizal fungal genera in the natural forest were \u003cem\u003eRussula\u003c/em\u003e, \u003cem\u003ePiloderma\u003c/em\u003e, \u003cem\u003eCortinarius\u003c/em\u003e, \u003cem\u003eAmphinema\u003c/em\u003e, \u003cem\u003eInocybe\u003c/em\u003e, and \u003cem\u003eMacowanites\u003c/em\u003e, whereas \u003cem\u003eMacowanites\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Thelephora\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Hygrophorus\u003c/em\u003e, \u003cem\u003eInocybe,\u003c/em\u003e and \u003cem\u003eRussula\u003c/em\u003e were the dominant ectomycorrhizal genera in the plantation (Fig. 3a). Among these dominant genera, \u003cem\u003eHygrophorus\u003c/em\u003e, \u003cem\u003eTomentella\u003c/em\u003e, \u003cem\u003eMacowanites\u003c/em\u003e, and \u003cem\u003eAmphinema\u003c/em\u003e were the main genera that caused the difference of ectomycorrhizal fungal communities between the forest types (Table S3). However, the genera that caused slight differences in ectomycorrhizal fungal communities between un-trenched and trenched plots were \u003cem\u003ePiloderma\u003c/em\u003e, \u003cem\u003eCortinarius\u003c/em\u003e, \u003cem\u003eSuillus\u003c/em\u003e, \u003cem\u003eLaccaria\u003c/em\u003e, and \u003cem\u003eCenococcum\u003c/em\u003e, among others (Table S3). The dominant genera of saprophytic fungal community in the natural forest were \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003ePleotrichocladium\u003c/em\u003e, \u003cem\u003eGymnostellatospora\u003c/em\u003e, \u003cem\u003eCladophialophora\u003c/em\u003e, and \u003cem\u003eunclassified_f__Hyaloscyphaceae\u003c/em\u003e, while the dominant genera in the plantation were \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003eArchaeorhizomyces\u003c/em\u003e, \u003cem\u003eCladophialophora\u003c/em\u003e, \u003cem\u003eTrichoderma,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGymnostellatospora\u0026nbsp;\u003c/em\u003e(Fig. 3b). The genera that caused the difference in saprophytic fungal communities between two forest types were \u003cem\u003eMortierella\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Glarea\u003c/em\u003e, and \u003cem\u003eunclassified_f__Clavariaceae\u003c/em\u003e, while the genera responsible for the differences between trenched and un-trenched plots were the two ascomycetes\u003cem\u003e\u0026nbsp;Glarea\u003c/em\u003e and \u003cem\u003eInfundichalara\u003c/em\u003e (Table S3)\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe plantation had higher reads of ectomycorrhizal community, ectomycorrhizal fungal biomass, and saprophytic fungal biomass, but lower reads of saprophytic fungal community and the ratio of bacterial and fungal biomass compared to the natural forest (Figs. 4a, 4d, 4e). Trenching significantly decreased the fungal biomass, saprophytic fungal biomass, ectomycorrhizal fungal biomass, and reads of the ectomycorrhizal fungal community (Figs. 4a, 4c, 4d). The effect size of trenching on ectomycorrhizal fungal biomass and ratio of bacterial and fungal biomass were greater in the natural forest than in the plantation (Fig. S3a, Fig. S3b). However, trenching significantly increased reads number of the saprophytic fungal community, and the increase was much higher in the plantation than in the natural forest (Fig. 4b; Fig. S3a, 3b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe result showed that the relative sequence read abundance of ectomycorrhizal fungi was significantly higher in the plantation than in the natural forest, but the relative sequence read abundance of saprophytic fungi was lower in the plantation than in the natural forest (Fig. 5). Trenching significantly reduced the relative sequence read abundance of ectomycorrhizal fungi in both forest types (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) (Fig. 5), whereas it significantly increased the relative sequence read abundances of saprotrophs (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) (Fig. 5, Table S4).\u003c/p\u003e\n\u003cp\u003eWe divided ectomycorrhizal fungi and saprophytic fungi into different functional groups according to ectomycorrhizal morphology or saprophytic fungal phylogeny. The results showed that the relative sequence read abundance of contact exploration type in the plantation was significantly higher than that in the natural forest (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), but trenching had no effect on it (Fig. 6a). Trenching significantly reduced the relative sequence read abundance of the medium-distance exploration type relative to the total ectomycorrhizal fungal reads (\u003cem\u003eP\u003c/em\u003e = 0.045) (Fig. 6c) and its total read abundance (Fig. S4b), with this effect being greater in natural forests than in plantation forests (Figs. S3c and S3d). Furthermore, the relative sequence read abundances of short-distance and long-distance exploration types, calculated relative to the total ectomycorrhizal fungal reads, showed no significant differences across forest types and trenching treatments (Figs. 6b and 6d). For the saprophytic fungal groups, the relative sequence read abundances of saprophytic ascomycetes, basidiomycetes, and mortierellomycetes, calculated relative to the total saprotrophic fungal reads, were significantly higher in the natural forest than in the plantation. However, the relative sequence read abundance of mucoromycetes was significantly lower in the natural forest compared to the plantation. After trenching, the relative sequence read abundances of saprophytic ascomycetes and mortierellomycetes, expressed as proportions of the total saprotrophic fungal reads, increased significantly (Fig. 6f, Fig. 6h, and Fig. 6i).