Genetic potential for N₂O metabolism in tree tissues: Insights into nitrogen cycling gene abundance and nosZ diversity across trees

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While soil microbial roles in N 2 O cycling are well studied, there is a major knowledge gap regarding the distribution and diversity of these microbes within tree ecosystems. In this study, we aimed to comprehensively assess the nitrogen (N) cycling gene abundance and the diversity of N 2 O-reducing microorganisms in shoots (leaves and terminal branches) and wood cores of four tree categories — European beech ( Fagus sylvatica ), European hornbeam ( Carpinus betulus ), birch ( Betula pendula and Betula pubescens ) and Norway spruce ( Picea abies ) across long transect. We assessed N 2 O exchange through shoot incubation experiments and measured internal N 2 O concentrations in stem wood. Inorganic N compounds were studied as indicators of microbial transformation, and a targeted metagenomic approach was used to analyze the relative abundance of N-cycling genes and nosZ clade I and II diversity. Our study revealed that hornbeam shoots showed potential N₂O emissions, while beech shoots indicated N₂O consumption in the incubation study. Birch had the highest internal stem wood N₂O concentration, and beech the lowest when compared to the ambient concentration. Metagenomic analysis confirmed the presence of key nitrification and denitrification genes in both tissues, with nosZ genes abundant in spruce shoots, birch wood cores, and beech wood cores—clade I dominating over clade II and Rhizobiales prevalent within clade I. These findings provide new insights into tree microbiome and its contribution to N 2 O exchange in tree-associated environments. tree-microbiome nosZ diversity nitrous oxide targeted metagenomic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Nitrous oxide (N 2 O) is a potent greenhouse gas with a global warming potential about 300 times greater than CO 2 over a 100-year horizon. Since the preindustrial era, atmospheric N 2 O concentrations have increased from 275 to 338 parts per billion (ppb), largely driven by anthropogenic activities such as the application of synthetic nitrogen fertilizers, cultivation of N 2 -fixing crops, and fossil fuel combustion [ 1 , 2 ]. Land-use changes, including wetland drainage, also contribute to elevated N 2 O emissions [ 3 , 4 ]. Microbial production of N 2 O through denitrification and nitrification processes in soils is considered the dominant source of N 2 O entering the atmosphere [ 5 ]. However, significant uncertainty remains in global N 2 O budget estimations due to the limited understanding of all potential sources and sinks [ 6 ]. Recent studies suggest that plants can also act as significant sources and sinks of N 2 O [ 7 , 8 ]. Yet, the microbial pathways and organisms responsible for N 2 O production and consumption within tree tissues remain poorly understood, despite their critical role in shaping tree-mediated N 2 O fluxes. Addressing this gap is crucial, as it directly influences our understanding of tree-associated microorganisms and their contribution to N 2 O dynamics. One approach to bridge this knowledge gap is through flux measurements, particularly from tree stems, which have been widely studied to quantify tree-mediated N 2 O exchange [ 7 , 9 – 12 ]. Research on phyllospheric N 2 O fluxes is only emerging [ 8 ]. Recent evidence suggests that microbial communities in the spruce phyllosphere have the potential for N 2 O exchange [ 13 ], and the canopy nitrification contributes significantly to forest N-cycling [ 14 ], indicating an active microbial role in these processes. Yet, the identity and function of microorganisms involved in N 2 O metabolism within different tree species and tissues remain largely unknown. This study addresses that gap by providing the first comprehensive assessment of tree-associated microbial contributions to N 2 O dynamics across multiple trees along a large transect. In this context, the present study aims to detect and compare the microbial communities involved in nitrogen (N) cycling metabolism within tree shoots, which include leaves and terminal branches, as well as wood cores of four tree categories. To achieve this, we utilized targeted metagenomics using probe capture [ 13 , 15 ] specifically designed to identify N 2 cycling genes in host-microbe systems, enabling us to evaluate the relative abundance of N-cycling genes, including bacterial and archaeal ammonia monooxygenase ( amoA ) and nitrite oxidoreductase ( nxrB) — key genes involved in nitrification, nitrite reductase ( nirK and nirS ), nitric oxide reductase ( norB ) and nitrous oxide reductase ( nosZ ), which are essential for denitrification. We also assess the diversity of nosZ clades I and II, which play a critical role in N 2 O reduction, and evaluate whether trees influence the relative abundance of N-cycling genes and distribution of nosZ microbial communities. To complement the molecular data, we conducted a short-term incubation experiment to measure N 2 O exchange from shoots and analyzed internal N 2 O concentrations in stem wood, providing functional evidence of N 2 O cycling activity. We quantified inorganic N compounds in tree tissues as indicators of microbial transformation. Ultimately, our study aims to enhance our understanding of the contributions of tree-associated microorganisms to N 2 O dynamics and to highlight the importance of aboveground tree-microbe interactions in forest N 2 O gas exchange. Methodology Sample collection for N 2 O metabolism analysis and nucleic acid extraction Ten forest sites were included in the study, several of which have previously reported N 2 O fluxes [ 4 , 16 , 17 ]. Samples of shoots (leaves/needles with terminal branches) and wood cores were collected from trees representing different dominant species: European hornbeam ( Carpinus betulus L.), European beech ( Fagus sylvatica L.), birch ( Betula pubescens Ehrh. and Betula pendula Roth.), and Norway spruce ( Picea abies (L.) H. Karst.). For analysis, downy birch and silver birch were combined and treated as a single category (‘birch’) due to their similar ecological characteristics [ 18 ]. Sampling was conducted between April and September 2022. To improve geographical and climatic representation, tree species from different climate zones were included. Samples were collected from sites in temperate forests (Lanžhot and Štítná nad Vláří, Czech Republic), hemiboreal forests (Agali II and Kiidjärve, Estonia; Bromarv, Finland) and boreal forests (Puijo and Kenttärova-Pallas, Finland). Table 1 Information on sampling sites, tree and soil characteristics. Species study sites Coordinates Site description Forest Stand height, Stem diameter at Breast height Soil type Soil pH Annual precipitation (mm) Mean Annual temperature (°C) Stem N 2 O flux Reference Hornbeam, Lanžhot, Czech Republic (L1) (48,68133°P, 16,94602°I) Temperate floodplain forest 60% of hornbeam, 20% ash, 15% of oak, 5% of elm, maple and tilia 23.4 m, 35 cm Eutric Humic Fluvisol, Haplic Fluvisol and Eutric Fluvisol 5.7 497 9.7 0.24 ± 0.34 µg N 2 O m − 2 h − 1 [ 16 , 19 ] Beech, Štítná nad Vláří, Czech Republic (L2) (49.02°N, 17.58°E) Temperate montane upland forest 115-year-old monoculture 32.2 m, 35 cm DBH Eutric (Stagnic) Cambisol 7.0 800 7.5 −3.8 µg N 2 O m − 2 h − 1 [ 16 , 20 ] Beech, Bromarv Rilax manor, Finland (L3) (59,95826°P, 23,06783°I) Hemiboreal upland 95-year-old mono-culture 30 m, 48 cm Inceptisols 4.2 758 NA NA [ 21 ] Birch, Agali-II, Tartu, Estonia (L4) (58°17'00.0"N 27°17'00.0"E) Hemiboreal wetland 65% of Downy birch and 35% of Norway spruce 15 m 14 cm Oxalis 5.9 650 4.8 *-23.69 ± 236.87 µg N 2 O m − 2 d − 1 [ 4 ] Spruce, Agali-II, Tartu, Estonia (L5) (58°17'00.0"N 27°17'00.0"E) Hemiboreal wetland 65% of Downy birch and 35% of Norway spruce 17 m 18.4 cm Oxalis 5.8 650 4.8 *6.96 ± 4.51 µg N 2 O m − 2 d − 1 [ 4 ] Birch, Kiidjärve, Tartu, Estonia (L6) (58,17896°P, 27,08408°I) Hemiboreal upland Monoculture, Birch 19.8 m 15.2 cm Alfisol, Sandy 5.2 NA NA NA [ 22 ] Birch, Puijo, Kuopio, Finland (L7) (62.908328, 27.659551) Boreal upland 165-year-old 90% spruce, 10% birch NA Podlozied soil 3.4 549 5 NA Spruce, Puijo, Kuopio, Finland (L8) 62°54′N, 27°39′E) Boreal upland 165-year-old 90% spruce, 10% birch 24-29.5 m 41–68 cm Podlozied soil 3.2 549 5 NA [ 23 ] Spruce, Kenttärova, Pallas, Finland (L9) (N67°59.237', E24°14.579') Subarctic upland 70–160-year-old mono culture spruce 13 m Podzol 4.4 484 -1.4 NA [ 24 ] *data from this study We collected one shoot and one wood core sample from each of three replicate trees (n = 3) per species at each site. All samples were collected from the north side of the tree. The standard sampling height was approximately 1.5 meters, except for the birch shoot samples from Agali, where the lowest branches were at around 10 meters height, and the beech and hornbeam shoot samples from the Czech Republic site, which were collected at approximately 6 meters height using scissors attached to a long, telescopic handle. Wood cores were taken using a 5 mm increment borer (Haglöf, Sweden), at a height of 1.5 meters. The length of the wood cores was 5–7 cm for both birch and spruce woods and 8–10 cm for beech woods. Samples of wood cores and shoots that were used for DNA extraction were immediately taken into 50 mL sterile Falcon tubes and instantly snap frozen with liquid N 2 , and stored at − 80°C. In addition, extra samples of shoots and wood cores were taken and separated into plastic bags (2 L) for further analysis. NO exchange measurements for shoots A small incubation experiment was conducted using detached shoots to evaluate their potential for N 2 O exchange and provide a functional context for metagenomic data. Approximately 10–20 g of fresh weight (FW) shoots were placed in 500 mL glass bottles containing 50 mL of 0.9% NaCl solution to maintain osmotic pressure, ensuring the solution touched the branches. Bottles were sealed with rubber septa and aluminum crimp caps, then supplemented with 120 mL of ambient indoor air. Incubation was carried out under controlled conditions: 12 hours in light (photosynthetically active radiation, PAR 300 µmol m⁻² s⁻¹) at 15°C, followed by 12 hours in darkness at 4°C. Blank bottles without plant material served as controls under identical conditions. Gas samples (20 mL) from the incubation bottles (samples and blank) were taken at 1, 24 and 72 h from the start of the incubation using polypropylene syringes (BD Plastipak™; Becton, Dickinson, and Company equipped with three-way stopcocks valve connected syringe needles (0.8 × 40 mm) (BD Precisionglide®) and transferred to pre-evacuated 12 mL Labco vials flushed with N 2 . The CO 2 and N 2 O concentrations from shoots were measured using an Agilent 7890B Gas Chromatograph (GC) (Agilent Technologies, Palo Alto, CA, USA) equipped with Gilson liquid handler GX271 autosampler (Gilson Inc., Middleton, WI, USA) and a Hayesep Q 80/100 mesh column and an electron capture detector (ECD) [ 25 ]. The method detection limit (MDL) was calculated according to USEPA guidelines. Seven blank replicates produced a standard deviation of 3.80 × 10⁻⁴ ng N 2 O g⁻¹ h⁻¹, resulting in an MDL of 1.19 × 10⁻³ ng N 2 O g⁻¹ h⁻¹. The N 2 O production or consumption potential rates over the incubation period were calculated using the ideal gas formula (Eq. 1). N 2 O exchanges \(\:=\left(\frac{V\varDelta\:CPM}{RTW}\right)\left(\frac{1}{\varDelta\:t}\right)\) Eq. 1 Where V = volume of gas phase in the incubation bottle (mm 3 ), ΔC = change in concentration of gas (ppm), P = air pressure (Pa), M = molecular mass of the gas (g mol − 1 ), T = temperature (K), R = universal gas constant (8.314 J mol − 1 K − 1 ), W = weight of sample (g), Δt = change in time (h) NO concentration measurements in stem wood bore holes N 2 O concentration in stem wood bore holes was measured using the previously described method [ 26 ]. After collecting wood cores, the increment borer was resealed to create an airtight chamber for gas measurements. Ambient concentration served as a reference for comparing stabilized N 2 O levels in wood bore holes. After 5 minutes, 20 mL gas samples were withdrawn from the sealed borer using syringes and injected into pre-evacuated, N 2 -flushed glass vials (Labco Limited, Lampeter, UK). N 2 O concentration in the stem wood was measured using gas chromatography equipped with ECD [ 25 ]. The detection limit for the GC analysis was calculated as 3 times the SD of the N 2 O standard gas concentration, which is 12 ppb. Because ambient N 2 O concentrations vary across sites, N 2 O concentrations in stem wood were compared with ambient air to determine the difference in N 2 O concentrations in stem wood. Negative values indicate lower N 2 O inside the wood than outside (potential consumption), while positive values indicate higher internal concentrations (potential production). Analysis of nutrient content in tree tissues Shoots and wood cores were snap-frozen in liquid N 2 , ground with a sterile mortar and pestle (Haldenwanger, Berlin, Germany) using liquid N 2 , and stored in 50 mL Falcon tubes at − 80°C. Tree tissues (1 g) were extracted