Dominant Tree Mycorrhizal Associations Affect Soil Nitrogen Transformation Rates Through Mediating Microbial Abundances in a Temperate Forest

Dominant Tree Mycorrhizal Associations Affect Soil Nitrogen Transformation Rates Through Mediating Microbial Abundances in a Temperate 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 Dominant Tree Mycorrhizal Associations Affect Soil Nitrogen Transformation Rates Through Mediating Microbial Abundances in a Temperate Forest Guigang Lin, Zuoqiang Yuan, Yansong Zhang, De-Hui Zeng, Xugao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-889787/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Tree-fungal symbioses are increasingly recognized to affect soil nitrogen (N) transformations, yet the role of soil microbes in the process is largely unclear. Soil microbes directly interact with trees and are a primary driver of many N transformation processes. Here, we explored the linkage among tree mycorrhizal associations, soil microbes and N transformation rates in a temperate forest of Northeast China. Across a gradient of increasing ectomycorrhizal (ECM) tree dominance, we measured soil acid-base chemistry, bacterial and fungal abundances, N-hydrolyzing enzyme activities, abundances and community composition of ammonia-oxidizing archaea (AOA) and bacteria, and net N mineralization and net nitrification rates. Results showed that soil pH, exchangeable base cations, inorganic N concentrations and N transformation rates decreased with increasing ECM tree dominance. The ECM tree dominance was negatively related to soil bacterial and AOA amoA gene abundances, and positively to soil fungal abundances and β- N -acetylglucosaminidase activities. These shifts in soil microbial abundances and enzyme activities along the mycorrhizal gradient were linked with the increase in soil acidity with increasing ECM tree dominance. Structural equation models revealed that ECM tree dominance was not directly related to N transformation rates, but indirectly to net N mineralization rates via affecting bacterial and fungal abundances, and indirectly to net nitrification rates via influencing AOA amoA gene abundances. Collectively, our results indicate that soil microbes provide a mechanistic link between mycorrhizal associations and soil N transformations, and suggest that shifts in forest mycorrhizal associations under global change could have profound consequences for biogeochemical cycling of temperate forests. General Biochemistry Ammonia oxidizer Fungal:bacterial ratio Hydrolytic enzyme Mycorrhizal type Plant-soil interaction Soil acid-base chemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Identifying and understanding factors that regulate soil nitrogen (N) transformations have long been a major theme for ecologists and soil scientists (Hobbie 1992; Knops et al. 2002). In forest ecosystems, variation in soil N cycling arises from both abiotic factors including climate and soil parent materials, and biotic factors such as dominant tree species (Hobbie 1992; Vitousek et al. 1997). Considering the profound effect of soil N availability on forest structure and function, understanding the mechanisms by which tree species affect N cycling is a critical priority under the context of global change that results in significant shifts in tree species composition (Jo et al. 2019; McDowell et al. 2020). Tree-fungal symbioses are recently recognized as an effective trait integrator that reflects and determines forest N cycling (Chapman et al. 2006; Phillips et al. 2013). Mounting evidence shows that forests dominated by ectomycorrhizal (ECM) trees are characterized by lower N availability and slower N transformation rates relative to forests dominated by arbuscular mycorrhizal (AM) trees (Phillips et al. 2013; Lin et al. 2017). One mechanism underlying this commonly observed pattern is suggested to be the difference between AM and ECM trees in mycorrhizal fungal traits (Phillips et al. 2013). Some ECM fungi can directly access organic N via secreting extracellular enzymes that results in high soil C:N ratios and slow N cycling rates, whereas AM fungi have limited enzymatic capabilities (Kohler et al. 2015). While such differences can lead to mycorrhizal-associated differences in N-cycling modes, the contribution of free-living soil microbes to these different modes is poorly understood. Free-living microbes constitute most soil microbial biomass in most ecosystems and are solely responsible for many N transformation processes such as nitrification (Kuypers et al. 2018). Consequently, other soil microbes, not just mycorrhizal fungi, should also be considered when exploring microbial mechanisms underlying mycorrhizal association effects on soil N cycling. Soil microbes directly drive N mineralization and nitrification that are two pivotal processes determining N availability to trees and forest productivity (Kuypers et al. 2018; Crowther et al. 2019). Nitrogen mineralization comprises the depolymerization of large-molecule organic N to small-molecule organic N and the ammonification of small-molecule organic N to NH 4 + -N (Schimel and Bennett 2004). The depolymerization step is catalyzed by the N-hydrolyzing enzymes that are primarily secreted by some saprotrophic and ECM fungi, and the ammonification step can be driven by most soil microbes (Isobe et al. 2020; Tatsumi et al. 2020). Nitrification is the microbial oxidation of NH 3 to NO 3 – -N via NO 2 – -N in which the NH 3 oxidation primarily driven by ammonia-oxidizing archaea (AOA) and bacteria (AOB) is the first and rate-limiting step (Prosser and Nicol 2012). Overall, soil N transformations is a step-by-step process driven by functionally distinct microbial groups, such that fully understanding how tree mycorrhizal associations affect soil N cycling should focus on each step of N transformation and its relevant microbial drivers (Tatsumi et al. 2020). There are several mechanisms by which tree mycorrhizal associations could affect soil microbes involved in N mineralization and nitrification (Cheeke et al. 2017; Mushinski et al. 2021). First, ECM fungi can suppress N-cycling microbes by competing nutrients and secreting antimicrobial compounds, while AM fungi can stimulate the growth of these microbes via exuding easily decomposable carbon (C) compounds (Cheng et al. 2012; Fernandez and Kennedy 2016). Moreover, ECM trees possess stronger soil acidifying ability relative to AM trees owing to their slower-decaying litter that retards the return of acid-buffering base cations from forest floors to mineral soils and greater root exudates that acidify soils (Yin et al. 2014; Keller and Phillips 2019). Low soil pH and base cation depletion could reduce soil microbial biomass, alter microbial community composition and inhibit N-hydrolyzing enzyme activities (Sinsabaugh et al. 2008; Rousk et al. 2009). Previous studies have shown that ECM-dominated soils are characterized by the less abundance and lower diversity of bacteria, fungi and ammonia oxidizers relative to AM-dominated soils (e.g. Bahram et al. 2020; Heděnec et al. 2020). Additionally, soil acid-base status also has a considerable effect on the quantity and quality of substrates relevant to N mineralization and nitrification. High solubility of Al 3+ and Fe 3+ under low soil pH can effectively reduce soil organic N (SON) availability for N mineralization by directly complexing with SON and serving as cation bridges between SON and clay particles (Charholm and Skyllberg 2013). The decrease in soil pH can exponentially reduce NH 3 availability for nitrification owing to the ionization of NH 3 to NH 4 + -N (Norman and Barrett 2016). Therefore, forest mycorrhizal associations could affect soil microbes and substrate availability relevant to N mineralization and nitrification through effects on soil acid-base status. Although previous studies have reported the differences between AM- and ECM-dominated forests in biogeochemical processes and soil microbes (e.g. Lin et al. 2017; Bahram et al. 2020), the potential linkage among forest mycorrhizal associations, acid-base status, soil microbes, and N cycling is largely unclear. To fill this gap, 39 plots were established across a gradient of increasing ECM tree dominance in an old-growth temperate forest of Northeast China. These plots almost evenly distribute in a 25-ha area and are under similar environmental conditions, such that this natural experimental design can largely eliminate the confounding effects of climate, soil parent material or topography. We measured soil acid-base chemistry, N-hydrolyzing enzyme activities, the abundance and composition of N-cycling microbes, substrate availability, and N cycling rates. Structural equation models were conducted to examine the direct and indirect effects of forest mycorrhizal associations, soil acid-base status, substrate availability and N-cycling microbes on net N mineralization and net nitrification rates. Specifically, we aimed to test the following hypotheses: (1) According to the mycorrhizal-associated nutrient economy framework (Phillips et al. 2013), soil acidity would increase and N transformation rates would decrease with increasing ECM tree dominance; (2) Changes in soil acid-base status along the mycorrhizal gradient would influence the activity and abundance of soil N-cycling microbes; (3) Forest mycorrhizal associations would affect soil N transformation rates via effects on soil N-cycling microbes and substrate availability. Materials And Methods Site description This study was conducted at a broad-leaved Korean pine mixed forest (BLKP) in the Changbai Mountain Natural Reserve of Northeast China (42°23′ N, 128°05′ E). This study site has a temperate continental climate, with mean annual temperature of 3.6 °C and mean annual precipitation of 700 mm. Soil of this study site is the Albi-Boric Argosols developed from volcanic ash, with the following properties: organic C of 85.28 g kg −1 , total N of 7.19 g kg −1 , and pH of 5.03 in the 0–10 cm soil layer. The BLKP forest has not been disturbed since at least 1700 (Yuan et al. 2016). Dominant tree species in the BLKP forest include Acer mono , Fraxinus mandshurica , Pinus koraiensis , Quercus mongolica and Tilia amurensis (Wang et al. 2010). In August 2018, 39 plots (20 m × 20 m) were established on the same soil type and similar topography along a gradient of increasing ECM tree dominance in a 25-ha area (Fig. S1). The terrain of this area is fairly gentle, with elevation ranging from 797.1 to 808.3 m. In each plot, individuals with diameter at breast height larger than 1-cm were measured and identified to species. Tree species were designated as AM or ECM types according to the FungalRoot database (Soudzilovskaia et al. 2020). When no mycorrhizal information was reported for some tree species, their mycorrhizal associations were designated based on mycorrhizal associations of their closely related species, considering the strong phylogenetic conservatism of mycorrhizal traits (Wurzburger et al. 2017). The ECM tree dominance of a given plot was calculated as the percentage of basal area of ECM trees to the total basal area of this plot. Given that all tree species except Populus ussuriensis only form one mycorrhizal type and P . ussuriensis just exists in one plot (Table S1), low ECM tree dominance indicates high AM tree dominance. Soil sampling and chemical analyses Soils were sampled from the upper 10-cm mineral soil using a 2.5-cm diameter stainless steel corer. In each plot, nine soil samples were collected in the 15 m × 15 m internal plot to avoid edge effects. Collected soil samples were placed into a cooler, transported to a laboratory and processed immediately. In the laboratory, soil samples were pooled by plot and then sieved through a 2-mm mesh to remove rocks and roots. Soil samples for measuring N availability and enzyme activities were immediately processed, for measuring soil C:N ratio and acid-base chemistry were air-dried, and for DNA extraction were stored at -80 °C. Aliquots of air-dried soils were milled to pass a 0.25-mm mesh. Milled samples were used to determine soil C and N concentrations, and natural abundance of 15 N by an elemental analyzer (vario MICRO cube, Elementar, Hanau, Germany) coupled to an isotope ratio mass spectrometer (IsoPrime 100, IsoPrime, Cheadle Hulme, UK). Soil natural 15 N abundance is expressed as: δ 15 N (‰) = ( R soil / R atm – 1) × 1000, where R soil and R atm are the 15 N: 14 N ratios of soil samples and atmospheric N 2 , respectively. For determining inorganic N (NH 4 + -N and NO 3 – -N) and dissolved organic N (DON) concentrations, fresh soils were extracted with 2-M KCl, shaken for 30 minutes and then filtered. Concentrations of NH 4 + -N and NO 3 – -N were measured by a continuous-flow autoanalyzer (AutoAnalyzer III, Bran + Luebbe GmbH, Germany). Total dissolved N concentration was measured colorimetrically after alkaline persulfate oxidation (Cabrera and Beare 1993). The DON concentration was calculated as the difference between total dissolved N and inorganic N concentrations. Net N mineralization and net nitrification rates were determined by quantifying changes in inorganic N and NO 3 – -N concentrations before and after a 14-day aerobic laboratory incubation at 25°C, respectively. Soil pH was measured in a slurry of 25-ml distilled water and 10-g air-dried soils using a bench-top electrode pH meter. For