\u003c/p\u003e\n\u003cp\u003eSoil function\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results showed that the natural forest had significantly higher activities of β-glucosidase (βG) and β-glucuronidase (βLU) than the plantation. However, trenching significantly decreased the activity of βG (Fig. 7a). The interaction between the forest type and trenching significantly influenced the activity of βLU (\u003cem\u003eP\u003c/em\u003e=0.009). Trenching significantly reduced the activity of βLU in the natural forest; however, it did not affect the activity of βLU in the plantation (Fig. 7b, Fig S5).\u003c/p\u003e\n\u003cp\u003eFor the nitrogen-cycling associated enzymes, the natural forest had a significantly higher activity of N-acetyl-β-D-glucosaminidase (NAG) than that of the plantation. Trenching significantly reduced the activity of NAG, but the effect size of trenching was greater in the natural forest (Fig. 7d, Fig. S5a, Fig. S5b). Similarly, the natural forest had a significantly higher leucine aminopeptidase (LAP) activity compared to the plantation, but was not affected by trenching (Fig. 7e). In contrast, we found that the natural forest had significantly lower activities of phenoloxidase (PHE) and peroxidase (PER) than the plantation, trenching increased the activity of PER in the natural forest (Fig. 7f and 7g), and the effect size of trenching on PER was greater in the natural forest than in the plantation. The activity of acid phosphatase (AP) was higher in the natural forest than in the plantation, but was not affected by trenching (Fig. 7c). In general, except for βG, the effect size of trenching on soil enzymes in natural forests was greater than that in plantation forests (Fig. S5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ammonification rate of soil in the plantation was significantly higher than that in the natural forest, but the nitrification rate and mineralization rate were significantly lower than that in the natural forest. Trenching significantly reduced the soil ammonification rate, but increased the soil nitrification rate, and the effect size of trenching on the soil nitrification or nitrogen mineralization rates was greater in natural forests than in the plantation (Fig. 8, Fig. S5c, Fig. S5d). Additionally, we found that trenching significantly increased the root litter decomposition rate in the plantation (\u003cem\u003eP\u003c/em\u003e=0.028), but had no effect on the root litter decomposition rate in the natural forest (\u003cem\u003eP\u003c/em\u003e=0.91) (Fig. 8d, Fig. S5c, Fig. S5d).\u003c/p\u003e\n\u003cp\u003eRelationship between soil properties, soil microbe, and function\u003c/p\u003e\n\u003cp\u003eFirst, we used random forest to identify the most important factors associated with soil function in terms of biomass, dominant genera, and functional groups (Table S5). Our results showed that soil ectomycorrhizal fungal biomass was the most important predictor for root litter mass loss in the plantation (\u003cem\u003eP\u003c/em\u003e=0.099), however, the relative sequence read abundance of ectomycorrhizal fungi, \u003cem\u003eCladophialophora\u003c/em\u003e, and fungal biomass were the most important predictors for root litter mass loss in the natural forest. The relative sequence read\u0026nbsp;abundances of Ascomycota and ectomycorrhizal fungi were the most important predictor of ammonification rate in the plantation, but the most important predictor of ammonification rate in the natural forest were the relative sequence read abundances of ectomycorrhizal fungi, medium-distance exploration type, \u003cem\u003eRussula\u003c/em\u003e, and Ascomycota. For nitrification rate, the ratio of bacterial biomass to fungal biomass-PLFAs, fungal biomass-PLFAs, ectomycorrhizal fungal biomass-ergosterol, and the relative sequence read abundance of saprophytic fungi were significantly associated with changes in nitrification rate in the natural forest. However, these factors were not identified as predictors for nitrification rate in the plantation (Table S5).