in 1 M KCl (3 mL), shaken for 1 h at 175 rpm (Heidolph, Schwabach, Germany), then centrifuged at 13,000 rpm for 5 min (Eppendorf, Horsholm, Denmark) for nutrient analysis, such as ammonium (NH 4 ⁺), nitrite (NO 2 ⁻), and nitrate (NO 3 ⁻). Supernatants were filtered through PES membrane filters (0.22 µm; Merck KGaA, Darmstadt, Germany) and stored at − 20°C until spectrophotometric analysis as described previously [ 27 , 28 ]. DNA extraction Community DNA was extracted from ground shoots and wood cores using the DNeasy® PowerSoil® Kit (Qiagen, Hilden, Germany) with minor modifications [ 29 ]. Briefly, 0.1–0.2 g of sample was homogenized in CD1 solution using Fastprep (Savant Fast Prep FP120 Bio 101, USA) for 30 s (2×) at 5.5 m s⁻¹ and vortexed for 10 min. After adding CD3 solution, samples were incubated for 1 h before continuing the protocol. DNA quality and concentration were checked using NanoDrop Lite (NanoDrop Technologies, Wilmington, NC, USA) and Qubit 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). After DNA extractions, both shoot and wood core samples (n ≥ 3 ) from each tree species were sent for targeted metagenomics (Arbor Biosciences, Arbor, Michigan, USA). Targeted metagenomics with probe capture and bioinformatic analyses To investigate the presence and diversity of tree-associated microorganisms capable of N 2 O reduction, a targeted metagenomics approach was used. This method, more sensitive than traditional metagenomics, enabled the detection of low-abundance microbial genes within the host-microbe ecosystem [ 13 , 15 ]. A curated target gene database of nitrification and denitrification genes for probe production was compiled using GenBank, BLAST, and HMM-based searches [ 15 ]. Gene-specific probes were designed using the MetCap pipeline [ 30 , 31 ], resulting in 263,111 unique probes. These were synthesized with biotin labelling (myBaits Custom-kit, Daicel Arbor Biosciences, Michigan, USA) for streptavidin-coated magnetic bead-based purification. DNA samples were fragmented, adapter-ligated, and hybridized with probes at 47°C for 72 hours. Post-hybridization, libraries were purified and pooled for sequencing on the Illumina NovaSeq platform with PE150 chemistry. The DNA libraries, probe hybridization and sequencing were done in myReads service of Daicel Arbor Biosciences (Arbor, Michigan, USA). Raw reads were quality-checked with FastQC and trimmed using Trimmomatic (Q > 30). To accurately identify functional genes, we employed advanced HMMER profiles [ 15 , 32 ] to meticulously search for sequences corresponding to each target gene against the gene-specific database, setting maximum E-value cut-off (E < 0.0001). Gene abundances were normalized to total read counts. Following this, we used graftM in conjunction with the Gappa tool, based on a robust nosZ reference phylogeny [ 33 ], to determine the taxonomic affiliations and relative abundances of nosZ clade I and clade II as previously described [ 34 ]. Statistical Analysis Data normality was assessed using the Shapiro-Wilk test. Normally distributed data were analyzed with one-way ANOVA and Tukey HSD, while non-normal data used Kruskal-Wallis with Dunn’s test and Bonferroni correction. Differences between tree and tree-tissues were tested with robust ANOVA via the Aligned Rank Transform method [ 35 ]. Spearman correlations were computed and visualized using the corrplot package. All analyses were performed in R (v4.4.0), with site-specific data provided in supplementary figures (S2–S6). Results Nutrient content in shoots and wood cores Ammonium (NH 4 + ), nitrite (NO 2 − ), and nitrate (NO 3 − ) concentrations were quantified in shoots and wood cores across all trees (Fig. 2 ) and corresponding site-specific data are presented in Fig. S2a and b. In shoots, birch exhibited the highest average NH 4 ⁺ content (2.0–5.0 µg g⁻¹ FW), followed by hornbeam and spruce, with no significant differences among these trees (Fig. 2 A). Beech had significantly lower NH 4 + levels (1.1–1.8 µg g⁻¹ FW; P < 0.05). NO 2 − content was significantly higher in spruce (2.0–3.3 µg g⁻¹ FW; P < 0.05) compared to beech and birch, while hornbeam did not differ significantly. Shoot NO 3 − content was highest in birch (0.9–3.4 µg g⁻¹ FW), followed by beech and hornbeam, with spruce showing significantly lower values (0.5–1.1 µg g⁻¹ FW; P < 0.05). In wood cores, no significant interspecific differences were detected for NH 4 + , NO 2 − , or NO 3 − . Birch and beech had the highest average NH 4 ⁺ levels (up to 39.7 and 30.7 µg g⁻¹ FW, respectively), while spruce showed the highest NO 2 − (0.8–2.9 µg g⁻¹ FW) and NO 3 − contents (1.6–5.7 µg g⁻¹ FW). Across tissues, NH 4 + was significantly lower in shoots than wood cores ( P < 0.001, Table 2 ), whereas NO 2 − was higher in shoots; NO 3 − did not differ significantly between tissues. N 2 O exchanges during incubation of shoots The N 2 O exchange rate during the incubation of shoots indicated varying potential for N 2 O production and consumption depending on trees (Fig. 2 c), with site-specific data provided in supplementary Fig. S2c. The tree shoots showed a significant difference ( P < 0.05) between trees: hornbeam exhibited a positive N 2 O exchange rate (0.002–0.007 ng N 2 O g⁻¹ h⁻¹), suggesting N 2 O emission. In contrast, beech showed a negative exchange rate (− 0.001 to − 0.017 ng N 2 O g⁻¹ h⁻¹), suggesting N 2 O consumption. Both trees exhibited N 2 O exchange rates that were significantly ( P < 0.05) different from the blank controls. We also measured CO 2 concentration during the incubation studies to check if the shoots were alive and respiring, and beech and spruce showed consumption (Fig. S1 ). N 2 O concentrations in wood bore holes The internal N 2 O concentration in wood bore holes varied across study sites (Fig. S2d). A negative value (e.g., − 9.74 ppb in beech) does not indicate a negative gas concentration but reflects the difference between the hole and ambient concentrations, representing potential N 2 O consumption by the wood (Fig. 2 d). Significant differences ( P < 0.05) were observed among tree species, with birch showing the highest ΔN 2 O concentration (mean 150.39 ppb). Relative abundance of genes indicating genetic potential for N 2 O metabolism The relative abundance of N-cycling genes varied significantly among trees in both shoots and wood cores (Fig. 3 ) (Corresponding site-specific data are shown in Fig. S3 and S4). In shoots, significant differences ( P < 0.05) were observed among trees for both denitrification genes ( nirK , nirS , norB , nosZ clade I and II) and nitrification genes (bacterial and archaeal amoA , nxrB ). Spruce shoots had the statistically highest ( P < 0.05) overall relative abundances of N-cycling genes, followed by birch, hornbeam, and beech (Fig. 3 a). In wood cores, significantly highest relative abundances ( P < 0.05) of nirK , nirS , norB , and nxrB were observed in birch and beech, followed by spruce and hornbeam. Bacterial amoA was significantly ( P < 0.05) more abundant in beech, followed by birch, hornbeam, and spruce (Fig. 3 b). In contrast, no significant differences among trees were observed for nosZ clade II and archaeal amoA gene. Across both shoots and wood cores, nosZ clade I was more abundant than clade II. In shoots, the relative abundance of nosZ clade I (1.41–24.06 per 100,000 reads) and Clade II (0.10–2.99 per 100,000 reads) was highest in spruce samples. In wood cores, nosZ clade I was significantly higher in beech and birch ( P < 0.05; 0.02–19.1 per 100,000 reads), whereas nosZ clade II reached its highest mean abundance in birch (0.01 to 0.62 per 100,000 reads). Diversity and distribution of nosZ genes among the trees In shoot tissues, nosZ clade I genes were predominantly found in Alphaproteobacteria, particularly Rhizobiales and Rhodospirillales , across most trees (Fig. 4 a). A higher diversity of nosZ clade I community was observed in spruce and birch shoots, and dominated by Rhizobiales , Rhodobacterales , Rhodospirillales , and Burkholderiales. Higher diversity of nosZ clade II communities was noted in spruce trees, which included Chitinophagales , Cytophagales , Sphingobacteriales , Campylobacterales , Flavobacteriales , Caldilineales , and Opitutales (Fig. 4 b). Notably, nosZ clade II communities were absent in hornbeam and beech shoots, and Rhodothermaceae showed very low abundance in birch. In contrast, wood cores displayed high diversity of nosZ clade I communities in beech and birch (Fig. 4 c). Shared taxa between these trees included Rhizobiales , Rhodobacterales , Rhodospirillales , Burkholderiales , Neisseriales , Nitrosomonadales , and Pseudomonadales . Birch wood cores also exhibited diverse nosZ clade II communities, comprising Chitinophagales , Sphingobacteriales , Rhodocyclales , and Opitutales (Fig. 4 d). No nosZ clade II communities were detected in hornbeam wood cores. Linkages between Nutrient contents, activity, and relative abundance of N-cycling microorganisms The study demonstrated significant variation in both N-cycling genes and nutrient contents (NH 4 + , NO 2 − ) across tree tissues, indicating tissue-specific distribution (Table 2 ). Additionally, the relative abundance of key N-cycling genes ( nirK , nirS , nosZ clades I and II, nxrB ) and NO 2 − content differed significantly among trees, suggesting tree-specific patterns (Table 2 ). Importantly, a strong interaction between tree categories and tissues (shoots and wood cores) influenced all N-cycling genes and NO 3 − content, underscoring that tree–tissue relationships jointly shape N-cycling dynamics. In shoots, NH 4 + content was significantly positively associated with the ratio of denitrification to nitrification ratio (Fig. 5 a), while NO 2 − content showed significant positive correlations with nirK , nirS , nosZ clade II, and nxrB . The N 2 O exchange rate was also significantly correlated with both the denitrification-to-nitrification gene ratio and shoot NH 4 + content. In wood cores, NH 4 + content exhibited significant positive correlations with nirK , nirS , norB , nosZ clade II, archaeal amoA , and nxrB (Fig. 5 b). Furthermore, Δ N 2 O concentration was significantly associated with nosZ clade I and the denitrification-to-nitrification gene ratio. A comparable relationship was observed in linear models assessing the linkage between N 2 O exchange, nutrient content, and the relative abundance of N-cycling genes (Table 3 ). Table 2 Non-parametric Aligned Rank Transform ANOVA was used to test for significant differences among tree tissues, trees and their interactions for each listed parameter. DF.res Tree tissues Trees Tree tissues*Trees F value P value F value P value F value P value nirK 43 15.12 < 0.001 2.80 0.05 17.21 < 0.001 nirS 43 14.24 < 0.001 3.36 < 0.05 16.86 < 0.001 norB 43 8.86 < 0.01 1.59 0.20 19.30 < 0.001 nosZ clade I 43 10.69 < 0.01 3.96 < 0.05 8.17 < 0.001 nosZ clade II 43 13.14 < 0.001 3.67 < 0.05 6.47 < 0.01 Bacterial amoA 43 12.95 < 0.001 1.56 0.21 3.77 < 0.05 Archaeal amoA 43 21.53 < 0.001 2.81 0.05 15.60 < 0.001 nxrB 43 14.34 < 0.001 3.83 < 0.05 11.70 < 0.001 Ammonia 41 34.85 < 0.001 1.79 0.16 1.64 0.19 Nitrite 41 22.29 < 0.001 7.72 < 0.001 0.67 0.57 Nitrate 41 0.03 0.87 0.80 0.50 4.44 < 0.01 Table 3 Step-wise linear models to examine the relationship between N 2 O exchanges with nutrient content and the relative abundance of N-cycling genes Model Selected models AIC Value F value P value Shoot N 2 O exchange ~ shoot ammonia + shoot nitrite + shoot nitrate Shoot ammonia -208.85 11.98 < 0.001 Shoot N 2 O exchange ~ nirK + nirS + norB + nxrB + archaeal amoA + bacterial amoA + nosZ + nosZ Clade I + nosZ Clade II + ratio (denitrification:nitrification) ratio (denitrification:nitrification) -205.18 6.96 < 0.05 Wood N 2 O concentration ~ nirK + nirS + norB + nxrB + archaeal amoA + bacterial amoA + nosZ + nosZ Clade I + nosZ Clade II + ratio (denitrification:nitrification) nosZ nosZ clade I ratio (denitrification:nitrification) -57.60 -57.53 -56.52 7.51 7.42 6.23 < 0.05 < 0.05 < 0.05 Discussion Nutrient Content and N 2 O Exchange and Its Influence on Microbial Activity The study revealed that both shoots and wood cores contain inorganic N compounds and may serve as potential active sites for N 2 O exchange, suggesting that internal microbial processes contribute to N 2 O cycling through N transformations. Shoots exhibited significantly higher NO 2 - concentrations than wood cores, likely because photosynthetic tissues actively assimilate NO 3 - . During this process, nitrate is first reduced to NO 2 - in the cytosol before further reduction to NH 4 + in chloroplasts, and the intermediate NO 2 - can accumulate in shoots [ 36 ]. Leaves also have easier access to atmospheric NO 3 - via stomatal uptake of nitrogen dioxide (NO 2 ) compared to wood cores [ 37 ], providing an additional route for assimilation. In contrast, wood cores contained higher NH 4 ⁺ concentrations, indicating potential nitrification within these tissues. Although wood cores contained higher NH 4 + , the concurrent increase in NO 3 - suggests active mineralization supplying substrate for nitrification. While NO 3 - levels were slightly higher in wood cores than in shoots, the difference