determining exchangeable base cations, air-dried soils were extracted with 1-M CH 3 COONH 4 , shaken for 30 minutes, centrifuged and then filtered. Extracts were analyzed to measure exchangeable Ca 2+ , Mg 2+ , K + and Na + concentrations by an inductively coupled plasma‐optical emission spectrometry (5100 ICP-OES, Agilent Technologies, Santa Clara, USA). Exchangeable bases are the sum of exchangeable Ca 2+ , Mg 2+ , K + and Na + concentrations. Extracellular enzyme assays Potential activities of two enzymes related to soil N cycling – β- N -acetylglucosaminidase (NAG) associated with chitin and peptidoglycan hydrolysis and leucine aminopeptidase (LAP) involved in hydrolyzing leucine and other amino acids from polypeptides – were measured using colorimetric methods (Parham and Deng 2000; Dick 2011; Mann et al. 2014). Fresh soils were transferred into centrifuge tubes containing 50-mM buffer (acetate buffer at pH = 5.5 for NAG and Tris buffer at pH = 8.0 for LAP). Then, reaction substrates were added to centrifuge tubes (3-mM p -nitrophenyl-β- N -acetylglucosaminide for NAG and 2-mM leucine p -nitroanilide for LAP). Centrifuge tubes were incubated at 25°C for 3 hours, after which 0.5-M NaOH and 0.5-M CaCl 2 were added to terminate the reaction. Centrifuge tubes were centrifuged and the absorbance of supernatants was measured at 405 nm using a UV-VIS spectrophotometer (UV-1750, Shimadzu, Kyoto, Japan). The background absorbance of soils (soils + buffer) and enzyme substrates (substrates + buffer) was subtracted from the sample absorbance. The resulting absorbance was compared with standard curves of p -nitrophenol for NAG and p -nitroaniline for LAP. Real-time quantitative PCR analyses Soil DNA was extracted from 0.5-g frozen soil using the FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, USA) following the manufacturer’s instructions. The DNA extracts were quantified by a NanoDrop spectrophotometer (Thermo Scientific, Waltham, USA). One subsample of DNA extracts was used for real-time quantitative PCR (qPCR) analyses, and the other was used for high-throughput sequencing after PCR amplification. Abundances of fungi, bacteria, and AOA and AOB amoA genes (encoding ammonia monooxygenase subunit A) were determined by qPCR on a 7500 Real-Time PCR System (Applied Biosystems, Foster City, USA) using primer pairs of 5.8S/ITS1f (Fierer et al. 2005), Eub338/Eub518 (Fierer et al. 2005), Arch-amoAF/Arch-amoAR (Francis et al. 2005) and amoA -1F/ amoA -2R (Rotthauwe et al. 1997), respectively. Samples, negative controls and standards were run in triplicate in 96-well plates. The 20-μl reaction mixtures contained 10-μl SYBR Premix Ex Taq, 0.4-μl forward and reverse primers, 2-μl DNA template (nuclease-free water for negative controls) and 7.2-μl nuclease-free water. Amplification conditions were 5 minutes at 95°C followed by 40 cycles of 5 seconds at 95°C, 30 seconds at 56°C and 40 seconds at 72°C. Standard curves were generated using 10-fold serial dilutions of plasmids containing corresponding DNA fragments. Briefly, PCR product of each target gene was generated according to previously mentioned primer pairs and amplification conditions, which was purified and cloned into pMD18-T vectors (Takara, Dalian, China). Then, vectors were transformed into Escherichia coli DH5α competent cells. After re-amplification and sequencing with the vector-specific primer pair of M13f/M13r, white positive clones with correct inserted DNA fragments were selected to extract plasmid DNA. Plasmid DNA was quantified and used as standards for qPCR. Amplification efficiencies of standard curves ranged from 86.5 to 96.4% with R 2 values >0.99. Melting-curve analyses, performed to evaluate the amplification specificity, resulted in a single peak. High-throughput sequencing and bioinformatic analyses The above-mentioned primer pairs including sample identifying barcodes were used to amplify amoA gene fragments of AOA and AOB for high-throughput sequencing. The PCR was conducted in 20-μl reaction mixtures containing 4-μl FastPfu buffer, 2-μl dNTPs, 0.8-μl forward and reverse primers, 0.4-μl FastPfu polymerase, 10-ng DNA template, 0.2-μl bovine serum albumin and nuclease-free water. Amplification conditions were 5 minutes at 95°C, followed by 35 cycles (30 seconds at 95°C, 30 seconds at 55°C and 45 seconds at 72°C), and a final extension of 10 minutes at 72°C. Triplicate PCR products per soil sample were pooled together, purified by an Agarose Gel DNA purification kit, and then quantified. After that, PCR products were normalized in equimolar amounts, and paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA). Sequence data are deposited in the NCBI SRA archive with accession numbers of PRJNA756050 for AOA and PRJNA756096 for AOB. Raw fastq files were demultiplexed and quality filtered using the QIIME pipeline (Caporaso et al. 2010). Raw reads that had low quality (average quality score <20 and length <150 bp) or contained ambiguous bases were removed. Moreover, raw reads that did not exactly match barcodes or primers were also removed. For AOB amoA reads, paired-end reads with overlapping base lengths >10 bp were merged using the FLASH software (Magoč and Salzberg 2011). This merging step was not conducted for AOA amoA reads because the large amplicon length (635 bp) resulted in the lack of overlapping regions between paired-end reads. Consequently, only forward reads were used for analyzing AOA community composition owing to their higher quality than the reverse reads. After deleting chimeras, high-quality sequences were assigned to operational taxonomic units (OTUs) at 97% similarity, and the most abundant sequence per OTU was selected as the representative sequence. Representative sequences were checked against the NCBI database (http://blast.ncbi.nlm.nih.gov/Blast.cgi), and non- amoA OTUs were deleted. Dominant OTUs (relative abundance >1%) were taxonomically classified through constructing neighbor-joining phylogenetic trees in the MEGA7 software (Kumar et al. 2016) using representative sequences of AOA or AOB amoA genes and taxonomically determined reference sequences. Reference sequences for AOA clusters were from Alves et al. (2013) and Lin et al. (2019), and for AOB clusters were from Avrahami and Conrad (2003) and Avrahami et al. (2003). Diversity (Shannon) and richness (Chao1) of AOA and AOB were calculated by the QIIME. These diversity indices were determined using rarefied OTU tables where the number of sequences per sample was rarefied to the minimum sequencing depth of the 39 samples (9500 sequences for AOA and 6500 sequences for AOB). Statistical analyses All statistical analyses were conducted using R (version 3.6.1; R Core Team 2019). Considering the potential spatial autocorrelation among plots (Fig. S1), generalized least-squares models were performed to analyze how variables related to N cycling, acid-base chemistry and soil microbes changed along the gradient of ECM tree dominance. These models were conducted using the gls function in the nlme package (Pinheiro et al. 2019) with ECM tree dominance as the independent variable, and soil properties as dependent variables. We fitted these models with and without a spherical autocorrelation structure, and selected models with lower Akaike Information Criterion values (Table S2). To meet the normality assumption, abundances of AOA amoA , AOB amoA , bacterial 16S rRNA and fungal ITS were log 10 -transformed. Additionally, changes in AOA and AOB community composition along the mycorrhizal gradient were visualized via non-metric multidimensional scaling (NMDS) ordinations based on Bray-Curtis dissimilarity matrices of rarefied OTU tables. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was conducted to test whether ECM tree dominance influenced AOA and AOB community composition. The NMDS and PERMANOVA were separately performed using the metaMDS and adonis functions in the vegan package (Oksanen et al. 2020). Multiple linear regression models were conducted using the lm function to examine whether soil chemical properties significantly influenced soil microbial abundances and N-hydrolyzing enzyme activities. For these models, model selection was performed according to the Akaike Information Criterion corrected for small sample size (AIC C ). The AIC C of all possible submodels was calculated by the dredge function in the MuMIn package (Bartoń 2018). If AIC C values of several submodels were smaller than two units relative to the best one (i.e. ΔAIC C <2.0), model averaging was conducted across these submodels to identify significant soil chemical variables. Furthermore, partial redundancy analysis was conducted using the rda function in the vegan package to examine whether soil chemical properties significantly influenced AOA and AOB community composition based on Hellinger-transformed OTU tables. A forward selection with 999 permutations was performed by the adonis function in the vegan package to identify statistically significant soil chemical variables. Structural equation models were conducted using the sem function in the lavaan package (Rosseel 2012) to test the direct and indirect effects of ECM tree dominance, soil acid-base chemistry, substrate availability and soil microbes on N transformation rates. The hypothesized relationships are that net N mineralization and net nitrification rates are determined by substrate availability and N-cycling microbes, which are mediated by ECM tree dominance through affecting soil acid-base chemistry (Fig. 1). In the model of net N mineralization rate, substrate availability is indicated by soil C:N ratio and DON concentration, and N-cycling microbes are indicated by gene abundances of bacterial 16S rRNA and fungal ITS and activities of NAG and LAP (Fig. 1a). In the model of net nitrification rate, substrate availability is indicated by NH 3 concentration, and N-cycling microbes are indicated by the community composition and amoA gene abundances of AOA and AOB (Fig. 1b). NH 3 concentration was calculated based on the Henderson-Hasselbalch equation assuming a p K a value of 9.25 (NH 3 = NH 4 + -N × 10 (pH–9.25) ; Norman and Barrett 2016). Community composition of AOA and AOB is indicated by the first axis of NMDS for each. In these two models, soil acid-base chemistry is a latent variable indicated by soil pH and exchangeable bases (Fig. 1). Final path models were obtained by removing paths with the highest probability values in a stepwise manner until all paths were significant (i.e. P <0.05). Fitness of structural equation models were evaluated by the Chi-square ( χ 2 ) test, comparative fit index (CFI) and standardized root mean square residual (SRMR). Results Forest mycorrhizal associations Across the 39 plots, ECM tree dominance ranged from 10.8% to 84.3% (Table S1). There were 10 AM-dominated plots (ECM tree dominance 70%). Plot-level total basal area ranged from 28.0 to 55.0 m 2 ha -1 , and did not significantly relate to ECM tree dominance ( P = 0.99; Fig. S2). Soil N cycling and acid-base chemistry All soil N cycling variables except DON concentration were significantly associated with ECM tree dominance (Fig. 2). Specifically, soil C:N ratio ranged from 10.7 to 14.4, and was positively related to ECM tree dominance ( P = 0.04; Fig. 2a). Soil total N ( P = 0.01), δ 15 N ( P <0.01), NH 4 + -N ( P = 0.01), NO 3 – -N ( P <0.01), and net N mineralization ( P = 0.02) and net nitrification rates ( P = 0.03) decreased linearly with increasing ECM tree dominance (Fig. 2b–2c, 2e–2h). Four of six acid-base chemical variables significantly linked with ECM tree dominance (Fig. 3). Soil pH ranged from 4.7 to 5.3, and decreased with increasing ECM tree dominance ( P <0.01; Fig. 3a). Soil exchangeable bases, Ca 2+ and Mg 2+ concentrations were negatively associated with ECM tree dominance (all P <0.01; Fig. 3b–3d). Soil exchangeable K + and Na + concentrations were not significantly related to ECM tree dominance (Fig. 3e–3f). Microbial abundances and N-hydrolyzing enzymes Across the mycorrhizal gradient, bacterial 16S rRNA gene abundance ( P = 0.02) decreased and fungal ITS gene abundance ( P <0.01) increased with increasing ECM tree dominance (Fig. 4a–4b). Consequently, fungal:bacterial ratio was positively correlated with ECM tree dominance ( P <0.01; Fig. S3). For N-hydrolyzing enzymes, NAG activity was positively related to ECM tree dominance ( P = 0.03; Fig. 4c), and there was no significant relationship between LAP activity and ECM tree dominance ( P = 0.06; Fig. 4d). Multiple linear regression revealed that bacterial 16S rRNA gene abundance was positively associated with soil pH ( Z = 2.10, P = 0.04; Table S3). Soil pH was negatively related to fungal ITS gene abundance ( Z = 2.21, P = 0.03) and NAG activity ( Z = 3.39, P <0.01; Table S3). Moreover, LAP activity was correlated with soil C:N ratio ( Z = 3.10, P <0.01), and exchangeable Na + ( Z = 3.01, P <0.01) and Ca 2+ concentrations ( Z = 1.98, P = 0.04; Table S3). A bundances and community composition of ammonia oxidizers The AOA amoA gene abundance, ranging from 1.2 × 10 7 to 3.3 × 10 8 g -1 soil, decreased with increasing ECM tree dominance ( P <0.01; Fig. 5a). There was no significant relationship between AOB amoA gene abundance and ECM tree dominance (Fig. 5b). The amoA gene abundance of AOA was 1–3 orders of magnitude higher than that of AOB, and the ratio of AOA to AOB amoA gene abundance was negatively