\u003c/p\u003e\n\u003cp\u003eBased on the above predictors identified by the random forest model, and combining changes in soil properties and soil enzymes, we constructed an SEM to examine the associations and potential pathways among these factors affecting root litter mass loss. In the natural forest, the final model explained 24% of the variation in root litter mass loss after trenching (CMIN/DF=1.032, CFI=0.997, RMSEA=0.04, X\u003csup\u003e2\u003c/sup\u003e=3.1, \u003cem\u003eP\u003c/em\u003e=0.38, Fig. 9a). The SEM results indicate that trenching was not associated with direct or indirect changes in root litter mass loss. However, trenching was associated with changes in soil PER enzyme activity and ectomycorrhizal fungal biomass, potentially mediated by its association with inorganic nitrogen content. In the plantation, the final model explained 67% of the variation in root litter mass loss after trenching (CMIN/DF=1.015, CFI=0.999, RMSEA=0.03, X\u003csup\u003e2\u003c/sup\u003e=2.0, \u003cem\u003eP\u003c/em\u003e=0.36; Fig. 9b). Trenching showed associations with root litter mass loss indirectly through NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and MBC (\u003cem\u003eP\u003c/em\u003e=0.016). Trenching was also linked to changes in ectomycorrhizal fungal biomass (\u003cem\u003eP\u003c/em\u003e=0.007) and fungal biomass (\u003cem\u003eP\u003c/em\u003e=0.01), which were further associated with root litter mass loss. Additionally, trenching was associated with changes in PER and PHE enzyme activities, mediated through NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and MBC contents. However, only PER activity showed a weak association with the rate of root litter mass loss (\u003cem\u003eP\u003c/em\u003e=0.089).\u003c/p\u003e\n\u003cp\u003eAn SEM was also constructed to explore the associations between these factors and soil nitrogen processes. In the natural forest, the final model explained 85% (CMIN/DF=0.37, CFI=1, RMSEA=0, X\u003csup\u003e2\u003c/sup\u003e=1.1, \u003cem\u003eP\u003c/em\u003e=0.78, Fig. 9c) and 96% (CMIN/DF=0.64, CFI=1, RMSEA=0, X\u003csup\u003e2\u003c/sup\u003e=3.1, \u003cem\u003eP\u003c/em\u003e=0.67, Fig. 9d) of the variation in ammonification and nitrification rates after trenching, respectively. The SEM results suggest that ectomycorrhizal fungal biomass was linked to nitrification rates through its association with NAG activity. Inorganic nitrogen was directly associated with ammonification and nitrification rates. In the plantation, the ammonification rate showed an association only with NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e levels.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEffects of forest type and trenching on saprophytic and ectomycorrhizal fungal communities\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThrough the previous conversion of the natural forest to the plantation, the two sites differed in soil physical and chemical properties as well as the community structure of saprophytic fungi and ectomycorrhizal fungi (Wang et al., 2023). In the soil, this was particularly expressed in the lower levels of soil C and N in the plantation compared to the natural forest. In the ectomycorrhizal community, the difference between the natural forest and the plantation was mainly due to differences in the sequence read abundance of four dominant taxa, resulting in a greater sequence read abundance of contact exploration type in the plantation (Fig. S4). In both forest types, trenching reduced the number of ectomycorrhizal reads and ectomycorrhizal fungal biomass, particularly in the natural forest. After trenching, fine roots die within 1-2 years as non-structural carbon reserves are depleted (Fernandez et al., 2020; Whalen et al., 2021), which also results in the death of ectomycorrhizal root tips and the associated mycelium in the soil. The greater decrease of ectomycorrhizal reads and ectomycorrhizal fungal biomass in the soil of natural forests may be linked to the greater sequence read abundance of long and medium-distance exploration types in the community. The long and medium-distance taxa \u003cem\u003ePiloderma\u003c/em\u003e, \u003cem\u003eCortinarius\u003c/em\u003e, \u003cem\u003eSuillus\u003c/em\u003e, and \u003cem\u003eCenococcum\u003c/em\u003e, contribute most to the difference between the un-trenched and trenched plots in both forest types. The proportionally greater decrease in long- and medium-distance exploration types could be due to either a higher demand for carbohydrates to maintain the large hyphal biomass or a faster turnover rate. A faster turnover rate is contrary to the supposition of Jörgensen et al. (2023), who suggested that long-distance exploration types have the longest turnover times.