was not significant, likely due to rapid assimilation in photosynthetic tissues. These nutrient patterns correlated with microbial relative abundance and N 2 O exchange. Elevated NO 2 - in shoots was associated with higher nxrB relative abundance, suggesting active NO 2 - oxidation and supporting previous reports that nxrB expression promotes NO 2 - accumulation [ 38 ]. Our study also revealed a positive correlation between shoot NO 2 - concentrations and nirK relative abundance, consistent with findings that elevated NO 2 - can stimulate denitrifying microbial activity and increase nirK abundance [ 39 ]. Variation in nutrient content, N-cycling relative abundance, and N 2 O exchange across trees indicates that physiological traits strongly influence microbial adaptability. Two-way ANOVA confirmed that trees significantly affect the abundance of key N-cycling genes ( nirK , nirS , nosZ clades I and II, bacterial amoA , nxrB ) and NO 2 ⁻ content, highlighting tree-specific impacts on microbial N 2 O-related processes. The high abundance of N-cycling genes in spruce shoots may be linked to the evergreen nature of spruce needles, which retain foliage year-round. In contrast, birch, beech, and hornbeam are broadleaf trees that shed leaves in autumn, limiting gas exchange and nutrient availability during part of the year. Trees can shape microbial diversity and abundance through functional traits and rhizosphere communities [ 40 ]. Incubation experiments demonstrated that shoot tissues could either emit or consume N 2 O, with beech shoots notably exhibiting negative exchange rates, indicative of N 2 O consumption, and hornbeam showing positive N 2 O exchange rates, possibly of N 2 O emission. Wood core gas concentration measurements further supported these findings, showing N 2 O emissions from birch and uptake in beech, suggesting tree-specific metabolic activity. Previous studies have reported N 2 O fluxes from tree stems and shoots, including spruce stems [ 11 ], birch stems [ 11 ], and Scots pine shoots and stems as N 2 O sources [ 10 ], as well as beech stems (Stitna, Czech Republic; [ 20 ]) and beech shoots (Freising, Germany; [ 8 ]) acting as atmospheric N 2 O sinks. Wood core internal gas concentrations indicated the possibility of N 2 O emission from birch, similar to findings by Ranniku et al. [ 7 ], who reported N 2 O emissions of 1.44 ± 0.22 µg N m⁻² h⁻¹ from birch stems in Estonia. However, variability in emissions across trees underscores the influence of environmental and physiological factors [ 4 , 10 , 41 ]. The abundance of lenticels in birch may have contributed to higher N 2 O emissions from stem wood, as birch possesses a greater density of lenticels compared to other species. This anatomical feature can facilitate bark-mediated gas diffusion [ 42 ]. Additionally, stem N 2 O emissions have been linked to soil N 2 O fluxes, especially when stem chambers are positioned near the soil surface [ 11 , 43 ]. This suggests that some observed fluxes may result from upward transport of soil-derived N 2 O. One of the limitations of this study is the absence of field-based N 2 O flux measurements, which would have enabled direct comparison of actual N 2 O dynamics within shoots. Nevertheless, based on our incubation experiments, we infer that N 2 O exchange occurs within shoots, with tree-specific variation. Notably, beech trees exhibited strong negative exchange rates in both shoots and wood cores, suggesting a potential for N 2 O consumption. Furthermore, the linkages between nutrient content, relative abundance of N-cycling genes, and N 2 O exchange imply that N 2 O reducers in shoots and wood cores may utilize NH 4 + for metabolic activities, supporting the role of internal microbial processes. However, passive atmospheric exchange cannot be entirely ruled out. Another limitation is that hornbeam was sampled from only one site; however, the inclusion of other broad-leaved trees—beech and birch—strengthens the comparative framework for assessing species-level variation in N 2 O-related microbial processes. Microbial Gene Abundance and Diversity The presence of key nitrification and denitrification genes in both shoots and wood cores suggested that these tissues harbor microbial communities capable of N 2 O transformation. Spruce and birch shoots exhibited higher relative abundances of these genes compared to other trees, while birch and beech wood cores showed elevated levels, indicating tissue and tree-specific microbial colonization. Additionally, across all trees, nosZ clade I organisms were more abundant than clade II, a pattern commonly observed in terrestrial ecosystems [ 13 , 44 ]. Song et al. [ 45 ] reported that higher diversity or abundance of nosZ clade II is linked to lower net N 2 O emissions or increased N 2 O consumption. In contrast, we observed the opposite pattern in spruce shoots and beech wood cores, which showed high abundance and diversity of nosZ clade I. Incubation experiments with beech and spruce shoots revealed negative N 2 O exchange rates, suggesting potential N 2 O consumption, while beech wood cores exhibited the lowest Δ N 2 O concentration, indicating a high potential for N 2 O uptake. However, in most cases, N-cycling genes were not detected in beech shoots, and the low abundances of nosZ clade I and nxrB could be due to limited sequencing depth; nevertheless, low relative abundance does not necessarily indicate low activity. Future studies should address this by quantifying absolute microbial counts and activity using metatranscriptomic or qPCR. Despite these limitations, our findings provide valuable insights into the potential for N 2 O metabolism within tree tissues. In our study, we identified diverse nosZ clade I organisms in both shoots (birch and spruce) and wood cores (beech, birch and spruce). Several common communities present in both tissues belong to the class Alphaproteobacteria (including Rhizobiales , Rhodobacterales , and Rhodospirillales ) and Betaproteobacteria ( Burkholderiales, Neisseriales ), with Rhizobiales orders notably dominant, indicating adaptability beyond root nodules [ 46 ]. Notably, Bradyrhizobium japonicum , a prominent Rhizobiales species, possesses a complete denitrification gene set, enabling efficient N 2 O reduction even under carbon-limited conditions [ 47 , 48 ]. Its occurrence, alongside taxa from Burkholderiales , Neisseriales , and Rhodobacterales , has recently been confirmed in spruce shoots [ 13 ], suggesting functional roles in aboveground plant tissues. Other clade I organisms, including Oceanospillales (uniquely present in wood cores) and Xanthomonadales (uniquely present in shoots), may contribute to N 2 O exchange through denitrification, indicating tissue-specific niches for specialized N 2 O-transforming microbes. Although clade II organisms were less abundant, they comprised a more diverse set of taxa in spruce shoots and birch wood cores, suggesting that even low-abundance taxa may play important roles in N 2 O reduction. Some common communities detected in shoot and wood cores include Chitinophagales , Sphingobacteriales , Campylobacterales , and Opitutales , previously reported in soils, lakes, and wastewater systems as denitrifiers [ 49 – 51 ]. The difference in communities detected across trees suggests that trees may influence N 2 O metabolising microbes. This aligns with previous studies showing that tree species affect soil microbial communities by affecting soil chemical properties [ 52 ] and that leaf traits affect fungal communities in trees [ 53 ]. Together, these findings highlight the presence of diverse N 2 O-reducing communities in aboveground tree tissues. Conclusion This study demonstrates that tree tissues, shoots and wood cores may serve as active sites of microbial N 2 O metabolism. Through targeted metagenomic analysis, we characterized the N 2 O-metabolizing microbial communities and revealed the relative abundance of key functional genes involved in nitrification and denitrification, including nosZ clades I and II. The diversity and distribution of these genes varied across tissues and trees, with spruce shoots, birch shoots, birch wood cores, and beech wood cores showing particularly high microbial relative abundance and diversity. Our findings suggest that internal nutrient availability, especially NH 4 + and NO 2 − , plays a significant role in shaping microbial community structure and function. The observed N 2 O exchange patterns, coupled with microbial relative abundance profiles, indicate that microbial processes within tree tissues likely contribute to N 2 O dynamics, although the potential influence of soil-derived or atmospheric N 2 O cannot be entirely excluded. Importantly, this study shows that trees may influence the composition and potential activity of N 2 O-metabolizing microbial communities, possibly through differences in inorganic N compounds, which vary among trees. These insights contribute to a growing understanding of the role of tree microbiome in terrestrial N 2 O cycling and underscore the need for further research using functional and isotopic approaches to quantify microbial contributions and clarify the mechanisms underlying N 2 O fluxes in forest ecosystems. Declarations Acknowledgements This study was supported by the Academy of Finland projects Nitrobiome, (grant No 342362, 346516, 361980) and the ACCC Flagship funded by the Academy of Finland (grant no 337550, 357905, 359343). UM received funding from Estonian Research Council (PRG2032), the European Union Horizon program [grant No 101079192 (MLTOM23003R)], the European Research Council (ERC) [grant No 101096403 (MLTOM23415R)]. KM received funding from project AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation [CZ.02.01.01/00/22_008/0004635]; the Ministry of Education, Youth and Sports of CR within the CzeCOS program (grant No LM2023048); and the Ministry of Education, Youth and Sports of CR within the LU - INTER-EXCELLENCE II (2022–2029) program (grant No LUC23162). We thank Kenttärova ICOS station and Finnish Metereological Institute for access to spruce forest; Kuopio City for access to Puijo SMEAR station spruce forest; Mikael Aminoff for giving permission to sample Beech forest in Bromarv, Raasepori. Competing interests None declared. Author contributions HS conceived the ideas and designed the methodology. KT, DP, JK, KS, KM and HS collected the sample. KT and MK conducted the experiments. DP performed the bioinformatic analysis. KT analysed the data and wrote the original draft. KT, DP, KS, UM, SH, KM, JP, and HS revised the draft. All authors contributed critically to the drafts and gave final approval for publication. Data availability The metagenomic sequencing data in this paper have been deposited at the SRA-NCBI (https://www.ncbi.nlm.nih.gov/) under BioProject accession no.: PRJNA1307397 (https://www.ncbi.nlm.nih.gov/bioproject/1307397). Supplementary Information Supplementary materials are provided. 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Ecol Evol 15:e71691. https://doi.org/10.1002/ece3.71691 Additional Declarations No competing interests reported. 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05:54:55","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93474,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/b4784688f861bd77710788a8.png"},{"id":98920245,"identity":"449e57ca-eb13-404a-bf9b-d3f961a71a65","added_by":"auto","created_at":"2025-12-24 05:54:55","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63175,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/8e9460f4e413315a63af8506.png"},{"id":99310081,"identity":"b9efd08d-28c2-4861-a92c-522367c980da","added_by":"auto","created_at":"2025-12-31 16:11:50","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161085,"visible":true,"origin":"","legend":"","description":"","filename":"82df543fa7954b448965b486a4b5ba7c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/14b0c54a826607a5c3ff1be2.xml"},{"id":99309991,"identity":"3108d5eb-4c8d-4065-ba37-53f9bbf04a1e","added_by":"auto","created_at":"2025-12-31 16:11:36","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172179,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/0fd1f0e00184dd42be0ca2df.html"},{"id":99310270,"identity":"7de7c88f-8f95-44a2-a9ba-e3b113bcb8e8","added_by":"auto","created_at":"2025-12-31 16:12:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":519480,"visible":true,"origin":"","legend":"\u003cp\u003eDescription of sites and techniques used in this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/ebc2a0ecfd981f1eb509f0a1.png"},{"id":98920226,"identity":"6ac160bc-0eba-4d49-9d67-52896d61a79d","added_by":"auto","created_at":"2025-12-24 05:54:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167633,"visible":true,"origin":"","legend":"\u003cp\u003eNutrient content and N\u003csub\u003e2\u003c/sub\u003eO dynamics in tree tissues collected from different locations. A: NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e- \u003c/sup\u003eand NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e concentrations in shoots, B: NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e in wood cores, C: N\u003csub\u003e2\u003c/sub\u003eO exchange of shoots, D: D N\u003csub\u003e2\u003c/sub\u003eO concentration in stem wood (C\u003csub\u003estem wood\u003c/sub\u003e – C\u003csub\u003eambient air\u003c/sub\u003e). The box plots showed all the data points in black dots, and black line inside the box represented the median (n \u003cstrong\u003e≥ \u003c/strong\u003e3). Significant differences were indicated by different letters and were determined separately for each nutrient across different trees (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Tukey or Dunn test) in A, for N\u003csub\u003e2\u003c/sub\u003eO exchanges across trees in B, for D N\u003csub\u003e2\u003c/sub\u003eO concentration across trees in D. Asterisk indicated statistical difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Wilcoxon) between trees and blank in C. No significant differences were observed for each nutrient across different trees in B.