associated with ECM tree dominance ( P <0.01; Fig. S4). Multiple linear regression showed that AOA amoA gene abundance was related to soil exchangeable Ca 2+ ( Z = 3.07, P <0.01) and K + concentrations ( Z = 2.20, P = 0.03; Table S3). The AOB amoA gene abundance was positively related to NH 4 + -N concentration ( Z = 2.31, P = 0.02; Table S3). A total of 376,874 high-quality sequences and 37 OTUs were obtained for AOA, and 445,450 high-quality sequences and 67 OTUs for AOB. Rarefaction curves for AOA and AOB OTUs of each soil sample reached saturation, indicating the adequate diversity coverage (Fig. S5). Phylogenetic analyses showed that dominant AOA OTUs (relative abundance >1%) were exclusively from the Nitrososphaera cluster (group I.1b lineage) and were grouped into clades A, B and E (Fig. S6). Dominant AOB OTUs (relative abundance >1%) were exclusively affiliated with the Nitrosospira genus and were grouped into clusters 1, 2, 3a, 3b, 4, 10 and 12 (Fig. S7). The Chao1 ( P = 0.02; Fig. 5c) and Shannon ( P = 0.02; Fig. 5e) indices of AOA decreased with increasing ECM tree abundance. No significant relationship was found between ECM tree dominance and Chao1 (Fig. 5d) and Shannon (Fig. 5f) indices of AOB. The PERMANOVA analysis revealed that there were significant changes in community composition of AOA ( F = 13.02, P <0.01; Fig. 6a) but not AOB ( F = 0.69, P = 0.64; Fig. 6b) along the mycorrhizal gradient. Partial redundancy analyses showed that AOA community composition was significantly related to soil C:N ratio ( F = 6.00, P = 0.02) and exchangeable Ca 2+ concentration ( F = 5.30, P = 0.02; Fig. S8a). The AOB community composition was correlated with soil exchangeable Ca 2+ concentration ( F = 3.00, P <0.01; Fig. S8b). Relationships among ECM tree dominance, soil microbes and N transformation rates For net N mineralization rates, the final structural equation model well fitted the data ( χ 2 = 18.43, P = 0.30, CFI = 0.97, SRMR = 0.08), and separately explained 58%, 9%, 34%, 26%, 37% and 38% of variation in soil acid-base chemistry, soil C:N ratio, bacterial 16S rRNA gene abundance, fungal ITS gene abundance, NAG activity and net N mineralization rate (Fig. 7a). Net N mineralization rate was directly and positively related to bacterial 16S rRNA gene abundance (standardized coefficient ( β ) = 0.46, P <0.01) and negatively related to fungal ITS gene abundance ( β = -0.33, P = 0.01; Fig. 7a). The ECM tree dominance had no significant direct effect on net N mineralization rates. In contrast, ECM tree dominance had significant indirect effects on net N mineralization rates via affecting bacterial 16S rRNA gene abundance ( β = -0.21, P = 0.01) and fungal ITS gene abundance ( β = -0.13, P = 0.04) that were mediated by soil acid-base chemistry (Fig. 7a). For net nitrification rates, the final structural equation model fitted the data well ( χ 2 = 5.13, P = 0.74, CFI = 1.00, SRMR = 0.04), and separately explained 56%, 71%, 31% and 40% of variation in soil acid-base chemistry, NH 3 concentration, AOA amoA gene abundance and net nitrification rate (Fig. 7b). The AOA amoA gene abundance was the only factor that had significant direct effects on net nitrification rate ( β = 0.63, P <0.01; Fig. 7b). Additionally, ECM tree dominance had significant indirect effects on net nitrification rate through influencing AOA amoA gene abundance that was mediated by soil acid-base chemistry ( β = -0.26, P <0.01; Fig. 7b). Discussion To comprehensively and mechanistically understand the relationship between forest mycorrhizal associations and soil N transformations, we focused on soil microbial drivers and substrate availability relevant to net N mineralization and net nitrification rates along a gradient of increasing ECM tree dominance. We showed that abundances of soil bacteria and AOA amoA gene decreased, and fungal abundance and NAG activity increased with increasing ECM tree dominance. These changes in soil microbial abundances and enzyme activities along the mycorrhizal gradient were associated with the increase in soil acidity with increasing ECM tree dominance. Structural equation models revealed that ECM tree dominance was not directly related to N transformation rates, but indirectly to net N mineralization rate via affecting soil bacterial and fungal abundances, and indirectly to net nitrification rate via influencing AOA amoA gene abundance. Our results suggest that soil microbes provide a mechanistic link between forest mycorrhizal associations and soil N transformation rates. Shifts in soil biogeochemistry along the mycorrhizal gradient Consistent with the mycorrhizal-associated nutrient economy model (Phillips et al. 2013), our results showed that soil inorganic N concentrations, N transformation rates and δ 15 N decreased with increasing ECM tree dominance (Fig. 2). This more conservative N cycling mode in ECM-dominated forests has also been demonstrated by local- (Midgley and Phillips 2016; Lin et al. 2018), regional- (Phillips et al. 2013) and global-scale studies (Averill et al. 2014; Lin et al. 2017). As such, shifts in forest mycorrhizal associations under N deposition and climate change may have profound environmental consequences owing to the potentially enhanced greenhouse gas emission and nitrate leaching under more open N cycling (Averill et al. 2018; Jo et al. 2019). Previous studies have reported the greater nitrate leaching and emissions of reactive nitrogen oxides in AM-dominated forests (Midgley and Phillips 2016; Mushinski et al. 2019). Apart from influencing soil N dynamics, forest mycorrhizal associations also had great effects on soil acid-base chemistry, characterized by the decrease in soil pH and exchangeable Ca 2+ and Mg 2+ concentrations with increasing ECM dominance (Fig. 3). The stronger soil acidity in ECM-dominated plots may be because compared with AM trees, roots of ECM trees secret greater organic acids (Brzostek et al. 2013; Yin et al. 2014) and the poorer decomposability of ECM tree litter retards the return of acid-buffering base cations from forest floors to mineral soils (Lin et al. 2018; Keller and Phillips 2019; See et al. 2019). Given the tight linkage between soil acid-base chemistry and microbial activities and community composition (Sinsabaugh et al. 2008; Fierer et al. 2009; Gurbry-Rangin et al. 2015), soil acid-base chemistry may be an important mediator between forest mycorrhizal associations and ecosystem processes. Net N mineralization rate is related to soil microbial abundances The well-known mechanism underlying the more conservative N cycling in ECM-dominated plots is that ECM fungi can mine N directly from soil organic matter via the enzymatic breakdown and Fenton reaction, while these capabilities are limited for AM fungi (Bödeker et al. 2014; Op De Beeck et al. 2018). Inconsistent with this mechanism, our structural equation model showed that ECM tree dominance did not directly relate to net N mineralization rate (Fig. 7a). The inexistence of this direct relationship may be because the dominant ECM fungal taxa in our study site do not produce oxidative enzymes or hydroxyl radicals. This speculation is supported by the current genome information revealing that not all ECM fungal taxa can acquire organic N bound in soil organic matter, indicating that effects of ECM fungi on ecological processes should depend on their community composition (Kohler et al. 2015; Pellitier and Zak 2018). Indeed, a recent study showed that the direction and magnitude of ECM fungal effects on litter decomposition were controlled by the community composition of ECM fungi (Fernandez et al. 2020). Our results revealed that ECM tree dominance indirectly linked with net N mineralization rates via affecting soil acid-base chemistry and then microbial abundances (Fig. 7a). The positive relationship between net N mineralization rates and bacterial abundances is expected since bacteria are a major biological driver of N mineralization (Petersen et al. 2012; Isobe et al. 2020). Although fungi also play a critical role in N mineralization, fungal abundance was negatively correlated with net N mineralization rates (Fig. 7a). A possible explanation for these results is that compared with fungi, bacteria have greater biological activities and faster turnover rates, such that bacteria process organic N to NH 4 + -N more rapidly (Crowther et al. 2019). Biogeochemical models and experimental studies have demonstrated that ecosystems with high fungal to bacterial ratios usually accompany with slow N transformation rates (Högberg et al. 2007; Waring et al. 2013). Consequently, the low bacterial abundance in ECM-dominated plots could be the reason for slow net N mineralization rates in these plots. Previous studies have also reported the decrease in bacterial abundance and increase in fungal abundance with increasing ECM tree dominance (Cheeke et al. 2017; Tatsumi et al. 2020; Cheeke et al. 2021), yet mechanisms underlying this result remain unclear. Our results showed that the change in relative abundance of different microbial groups along the mycorrhizal gradient was related to mycorrhizal-associated differences in soil pH (Table S3). A possible reason for this result is that compared with fungi, bacteria are less acid tolerant, leading to the high fungal dominance in ECM-dominated soils with strong acidity (Rousk et al. 2009). Apart from microbial drivers, forest mycorrhizal associations can also affect N mineralization via influencing substrate availability such as soil C:N ratio and DON concentration (Averill et al. 2014; Lin et al. 2017). Although soil C:N ratio varied along the mycorrhizal gradient, it was not significantly associated with net N mineralization rates (Fig. 7a). This non-significant relationship may be due to the narrow range of variation in soil C:N ratios (i.e. 10.7–14.4) in our local-scale study (Fig. 2a). Moreover, our results showed that DON concentration did not significantly vary along the mycorrhizal gradient and was unrelated to net N mineralization rate (Fig. 2d), although ECM-dominated plots had high potential β- N -acetylglucosaminidase activities (Fig. 4c). The decoupling between N-hydrolyzing enzyme activities and DON concentration may be because the substrate is in short supply by upstream reactions that limit the high potential hydrolyzing enzyme activities in ECM-dominated plots. These upstream reactions include the degradation of complex soil organic matter and destabilization of mineral-bound proteinaceous compounds, resulting in the release of protein and chitin that can be depolymerized by N-hydrolyzing enzymes (Kieloaho et al. 2016; Jilling et al. 2018). The degradation and destabilization reactions can be catalyzed by oxidative enzymes, such that oxidative enzyme activities are critical in determining N mineralization (Zhu et al. 2014; Jilling et al. 2018). For instance, Kieloaho et al. (2016) revealed that activities of oxidative enzymes rather than N-hydrolyzing enzymes were tightly related to N mineralization rates. Considering the commonly observed positive relationship between soil pH and oxidative enzymes activities (Sinsabaugh et al. 2008), the strong soil acidity in ECM-dominated forests may inhibit oxidative enzyme activities and thus decrease substrate availability for N-hydrolyzing enzymes. Net nitrification rate is related to AOA amoA abundances Consistent with net N mineralization rate, our results also revealed that net nitrification rate was related to microbial abundance but not to substrate availability (Fig. 7b). The non-significant NH 3 effect may be because NH 3 concentration (0.06–0.57 μM; Fig. S9) was exactly within the range of the half-saturation constant for NH 3 of AOA (0.04–5.2 μM; Kits et al. 2017, Shi et al. 2018), indicating that NH 3 availability may not be a limiting factor for AOA performing nitrification. In contrast, AOB have low substrate affinity with the half-saturation constant for NH 3 ranging from 1.77 to 200 μM (Kits et al. 2017, Shi et al. 2018), beyond the NH 3 concentration in our study site. Consequently, differences in the NH 3 affinity could explain that net nitrification rate was related to amoA abundance of AOA rather than that of AOB (Fig. 7b). This result is consistent with previous studies showing that NH 3 concentration led to niche specialization and differentiation between AOA and AOB (Prosser and Nicol 2012), and AOA played a pivotal role in nitrification of acidic soils (Yao et al. 2013; Mushinski et al. 2019). Apart from abundances, community composition of AOA can also control nitrification rates owing to the functional heterogeneity among AOA lineages (Alves et al. 2013). However, our result showed that AOA community composition was not associated with net nitrification rates (Fig. 7b). This result may be due to the low AOA diversity in our study site in which functional similar clade B and clade E accounted for 92.5% of total sequences (Fig. S6). Our results showed significant changes in amoA abundances and community composition of AOA along the mycorrhizal gradient (Figs. 5a, 6a), which is consistent with previous studies (Tatsumi et al. 2020; Mushinski et al. 2021). These changes along the mycorrhizal gradient have been attributed to mycorrhizal-associated differences in soil pH (Gubry-Rangin et al. 2015; Mushinski et al. 2019). Inconsistent with this expectation, exchangeable Ca 2+ concentration rather than soil pH was related