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn both forest types, soil fungal biomass decreased significantly after trenching. However, the decrease was less pronounced in the plantation, potentially due to the relative increase in saprophytic fungi sequence read abundance. Although the biomass of saprophytic fungi, as measured by ergosterol, did not show an increase after trenching, it is important to recognize the limitations of ergosterol as a biomarker. Not all saprophytic fungi contain ergosterol. For instance, studies have revealed that \u003cem\u003eMortierella\u003c/em\u003e species do not contain ergosterol but instead contain desmosterol (Olsson et al., 2003). \u003cem\u003eMortierella sp\u003c/em\u003e was the dominant genus in the two forest types, accounting for about 40% of the entire saprophytic fungal reads. Therefore, the saprophytic fungal biomass characterized by ergosterol is likely underestimated in our study. Moreover, our study showed that MBC increased significantly after trenching, with the proportion of increase being significantly higher in the plantation than in the natural forest. However, this increase in MBC is difficult to reconcile with the observed decrease in fungal and bacterial biomass. It is possible that the MBC increase reflects contributions from other microbial groups or a shift in microbial activity rather than absolute biomass changes. These results highlight the complexity of microbial responses to trenching and the need for complementary approaches to disentangle the underlying mechanisms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA potential limitation of our study arises from the use of the estimation technique by Bååth et al. (2004) in trenched vs. untrenched plots. Trenching inherently introduces disturbances such as the cessation of root carbon inputs and microbial community shifts, which overlap with the effects simulated during incubation. This overlap complicates the interpretation of microbial biomass and activity changes, as the observed effects may represent cumulative impacts of trenching and incubation rather than distinct incubation effects. While we focused on relative differences between treatments to mitigate this issue, these findings should be interpreted with caution. Future studies could address this limitation by incorporating additional controls, such as non-incubated trenched plots, or employing less invasive methods like in situ microbial activity measurements or isotopic tracing to disentangle these overlapping effects. Despite this caveat, our study provides valuable insights into microbial community dynamics and soil carbon processes under reduced carbon input conditions.\u003c/p\u003e\n\u003cp\u003eFrom the analysis of fungal reads, it is evident that ectomycorrhizal fungi outnumber saprophytic fungi by several multiples, suggesting that ectomycorrhizal fungi constitute the majority of fungal biomass. The observed trend in the abundance of ectomycorrhizal fungal reads aligns with the changes in fungal biomass, providing further support for this inference. The higher biomass of fungi in the plantation was also due to the higher biomass of ectomycorrhizal fungi. Studies have shown that more ectomycorrhizal fungi are recruited in nutrient-poor soils (Corrales et al., 2018). Trenching significantly increased the relative sequence read abundance of the saprophytic fungi in both forest types. The most important changes in terms of sequence read abundance were shown for ascomycetes and mortierellomycetes (Fig. S4). An increase in ascomycetes, as shown in other studies after trenching (Mayer et al., 2021) and girdling (Mayer et al., 2023) in other forest types, is related to increased decomposition (Mayer et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCommunity structure of ectomycorrhizal fungi was not significantly affected after one year of trenching. It may be due to the fact that the reduction of dissoved organic carbon in the early stage of treatment may have a more obvious effect on the species or genus with a relatively high demand for dissolved organic carbon, such as some medium- and long- distance exploration types, and these species or genus only account for a small proportion in the ectomycorrhizal fungal community. Therefore, one year of treatment did not lead to significant changes in ectomycorrhizal fungal community.\u003c/p\u003e\n\u003cp\u003eEffect of forest type and trenching on soil enzyme activity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were clear differences between the forest types for most of the hydrolase and oxidase enzymes in soils. The clear differences in the activity of PER was not reflected in differences between the forest types in decomposition. Contrary to the results of Mayer et al. (2021) where the activity of peroxidase increased with soil fertility, the plantation with the lower levels of N had the highest peroxidase activity. In Swedish boreal forests, the activity of Mn-peroxidase was significantly correlated with the presence of\u003cem\u003e\u0026nbsp;Cortinarius\u003c/em\u003e. However, our results showed no significant difference in the relative abundance of \u003cem\u003eCortinarius\u003c/em\u003e between the natural forest and the plantation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTrenching and the subsequent loss of fine roots and mycorrhizal fungi resulted in lower activities of β-glucosidase and β-glucuronidase, N-acetyl-β-D-glucosaminidase, and acid phosphatase (\u003cem\u003eP\u003c/em\u003e=0.074). As similar loss activity of these enzymes was found in a clear cut \u003cem\u003ePicea abies\u003c/em\u003e forest (Kohout et al., 2018). This pattern may indicate that these enzymes are secreted by fine roots or mycorrhizal