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/8ebbc9dd6fe28627c2719b0d.png"},{"id":98920233,"identity":"9bdf4918-a236-412c-b309-a77168811ad5","added_by":"auto","created_at":"2025-12-24 05:54:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187492,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundances of denitrification and nitrification genes in (a) shoots and (b) wood cores from different trees. Relative abundances were calculated as total counts in relation to total reads. The data were log-transformed for better visualization. \u0026nbsp;The box plots showed all the data points in black dots, and black line inside the box represented the median (n \u003cstrong\u003e≥ 3)\u003c/strong\u003e. Significant differences were indicated by different letters and were determined separately for each gene across different trees (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Dunn test) in a and b. Trees where N-cycling genes showed no reads were marked with symbol @. \u003cem\u003enosZ\u003c/em\u003e clade II and archaeal \u003cem\u003eamoA\u003c/em\u003e in b showed no significant difference among trees.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/1c88dbb5188c2e4f4a0f59cc.png"},{"id":98920227,"identity":"700cdade-6d67-4f7b-9db0-1376f47238aa","added_by":"auto","created_at":"2025-12-24 05:54:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":252037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003enosZ\u003c/em\u003e clade I and II distribution based on the classification at the order level in shoots (a and b) and in wood cores (c and d).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/a243cd33ae4a42e78a66b1ec.png"},{"id":98920229,"identity":"1180d0b6-b334-4ba0-82f6-ef65e42cbb86","added_by":"auto","created_at":"2025-12-24 05:54:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":271046,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis for all the environmental and microbial parameters of the shoot (a) and wood cores (b). The color showed the Spearman correlation coefficient and indicated positive and negative correlation. Asterisk represented a significant difference of 0.01 (**) and 0.05 (*) between parameters.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/7748a055075a46bd67b8a3f0.png"},{"id":107350841,"identity":"b52ee86e-2f5d-4b6b-8623-0b25a4e5e693","added_by":"auto","created_at":"2026-04-20 16:05:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2047804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/88253391-2a29-474c-a79e-262fedba36fd.pdf"},{"id":98920238,"identity":"93a860aa-9277-441e-aced-d06debe4466f","added_by":"auto","created_at":"2025-12-24 05:54:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3100049,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8339723/v1/b862f86b2c6ec72f3f0fe598.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic potential for N₂O metabolism in tree tissues: Insights into nitrogen cycling gene abundance and nosZ diversity across trees","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO) is a potent greenhouse gas with a global warming potential about 300 times greater than CO\u003csub\u003e2\u003c/sub\u003e over a 100-year horizon. Since the preindustrial era, atmospheric N\u003csub\u003e2\u003c/sub\u003eO concentrations have increased from 275 to 338 parts per billion (ppb), largely driven by anthropogenic activities such as the application of synthetic nitrogen fertilizers, cultivation of N\u003csub\u003e2\u003c/sub\u003e-fixing crops, and fossil fuel combustion [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Land-use changes, including wetland drainage, also contribute to elevated N\u003csub\u003e2\u003c/sub\u003eO emissions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Microbial production of N\u003csub\u003e2\u003c/sub\u003eO through denitrification and nitrification processes in soils is considered the dominant source of N\u003csub\u003e2\u003c/sub\u003eO entering the atmosphere [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, significant uncertainty remains in global N\u003csub\u003e2\u003c/sub\u003eO budget estimations due to the limited understanding of all potential sources and sinks [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies suggest that plants can also act as significant sources and sinks of N\u003csub\u003e2\u003c/sub\u003eO [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Yet, the microbial pathways and organisms responsible for N\u003csub\u003e2\u003c/sub\u003eO production and consumption within tree tissues remain poorly understood, despite their critical role in shaping tree-mediated N\u003csub\u003e2\u003c/sub\u003eO fluxes.\u003c/p\u003e \u003cp\u003eAddressing this gap is crucial, as it directly influences our understanding of tree-associated microorganisms and their contribution to N\u003csub\u003e2\u003c/sub\u003eO dynamics. One approach to bridge this knowledge gap is through flux measurements, particularly from tree stems, which have been widely studied to quantify tree-mediated N\u003csub\u003e2\u003c/sub\u003eO exchange [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Research on phyllospheric N\u003csub\u003e2\u003c/sub\u003eO fluxes is only emerging [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent evidence suggests that microbial communities in the spruce phyllosphere have the potential for N\u003csub\u003e2\u003c/sub\u003eO exchange [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the canopy nitrification contributes significantly to forest N-cycling [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], indicating an active microbial role in these processes. Yet, the identity and function of microorganisms involved in N\u003csub\u003e2\u003c/sub\u003eO metabolism within different tree species and tissues remain largely unknown. This study addresses that gap by providing the first comprehensive assessment of tree-associated microbial contributions to N\u003csub\u003e2\u003c/sub\u003eO dynamics across multiple trees along a large transect.\u003c/p\u003e \u003cp\u003eIn this context, the present study aims to detect and compare the microbial communities involved in nitrogen (N) cycling metabolism within tree shoots, which include leaves and terminal branches, as well as wood cores of four tree categories. To achieve this, we utilized targeted metagenomics using probe capture [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] specifically designed to identify N\u003csub\u003e2\u003c/sub\u003e cycling genes in host-microbe systems, enabling us to evaluate the relative abundance of N-cycling genes, including bacterial and archaeal ammonia monooxygenase (\u003cem\u003eamoA\u003c/em\u003e) and nitrite oxidoreductase (\u003cem\u003enxrB)\u003c/em\u003e \u0026mdash; key genes involved in nitrification, nitrite reductase (\u003cem\u003enirK\u003c/em\u003e and \u003cem\u003enirS\u003c/em\u003e), nitric oxide reductase (\u003cem\u003enorB\u003c/em\u003e) and nitrous oxide reductase (\u003cem\u003enosZ\u003c/em\u003e), which are essential for denitrification. We also assess the diversity of \u003cem\u003enosZ\u003c/em\u003e clades I and II, which play a critical role in N\u003csub\u003e2\u003c/sub\u003eO reduction, and evaluate whether trees influence the relative abundance of N-cycling genes and distribution of \u003cem\u003enosZ\u003c/em\u003e microbial communities. To complement the molecular data, we conducted a short-term incubation experiment to measure N\u003csub\u003e2\u003c/sub\u003eO exchange from shoots and analyzed internal N\u003csub\u003e2\u003c/sub\u003eO concentrations in stem wood, providing functional evidence of N\u003csub\u003e2\u003c/sub\u003eO cycling activity. We quantified inorganic N compounds in tree tissues as indicators of microbial transformation. Ultimately, our study aims to enhance our understanding of the contributions of tree-associated microorganisms to N\u003csub\u003e2\u003c/sub\u003eO dynamics and to highlight the importance of aboveground tree-microbe interactions in forest N\u003csub\u003e2\u003c/sub\u003eO gas exchange.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection for N\u003csub\u003e2\u003c/sub\u003eO metabolism analysis and nucleic acid extraction\u003c/h2\u003e \u003cp\u003eTen forest sites were included in the study, several of which have previously reported N\u003csub\u003e2\u003c/sub\u003eO fluxes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Samples of shoots (leaves/needles with terminal branches) and wood cores were collected from trees representing different dominant species: European hornbeam (\u003cem\u003eCarpinus betulus\u003c/em\u003e L.), European beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e L.), birch (\u003cem\u003eBetula pubescens\u003c/em\u003e Ehrh. and \u003cem\u003eBetula pendula\u003c/em\u003e Roth.), and Norway spruce (\u003cem\u003ePicea abies\u003c/em\u003e (L.) H. Karst.). For analysis, downy birch and silver birch were combined and treated as a single category (\u0026lsquo;birch\u0026rsquo;) due to their similar ecological characteristics [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Sampling was conducted between April and September 2022. To improve geographical and climatic representation, tree species from different climate zones were included. Samples were collected from sites in temperate forests (Lanžhot and Št\u0026iacute;tn\u0026aacute; nad Vl\u0026aacute;ř\u0026iacute;, Czech Republic), hemiboreal forests (Agali II and Kiidj\u0026auml;rve, Estonia; Bromarv, Finland) and boreal forests (Puijo and Kentt\u0026auml;rova-Pallas, Finland).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation on sampling sites, tree and soil characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies study sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStand height, Stem diameter at Breast height\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSoil pH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnnual precipitation (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean Annual temperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStem N\u003csub\u003e2\u003c/sub\u003eO flux\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHornbeam, Lanžhot, Czech Republic (L1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(48,68133\u0026deg;P, 16,94602\u0026deg;I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemperate floodplain forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60% of hornbeam, 20% ash, 15% of oak, 5% of elm, maple and tilia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.4 m,\u003c/p\u003e \u003cp\u003e35 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEutric Humic Fluvisol, Haplic Fluvisol and Eutric Fluvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 \u0026micro;g N\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeech, Št\u0026iacute;tn\u0026aacute; nad Vl\u0026aacute;ř\u0026iacute;, Czech Republic (L2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(49.02\u0026deg;N, 17.58\u0026deg;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemperate montane upland forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115-year-old monoculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.2 m,\u003c/p\u003e \u003cp\u003e35 cm DBH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEutric (Stagnic) Cambisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;3.8 \u0026micro;g N\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeech, Bromarv Rilax manor, Finland\u003c/p\u003e \u003cp\u003e(L3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(59,95826\u0026deg;P, 23,06783\u0026deg;I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemiboreal upland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95-year-old mono-culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m, 48 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInceptisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirch, Agali-II, Tartu, Estonia\u003c/p\u003e \u003cp\u003e(L4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(58\u0026deg;17'00.0\"N 27\u0026deg;17'00.0\"E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemiboreal wetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65% of Downy birch and 35% of Norway spruce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 m\u003c/p\u003e \u003cp\u003e14 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOxalis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e*-23.69\u0026thinsp;\u0026plusmn;\u0026thinsp;236.87 \u0026micro;g N\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpruce, Agali-II, Tartu, Estonia\u003c/p\u003e \u003cp\u003e(L5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(58\u0026deg;17'00.0\"N 27\u0026deg;17'00.0\"E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemiboreal wetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65% of Downy birch and 35% of Norway spruce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 m\u003c/p\u003e \u003cp\u003e18.4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOxalis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e*6.