to AOA amoA abundance (Table S3) and community composition (Fig. S8a). Considering that soil pH affects AOA activities mainly through influencing the ionization equilibrium between NH 3 and NH 4 + -N (Prosser and Nicol 2012; Norman and Barrett 2016), the sufficient NH 3 supply in our study site may be the reason for the non-significant relationship between soil pH and AOA amoA abundances. The positive link between AOA amoA abundance and exchangeable Ca 2+ concentration has also been previously reported (Yao et al. 2013; Ciccolini et al. 2016), yet mechanisms underlying this link are still unknown. Consequently, mechanistic studies are needed to explore the role of Ca 2+ in nitrification under the context of globally increasing N deposition that leads to soil acidification and Ca 2+ leaching (Bowman et al. 2008). Conclusions Our results showed that forest mycorrhizal associations were indirectly related to soil N transformation rates via affecting soil microbial abundances that were mediated by mycorrhizal-associated differences in soil acid-base chemistry. These results highlight that soil acid-base chemistry is a critical mediator between forest mycorrhizal associations and N-cycling microbes, and indicate that soil microbes provide a mechanistic link between mycorrhizal associations and soil N cycling. Moreover, these findings have important implications for understanding forest community structure and function. First, our results suggest that while previous studies have reported that N deposition favors AM trees over ECM trees (Averill et al 2018; Jo et al. 2019), intensified soil acidification under high N deposition may offset such effect owing to the more adaptive of ECM trees to acidic soils. Considering that AM-dominated forests harbor more ammonia oxidizers, shifts in forest mycorrhizal associations under global change could have significant consequences for water quality via affecting NO 3 - -N leaching and important feedbacks to climate change via influencing N oxide emissions, since these two processes are tightly related to ammonia oxidizers (Mushinski et al. 2019; Prosser et al. 2020). Moreover, if our results can be held across wide climatic and edaphic conditions, it is feasible to incorporate tree mycorrhizal associations into ecological models to predict soil N dynamics. Declarations Acknowledgements We are grateful to the National Research Station of Changbai Mountain Forest Ecosystems for providing the experimental site and relevant supports. This work was supported by the National Natural Science Foundation of China (Nos. 31700538 and 31830015) and the Youth Innovation Promotion Association CAS (No. 2019200). Authors' contributions GL, DZ, XW conceived the study; GL, ZY and YZ collected and analyzed the data; All authors contributed critically to the drafts and gave final approval for publication. Data availability The datasets generated during and/or analysed during the current study are available from corresponding authors on reasonable request. Conflict of interest The authors declare that they have no conflict of interest. References Alves RJE, Wanek W, Zappe A, Richter A, Svenning MM, Schleper C, Urich T (2013) Nitrification rates in Arctic soils are associated with functionally distinct populations of ammonia-oxidizing archaea. ISME J 7:1620–1631 Averill C, Dietze MC, Bhatnagar JM (2018) Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob Change Biol 24:4544–4553 Averill C, Turner BL, Finzi AC (2014) Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. 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Soil Biol Biochem 76:183-192 Supplementary Files Supportinginformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 04 Nov, 2021 Reviews received at journal 10 Sep, 2021 Reviewers invited by journal 08 Sep, 2021 Editor invited by journal 08 Sep, 2021 Editor assigned by journal 08 Sep, 2021 First submitted to journal 07 Sep, 2021 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-889787","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":50967271,"identity":"4e9d82f0-8d77-48ad-a15e-b467194869f0","order_by":0,"name":"Guigang Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACPmYQWSFBghY2sJYzJGkBEYxtJOhgYGPnPfzy6zwLeX4G5ocfGGruEOMwvjRr2W0ShjMb2IwlGI49I0YLj5mx5DYJxg0HGMwYGBsOE6tljoT9/gPs34jWYvzwY4NE4gYGHhJsYWY4JpE84zBPsUTCMSK08POfMf74o6bOtr+9feOHDzVEaAFZJM0DokBxmkCUBqDajz+IVDkKRsEoGAUjFAAARHIuZKTwl4UAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4473-2708","institution":"Institute of Applied Ecology Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Guigang","middleName":"","lastName":"Lin","suffix":""},{"id":50967272,"identity":"3b7a62ef-ab62-43b0-99bb-63b27d664b8f","order_by":1,"name":"Zuoqiang Yuan","email":"","orcid":"","institution":"Institute of Applied Ecology Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zuoqiang","middleName":"","lastName":"Yuan","suffix":""},{"id":50967273,"identity":"8233ba37-ce95-445f-bace-040908d74251","order_by":2,"name":"Yansong Zhang","email":"","orcid":"","institution":"Institute of Applied Ecology Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yansong","middleName":"","lastName":"Zhang","suffix":""},{"id":50967274,"identity":"f62941eb-de76-4774-9c65-fe2b3b1b77fd","order_by":3,"name":"De-Hui Zeng","email":"","orcid":"","institution":"Institute of Applied Ecology Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"De-Hui","middleName":"","lastName":"Zeng","suffix":""},{"id":50967275,"identity":"8f9f0624-bb80-47d3-91e9-d62da4604b5b","order_by":4,"name":"Xugao Wang","email":"","orcid":"","institution":"Institute of Applied Ecology Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xugao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2021-09-09 11:33:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-889787/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-889787/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13831219,"identity":"e8faa300-e60f-4f0a-834d-5463025a49d8","added_by":"auto","created_at":"2021-09-21 15:08:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104431,"visible":true,"origin":"","legend":"Conceptual models depicting the direct and indirect effects of ECM tree dominance, soil acid-base chemistry, substrate availability, and soil microbes on net N mineralization (a) and net nitrification rates (b). Soil acid-base chemistry is a latent variable indicated by soil pH and exchangeable (exch.) bases. DON, dissolved organic N; AOA, ammonia-oxidizing archaea; AOB, ammonia-oxidizing bacteria.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/394d50a4ec39cbb6cd0ee791.jpg"},{"id":13831642,"identity":"495ba03f-988f-4111-943a-e64c0faaef2e","added_by":"auto","created_at":"2021-09-21 15:11:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117694,"visible":true,"origin":"","legend":"Changes in soil N dynamics with increasing ECM tree dominance (n = 39). Statistical results are obtained from generalized least-squares models with a spherical autocorrelation structure for soil C:N ratio, and without the spherical autocorrelation structure for other variables (Table S2). Net N min, net N mineralization rate; net nitri, net nitrification rate.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/eecc7d90558823b540ebf456.jpg"},{"id":13831641,"identity":"a2de3450-0b42-4155-97fd-3bc60729c1cd","added_by":"auto","created_at":"2021-09-21 15:11:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134995,"visible":true,"origin":"","legend":"Changes in soil acid-base chemistry with increasing ECM tree dominance (n = 39). Statistical results are obtained from generalized least-squares models with a spherical autocorrelation structure for soil pH, and without the spherical autocorrelation structure for other variables (Table S2). Exchangeable bases are the sum of exchangeable Ca2+, Mg2+, K+ and Na+ concentrations.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/837744a257064d27786495ce.jpg"},{"id":13831224,"identity":"fa692ac4-bc88-4dff-9f94-02989c59be7c","added_by":"auto","created_at":"2021-09-21 15:08:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159751,"visible":true,"origin":"","legend":"Changes in bacterial 16S rRNA and fungal ITS gene abundances, and N-hydrolyzing enzyme activities with increasing ECM tree dominance (n = 39). Statistical results are obtained from generalized least-squares models (Table S2). NAG, β-N-acetylglucosaminidase; LAP, leucine aminopeptidase.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/e0364e1b408caf7328b162dd.jpg"},{"id":13831217,"identity":"bfc52577-a0c5-48f8-a388-161f030ccf5f","added_by":"auto","created_at":"2021-09-21 15:08:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120703,"visible":true,"origin":"","legend":"Changes in amoA gene abundances and diversity of ammonia-oxidizing archaea (AOA) and bacteria (AOB) with increasing ECM tree dominance (n = 39). Statistical results are obtained from generalized least-squares models with a spherical autocorrelation structure for AOA Shannon index, and without the spherical autocorrelation structure for other variables (Table S2).","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/a898dcaae50d93125d0f860e.jpg"},{"id":13831221,"identity":"8e714c83-88b2-4408-8204-9f01f5835c40","added_by":"auto","created_at":"2021-09-21 15:08:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":85651,"visible":true,"origin":"","legend":"Non-metric multidimensional scaling (NMDS) ordinations showing changes in community composition of ammonia-oxidizing archaea (AOA) and bacteria (AOB) along the mycorrhizal gradient (n = 39). Statistical results are obtained from the permutational multivariate analysis of variance (PERMANOVA).","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/0a1a379910f7d7b9f21ae40e.jpg"},{"id":13831222,"identity":"9bf792eb-cfbe-4a2a-9070-56f342122dad","added_by":"auto","created_at":"2021-09-21 15:08:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95350,"visible":true,"origin":"","legend":"Final structural equation models depicting the direct and indirect effects of ECM tree dominance, soil acid-base chemistry, substrate availability, and soil microbes on net N mineralization (a) and net nitrification rates (b). Soil acid-base chemistry is a latent variable indicated by soil pH and exchangeable (exch.) bases. Final models are obtained by removing paths with the highest probability values in a stepwise manner from full models shown in Fig. 1 until all paths are significant. R2-values represent the percentage of variation of response variables explained by all paths. Numbers next to the arrows are standardized path coefficients. *, P \u003c0.05; **, P \u003c0.01. AOA, ammonia-oxidizing archaea.","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/7dfc6144e80d0d8f8a7cb713.jpg"},{"id":13831647,"identity":"4dd150b5-7da8-41c1-a4f8-1b41870ec6e0","added_by":"auto","created_at":"2021-09-21 15:11:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1061811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/7a63aaa9-9885-464c-b564-5b2a3f4b3120.pdf"},{"id":13831643,"identity":"ffb5934e-97dc-46ee-85da-7e8037b4f67f","added_by":"auto","created_at":"2021-09-21 15:11:08","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2180010,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-889787/v1/fbb67285f09b169bf9e4382d.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDominant Tree Mycorrhizal Associations Affect Soil Nitrogen Transformation Rates Through Mediating Microbial Abundances in a Temperate Forest\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdentifying and understanding factors that regulate soil nitrogen (N) transformations have long been a major theme for ecologists and soil scientists (Hobbie 1992; Knops et al. 2002).\u0026nbsp;In forest ecosystems, variation in soil N cycling arises from both abiotic factors including climate and soil parent materials, and biotic factors such as dominant tree species\u0026nbsp;(Hobbie 1992; Vitousek et al. 1997).\u0026nbsp;Considering the profound effect of soil N availability on forest structure and function, understanding the mechanisms by which tree species affect N cycling is a critical priority under the context of global change that results in significant shifts in tree species composition (Jo et al. 2019; McDowell et al. 2020).\u003c/p\u003e\n\u003cp\u003eTree-fungal symbioses are recently recognized\u0026nbsp;as an effective trait integrator that reflects and determines forest N cycling\u0026nbsp;(Chapman et al. 2006; Phillips et al. 2013).\u0026nbsp;Mounting evidence shows that forests dominated by ectomycorrhizal (ECM) trees are characterized by lower N availability and slower N transformation rates relative to forests dominated by arbuscular mycorrhizal (AM) trees (Phillips et al. 2013; Lin et al. 2017). One mechanism underlying this commonly observed pattern\u0026nbsp;is suggested to be the difference between AM and ECM trees in mycorrhizal fungal traits\u0026nbsp;(Phillips et al. 2013). Some ECM fungi can directly access organic N via secreting extracellular enzymes that results in high soil C:N ratios and slow N cycling rates, whereas AM fungi have limited enzymatic capabilities (Kohler et al. 2015). While such differences can lead to mycorrhizal-associated differences in N-cycling modes, the contribution of free-living soil microbes to these different modes is poorly understood. Free-living microbes constitute most soil microbial biomass in most ecosystems and are solely responsible for many N transformation processes such as nitrification (Kuypers et al. 2018).