fungi, or that their activity stimulates microorganisms to produce these enzymes. However, within a forest type, only in natural forests did trenching significantly affect soil enzyme levels, shown as a decrease in the activity of β-glucuronidase and an increase in the activity of peroxidase. In a boreal forest, a decrease in Mn-peroxidase after trenching is used as evidence to support the direct involvement of ectomycorrhizal fungi in decomposition (Sterkenburg et al., 2018). In the natural forest, the increase in peroxidase was associated with an increase in the relative abundance of\u003cem\u003e\u0026nbsp;Inocybe\u003c/em\u003e, a genus known for the ability to break down organic substrates (Bahram et al., 2018; Courty et al., 2005). While these findings provide preliminary insights into the functional shifts in microbial communities, the lack of replication at a larger scale limits their generalizability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN mineralization and the ectomycorrhizal fungal community\u003c/p\u003e\n\u003cp\u003eNitrogen mineralization from organic N to NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003eis a series of linked reactions in which the first step, depolymerization, is mediated by fungi, and the subsequent steps to NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003earemediated by bacteria (Levy-Booth et al., 2014). The incubation of both the natural forest and the plantation soils showed negative net ammonification rates, characteristics of microbial immobilization, or high rates of nitrification (Turner et al., 2007). In the incubated soil taken from the natural forest after trenching, the net ammonification rate became more negative and the nitrification rate increased compared to soil from the un-trenched plots. The relationship between the ammonification and nitrification rates suggests removal of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e through nitrification is the primary cause of the negative net ammonification rates. As a consequence trenching increased the net N mineralization rate in the natural forest, but not in the plantation. However, in situ, trenching resulted in an increase in both the soil solution levels of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003ein the natural forest, and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e in the plantation. This suggests that either the increase in saprotrophic fungi increases the rates of organic N depolymerization, or that the ectomycorrhizas were actively involved in the removal of N (Hobbie and Agerer 2010). However, the significant correlation between the ectomycorrhizal fungal biomass (ergosterol) and soil NAG enzymes supports the idea that these fungi are involved in depolymerization (Fig. 9c). The effect of trenching on nitrification rate of natural forest is greater than that of plantation forest. This may be due to the increase in the ratio of bacterial to fungal biomass caused by the substantial reduction of ectomycorrhizal fungal biomass in natural forests, thus affecting enzyme activity related to nitrogen conversion (NAG). This view is supported by our random forest analysis, which found that the ratio of bacterial to fungal biomass is the most important predictor of nitrification rate in natural forests (Table S5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite all of the changes in the ectomycorrhizal and saprophytic fungal communities, only in the plantation did trenching result in higher rates of decomposition of root material. This would suggest that under conditions of low N availability, even ectomycorrhizal communities with a dominance of contact exploration types can affect root litter decomposition. However, the limited sample size restricts our ability to fully elucidate the complex interactions among saprophytic fungi, ectomycorrhizal fungi, and bacteria in driving N cycling and decomposition processes. These results should be regarded as preliminary observations, providing a foundation for future studies. To address the limitations of this study and build on these findings, future research should aim to increase sample sizes and field replicates to enhance the statistical power and robustness of results, while also conducting multi-site studies to assess the generalizability of these patterns across diverse forest ecosystems.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe initial ectomycorrhizal fungal communities of the two forest types were different. The natural forest was dominated by the medium and long-distance exploration types, while the plantation was dominated by the contact exploration type. The ectomycorrhizal fungal communities of the natural forest were more sensitive to trenching, showing a sharp decline in ectomycorrhizal fungal biomass. Moreover, Trenching had greater effects on soil enzyme activity and nitrogen mineralization in the natural forest. The changes of NAG enzyme activity related to nitrogen conversion in natural forest soil after trenching were correlated with the changes of ectomycorrhizal fungal biomass, further affecting the soil nitrogen process. Under conditions of low N availability, even ectomycorrhizal communities with a dominance of contact exploration types can affect root litter decompostion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 32271849, 31870602), the Program of Sichuan Applied Basic Research Foundation (2023NSFSC0120 and 2024NSFSC0354), the Chinese Postdoctoral Science Foundation (2022M722296 and 2023M732500). DLG was supported by the project EXCELLENTIA under the Horizon Europe research and innovation programme, grant agreement N°101087262.