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51 \u0026micro;g N\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirch, Kiidj\u0026auml;rve, Tartu, Estonia\u003c/p\u003e \u003cp\u003e(L6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(58,17896\u0026deg;P, 27,08408\u0026deg;I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemiboreal upland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonoculture, Birch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.8 m\u003c/p\u003e \u003cp\u003e15.2 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlfisol, Sandy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirch, Puijo, Kuopio, Finland\u003c/p\u003e \u003cp\u003e(L7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(62.908328, 27.659551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoreal upland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165-year-old 90% spruce, 10% birch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePodlozied soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpruce, Puijo, Kuopio, Finland (L8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u0026deg;54\u0026prime;N, 27\u0026deg;39\u0026prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoreal upland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165-year-old 90% spruce, 10% birch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24-29.5 m\u003c/p\u003e \u003cp\u003e41\u0026ndash;68 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePodlozied soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpruce, Kentt\u0026auml;rova, Pallas, Finland\u003c/p\u003e \u003cp\u003e(L9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N67\u0026deg;59.237', E24\u0026deg;14.579')\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubarctic upland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u0026ndash;160-year-old mono culture spruce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePodzol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*data from this study\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe collected one shoot and one wood core sample from each of three replicate trees (n\u0026thinsp;=\u0026thinsp;3) per species at each site. All samples were collected from the north side of the tree. The standard sampling height was approximately 1.5 meters, except for the birch shoot samples from Agali, where the lowest branches were at around 10 meters height, and the beech and hornbeam shoot samples from the Czech Republic site, which were collected at approximately 6 meters height using scissors attached to a long, telescopic handle. Wood cores were taken using a 5 mm increment borer (Hagl\u0026ouml;f, Sweden), at a height of 1.5 meters. The length of the wood cores was 5\u0026ndash;7 cm for both birch and spruce woods and 8\u0026ndash;10 cm for beech woods. Samples of wood cores and shoots that were used for DNA extraction were immediately taken into 50 mL sterile Falcon tubes and instantly snap frozen with liquid N\u003csub\u003e2\u003c/sub\u003e, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. In addition, extra samples of shoots and wood cores were taken and separated into plastic bags (2 L) for further analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNO exchange measurements for shoots\u003c/h3\u003e\n\u003cp\u003eA small incubation experiment was conducted using detached shoots to evaluate their potential for N\u003csub\u003e2\u003c/sub\u003eO exchange and provide a functional context for metagenomic data. Approximately 10\u0026ndash;20 g of fresh weight (FW) shoots were placed in 500 mL glass bottles containing 50 mL of 0.9% NaCl solution to maintain osmotic pressure, ensuring the solution touched the branches. Bottles were sealed with rubber septa and aluminum crimp caps, then supplemented with 120 mL of ambient indoor air. Incubation was carried out under controlled conditions: 12 hours in light (photosynthetically active radiation, PAR 300 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;) at 15\u0026deg;C, followed by 12 hours in darkness at 4\u0026deg;C. Blank bottles without plant material served as controls under identical conditions. Gas samples (20 mL) from the incubation bottles (samples and blank) were taken at 1, 24 and 72 h from the start of the incubation using polypropylene syringes (BD Plastipak\u0026trade;; Becton, Dickinson, and Company equipped with three-way stopcocks valve connected syringe needles (0.8 \u0026times; 40 mm) (BD Precisionglide\u0026reg;) and transferred to pre-evacuated 12 mL Labco vials flushed with N\u003csub\u003e2\u003c/sub\u003e. The CO\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO concentrations from shoots were measured using an Agilent 7890B Gas Chromatograph (GC) (Agilent Technologies, Palo Alto, CA, USA) equipped with Gilson liquid handler GX271 autosampler (Gilson Inc., Middleton, WI, USA) and a Hayesep Q 80/100 mesh column and an electron capture detector (ECD) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The method detection limit (MDL) was calculated according to USEPA guidelines. Seven blank replicates produced a standard deviation of 3.80 \u0026times; 10⁻⁴ ng N\u003csub\u003e2\u003c/sub\u003eO g⁻\u0026sup1; h⁻\u0026sup1;, resulting in an MDL of 1.19 \u0026times; 10⁻\u0026sup3; ng N\u003csub\u003e2\u003c/sub\u003eO g⁻\u0026sup1; h⁻\u0026sup1;. The N\u003csub\u003e2\u003c/sub\u003eO production or consumption potential rates over the incubation period were calculated using the ideal gas formula (Eq.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO exchanges \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\left(\\frac{V\\varDelta\\:CPM}{RTW}\\right)\\left(\\frac{1}{\\varDelta\\:t}\\right)\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eWhere V\u0026thinsp;=\u0026thinsp;volume of gas phase in the incubation bottle (mm\u003csup\u003e3\u003c/sup\u003e), ΔC\u0026thinsp;=\u0026thinsp;change in concentration of gas (ppm), P\u0026thinsp;=\u0026thinsp;air pressure (Pa), M\u0026thinsp;=\u0026thinsp;molecular mass of the gas (g mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), T\u0026thinsp;=\u0026thinsp;temperature (K), R\u0026thinsp;=\u0026thinsp;universal gas constant (8.314 J mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e K\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), W\u0026thinsp;=\u0026thinsp;weight of sample (g), Δt\u0026thinsp;=\u0026thinsp;change in time (h)\u003c/p\u003e\n\u003ch3\u003eNO concentration measurements in stem wood bore holes\u003c/h3\u003e\n\u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO concentration in stem wood bore holes was measured using the previously described method [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. After collecting wood cores, the increment borer was resealed to create an airtight chamber for gas measurements. Ambient concentration served as a reference for comparing stabilized N\u003csub\u003e2\u003c/sub\u003eO levels in wood bore holes. After 5 minutes, 20 mL gas samples were withdrawn from the sealed borer using syringes and injected into pre-evacuated, N\u003csub\u003e2\u003c/sub\u003e-flushed glass vials (Labco Limited, Lampeter, UK). N\u003csub\u003e2\u003c/sub\u003eO concentration in the stem wood was measured using gas chromatography equipped with ECD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The detection limit for the GC analysis was calculated as 3 times the SD of the N\u003csub\u003e2\u003c/sub\u003eO standard gas concentration, which is 12 ppb. Because ambient N\u003csub\u003e2\u003c/sub\u003eO concentrations vary across sites, N\u003csub\u003e2\u003c/sub\u003eO concentrations in stem wood were compared with ambient air to determine the difference in N\u003csub\u003e2\u003c/sub\u003eO concentrations in stem wood. Negative values indicate lower N\u003csub\u003e2\u003c/sub\u003eO inside the wood than outside (potential consumption), while positive values indicate higher internal concentrations (potential production).\u003c/p\u003e\n\u003ch3\u003eAnalysis of nutrient content in tree tissues\u003c/h3\u003e\n\u003cp\u003eShoots and wood cores were snap-frozen in liquid N\u003csub\u003e2\u003c/sub\u003e, ground with a sterile mortar and pestle (Haldenwanger, Berlin, Germany) using liquid N\u003csub\u003e2\u003c/sub\u003e, and stored in 50 mL Falcon tubes at \u0026minus;\u0026thinsp;80\u0026deg;C. Tree tissues (1 g) were extracted in 1 M KCl (3 mL), shaken for 1 h at 175 rpm (Heidolph, Schwabach, Germany), then centrifuged at 13,000 rpm for 5 min (Eppendorf, Horsholm, Denmark) for nutrient analysis, such as ammonium (NH\u003csub\u003e4\u003c/sub\u003e⁺), nitrite (NO\u003csub\u003e2\u003c/sub\u003e⁻), and nitrate (NO\u003csub\u003e3\u003c/sub\u003e⁻). Supernatants were filtered through PES membrane filters (0.22 \u0026micro;m; Merck KGaA, Darmstadt, Germany) and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until spectrophotometric analysis as described previously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDNA extraction\u003c/h3\u003e\n\u003cp\u003eCommunity DNA was extracted from ground shoots and wood cores using the DNeasy\u0026reg; PowerSoil\u0026reg; Kit (Qiagen, Hilden, Germany) with minor modifications [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Briefly, 0.1\u0026ndash;0.2 g of sample was homogenized in CD1 solution using Fastprep (Savant Fast Prep FP120 Bio 101, USA) for 30 s (2\u0026times;) at 5.5 m s⁻\u0026sup1; and vortexed for 10 min. After adding CD3 solution, samples were incubated for 1 h before continuing the protocol. DNA quality and concentration were checked using NanoDrop Lite (NanoDrop Technologies, Wilmington, NC, USA) and Qubit 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). After DNA extractions, both shoot and wood core samples (n\u0026thinsp;\u0026ge;\u0026thinsp;3\u003cb\u003e)\u003c/b\u003e from each tree species were sent for targeted metagenomics (Arbor Biosciences, Arbor, Michigan, USA).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTargeted metagenomics with probe capture and bioinformatic analyses\u003c/h2\u003e \u003cp\u003eTo investigate the presence and diversity of tree-associated microorganisms capable of N\u003csub\u003e2\u003c/sub\u003eO reduction, a targeted metagenomics approach was used. This method, more sensitive than traditional metagenomics, enabled the detection of low-abundance microbial genes within the host-microbe ecosystem [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA curated target gene database of nitrification and denitrification genes for probe production was compiled using GenBank, BLAST, and HMM-based searches [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Gene-specific probes were designed using the MetCap pipeline [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], resulting in 263,111 unique probes. These were synthesized with biotin labelling (myBaits Custom-kit, Daicel Arbor Biosciences, Michigan, USA) for streptavidin-coated magnetic bead-based purification. DNA samples were fragmented, adapter-ligated, and hybridized with probes at 47\u0026deg;C for 72 hours. Post-hybridization, libraries were purified and pooled for sequencing on the Illumina NovaSeq platform with PE150 chemistry. The DNA libraries, probe hybridization and sequencing were done in myReads service of Daicel Arbor Biosciences (Arbor, Michigan, USA). Raw reads were quality-checked with FastQC and trimmed using Trimmomatic (Q\u0026thinsp;\u0026gt;\u0026thinsp;30). To accurately identify functional genes, we employed advanced HMMER profiles [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] to meticulously search for sequences corresponding to each target gene against the gene-specific database, setting maximum E-value cut-off (E\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Gene abundances were normalized to total read counts. Following this, we used graftM in conjunction with the Gappa tool, based on a robust \u003cem\u003enosZ\u003c/em\u003e reference phylogeny [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], to determine the taxonomic affiliations and relative abundances of \u003cem\u003enosZ\u003c/em\u003e clade I and clade II as previously described [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData normality was assessed using the Shapiro-Wilk test. Normally distributed data were analyzed with one-way ANOVA and Tukey HSD, while non-normal data used Kruskal-Wallis with Dunn\u0026rsquo;s test and Bonferroni correction. Differences