\u0026nbsp;Consequently, other soil microbes, not just mycorrhizal fungi, should also be considered when exploring microbial mechanisms underlying mycorrhizal association effects on soil N cycling.\u003c/p\u003e\n\u003cp\u003eSoil microbes directly drive N mineralization and nitrification that are two pivotal processes determining N availability to trees and forest productivity (Kuypers et al. 2018; Crowther et al. 2019). Nitrogen mineralization comprises the depolymerization of large-molecule organic N to small-molecule organic N and the ammonification of small-molecule organic N to NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (Schimel and Bennett 2004). The depolymerization step is catalyzed by the N-hydrolyzing enzymes that are primarily secreted by some saprotrophic and ECM fungi, and the ammonification step can be driven by most soil microbes (Isobe et al. 2020; Tatsumi et al. 2020). Nitrification is the microbial oxidation of NH\u003csub\u003e3\u003c/sub\u003e to NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e-N via NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e-N in which the NH\u003csub\u003e3\u003c/sub\u003e oxidation primarily driven by ammonia-oxidizing archaea (AOA) and bacteria (AOB) is the first and rate-limiting step (Prosser and Nicol 2012). Overall, soil N transformations is a step-by-step process driven by functionally distinct microbial groups, such that fully understanding how tree mycorrhizal associations affect soil N cycling should focus on each step of N transformation and its relevant microbial drivers (Tatsumi et al. 2020).\u003c/p\u003e\n\u003cp\u003eThere are several mechanisms by which tree mycorrhizal associations could affect soil microbes involved in N mineralization and nitrification (Cheeke et al. 2017; Mushinski et al. 2021). First, ECM fungi can suppress N-cycling microbes by competing nutrients and secreting antimicrobial compounds, while AM fungi can stimulate the growth of these microbes via exuding easily decomposable carbon (C) compounds (Cheng et al. 2012; Fernandez and Kennedy 2016).\u0026nbsp;Moreover, ECM trees possess stronger soil acidifying ability relative to AM trees owing to their slower-decaying litter that retards the return of acid-buffering base cations from forest floors to mineral soils and greater root exudates that acidify soils\u0026nbsp;(Yin et al. 2014; Keller and Phillips 2019).\u0026nbsp;Low soil pH and base cation depletion could reduce soil microbial biomass, alter microbial community composition and inhibit N-hydrolyzing enzyme activities (Sinsabaugh et al. 2008; Rousk et al. 2009). Previous studies have shown that ECM-dominated soils are characterized by the less abundance and lower diversity of bacteria, fungi and\u0026nbsp;ammonia oxidizers\u0026nbsp;relative to AM-dominated soils (e.g. Bahram et al. 2020; Heděnec\u0026nbsp;et al. 2020). Additionally, soil acid-base status also has a considerable effect on the quantity and quality of substrates relevant to N mineralization and nitrification. High solubility of Al\u003csup\u003e3+\u003c/sup\u003e and Fe\u003csup\u003e3+\u003c/sup\u003e under low soil pH can effectively reduce soil organic N (SON) availability for N mineralization by directly complexing with SON and serving as cation bridges between SON and clay particles (Charholm and Skyllberg 2013). The decrease in soil pH can exponentially reduce NH\u003csub\u003e3\u003c/sub\u003e availability for nitrification owing to the ionization of NH\u003csub\u003e3\u003c/sub\u003e to NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (Norman and Barrett 2016). Therefore,\u0026nbsp;forest mycorrhizal associations could affect soil microbes and substrate availability relevant to N mineralization and nitrification through effects on soil acid-base status.\u003c/p\u003e\n\u003cp\u003eAlthough previous studies have reported the differences between AM- and ECM-dominated forests in biogeochemical processes and soil microbes (e.g. Lin et al. 2017; Bahram et al. 2020), the potential linkage among forest mycorrhizal associations, acid-base status, soil microbes, and N cycling is largely unclear. To fill this gap, 39 plots were established across a gradient of increasing ECM tree dominance in an old-growth temperate forest of Northeast China. These plots almost evenly distribute in a 25-ha area and are under similar environmental conditions, such that this natural experimental design can largely eliminate the confounding effects of climate, soil parent material or topography. We measured soil acid-base chemistry, N-hydrolyzing enzyme activities, the abundance and composition of N-cycling microbes, substrate availability, and N cycling rates. Structural equation models were conducted to examine the direct and indirect effects of forest mycorrhizal associations, soil acid-base status, substrate availability and N-cycling microbes on net N mineralization and net nitrification rates. Specifically, we aimed to test the following hypotheses: (1) According to the mycorrhizal-associated nutrient economy framework (Phillips et al. 2013), soil acidity would increase and N transformation rates would decrease with increasing ECM tree dominance; (2) Changes in soil acid-base status along the mycorrhizal gradient would influence the activity and abundance of soil N-cycling microbes; (3) Forest mycorrhizal associations would affect soil N transformation rates via effects on soil N-cycling microbes and substrate availability.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eSite description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted at a broad-leaved Korean pine mixed forest (BLKP) in the Changbai Mountain Natural Reserve of\u0026nbsp;Northeast China\u0026nbsp;(42\u0026deg;23\u0026prime; N, 128\u0026deg;05\u0026prime; E). This study site has a temperate continental climate, with mean annual temperature of 3.6 \u0026deg;C and mean annual precipitation of 700 mm.\u0026nbsp;Soil of this study site is the Albi-Boric Argosols developed from volcanic ash, with the following properties: organic C of 85.28 g kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e, total N of 7.19 g kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e, and pH of 5.03 in the 0\u0026ndash;10 cm soil layer. The BLKP forest has not been disturbed since at least 1700 (Yuan et al. 2016). Dominant tree species in the BLKP forest include\u003cem\u003e\u0026nbsp;Acer mono\u003c/em\u003e, \u003cem\u003eFraxinus mandshurica\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Pinus koraiensis\u003c/em\u003e, \u003cem\u003eQuercus mongolica\u003c/em\u003e and \u003cem\u003eTilia amurensis\u003c/em\u003e (Wang et al. 2010).\u003c/p\u003e\n\u003cp\u003eIn August 2018,\u0026nbsp;39 plots (20 m \u0026times; 20 m) were established\u0026nbsp;on the same soil type and similar topography\u0026nbsp;along a gradient of increasing ECM tree dominance\u0026nbsp;in a 25-ha area (Fig. S1). The terrain of this area is fairly gentle, with elevation ranging from 797.1 to 808.3 m. In each plot, individuals with diameter at breast height larger than 1-cm were measured and identified to species. Tree species were designated as AM or ECM types according to the FungalRoot database (Soudzilovskaia et al. 2020). When no mycorrhizal information was reported for some tree species, their mycorrhizal associations were designated based on mycorrhizal associations of their closely related species, considering the strong phylogenetic conservatism of mycorrhizal traits (Wurzburger et al. 2017). The ECM tree dominance of a given plot was calculated as the percentage of basal area of ECM trees to the total basal area of this plot. Given that all tree species except \u003cem\u003ePopulus ussuriensis\u0026nbsp;\u003c/em\u003eonly form one mycorrhizal type and \u003cem\u003eP\u003c/em\u003e. \u003cem\u003eussuriensis\u0026nbsp;\u003c/em\u003ejust exists in one plot (Table S1), low ECM tree dominance indicates high AM tree dominance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil sampling and chemical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoils were sampled from the upper 10-cm mineral soil using a 2.5-cm diameter stainless steel corer. In each plot, nine soil samples were collected in the 15 m \u0026times; 15 m internal plot to avoid edge effects. Collected soil samples were placed into a cooler, transported to a laboratory and processed immediately. In the laboratory, soil samples were pooled by plot and then sieved through a 2-mm mesh to remove rocks and roots. Soil samples for measuring N\u0026nbsp;availability and enzyme activities were immediately processed, for measuring soil C:N ratio and acid-base chemistry were air-dried, and for DNA extraction were stored at -80 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003eAliquots of air-dried soils were milled to pass a 0.25-mm mesh. Milled samples were used to determine soil C and N concentrations, and natural abundance of \u003csup\u003e15\u003c/sup\u003eN by an\u0026nbsp;elemental analyzer (vario MICRO cube, Elementar, Hanau, Germany) coupled to an isotope ratio mass spectrometer (IsoPrime 100, IsoPrime, Cheadle Hulme, UK). Soil natural\u0026nbsp;\u003csup\u003e15\u003c/sup\u003eN abundance is expressed as:\u0026nbsp;\u0026delta;\u003csup\u003e15\u003c/sup\u003eN\u0026nbsp;(\u0026permil;) = (\u003cem\u003eR\u003c/em\u003e\u003csub\u003esoil\u003c/sub\u003e / \u003cem\u003eR\u003c/em\u003e\u003csub\u003eatm\u003c/sub\u003e \u0026ndash;\u0026nbsp;1)\u0026nbsp;\u0026times; 1000, where\u0026nbsp;\u003cem\u003eR\u003c/em\u003e\u003csub\u003esoil\u003c/sub\u003e and\u0026nbsp;\u003cem\u003eR\u003c/em\u003e\u003csub\u003eatm\u003c/sub\u003e are the \u003csup\u003e15\u003c/sup\u003eN:\u003csup\u003e14\u003c/sup\u003eN ratios of soil samples and atmospheric N\u003csub\u003e2\u003c/sub\u003e, respectively. For determining inorganic N (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and\u0026nbsp;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e-N) and dissolved organic N (DON) concentrations, fresh\u0026nbsp;soils were extracted with 2-M KCl, shaken for 30 minutes and then filtered. Concentrations of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e-N were measured by a continuous-flow autoanalyzer (AutoAnalyzer III, Bran + Luebbe GmbH, Germany). Total dissolved N concentration was measured colorimetrically after alkaline persulfate oxidation (Cabrera and Beare 1993). The DON concentration was calculated as the difference between total dissolved N and inorganic N concentrations. Net N mineralization and net nitrification rates were determined by quantifying changes in inorganic N and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e-N concentrations before and after a 14-day aerobic laboratory incubation at 25\u0026deg;C, respectively.\u003c/p\u003e\n\u003cp\u003eSoil pH was measured in a slurry of 25-ml distilled water and 10-g air-dried soils using a bench-top electrode pH meter. For determining exchangeable base cations, air-dried soils were extracted with 1-M CH\u003csub\u003e3\u003c/sub\u003eCOONH\u003csub\u003e4\u003c/sub\u003e, shaken for 30 minutes, centrifuged and then filtered. Extracts were analyzed to measure exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e concentrations by an inductively coupled plasma‐optical emission spectrometry (5100 ICP-OES, Agilent Technologies, Santa Clara, USA). Exchangeable bases are the sum of exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e concentrations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtracellular enzyme assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential activities of two enzymes related to soil N cycling \u0026ndash;\u0026nbsp;\u0026beta;-\u003cem\u003eN\u003c/em\u003e-acetylglucosaminidase\u0026nbsp;(NAG) associated with chitin and peptidoglycan hydrolysis and leucine aminopeptidase (LAP) involved in hydrolyzing leucine and other amino acids from polypeptides \u0026ndash; were measured using colorimetric methods (Parham and Deng 2000; Dick 2011; Mann et al. 2014). Fresh soils were transferred into centrifuge tubes containing 50-mM buffer (acetate buffer at pH = 5.5 for NAG and Tris buffer at pH = 8.0 for LAP). Then, reaction substrates were added to centrifuge tubes (3-mM \u003cem\u003ep\u003c/em\u003e-nitrophenyl-\u0026beta;-\u003cem\u003eN\u003c/em\u003e-acetylglucosaminide\u0026nbsp;for NAG and 2-mM leucine \u003cem\u003ep\u003c/em\u003e-nitroanilide for LAP). Centrifuge tubes were incubated at 25\u0026deg;C for 3 hours, after which 0.5-M NaOH and 0.5-M CaCl\u003csub\u003e2\u003c/sub\u003e were added to terminate the reaction. Centrifuge tubes were\u0026nbsp;centrifuged and the absorbance of supernatants was measured at 405 nm using\u0026nbsp;a UV-VIS spectrophotometer (UV-1750, Shimadzu, Kyoto, Japan). The background absorbance of soils (soils + buffer) and enzyme substrates (substrates + buffer) was subtracted from the sample absorbance. The resulting absorbance was compared with standard curves of \u003cem\u003ep\u003c/em\u003e-nitrophenol for NAG and \u003cem\u003ep\u003c/em\u003e-nitroaniline for LAP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReal-time quantitative PCR analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil DNA was extracted from 0.5-g\u0026nbsp;frozen soil using the FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, USA) following the manufacturer\u0026rsquo;s instructions. The DNA extracts were quantified by a NanoDrop spectrophotometer (Thermo Scientific, Waltham, USA). One subsample of DNA extracts was used for real-time quantitative PCR (qPCR) analyses, and the other was used for high-throughput sequencing after PCR amplification.