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLixia Wang, Douglas Godbold and Zhenfeng Xu contributed to the field experiment conception and design. Field sampling and Lab work were performed by Haoying Gao, Shuangjia Fu, and Huichao Li. Data collection and analyses were conducted by Lin Xu, Li Zhang, and Han Li. Chengming You, Sining Liu, Hongwei Xu, Jiao Li, and Bo Tan provided valuable input through discussions and support for the statistical analyses, and all authors contributed to the manuscript writing and revisions. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data presented in this study are openly available in in the NCBI Sequence Read Archive and included in the BioProject with accession number PRJNA1010565. All other data presented in this study are available in this article and the respective supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgerer R (2001) Exploration types of ectomycorrhizae. Mycorrhiza 11: 107-114. https://doi.org/10.1007/s005720100108\u003c/li\u003e\n\u003cli\u003eAubrey DP, Teskey RO (2018) Stored root carbohydrates can maintain root respiration for extended periods. 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Geoderma 433: 116448. https://doi.org/10.1016/j.geoderma.2023.116448\u003c/li\u003e\n\u003cli\u003eWang L, Otgonsuren B, Godbold DL (2017) Mycorrhizas and soil ecosystem function of co-existing woody vegetation islands at the alpine tree line. Plant Soil 411: 467-481. https://doi.org/10.1007/s11104-016-3047-2\u003c/li\u003e\n\u003cli\u003eWhalen ED, Lounsbury N, Geyer K, Anthony M, Morrison E, van Diepen LTA, Le Moine J, Nadelhoffer K, vanden Enden L, Simpson MJ, Frey SD (2021) Root control of fungal communities and soil carbon stocks in a temperate forest. Soil Biol. Biochem. 161: 108390. https://doi.org/10.1016/j.soilbio.2021.108390\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 Soil property (Means\u0026plusmn;SE) of un-trenched and trenched plots in the natural forest and plantation (n = 3).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"803\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003eNatural forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003ePlantation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eForest type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eTrenching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eF\u0026times;T\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUn-trenched\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eTrenched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUn-trenched\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eTrenched\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eDOC\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e323\u0026plusmn;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e275\u0026plusmn;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e411\u0026plusmn;97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e214\u0026plusmn;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e32.1\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e48.5\u0026plusmn;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e15.5\u0026plusmn;1.2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e32.3\u0026plusmn;3.8a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e(mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e15.4\u0026plusmn;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e24.8\u0026plusmn;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e6.9\u0026plusmn;1.2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e21.5\u0026plusmn;2.8a\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u0026nbsp;\u003c/sup\u003e(mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e16.8\u0026plusmn;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e23.7\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e8.6\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e10.8\u0026plusmn;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eMBC\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1847\u0026plusmn;214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2127\u0026plusmn;118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1767\u0026plusmn;313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2871\u0026plusmn;354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eMBN\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e92\u0026plusmn;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e91\u0026plusmn;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e125\u0026plusmn;22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e154\u0026plusmn;21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eMBP\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2947\u0026plusmn;526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2730\u0026plusmn;495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e781\u0026plusmn;228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1719\u0026plusmn;375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: DOC: soil dissolved organic carbon;\u0026nbsp;NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e: soil ammonium nitrogen;\u0026nbsp;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e: soil nitrate-nitrogen; MBN: soil microbial biomass nitrogen; MBC: soil microbial biomass carbon; MBP: soil microbial biomass phosphorus\u003c/p\u003e\n\u003cp\u003eThe results of mixed-effect analysis of variance (ANOVA) with forest type, trenching, and their interaction as fixed factors and sampling plot as a random factor are shown (*, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01; ***, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Values suggesting significant effects are given in bold.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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},"keywords":"ectomycorrhizal fungi, exploration type, nitrogen mineralization, root litter decomposition, saprophytic fungi, soil enzyme activity","lastPublishedDoi":"10.21203/rs.3.rs-6883380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6883380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground and Aims\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEctomycorrhizal (ECM) fungi interact with saprotrophic fungi and bacteria, thereby influencing soil decomposition and nitrogen (N) mineralization. However, how the functional composition of ECM communities (i.e., exploration types) affects these processes under varying levels of N availability remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a soil trenching experiment to manipulate root-associated fungal communities in two forest types—natural forest (higher N availability, dominated by long- and medium-distance ECM types) and plantation (lower N availability, dominated by contact exploration types). We evaluated the effects of trenching on fungal biomass, community composition, soil enzyme activities, nitrogen mineralization, and root decomposition.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTrenching significantly reduced ECM fungal biomass and the relative abundance of medium-distance exploration types, particularly in the natural forest. In contrast, saprotrophic fungal sequence read abundance increased more in the plantation. Enzyme activities (except β-glucosidase) and nitrification rates were more strongly affected by trenching in the natural forest, where nitrification was positively correlated with the activities of leucine aminopeptidase and N-acetyl-β-D-glucosaminidase, and negatively correlated with ECM fungal biomass. Root decomposition increased only in the plantation and was also negatively correlated with ECM fungal biomass.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eECM exploration types influence soil N cycling and decomposition through their effects on fungal biomass and enzyme activity, with these impacts modulated by soil N availability. In low-N soils dominated by contact exploration type-ECM fungi, ECM communities exert a suppressive effect on decomposition. These findings underscore the role of ECM functional traits in shaping belowground processes under changing forest conditions.\u003c/p\u003e","manuscriptTitle":"Ectomycorrhizal exploration types mediate soil decomposition and nitrogen dynamics of sub-alpine forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-19 17:05:54","doi":"10.21203/rs.3.rs-6883380/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-09-12T15:25:35+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-23T20:56:54+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-18T01:37:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-06-14T00:21:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T02:29:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-06-12T18:44:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"03594faf-34cf-475e-8091-53d364cce88e","owner":[],"postedDate":"June 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:01:29+00:00","versionOfRecord":{"articleIdentity":"rs-6883380","link":"https://doi.org/10.1007/s11104-025-08097-9","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2025-11-20 15:57:44","publishedOnDateReadable":"November 20th, 2025"},"versionCreatedAt":"2025-06-19 17:05:54","video":"","vorDoi":"10.1007/s11104-025-08097-9","vorDoiUrl":"https://doi.org/10.1007/s11104-025-08097-9","workflowStages":[]},"version":"v1","identity":"rs-6883380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6883380","identity":"rs-6883380","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}