between tree and tree-tissues were tested with robust ANOVA via the Aligned Rank Transform method [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Spearman correlations were computed and visualized using the corrplot package. All analyses were performed in R (v4.4.0), with site-specific data provided in supplementary figures (S2\u0026ndash;S6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eNutrient content in shoots and wood cores\u003c/h2\u003e\n \u003cp\u003eAmmonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), and nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) concentrations were quantified in shoots and wood cores across all trees (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and corresponding site-specific data are presented in Fig. S2a and b. In shoots, birch exhibited the highest average NH\u003csub\u003e4\u003c/sub\u003e⁺ content (2.0\u0026ndash;5.0 \u0026micro;g g⁻\u0026sup1; FW), followed by hornbeam and spruce, with no significant differences among these trees (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Beech had significantly lower NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e levels (1.1\u0026ndash;1.8 \u0026micro;g g⁻\u0026sup1; FW; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e content was significantly higher in spruce (2.0\u0026ndash;3.3 \u0026micro;g g⁻\u0026sup1; FW; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to beech and birch, while hornbeam did not differ significantly. Shoot NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e content was highest in birch (0.9\u0026ndash;3.4 \u0026micro;g g⁻\u0026sup1; FW), followed by beech and hornbeam, with spruce showing significantly lower values (0.5\u0026ndash;1.1 \u0026micro;g g⁻\u0026sup1; FW; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In wood cores, no significant interspecific differences were detected for NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, or NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e. Birch and beech had the highest average NH\u003csub\u003e4\u003c/sub\u003e⁺ levels (up to 39.7 and 30.7 \u0026micro;g g⁻\u0026sup1; FW, respectively), while spruce showed the highest NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (0.8\u0026ndash;2.9 \u0026micro;g g⁻\u0026sup1; FW) and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e contents (1.6\u0026ndash;5.7 \u0026micro;g g⁻\u0026sup1; FW). Across tissues, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e was significantly lower in shoots than wood cores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e was higher in shoots; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e did not differ significantly between tissues.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eN\u003csub\u003e2\u003c/sub\u003eO exchanges during incubation of shoots\u003c/h2\u003e\n \u003cp\u003eThe N\u003csub\u003e2\u003c/sub\u003eO exchange rate during the incubation of shoots indicated varying potential for N\u003csub\u003e2\u003c/sub\u003eO production and consumption depending on trees (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec), with site-specific data provided in supplementary Fig. S2c. The tree shoots showed a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between trees: hornbeam exhibited a positive N\u003csub\u003e2\u003c/sub\u003eO exchange rate (0.002\u0026ndash;0.007 ng N\u003csub\u003e2\u003c/sub\u003eO g⁻\u0026sup1; h⁻\u0026sup1;), suggesting N\u003csub\u003e2\u003c/sub\u003eO emission. In contrast, beech showed a negative exchange rate (\u0026minus;\u0026thinsp;0.001 to \u0026minus;\u0026thinsp;0.017 ng N\u003csub\u003e2\u003c/sub\u003eO g⁻\u0026sup1; h⁻\u0026sup1;), suggesting N\u003csub\u003e2\u003c/sub\u003eO consumption. Both trees exhibited N\u003csub\u003e2\u003c/sub\u003eO exchange rates that were significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) different from the blank controls. We also measured CO\u003csub\u003e2\u003c/sub\u003e concentration during the incubation studies to check if the shoots were alive and respiring, and beech and spruce showed consumption (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eN\u003csub\u003e2\u003c/sub\u003eO concentrations in wood bore holes\u003c/h2\u003e\n \u003cp\u003eThe internal N\u003csub\u003e2\u003c/sub\u003eO concentration in wood bore holes varied across study sites (Fig. S2d). A negative value (e.g., \u0026minus;\u0026thinsp;9.74 ppb in beech) does not indicate a negative gas concentration but reflects the difference between the hole and ambient concentrations, representing potential N\u003csub\u003e2\u003c/sub\u003eO consumption by the wood (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed). Significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed among tree species, with birch showing the highest \u0026Delta;N\u003csub\u003e2\u003c/sub\u003eO concentration (mean 150.39 ppb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eRelative abundance of genes indicating genetic potential for N\u003csub\u003e2\u003c/sub\u003eO metabolism\u003c/h2\u003e\n \u003cp\u003eThe relative abundance of N-cycling genes varied significantly among trees in both shoots and wood cores (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) (Corresponding site-specific data are shown in Fig. S3 and S4). In shoots, significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed among trees for both denitrification genes (\u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enirS\u003c/em\u003e, \u003cem\u003enorB\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e clade I and II) and nitrification genes (bacterial and archaeal \u003cem\u003eamoA\u003c/em\u003e, \u003cem\u003enxrB\u003c/em\u003e). Spruce shoots had the statistically highest (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) overall relative abundances of N-cycling genes, followed by birch, hornbeam, and beech (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). In wood cores, significantly highest relative abundances (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of \u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enirS\u003c/em\u003e, \u003cem\u003enorB\u003c/em\u003e, and \u003cem\u003enxrB\u003c/em\u003e were observed in birch and beech, followed by spruce and hornbeam. Bacterial \u003cem\u003eamoA\u003c/em\u003e was significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) more abundant in beech, followed by birch, hornbeam, and spruce (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). In contrast, no significant differences among trees were observed for \u003cem\u003enosZ\u003c/em\u003e clade II and archaeal \u003cem\u003eamoA\u003c/em\u003e gene. Across both shoots and wood cores, \u003cem\u003enosZ\u003c/em\u003e clade I was more abundant than clade II. In shoots, the relative abundance of \u003cem\u003enosZ\u003c/em\u003e clade I (1.41\u0026ndash;24.06 per 100,000 reads) and Clade II (0.10\u0026ndash;2.99 per 100,000 reads) was highest in spruce samples. In wood cores, \u003cem\u003enosZ\u003c/em\u003e clade I was significantly higher in beech and birch (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 0.02\u0026ndash;19.1 per 100,000 reads), whereas \u003cem\u003enosZ\u003c/em\u003e clade II reached its highest mean abundance in birch (0.01 to 0.62 per 100,000 reads).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDiversity and distribution of\u003c/strong\u003e \u003cstrong\u003enosZ\u003c/strong\u003e \u003cstrong\u003egenes among the trees\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn shoot tissues, \u003cem\u003enosZ\u003c/em\u003e clade I genes were predominantly found in Alphaproteobacteria, particularly \u003cem\u003eRhizobiales\u003c/em\u003e and \u003cem\u003eRhodospirillales\u003c/em\u003e, across most trees (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). A higher diversity of \u003cem\u003enosZ\u003c/em\u003e clade I community was observed in spruce and birch shoots, and dominated by \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eRhodobacterales\u003c/em\u003e, \u003cem\u003eRhodospirillales\u003c/em\u003e, and \u003cem\u003eBurkholderiales.\u003c/em\u003e Higher diversity of \u003cem\u003enosZ\u003c/em\u003e clade II communities was noted in spruce trees, which included \u003cem\u003eChitinophagales\u003c/em\u003e, \u003cem\u003eCytophagales\u003c/em\u003e, \u003cem\u003eSphingobacteriales\u003c/em\u003e, \u003cem\u003eCampylobacterales\u003c/em\u003e, \u003cem\u003eFlavobacteriales\u003c/em\u003e, \u003cem\u003eCaldilineales\u003c/em\u003e, and \u003cem\u003eOpitutales\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Notably, \u003cem\u003enosZ\u003c/em\u003e clade II communities were absent in hornbeam and beech shoots, and \u003cem\u003eRhodothermaceae\u003c/em\u003e showed very low abundance in birch. In contrast, wood cores displayed high diversity of \u003cem\u003enosZ\u003c/em\u003e clade I communities in beech and birch (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). Shared taxa between these trees included \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eRhodobacterales\u003c/em\u003e, \u003cem\u003eRhodospirillales\u003c/em\u003e, \u003cem\u003eBurkholderiales\u003c/em\u003e, \u003cem\u003eNeisseriales\u003c/em\u003e, \u003cem\u003eNitrosomonadales\u003c/em\u003e, and \u003cem\u003ePseudomonadales\u003c/em\u003e. Birch wood cores also exhibited diverse \u003cem\u003enosZ\u003c/em\u003e clade II communities, comprising \u003cem\u003eChitinophagales\u003c/em\u003e, \u003cem\u003eSphingobacteriales\u003c/em\u003e, \u003cem\u003eRhodocyclales\u003c/em\u003e, and \u003cem\u003eOpitutales\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed). No \u003cem\u003enosZ\u003c/em\u003e clade II communities were detected in hornbeam wood cores.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eLinkages between Nutrient contents, activity, and relative abundance of N-cycling microorganisms\u003c/h2\u003e\n \u003cp\u003eThe study demonstrated significant variation in both N-cycling genes and nutrient contents (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) across tree tissues, indicating tissue-specific distribution (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the relative abundance of key N-cycling genes (\u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enirS\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e clades I and II, \u003cem\u003enxrB\u003c/em\u003e) and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e content differed significantly among trees, suggesting tree-specific patterns (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Importantly, a strong interaction between tree categories and tissues (shoots and wood cores) influenced all N-cycling genes and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e content, underscoring that tree\u0026ndash;tissue relationships jointly shape N-cycling dynamics.\u003c/p\u003e\n \u003cp\u003eIn shoots, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e content was significantly positively associated with the ratio of denitrification to nitrification ratio (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea), while NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e content showed significant positive correlations with \u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enirS\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e clade II, and \u003cem\u003enxrB\u003c/em\u003e. The N\u003csub\u003e2\u003c/sub\u003eO exchange rate was also significantly correlated with both the denitrification-to-nitrification gene ratio and shoot NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e content. In wood cores, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e content exhibited significant positive correlations with \u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enirS\u003c/em\u003e, \u003cem\u003enorB\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e clade II, archaeal \u003cem\u003eamoA\u003c/em\u003e, and \u003cem\u003enxrB\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). Furthermore, \u0026Delta; N\u003csub\u003e2\u003c/sub\u003eO concentration was significantly associated with \u003cem\u003enosZ\u003c/em\u003e clade I and the denitrification-to-nitrification gene ratio. A comparable relationship was observed in linear models assessing the linkage between N\u003csub\u003e2\u003c/sub\u003eO exchange, nutrient content, and the relative abundance of N-cycling genes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNon-parametric Aligned Rank Transform ANOVA was used to test for significant differences among tree tissues, trees and their interactions for each listed parameter.