\u003c/p\u003e\n\u003cp\u003eAbundances of fungi, bacteria, and AOA and AOB \u003cem\u003eamoA\u003c/em\u003e genes (encoding ammonia monooxygenase subunit A) were determined by qPCR on\u0026nbsp;a 7500 Real-Time PCR System (Applied Biosystems, Foster City, USA) using primer pairs of 5.8S/ITS1f (Fierer et al. 2005), Eub338/Eub518 (Fierer et al. 2005), Arch-amoAF/Arch-amoAR (Francis et al. 2005) and \u003cem\u003eamoA\u003c/em\u003e-1F/\u003cem\u003eamoA\u003c/em\u003e-2R (Rotthauwe et al. 1997), respectively. Samples, negative controls and standards were run in triplicate in 96-well plates. The 20-\u0026mu;l reaction mixtures contained 10-\u0026mu;l SYBR Premix Ex Taq, 0.4-\u0026mu;l forward and reverse primers, 2-\u0026mu;l DNA template (nuclease-free water\u0026nbsp;for negative controls) and 7.2-\u0026mu;l nuclease-free water. Amplification conditions were 5 minutes at 95\u0026deg;C followed by 40 cycles of 5 seconds at 95\u0026deg;C, 30 seconds at 56\u0026deg;C and 40 seconds at 72\u0026deg;C. Standard curves were generated using 10-fold serial dilutions of plasmids containing corresponding DNA fragments. Briefly, PCR product of each target gene was generated according to previously mentioned primer pairs and amplification conditions, which was purified and cloned into pMD18-T vectors (Takara, Dalian, China). Then, vectors were transformed into \u003cem\u003eEscherichia coli\u003c/em\u003e DH5\u0026alpha; competent cells. After re-amplification and sequencing with the vector-specific primer pair of M13f/M13r, white positive clones with correct inserted DNA fragments were selected to extract plasmid DNA. Plasmid DNA was quantified and used as standards for qPCR. Amplification efficiencies of standard curves ranged from 86.5 to 96.4% with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values \u0026gt;0.99. Melting-curve analyses, performed to evaluate the amplification specificity, resulted in a single peak.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-throughput sequencing and bioinformatic analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above-mentioned primer pairs including sample identifying barcodes were used to amplify \u003cem\u003eamoA\u003c/em\u003e gene fragments of AOA and AOB for high-throughput sequencing.\u0026nbsp;The PCR was conducted in\u0026nbsp;20-\u0026mu;l reaction mixtures containing 4-\u0026mu;l FastPfu buffer, 2-\u0026mu;l dNTPs, 0.8-\u0026mu;l forward and reverse primers, 0.4-\u0026mu;l FastPfu polymerase, 10-ng DNA template, 0.2-\u0026mu;l bovine serum albumin and nuclease-free water. Amplification conditions were 5 minutes at 95\u0026deg;C, followed by 35 cycles (30 seconds at 95\u0026deg;C, 30 seconds at 55\u0026deg;C and 45 seconds at 72\u0026deg;C), and a final extension of 10 minutes at 72\u0026deg;C. Triplicate PCR products per soil sample were pooled together, purified by an Agarose Gel DNA purification kit, and then quantified. After that, PCR products were normalized in equimolar amounts, and\u0026nbsp;paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, USA). Sequence data are deposited in the NCBI SRA archive with accession numbers of PRJNA756050 for AOA and PRJNA756096 for AOB.\u003c/p\u003e\n\u003cp\u003eRaw fastq files were demultiplexed and quality filtered using the QIIME pipeline (Caporaso et al. 2010). Raw reads that had low quality (average quality score \u0026lt;20 and length \u0026lt;150 bp) or contained ambiguous bases were removed. Moreover, raw reads that did not exactly match barcodes or primers were also removed.\u0026nbsp;For AOB \u003cem\u003eamoA\u003c/em\u003e reads,\u0026nbsp;paired-end reads with overlapping base lengths \u0026gt;10 bp were merged using the FLASH software (Magoč\u0026nbsp;and Salzberg 2011). This merging step was not conducted for AOA \u003cem\u003eamoA\u003c/em\u003e reads because the large amplicon length (635 bp) resulted in the lack of overlapping regions between paired-end reads. Consequently, only forward reads were used for analyzing AOA community composition owing to their higher quality than the reverse reads. After deleting chimeras, high-quality sequences were assigned to operational taxonomic units (OTUs) at 97% similarity, and the most abundant sequence per OTU was selected as the representative sequence. Representative sequences were checked against the NCBI database (http://blast.ncbi.nlm.nih.gov/Blast.cgi), and non-\u003cem\u003eamoA\u003c/em\u003e OTUs were deleted. Dominant OTUs (relative abundance \u0026gt;1%) were taxonomically classified through constructing neighbor-joining phylogenetic trees in the MEGA7 software (Kumar et al. 2016) using representative sequences of AOA or AOB \u003cem\u003eamoA\u003c/em\u003e genes and taxonomically determined reference sequences. Reference sequences for AOA clusters were from Alves et al. (2013) and Lin et al. (2019), and for AOB clusters were from Avrahami and Conrad (2003) and Avrahami et al. (2003).\u0026nbsp;Diversity (Shannon) and richness (Chao1) of AOA and AOB were calculated by the QIIME. These diversity indices were determined using\u0026nbsp;rarefied OTU tables where the number of sequences per sample was rarefied to the minimum sequencing depth of the 39 samples (9500 sequences for AOA and 6500 sequences for AOB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using R (version 3.6.1; R Core Team 2019). Considering the potential spatial autocorrelation among plots (Fig. S1), generalized least-squares models were performed to analyze how variables related to N cycling, acid-base chemistry and soil microbes changed along the gradient of ECM tree dominance. These models were conducted using the \u003cem\u003egls\u003c/em\u003e function in the nlme package (Pinheiro et al. 2019) with ECM tree dominance as the independent variable, and soil properties as dependent variables. We fitted these models with and without a spherical autocorrelation structure, and selected models with lower Akaike Information Criterion values (Table S2). To meet the normality assumption, abundances of AOA \u003cem\u003eamoA\u003c/em\u003e, AOB \u003cem\u003eamoA\u003c/em\u003e, bacterial 16S rRNA and fungal ITS were log\u003csub\u003e10\u003c/sub\u003e-transformed.\u0026nbsp;Additionally, changes in\u0026nbsp;AOA and AOB community composition\u0026nbsp;along the mycorrhizal gradient were visualized via non-metric multidimensional scaling (NMDS) ordinations based on Bray-Curtis dissimilarity matrices of\u0026nbsp;rarefied\u0026nbsp;OTU tables.\u0026nbsp;Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was conducted to test whether\u0026nbsp;ECM tree dominance influenced AOA and AOB community composition.\u0026nbsp;The NMDS and\u0026nbsp;PERMANOVA were separately performed using the \u003cem\u003emetaMDS\u003c/em\u003e and \u003cem\u003eadonis\u003c/em\u003e functions in the vegan package\u0026nbsp;(Oksanen et al. 2020).\u003c/p\u003e\n\u003cp\u003eMultiple linear regression models were conducted using the \u003cem\u003elm\u003c/em\u003e function to examine whether soil chemical properties significantly influenced soil microbial abundances and N-hydrolyzing enzyme activities. For these models, model selection was performed according to the Akaike Information Criterion corrected for small sample size (AIC\u003csub\u003eC\u003c/sub\u003e). The AIC\u003csub\u003eC\u003c/sub\u003e of all possible submodels was calculated by the \u003cem\u003edredge\u003c/em\u003e function in the MuMIn package (Bartoń 2018). If AIC\u003csub\u003eC\u003c/sub\u003e values of several submodels were smaller than two units relative to the best one (i.e. \u0026Delta;AIC\u003csub\u003eC\u003c/sub\u003e \u0026lt;2.0), model averaging was conducted across these submodels to identify significant soil chemical variables. Furthermore, partial redundancy analysis was conducted using the \u003cem\u003erda\u003c/em\u003e function in the vegan package to examine whether soil chemical properties significantly influenced AOA and AOB community composition based on Hellinger-transformed OTU tables.\u0026nbsp;A forward selection\u0026nbsp;with 999 permutations was performed by the \u003cem\u003eadonis\u003c/em\u003e function in the vegan package to\u0026nbsp;identify statistically significant soil chemical variables.\u003c/p\u003e\n\u003cp\u003eStructural equation models\u0026nbsp;were conducted using the \u003cem\u003esem\u003c/em\u003e function in the lavaan package (Rosseel 2012) to\u0026nbsp;test the direct and indirect effects of ECM tree dominance, soil acid-base chemistry, substrate availability and soil microbes on N transformation rates. The hypothesized relationships are that net N mineralization and net nitrification rates\u0026nbsp;are determined by substrate availability and N-cycling\u0026nbsp;microbes, which are mediated by ECM tree dominance through affecting soil acid-base chemistry\u0026nbsp;(Fig. 1).\u0026nbsp;In the model of net N mineralization rate, substrate availability is indicated by soil C:N ratio and DON concentration, and N-cycling microbes are indicated by gene abundances of bacterial 16S rRNA and fungal ITS and activities of NAG and LAP (Fig. 1a). In the model of net nitrification rate, substrate availability is indicated by NH\u003csub\u003e3\u003c/sub\u003e concentration, and N-cycling microbes are indicated by the community composition and \u003cem\u003eamoA\u003c/em\u003e gene abundances of AOA and AOB\u0026nbsp;(Fig. 1b).\u0026nbsp;NH\u003csub\u003e3\u003c/sub\u003e concentration was calculated based on the Henderson-Hasselbalch equation assuming a p\u003cem\u003eK\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e value of 9.25 (NH\u003csub\u003e3\u003c/sub\u003e = NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u0026nbsp;\u0026times;\u0026nbsp;10\u003csup\u003e(pH\u0026ndash;9.25)\u003c/sup\u003e; Norman and Barrett 2016). Community composition of AOA and AOB is indicated by the first axis of NMDS for each. In these two models, soil acid-base chemistry is a latent variable indicated by soil pH and exchangeable bases (Fig. 1). Final path models were obtained by removing paths with the highest probability values in a stepwise manner until all paths were significant (i.e. \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05).\u0026nbsp;Fitness of structural equation models were evaluated by the Chi-square (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) test, comparative fit index (CFI) and standardized root mean square residual (SRMR).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eForest mycorrhizal associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross the 39 plots, ECM tree dominance ranged from 10.8% to 84.3% (Table S1). There were 10 AM-dominated plots (ECM tree dominance \u0026lt;30%), 20 mixed plots (30%\u0026le;\u0026nbsp;ECM tree dominance\u0026nbsp;\u0026le;70%), and 9 ECM-dominated plots (ECM tree dominance \u0026gt;70%). Plot-level total basal area ranged from 28.0 to 55.0 m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e-1\u003c/sup\u003e, and did not significantly relate to ECM tree dominance (\u003cem\u003eP\u003c/em\u003e = 0.99; Fig. S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil N cycling and acid-base chemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll soil N cycling variables except DON concentration were significantly associated with ECM tree dominance\u0026nbsp;(Fig. 2). Specifically, soil C:N ratio ranged from 10.7 to 14.4, and was positively related to ECM tree dominance (\u003cem\u003eP\u003c/em\u003e = 0.04; Fig. 2a). Soil total N (\u003cem\u003eP\u003c/em\u003e = 0.01),\u0026nbsp;\u0026delta;\u003csup\u003e15\u003c/sup\u003eN (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01),\u0026nbsp;NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (\u003cem\u003eP\u003c/em\u003e = 0.01), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e-N (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01), and net N mineralization (\u003cem\u003eP\u003c/em\u003e = 0.02) and net nitrification rates (\u003cem\u003eP\u003c/em\u003e = 0.03) decreased linearly with increasing ECM tree dominance (Fig. 2b\u0026ndash;2c, 2e\u0026ndash;2h).\u003c/p\u003e\n\u003cp\u003eFour of six acid-base chemical variables significantly linked with ECM tree dominance\u0026nbsp;(Fig. 3). Soil pH ranged from 4.7 to 5.3, and decreased with increasing ECM tree dominance (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. 3a). Soil exchangeable bases, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e concentrations were negatively associated with ECM tree dominance (all \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. 3b\u0026ndash;3d). Soil exchangeable K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e concentrations were not significantly related to ECM tree dominance (Fig. 3e\u0026ndash;3f).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobial abundances and N-hydrolyzing enzymes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross the mycorrhizal gradient, bacterial 16S rRNA gene abundance (\u003cem\u003eP\u003c/em\u003e = 0.02) decreased and fungal ITS gene abundance (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01) increased with increasing ECM tree dominance (Fig. 4a\u0026ndash;4b). Consequently, fungal:bacterial ratio was positively correlated with ECM tree dominance (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. S3). For N-hydrolyzing enzymes, NAG activity was positively related to ECM tree dominance (\u003cem\u003eP\u003c/em\u003e = 0.03; Fig. 4c), and there was no significant relationship between LAP activity and ECM tree dominance (\u003cem\u003eP\u003c/em\u003e = 0.06; Fig. 4d).\u003c/p\u003e\n\u003cp\u003eMultiple linear regression revealed that bacterial 16S rRNA gene abundance\u0026nbsp;was positively associated with soil pH (\u003cem\u003eZ\u003c/em\u003e = 2.10, \u003cem\u003eP\u003c/em\u003e = 0.04; Table S3). Soil pH was negatively related to fungal ITS gene abundance (\u003cem\u003eZ\u003c/em\u003e = 2.21, \u003cem\u003eP\u003c/em\u003e = 0.03) and NAG activity (\u003cem\u003eZ\u003c/em\u003e = 3.39, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Table S3). Moreover, LAP activity was correlated with soil C:N ratio (\u003cem\u003eZ\u003c/em\u003e = 3.10, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01), and exchangeable Na\u003csup\u003e+\u003c/sup\u003e (\u003cem\u003eZ\u003c/em\u003e = 3.01, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01) and Ca\u003csup\u003e2+\u003c/sup\u003e concentrations (\u003cem\u003eZ\u003c/em\u003e = 1.98, \u003cem\u003eP\u003c/em\u003e = 0.04; Table S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ebundances and community composition of ammonia oxidizers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AOA \u003cem\u003eamoA\u003c/em\u003e gene abundance, ranging from 1.2\u0026nbsp;\u0026times;\u0026nbsp;10\u003csup\u003e7\u003c/sup\u003e to 3.3\u0026nbsp;\u0026times;\u0026nbsp;10\u003csup\u003e8\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e soil, decreased with increasing ECM tree dominance (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. 5a). There was no significant relationship between AOB \u003cem\u003eamoA\u003c/em\u003e gene abundance and ECM tree dominance (Fig. 5b). The \u003cem\u003eamoA\u003c/em\u003e gene abundance of AOA was 1\u0026ndash;3 orders of magnitude higher than that of AOB, and\u0026nbsp;the ratio of AOA to AOB \u003cem\u003eamoA\u003c/em\u003e gene abundance was negatively associated with ECM tree dominance\u0026nbsp;(\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. S4). Multiple linear regression showed that\u0026nbsp;AOA \u003cem\u003eamoA\u0026nbsp;\u003c/em\u003egene abundance was related to soil exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e (\u003cem\u003eZ\u003c/em\u003e = 3.07, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01)\u0026nbsp;and K\u003csup\u003e+\u003c/sup\u003e concentrations (\u003cem\u003eZ\u003c/em\u003e = 2.20, \u003cem\u003eP\u003c/em\u003e = 0.03; Table S3). The AOB \u003cem\u003eamoA\u003c/em\u003e gene abundance was positively related to NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N concentration (\u003cem\u003eZ\u003c/em\u003e = 2.31, \u003cem\u003eP\u003c/em\u003e = 0.02; Table S3).\u003c/p\u003e\n\u003cp\u003eA total of 376,874\u0026nbsp;high-quality sequences and 37 OTUs were obtained for AOA, and 445,450 high-quality sequences and 67 OTUs for AOB.\u0026nbsp;Rarefaction curves for AOA and AOB OTUs of each soil sample reached saturation, indicating the adequate diversity coverage (Fig. S5).\u0026nbsp;Phylogenetic analyses showed that\u0026nbsp;dominant AOA OTUs (relative abundance \u0026gt;1%)\u0026nbsp;were exclusively from the \u003cem\u003eNitrososphaera\u003c/em\u003e cluster (group I.1b lineage) and were grouped into clades A, B and E (Fig. S6).\u0026nbsp;Dominant AOB OTUs (relative abundance \u0026gt;1%) were exclusively affiliated with the \u003cem\u003eNitrosospira\u003c/em\u003e genus and were grouped into clusters 1, 2, 3a, 3b, 4, 10 and 12 (Fig. S7).\u003c/p\u003e\n\u003cp\u003eThe Chao1 (\u003cem\u003eP\u003c/em\u003e = 0.02; Fig. 5c) and Shannon (\u003cem\u003eP\u003c/em\u003e = 0.02; Fig. 5e) indices of AOA decreased with increasing ECM tree abundance. No significant relationship was found between ECM tree dominance and Chao1 (Fig. 5d) and Shannon (Fig. 5f) indices of AOB. The\u0026nbsp;PERMANOVA\u0026nbsp;analysis revealed that there were significant changes in community composition of AOA (\u003cem\u003eF\u003c/em\u003e = 13.02, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. 6a) but not AOB (\u003cem\u003eF\u003c/em\u003e = 0.69, \u003cem\u003eP\u003c/em\u003e = 0.64; Fig. 6b) along the mycorrhizal gradient. Partial redundancy analyses showed that AOA community composition was significantly related to soil C:N ratio (\u003cem\u003eF\u003c/em\u003e = 6.00, \u003cem\u003eP\u003c/em\u003e = 0.02) and exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e concentration (\u003cem\u003eF\u003c/em\u003e = 5.30, \u003cem\u003eP\u003c/em\u003e = 0.02; Fig. S8a). The AOB community composition was correlated with soil exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e concentration (\u003cem\u003eF\u003c/em\u003e = 3.00, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. S8b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationships among ECM tree dominance, soil microbes and N transformation rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor net N mineralization rates, the final\u0026nbsp;structural equation\u0026nbsp;model well fitted the data (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 18.43, \u003cem\u003eP\u003c/em\u003e = 0.30, CFI = 0.97, SRMR = 0.08), and separately explained 58%, 9%, 34%, 26%, 37% and 38% of variation in soil acid-base chemistry, soil C:N ratio, bacterial 16S rRNA gene abundance, fungal ITS gene abundance, NAG activity and net N mineralization rate (Fig. 7a).\u0026nbsp;Net N mineralization rate was directly and positively related to bacterial 16S rRNA gene abundance (standardized coefficient (\u003cem\u003e\u0026beta;\u003c/em\u003e) = 0.46, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01) and negatively related to fungal ITS gene abundance (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.33, \u003cem\u003eP\u003c/em\u003e = 0.01; Fig. 7a). The ECM tree dominance had no significant direct effect on net N mineralization rates. In contrast, ECM tree dominance had significant indirect effects on net N mineralization rates via affecting\u0026nbsp;bacterial 16S rRNA gene abundance\u0026nbsp;(\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.21, \u003cem\u003eP\u003c/em\u003e = 0.01) and fungal ITS gene abundance\u0026nbsp;(\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.13, \u003cem\u003eP\u003c/em\u003e = 0.04) that were mediated by soil acid-base chemistry (Fig. 7a).\u003c/p\u003e\n\u003cp\u003eFor net nitrification rates, the final structural equation model fitted the data well (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 5.13, \u003cem\u003eP\u003c/em\u003e = 0.74, CFI = 1.00, SRMR = 0.04), and separately explained 56%, 71%, 31% and 40% of variation in soil acid-base chemistry, NH\u003csub\u003e3\u003c/sub\u003e concentration, AOA \u003cem\u003eamoA\u003c/em\u003e gene abundance and net nitrification rate (Fig. 7b). The AOA \u003cem\u003eamoA\u003c/em\u003e gene abundance was the only factor that had significant direct effects on net nitrification rate (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.63, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. 7b). Additionally, ECM tree dominance had significant indirect effects on net nitrification rate through influencing AOA \u003cem\u003eamoA\u003c/em\u003e gene abundance that was mediated by soil acid-base chemistry\u0026nbsp;(\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.26, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01; Fig. 7b).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo comprehensively and mechanistically understand the relationship between forest mycorrhizal associations and soil N transformations, we focused on soil microbial drivers and substrate availability relevant to net N mineralization and net nitrification rates along a gradient of increasing ECM tree dominance. We showed that abundances of soil bacteria and AOA \u003cem\u003eamoA\u003c/em\u003e gene decreased, and fungal abundance and NAG activity increased with increasing ECM tree dominance. These changes in soil microbial abundances and enzyme activities along the mycorrhizal gradient were associated with the increase in soil acidity with increasing ECM tree dominance.\u0026nbsp;Structural equation models revealed that ECM tree dominance was not directly related to N transformation rates, but indirectly to net N mineralization rate via affecting soil bacterial and fungal abundances, and indirectly to net nitrification rate via influencing AOA \u003cem\u003eamoA\u003c/em\u003e gene abundance.\u0026nbsp;Our results suggest that soil microbes provide a mechanistic link between forest mycorrhizal associations and soil N transformation rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShifts in soil biogeochemistry along the mycorrhizal gradient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with the mycorrhizal-associated nutrient economy model (Phillips et al. 2013), our results showed that soil inorganic N concentrations, N transformation rates and\u0026nbsp;\u0026delta;\u003csup\u003e15\u003c/sup\u003eN\u0026nbsp;decreased with increasing ECM tree dominance\u0026nbsp;(Fig. 2). This more conservative N cycling mode in ECM-dominated forests has also been demonstrated by local- (Midgley and Phillips 2016; Lin et al. 2018), regional- (Phillips et al. 2013) and global-scale studies (Averill et al. 2014; Lin et al. 2017). As such, shifts in forest mycorrhizal associations under N deposition and climate change may have profound environmental consequences owing to the potentially enhanced greenhouse gas emission and nitrate leaching under more open N cycling (Averill et al. 2018; Jo et al. 2019). Previous studies have reported the greater nitrate leaching and emissions of reactive nitrogen oxides in AM-dominated forests (Midgley and Phillips 2016;\u0026nbsp;Mushinski et al. 2019).\u003c/p\u003e\n\u003cp\u003eApart from influencing soil N dynamics, forest mycorrhizal associations also had great effects on soil acid-base chemistry, characterized by the decrease in soil pH and exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e concentrations with increasing ECM dominance (Fig. 3). The stronger soil acidity in ECM-dominated plots may be because compared with AM trees, roots of ECM trees secret greater organic acids (Brzostek et al. 2013; Yin et al. 2014) and the poorer decomposability of ECM tree litter retards the return of acid-buffering base cations from forest floors to mineral soils (Lin et al. 2018; Keller and Phillips 2019; See et al. 2019). Given the tight linkage between soil acid-base chemistry and microbial activities and community composition (Sinsabaugh et al. 2008; Fierer et al. 2009; Gurbry-Rangin et al. 2015), soil acid-base chemistry may be an important mediator between forest mycorrhizal associations and ecosystem processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNet N mineralization rate is related to soil microbial abundances\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe well-known mechanism underlying the more conservative N cycling in ECM-dominated plots is that ECM fungi can mine N directly from soil organic matter via the enzymatic breakdown and Fenton reaction, while these capabilities are limited for AM fungi (B\u0026ouml;deker et al. 2014; Op De Beeck et al. 2018). Inconsistent with this mechanism, our\u0026nbsp;structural equation model showed that ECM tree dominance did not directly relate to net N mineralization rate\u0026nbsp;(Fig. 7a). The inexistence of this direct relationship may be because the dominant ECM fungal taxa in our study site do not produce oxidative enzymes or hydroxyl radicals. This speculation is supported by the current genome information revealing that not all ECM fungal taxa can acquire organic N bound in soil organic matter, indicating that effects of ECM fungi on ecological processes should depend on their community composition (Kohler et al. 2015; Pellitier and Zak 2018). Indeed, a recent study showed that the direction and magnitude of ECM fungal effects on litter decomposition were controlled by the community composition of ECM fungi (Fernandez et al. 2020).\u003c/p\u003e\n\u003cp\u003eOur results revealed that ECM tree dominance indirectly linked with net N mineralization rates via affecting soil acid-base chemistry and then microbial abundances (Fig. 7a). The positive relationship between net N mineralization rates and bacterial abundances is expected since bacteria are a major biological driver of N mineralization (Petersen et al. 2012; Isobe et al. 2020).\u0026nbsp;Although fungi also play a critical role in N mineralization, fungal abundance was negatively correlated with net N mineralization rates (Fig. 7a). A possible explanation for these results is that compared with fungi, bacteria have greater biological activities and faster turnover rates, such that bacteria process organic N to NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N more rapidly (Crowther et al. 2019). Biogeochemical models and experimental studies have demonstrated that ecosystems with high\u0026nbsp;fungal to bacterial ratios usually accompany with slow N transformation rates (H\u0026ouml;gberg et al. 2007; Waring et al. 2013).