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDF.res\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTree tissues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTrees\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTree tissues*Trees\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enirK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enirS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enorB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enosZ\u003c/em\u003e clade I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enosZ\u003c/em\u003e clade II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacterial \u003cem\u003eamoA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArchaeal \u003cem\u003eamoA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enxrB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmmonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStep-wise linear models to examine the relationship between N\u003csub\u003e2\u003c/sub\u003eO exchanges with nutrient content and the relative abundance of N-cycling genes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSelected models\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShoot N\u003csub\u003e2\u003c/sub\u003eO exchange ~\u003c/p\u003e\n \u003cp\u003eshoot ammonia\u0026thinsp;+\u0026thinsp;shoot nitrite\u0026thinsp;+\u0026thinsp;shoot nitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShoot ammonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-208.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShoot N\u003csub\u003e2\u003c/sub\u003eO exchange ~\u003c/p\u003e\n \u003cp\u003e\u003cem\u003enirK\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enirS\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enorB\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enxrB\u003c/em\u003e\u0026thinsp;+\u0026thinsp;archaeal \u003cem\u003eamoA\u003c/em\u003e\u0026thinsp;+\u0026thinsp;bacterial \u003cem\u003eamoA\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enosZ\u003c/em\u003e\u0026thinsp;+\u0026thinsp;nosZ Clade I\u0026thinsp;+\u0026thinsp;\u003cem\u003enosZ\u003c/em\u003e Clade II\u0026thinsp;+\u0026thinsp;ratio (denitrification:nitrification)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eratio (denitrification:nitrification)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-205.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWood N\u003csub\u003e2\u003c/sub\u003eO concentration ~\u003c/p\u003e\n \u003cp\u003e\u003cem\u003enirK\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enirS\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enorB\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enxrB\u003c/em\u003e\u0026thinsp;+\u0026thinsp;archaeal \u003cem\u003eamoA\u003c/em\u003e\u0026thinsp;+\u0026thinsp;bacterial \u003cem\u003eamoA\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enosZ\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003enosZ\u003c/em\u003e Clade I\u0026thinsp;+\u0026thinsp;\u003cem\u003enosZ\u003c/em\u003e Clade II\u0026thinsp;+\u0026thinsp;ratio (denitrification:nitrification)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003enosZ\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003enosZ\u003c/em\u003e clade I\u003c/p\u003e\n \u003cp\u003eratio (denitrification:nitrification)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-57.60\u003c/p\u003e\n \u003cp\u003e-57.53\u003c/p\u003e\n \u003cp\u003e-56.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003cp\u003e7.42\u003c/p\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNutrient Content and N\u003csub\u003e2\u003c/sub\u003eO Exchange and Its Influence on Microbial Activity\u003c/h2\u003e \u003cp\u003eThe study revealed that both shoots and wood cores contain inorganic N compounds and may serve as potential active sites for N\u003csub\u003e2\u003c/sub\u003eO exchange, suggesting that internal microbial processes contribute to N\u003csub\u003e2\u003c/sub\u003eO cycling through N transformations. Shoots exhibited significantly higher NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e concentrations than wood cores, likely because photosynthetic tissues actively assimilate NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e. During this process, nitrate is first reduced to NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e in the cytosol before further reduction to NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e in chloroplasts, and the intermediate NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e can accumulate in shoots [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Leaves also have easier access to atmospheric NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e via stomatal uptake of nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) compared to wood cores [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], providing an additional route for assimilation. In contrast, wood cores contained higher NH\u003csub\u003e4\u003c/sub\u003e⁺ concentrations, indicating potential nitrification within these tissues. Although wood cores contained higher NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, the concurrent increase in NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e suggests active mineralization supplying substrate for nitrification. While NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e levels were slightly higher in wood cores than in shoots, the difference was not significant, likely due to rapid assimilation in photosynthetic tissues. These nutrient patterns correlated with microbial relative abundance and N\u003csub\u003e2\u003c/sub\u003eO exchange. Elevated NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e in shoots was associated with higher \u003cem\u003enxrB\u003c/em\u003e relative abundance, suggesting active NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e oxidation and supporting previous reports that \u003cem\u003enxrB\u003c/em\u003e expression promotes NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e accumulation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Our study also revealed a positive correlation between shoot NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e concentrations and \u003cem\u003enirK\u003c/em\u003e relative abundance, consistent with findings that elevated NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e can stimulate denitrifying microbial activity and increase \u003cem\u003enirK\u003c/em\u003e abundance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Variation in nutrient content, N-cycling relative abundance, and N\u003csub\u003e2\u003c/sub\u003eO exchange across trees indicates that physiological traits strongly influence microbial adaptability. Two-way ANOVA confirmed that trees significantly affect the abundance of key N-cycling genes (\u003cem\u003enirK\u003c/em\u003e, \u003cem\u003enirS\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e clades I and II, bacterial \u003cem\u003eamoA\u003c/em\u003e, \u003cem\u003enxrB\u003c/em\u003e) and NO\u003csub\u003e2\u003c/sub\u003e⁻ content, highlighting tree-specific impacts on microbial N\u003csub\u003e2\u003c/sub\u003eO-related processes. The high abundance of N-cycling genes in spruce shoots may be linked to the evergreen nature of spruce needles, which retain foliage year-round. In contrast, birch, beech, and hornbeam are broadleaf trees that shed leaves in autumn, limiting gas exchange and nutrient availability during part of the year. Trees can shape microbial diversity and abundance through functional traits and rhizosphere communities [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIncubation experiments demonstrated that shoot tissues could either emit or consume N\u003csub\u003e2\u003c/sub\u003eO, with beech shoots notably exhibiting negative exchange rates, indicative of N\u003csub\u003e2\u003c/sub\u003eO consumption, and hornbeam showing positive N\u003csub\u003e2\u003c/sub\u003eO exchange rates, possibly of N\u003csub\u003e2\u003c/sub\u003eO emission. Wood core gas concentration measurements further supported these findings, showing N\u003csub\u003e2\u003c/sub\u003eO emissions from birch and uptake in beech, suggesting tree-specific metabolic activity. Previous studies have reported N\u003csub\u003e2\u003c/sub\u003eO fluxes from tree stems and shoots, including spruce stems [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], birch stems [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and Scots pine shoots and stems as N\u003csub\u003e2\u003c/sub\u003eO sources [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], as well as beech stems (Stitna, Czech Republic; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]) and beech shoots (Freising, Germany; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]) acting as atmospheric N\u003csub\u003e2\u003c/sub\u003eO sinks. Wood core internal gas concentrations indicated the possibility of N\u003csub\u003e2\u003c/sub\u003eO emission from birch, similar to findings by Ranniku et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], who reported N\u003csub\u003e2\u003c/sub\u003eO emissions of 1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 \u0026micro;g N m⁻\u0026sup2; h⁻\u0026sup1; from birch stems in Estonia. However, variability in emissions across trees underscores the influence of environmental and physiological factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The abundance of lenticels in birch may have contributed to higher N\u003csub\u003e2\u003c/sub\u003eO emissions from stem wood, as birch possesses a greater density of lenticels compared to other species. This anatomical feature can facilitate bark-mediated gas diffusion [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, stem N\u003csub\u003e2\u003c/sub\u003eO emissions have been linked to soil N\u003csub\u003e2\u003c/sub\u003eO fluxes, especially when stem chambers are positioned near the soil surface [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This suggests that some observed fluxes may result from upward transport of soil-derived N\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e \u003cp\u003eOne of the limitations of this study is the absence of field-based N\u003csub\u003e2\u003c/sub\u003eO flux measurements, which would have enabled direct comparison of actual N\u003csub\u003e2\u003c/sub\u003eO dynamics within shoots. Nevertheless, based on our incubation experiments, we infer that N\u003csub\u003e2\u003c/sub\u003eO exchange occurs within shoots, with tree-specific variation. Notably, beech trees exhibited strong negative exchange rates in both shoots and wood cores, suggesting a potential for N\u003csub\u003e2\u003c/sub\u003eO consumption. Furthermore, the linkages between nutrient content, relative abundance of N-cycling genes, and N\u003csub\u003e2\u003c/sub\u003eO exchange imply that N\u003csub\u003e2\u003c/sub\u003eO reducers in shoots and wood cores may utilize NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e for metabolic activities, supporting the role of internal microbial processes. However, passive atmospheric exchange cannot be entirely ruled out. Another limitation is that hornbeam was sampled from only one site; however, the inclusion of other broad-leaved trees\u0026mdash;beech and birch\u0026mdash;strengthens the comparative framework for assessing species-level variation in N\u003csub\u003e2\u003c/sub\u003eO-related microbial processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial Gene Abundance and Diversity\u003c/h2\u003e \u003cp\u003eThe presence of key nitrification and denitrification genes in both shoots and wood cores suggested that these tissues harbor microbial communities capable of N\u003csub\u003e2\u003c/sub\u003eO transformation. Spruce and birch shoots exhibited higher relative abundances of these genes compared to other trees, while birch and beech wood cores showed elevated levels, indicating tissue and tree-specific microbial colonization. Additionally, across all trees, \u003cem\u003enosZ\u003c/em\u003e clade I organisms were more abundant than clade II, a pattern commonly observed in terrestrial ecosystems [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Song et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] reported that higher diversity or abundance of \u003cem\u003enosZ\u003c/em\u003e clade II is linked to lower net N\u003csub\u003e2\u003c/sub\u003eO emissions or increased N\u003csub\u003e2\u003c/sub\u003eO consumption. In contrast, we observed the opposite pattern in spruce shoots and beech wood cores, which showed high abundance and diversity of \u003cem\u003enosZ\u003c/em\u003e clade I. Incubation experiments with beech and spruce shoots revealed negative N\u003csub\u003e2\u003c/sub\u003eO exchange rates, suggesting potential N\u003csub\u003e2\u003c/sub\u003eO consumption, while beech wood cores exhibited the lowest Δ N\u003csub\u003e2\u003c/sub\u003eO concentration, indicating a high potential for N\u003csub\u003e2\u003c/sub\u003eO uptake. However, in most cases, N-cycling genes were not detected in beech shoots, and the low abundances of \u003cem\u003enosZ\u003c/em\u003e clade I and \u003cem\u003enxrB\u003c/em\u003e could be due to limited sequencing depth; nevertheless, low relative abundance does not necessarily indicate low activity. Future studies should address this by quantifying absolute microbial counts and activity using metatranscriptomic or qPCR. Despite these limitations, our findings provide valuable insights into the potential for N\u003csub\u003e2\u003c/sub\u003eO metabolism within tree tissues.