\u0026nbsp;Consequently, the low bacterial abundance in ECM-dominated plots could be the reason for slow net N mineralization rates in these plots. Previous studies have also reported\u0026nbsp;the decrease in bacterial abundance and increase in fungal abundance with increasing ECM tree dominance (Cheeke et al. 2017; Tatsumi et al. 2020; Cheeke et al. 2021), yet mechanisms underlying this result remain unclear. Our results showed that the change in relative abundance of different microbial groups along the mycorrhizal gradient was related to mycorrhizal-associated differences in soil pH (Table S3). A possible reason for this result is that compared with fungi, bacteria are less acid tolerant, leading to the high fungal dominance in ECM-dominated soils with strong acidity (Rousk et al. 2009).\u003c/p\u003e\n\u003cp\u003eApart from microbial drivers, forest mycorrhizal associations can also affect N mineralization via influencing substrate availability such as soil C:N ratio and DON concentration (Averill et al. 2014; Lin et al. 2017). Although soil C:N ratio varied along the mycorrhizal gradient,\u0026nbsp;it was not significantly associated with net N mineralization rates (Fig. 7a). This non-significant relationship may be due to the narrow range of variation in soil C:N ratios (i.e. 10.7\u0026ndash;14.4) in our local-scale study (Fig. 2a). Moreover, our results showed that DON concentration did not significantly vary along the mycorrhizal gradient and was unrelated to net N mineralization rate (Fig. 2d), although ECM-dominated plots had high potential\u0026nbsp;\u0026beta;-\u003cem\u003eN\u003c/em\u003e-acetylglucosaminidase activities (Fig. 4c). The decoupling between N-hydrolyzing enzyme activities and DON concentration may be because the substrate is in short supply by upstream reactions that limit the high potential hydrolyzing enzyme activities in ECM-dominated plots. These upstream reactions include the degradation of complex soil organic matter and destabilization of mineral-bound proteinaceous compounds, resulting in the release of protein and chitin that can be depolymerized by\u0026nbsp;N-hydrolyzing enzymes (Kieloaho et al. 2016; Jilling et al. 2018). The degradation and destabilization reactions can be catalyzed by oxidative enzymes, such that oxidative enzyme activities are critical in determining N mineralization (Zhu et al. 2014; Jilling et al. 2018). For instance, Kieloaho et al. (2016) revealed that activities of oxidative enzymes rather than N-hydrolyzing enzymes were tightly related to N mineralization rates. Considering the commonly observed positive relationship between soil pH and oxidative enzymes activities (Sinsabaugh et al. 2008), the strong soil acidity in ECM-dominated forests may inhibit oxidative enzyme activities and thus decrease substrate availability for N-hydrolyzing enzymes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNet nitrification rate is related to AOA \u003cem\u003eamoA\u003c/em\u003e abundances\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with net N mineralization rate, our results also revealed that net nitrification rate was related to microbial abundance but not to substrate availability (Fig. 7b). The non-significant NH\u003csub\u003e3\u003c/sub\u003e effect may be because\u0026nbsp;NH\u003csub\u003e3\u003c/sub\u003e concentration (0.06\u0026ndash;0.57\u0026nbsp;\u0026mu;M; Fig. S9) was exactly within the range of the half-saturation constant for NH\u003csub\u003e3\u003c/sub\u003e of AOA (0.04\u0026ndash;5.2\u0026nbsp;\u0026mu;M; Kits et al. 2017, Shi et al. 2018), indicating that NH\u003csub\u003e3\u003c/sub\u003e availability may not be a limiting factor for AOA performing nitrification. In contrast, AOB have low substrate affinity with the half-saturation constant for NH\u003csub\u003e3\u003c/sub\u003e ranging from 1.77 to 200\u0026nbsp;\u0026mu;M (Kits et al. 2017, Shi et al. 2018), beyond the NH\u003csub\u003e3\u003c/sub\u003e concentration in our study site. Consequently, differences in the NH\u003csub\u003e3\u003c/sub\u003e affinity could explain that net nitrification rate was related to \u003cem\u003eamoA\u003c/em\u003e abundance of AOA rather than that of AOB (Fig. 7b). This result is consistent with previous studies showing that NH\u003csub\u003e3\u003c/sub\u003e concentration led to niche specialization and differentiation between AOA and AOB (Prosser and Nicol 2012), and AOA played a pivotal role in nitrification of acidic soils (Yao et al. 2013;\u0026nbsp;Mushinski et al. 2019). Apart from abundances, community composition of AOA can also control nitrification rates owing to the functional heterogeneity among AOA lineages (Alves et al. 2013). However, our result showed that AOA community composition was not associated with net nitrification rates (Fig. 7b). This result may be due to the low AOA diversity in our study site\u0026nbsp;in which\u0026nbsp;functional similar clade B and clade E accounted for 92.5% of total sequences (Fig. S6).\u003c/p\u003e\n\u003cp\u003eOur results showed significant changes in \u003cem\u003eamoA\u003c/em\u003e abundances and community composition of AOA along the mycorrhizal gradient (Figs. 5a, 6a), which is consistent with previous studies (Tatsumi et al. 2020; Mushinski et al. 2021). These changes along the mycorrhizal gradient have been attributed to mycorrhizal-associated differences in soil pH (Gubry-Rangin et al. 2015; Mushinski et al. 2019). Inconsistent with this expectation, exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e concentration rather than soil pH was related to AOA \u003cem\u003eamoA\u003c/em\u003e abundance (Table S3) and community composition (Fig. S8a). Considering that soil pH affects AOA activities mainly through influencing the ionization equilibrium between NH\u003csub\u003e3\u003c/sub\u003e and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (Prosser and Nicol 2012; Norman and Barrett 2016), the sufficient NH\u003csub\u003e3\u003c/sub\u003e supply in our study site may be the reason for the non-significant relationship between soil pH and AOA \u003cem\u003eamoA\u003c/em\u003e abundances. The positive link between AOA \u003cem\u003eamoA\u003c/em\u003e abundance and exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e concentration has also been previously reported (Yao et al. 2013; Ciccolini et al. 2016), yet mechanisms underlying this link are still unknown. Consequently, mechanistic studies are needed to explore the role of Ca\u003csup\u003e2+\u003c/sup\u003e in nitrification under the context of globally increasing N deposition that leads to soil acidification and Ca\u003csup\u003e2+\u003c/sup\u003e leaching (Bowman et al. 2008).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results showed that forest mycorrhizal associations were indirectly related to soil N transformation rates via affecting soil microbial abundances that were mediated by mycorrhizal-associated differences in soil acid-base chemistry. These results\u0026nbsp;highlight that soil acid-base chemistry is a critical mediator between forest mycorrhizal associations and N-cycling microbes, and indicate that soil microbes provide a mechanistic link between mycorrhizal associations and soil N cycling.\u0026nbsp;Moreover, these findings have important implications for understanding forest community structure and function.\u0026nbsp;First, our results suggest that while previous studies have reported that N deposition favors AM trees over ECM trees (Averill et al 2018; Jo et al. 2019), intensified soil acidification under high N deposition may offset such effect owing to the more adaptive of ECM trees to acidic soils. Considering that AM-dominated forests harbor more ammonia oxidizers, shifts in forest mycorrhizal associations under global change could have significant consequences for water quality via affecting NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N leaching and important feedbacks to climate change via influencing N oxide emissions, since these two processes are tightly related to ammonia oxidizers (Mushinski et al. 2019; Prosser et al. 2020). Moreover, if our results can be held across wide climatic and edaphic conditions, it is feasible to incorporate tree mycorrhizal associations into ecological models to predict soil N dynamics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe are grateful to the National Research Station of Changbai Mountain Forest Ecosystems for providing the experimental site and relevant supports. This work was supported by the National Natural Science Foundation of China (Nos. 31700538 and 31830015) and the Youth Innovation Promotion Association CAS (No. 2019200).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003eGL, DZ, XW\u0026nbsp;conceived the study; GL, ZY and YZ collected and analyzed the data; All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are available from corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAlves RJE, Wanek W, Zappe A, Richter A, Svenning MM, Schleper C, Urich T (2013) Nitrification rates in Arctic soils are associated with functionally distinct populations of ammonia-oxidizing archaea. 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Soil Biol Biochem 76:183-192\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":"biogeochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biog","sideBox":"Learn more about [Biogeochemistry](https://www.springer.com/journal/10533)","snPcode":"10533","submissionUrl":"https://submission.nature.com/new-submission/10533/3","title":"Biogeochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ammonia oxidizer, Fungal:bacterial ratio, Hydrolytic enzyme, Mycorrhizal type, Plant-soil interaction, Soil acid-base chemistry","lastPublishedDoi":"10.21203/rs.3.rs-889787/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-889787/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTree-fungal symbioses are increasingly recognized to affect soil nitrogen (N) transformations, yet the role of soil microbes in the process is largely unclear. Soil microbes directly interact with trees and are a primary driver of many N transformation processes. Here, we explored the linkage among tree mycorrhizal associations, soil microbes and N transformation rates in a temperate forest of Northeast China. Across a gradient of increasing ectomycorrhizal (ECM) tree dominance, we measured soil acid-base chemistry, bacterial and fungal abundances, N-hydrolyzing enzyme activities, abundances and community composition of ammonia-oxidizing archaea (AOA) and bacteria, and net N mineralization and net nitrification rates. Results showed that soil pH, exchangeable base cations, inorganic N concentrations and N transformation rates decreased with increasing ECM tree dominance. The ECM tree dominance was negatively related to soil bacterial and AOA \u003cem\u003eamoA\u003c/em\u003e gene abundances, and positively to soil fungal abundances and β-\u003cem\u003eN\u003c/em\u003e-acetylglucosaminidase activities. These shifts in soil microbial abundances and enzyme activities along the mycorrhizal gradient were linked with the increase in soil acidity with increasing ECM tree dominance. Structural equation models revealed that ECM tree dominance was not directly related to N transformation rates, but indirectly to net N mineralization rates via affecting bacterial and fungal abundances, and indirectly to net nitrification rates via influencing AOA \u003cem\u003eamoA\u003c/em\u003e gene abundances. Collectively, our results indicate that soil microbes provide a mechanistic link between mycorrhizal associations and soil N transformations, and suggest that shifts in forest mycorrhizal associations under global change could have profound consequences for biogeochemical cycling of temperate forests.\u003c/p\u003e","manuscriptTitle":"Dominant Tree Mycorrhizal Associations Affect Soil Nitrogen Transformation Rates Through Mediating Microbial Abundances in a Temperate Forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-21 15:08:06","doi":"10.21203/rs.3.rs-889787/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2021-11-04T21:58:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-09-10T20:23:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-09-08T21:39:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Biogeochemistry","date":"2021-09-08T17:42:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-09-08T09:24:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biogeochemistry","date":"2021-09-07T04:41:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biogeochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biog","sideBox":"Learn more about [Biogeochemistry](https://www.springer.com/journal/10533)","snPcode":"10533","submissionUrl":"https://submission.nature.com/new-submission/10533/3","title":"Biogeochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"937a38a1-025a-48eb-8900-ab4bb48693ec","owner":[],"postedDate":"September 21st, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":7334766,"name":"General Biochemistry"}],"tags":[],"updatedAt":"2022-02-15T17:22:56+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-21 15:08:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-889787","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-889787","identity":"rs-889787","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}