\u003c/p\u003e \u003cp\u003eIn our study, we identified diverse \u003cem\u003enosZ\u003c/em\u003e clade I organisms in both shoots (birch and spruce) and wood cores (beech, birch and spruce). Several common communities present in both tissues belong to the class Alphaproteobacteria (including \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eRhodobacterales\u003c/em\u003e, and \u003cem\u003eRhodospirillales\u003c/em\u003e) and Betaproteobacteria (\u003cem\u003eBurkholderiales, Neisseriales\u003c/em\u003e), with \u003cem\u003eRhizobiales\u003c/em\u003e orders notably dominant, indicating adaptability beyond root nodules [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Notably, \u003cem\u003eBradyrhizobium japonicum\u003c/em\u003e, a prominent \u003cem\u003eRhizobiales\u003c/em\u003e species, possesses a complete denitrification gene set, enabling efficient N\u003csub\u003e2\u003c/sub\u003eO reduction even under carbon-limited conditions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Its occurrence, alongside taxa from \u003cem\u003eBurkholderiales\u003c/em\u003e, \u003cem\u003eNeisseriales\u003c/em\u003e, and \u003cem\u003eRhodobacterales\u003c/em\u003e, has recently been confirmed in spruce shoots [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], suggesting functional roles in aboveground plant tissues. Other clade I organisms, including \u003cem\u003eOceanospillales\u003c/em\u003e (uniquely present in wood cores) and \u003cem\u003eXanthomonadales\u003c/em\u003e (uniquely present in shoots), may contribute to N\u003csub\u003e2\u003c/sub\u003eO exchange through denitrification, indicating tissue-specific niches for specialized N\u003csub\u003e2\u003c/sub\u003eO-transforming microbes. Although clade II organisms were less abundant, they comprised a more diverse set of taxa in spruce shoots and birch wood cores, suggesting that even low-abundance taxa may play important roles in N\u003csub\u003e2\u003c/sub\u003eO reduction. Some common communities detected in shoot and wood cores include \u003cem\u003eChitinophagales\u003c/em\u003e, \u003cem\u003eSphingobacteriales\u003c/em\u003e, \u003cem\u003eCampylobacterales\u003c/em\u003e, and \u003cem\u003eOpitutales\u003c/em\u003e, previously reported in soils, lakes, and wastewater systems as denitrifiers [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The difference in communities detected across trees suggests that trees may influence N\u003csub\u003e2\u003c/sub\u003eO metabolising microbes. This aligns with previous studies showing that tree species affect soil microbial communities by affecting soil chemical properties [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and that leaf traits affect fungal communities in trees [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Together, these findings highlight the presence of diverse N\u003csub\u003e2\u003c/sub\u003eO-reducing communities in aboveground tree tissues.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that tree tissues, shoots and wood cores may serve as active sites of microbial N\u003csub\u003e2\u003c/sub\u003eO metabolism. Through targeted metagenomic analysis, we characterized the N\u003csub\u003e2\u003c/sub\u003eO-metabolizing microbial communities and revealed the relative abundance of key functional genes involved in nitrification and denitrification, including \u003cem\u003enosZ\u003c/em\u003e clades I and II. The diversity and distribution of these genes varied across tissues and trees, with spruce shoots, birch shoots, birch wood cores, and beech wood cores showing particularly high microbial relative abundance and diversity. Our findings suggest that internal nutrient availability, especially NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, plays a significant role in shaping microbial community structure and function. The observed N\u003csub\u003e2\u003c/sub\u003eO exchange patterns, coupled with microbial relative abundance profiles, indicate that microbial processes within tree tissues likely contribute to N\u003csub\u003e2\u003c/sub\u003eO dynamics, although the potential influence of soil-derived or atmospheric N\u003csub\u003e2\u003c/sub\u003eO cannot be entirely excluded. Importantly, this study shows that trees may influence the composition and potential activity of N\u003csub\u003e2\u003c/sub\u003eO-metabolizing microbial communities, possibly through differences in inorganic N compounds, which vary among trees. These insights contribute to a growing understanding of the role of tree microbiome in terrestrial N\u003csub\u003e2\u003c/sub\u003eO cycling and underscore the need for further research using functional and isotopic approaches to quantify microbial contributions and clarify the mechanisms underlying N\u003csub\u003e2\u003c/sub\u003eO fluxes in forest ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the\u0026nbsp;Academy of Finland projects\u0026nbsp;Nitrobiome, (grant No 342362, 346516, 361980) and the ACCC Flagship funded by the Academy of Finland (grant no 337550, 357905, 359343). UM received funding from Estonian Research Council (PRG2032), the European Union Horizon program [grant No 101079192 (MLTOM23003R)], the European Research Council (ERC) [grant No 101096403 (MLTOM23415R)]. KM received funding from project AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation [CZ.02.01.01/00/22_008/0004635]; the Ministry of Education, Youth and Sports of CR within the CzeCOS program (grant No LM2023048); and the Ministry of Education, Youth and Sports of CR within the LU - INTER-EXCELLENCE II (2022\u0026ndash;2029) program (grant No LUC23162). We thank Kentt\u0026auml;rova ICOS station and Finnish Metereological Institute for access to spruce forest; Kuopio City for access to Puijo SMEAR station spruce forest; Mikael Aminoff for giving permission to sample Beech forest in Bromarv, Raasepori.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHS conceived the ideas and designed the methodology. KT, DP, JK, KS, KM and HS collected the sample. KT and MK conducted the experiments. DP performed the bioinformatic analysis. KT analysed the data and wrote the original draft. KT, DP, KS, UM, SH, KM, JP, and HS revised the draft. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metagenomic sequencing data in this paper have been deposited at the SRA-NCBI (https://www.ncbi.nlm.nih.gov/) under BioProject accession no.: PRJNA1307397 (https://www.ncbi.nlm.nih.gov/bioproject/1307397).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary materials are provided.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThoning K, Dlugokencky E, Lan X, NOAA Global Monitoring Laboratory (2022) Trends in globally-averaged CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and SF\u003csub\u003e6\u003c/sub\u003e\u003c/li\u003e\n\u003cli\u003eNevison C, Andrews A, Thoning K, et al (2018) Nitrous Oxide Emissions Estimated With the CarbonTracker‐Lagrange North American Regional Inversion Framework. 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Biochem Soc Trans 33:141\u0026ndash;144. https://doi.org/10.1042/BST0330141\u003c/li\u003e\n\u003cli\u003eGao Y, \u0026Oslash;verlie Arntzen M, Kjos M, et al (2023) Denitrification by \u003cem\u003eBradyrhizobi\u003c/em\u003ea under Feast and Famine and the Role of the bc1 Complex in Securing Electrons for N\u003csub\u003e2\u003c/sub\u003eO Reduction. Appl Environ Microbiol 89:e01745-22. https://doi.org/10.1128/aem.01745-22\u003c/li\u003e\n\u003cli\u003eLlorens-Mar\u0026egrave;s T, Yooseph S, Goll J, et al (2015) Connecting biodiversity and potential functional role in modern euxinic environments by microbial metagenomics. ISME J 9:1648\u0026ndash;1661. https://doi.org/10.1038/ismej.2014.254\u003c/li\u003e\n\u003cli\u003eHester ER, Harpenslager SF, Van Diggelen JMH, et al (2018) Linking Nitrogen Load to the Structure and Function of Wetland Soil and Rhizosphere Microbial Communities. mSystems 3:10.1128/msystems.00214-17. https://doi.org/10.1128/msystems.00214-17\u003c/li\u003e\n\u003cli\u003eTakatsu Y, Miyamoto T, Hashidoko Y (2020) An Unknown Non-denitrifier Bacterium Isolated from Soil Actively Reduces Nitrous Oxide under High pH Conditions. Microbes Environ 35:n/a. https://doi.org/10.1264/jsme2.ME20100\u003c/li\u003e\n\u003cli\u003eFrene JP, Lawson SS, Lue Sue ND, et al (2024) Effects of tree species identity on soil microbial communities in \u003cem\u003eJuglans nigra\u003c/em\u003e and \u003cem\u003eQuercus rubra\u003c/em\u003e plantations. Front Microbiol 15:1442026. https://doi.org/10.3389/fmicb.2024.1442026\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;hler M, Castro S\u0026aacute;nchez‐Bermejo P, H\u0026auml;hn G, et al (2025) Foliar Endophytic Fungal Communities Are Driven by Leaf Traits\u0026mdash;Evidence From a Temperate Tree Diversity Experiment. Ecol Evol 15:e71691. https://doi.org/10.1002/ece3.71691\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"tree-microbiome, nosZ diversity, nitrous oxide, targeted metagenomic","lastPublishedDoi":"10.21203/rs.3.rs-8339723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8339723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO) is a potent greenhouse gas, and microorganisms play a crucial role in its metabolism. While soil microbial roles in N\u003csub\u003e2\u003c/sub\u003eO cycling are well studied, there is a major knowledge gap regarding the distribution and diversity of these microbes within tree ecosystems. In this study, we aimed to comprehensively assess the nitrogen (N) cycling gene abundance and the diversity of N\u003csub\u003e2\u003c/sub\u003eO-reducing microorganisms in shoots (leaves and terminal branches) and wood cores of four tree categories \u0026mdash; European beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e), European hornbeam (\u003cem\u003eCarpinus betulus\u003c/em\u003e), birch (\u003cem\u003eBetula pendula\u003c/em\u003e and \u003cem\u003eBetula pubescens\u003c/em\u003e) and Norway spruce (\u003cem\u003ePicea abies\u003c/em\u003e) across long transect. We assessed N\u003csub\u003e2\u003c/sub\u003eO exchange through shoot incubation experiments and measured internal N\u003csub\u003e2\u003c/sub\u003eO concentrations in stem wood. Inorganic N compounds were studied as indicators of microbial transformation, and a targeted metagenomic approach was used to analyze the relative abundance of N-cycling genes and \u003cem\u003enosZ\u003c/em\u003e clade I and II diversity. Our study revealed that hornbeam shoots showed potential N₂O emissions, while beech shoots indicated N₂O consumption in the incubation study. Birch had the highest internal stem wood N₂O concentration, and beech the lowest when compared to the ambient concentration. Metagenomic analysis confirmed the presence of key nitrification and denitrification genes in both tissues, with \u003cem\u003enosZ\u003c/em\u003e genes abundant in spruce shoots, birch wood cores, and beech wood cores\u0026mdash;clade I dominating over clade II and \u003cem\u003eRhizobiales\u003c/em\u003e prevalent within clade I. These findings provide new insights into tree microbiome and its contribution to N\u003csub\u003e2\u003c/sub\u003eO exchange in tree-associated environments.\u003c/p\u003e","manuscriptTitle":"Genetic potential for N₂O metabolism in tree tissues: Insights into nitrogen cycling gene abundance and nosZ diversity across trees","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-24 05:54:49","doi":"10.21203/rs.3.rs-8339723/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-06T14:28:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T09:22:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T10:04:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T12:50:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328586052717302099198720699791076233242","date":"2026-01-14T03:24:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223562028843021835126867545162890388635","date":"2026-01-12T19:25:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85204096923349530164163456128528025109","date":"2026-01-12T05:08:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T12:19:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38000310889930255942503164628668415738","date":"2026-01-05T10:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113471220380376801419088936040448386489","date":"2025-12-19T08:06:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T16:49:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T04:18:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T04:17:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2025-12-11T19:23:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1146a3f7-5318-4c92-808d-a36e386c1894","owner":[],"postedDate":"December 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:02:45+00:00","versionOfRecord":{"articleIdentity":"rs-8339723","link":"https://doi.org/10.1007/s00248-026-02773-8","journal":{"identity":"microbial-ecology","isVorOnly":false,"title":"Microbial Ecology"},"publishedOn":"2026-04-18 15:59:31","publishedOnDateReadable":"April 18th, 2026"},"versionCreatedAt":"2025-12-24 05:54:49","video":"","vorDoi":"10.1007/s00248-026-02773-8","vorDoiUrl":"https://doi.org/10.1007/s00248-026-02773-8","workflowStages":[]},"version":"v1","identity":"rs-8339723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8339723","identity":"rs-8339723","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}