Intraspecific plant trait variation drives bacterial community assembly while soil properties govern fungal communities under long-term nitrogen enrichment in a wetland

Intraspecific plant trait variation drives bacterial community assembly while soil properties govern fungal communities under long-term nitrogen enrichment in a wetland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intraspecific plant trait variation drives bacterial community assembly while soil properties govern fungal communities under long-term nitrogen enrichment in a wetland Zhaodong Cui, Mingyi Chen, Wenyu Wu, Jianyu Wang, Yutong Ma, Haixiu Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9417578/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Long-term nitrogen addition alters plant economic strategy and soil environment, yet whether bacteria and fungi respond through the same plant trait axis remains unresolved. Methods Using a 16-year nitrogen addition experiment in a Deyeuxia angustifolia -dominated wetland, we integrated plant functional traits, soil properties, and microbial sequencing data. Results Intraspecific trait variation formed a two-dimensional economic space: a fast-slow resource-use axis and an aboveground-belowground allocation axis. Bacterial community composition tracked plant trait gradients (explaining 8.0% of variation), whereas fungal communities responded more strongly to soil nutrient-moisture gradients (5.0%). Belowground-biased allocation enriched cosmopolitan bacteria and putative N-cycling and redox-related taxa, while aboveground-biased allocation favored specialist bacteria and putative P-cycling fungi. Conclusions These findings demonstrate that subtle intraspecific trait shifts drive divergent microbial filtering pathways, with implications for carbon-nutrient cycling in wetlands under nitrogen deposition. intraspecific trait variation nitrogen deposition plant economic spectrum trait plasticity Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The plant economics spectrum describes plant strategies of resource acquisition and allocation. These strategies influence microbial community structure and function through nutrient inputs to soil (Isaac et al. 2017 ; Reich, 2014 ; Roumet et al. 2016 ; Falkowski et al. 2008 ; Knight et al. 2024 ; Díaz et al. 2016 ). Most studies on plant traits and microbial communities have focused on interspecific differences. They usually compare root traits, leaf traits, or mycorrhizal types among species to explain microbial functional differentiation (Lin et al. 2024 ; Brzostek et al. 2013 ; Henneron et al. 2020 ; Huang et al. 2021 ). This approach shows the ecological importance of plant traits. It does not separate intraspecific trait adjustment from species replacement. Recent studies have started to address intraspecific trait variation (ITV). Lin et al. ( 2024 ) showed that root traits at the intraspecific scale explained shifts in microbial functional groups. Most of these studies, however, were based on spatial gradients or short-term treatments. Most previous studies also examined either bacteria or fungi alone, or focused on one microbial function (Ho et al. 2017 ; Mouginot et al. 2014 ). Direct comparison of bacterial and fungal responses within one ecological framework remains rare (Leff et al. 2018 ; Barberán et al. 2015 ; Wan et al. 2021 ; Zheng et al. 2019 ). Plant strategy under long-term environmental change is therefore still poorly linked to niche differentiation across multiple microbial groups. Long-term nitrogen addition provides a suitable system to test this link. Nitrogen addition changes microbial diversity, functional gene abundance, and key taxa related to N cycling, redox processes, and litter decomposition (Li et al. 2025 ; Niu et al. 2021 ). Most studies, however, used a simple soil-microbe framework. They did not include the joint effects of plant traits and soil environment. Other studies showed that nitrogen addition changed plant resource-use strategy (Guo et al. 2022 ; Lu et al. 2011 ). Few studies directly linked these trait shifts to responses of specific microbial functional groups. Two questions therefore remain open. Can soil change reshape microbial community structure through plant trait reorganization? Do different microbial groups respond along the same plant strategy axis? Trait-based ecology provides a framework to study plant-microbe coupling (Laliberté 2017 ; Bardgett et al. 2014 ). Most evidence, however, comes from interspecific comparisons or short-term experiments. Intraspecific analysis requires a stable field system with a clear external driver. The long-term nitrogen addition experiment on the Sanjiang Plain provides such a system. The background environment is relatively uniform. The nitrogen gradient is clear. This system allows analysis of coordinated responses among plant traits, soil environment, and multiple microbial groups. This study aimed to clarify links between intraspecific trait variation and soil microbial communities. Deyeuxia angustifolia was expected to show systematic intraspecific trait differentiation under different nitrogen treatments. Based on this expectation, H 1 proposed that intraspecific trait variation in Deyeuxia angustifolia would form a two-dimensional plant economic space defined by a fast-slow strategy axis and an aboveground-belowground allocation axis, consistent with patterns reported in other plant species (Liu et al. 2023 ; Lin et al. 2024 ; Díaz et al. 2016 ). H 2 proposed that fungal community structure would follow soil physicochemical properties more closely, whereas bacterial community structure would follow plant trait variation more closely, because fungi are usually more sensitive to soil environmental gradients and bacteria are more closely linked to plant traits (Fanin et al. 2013 ). Nitrogen addition in this ecosystem usually causes relative phosphorus (P) limitation. Based on this pattern, H 3 proposed that the relative abundances of ecological indicator groups would vary systematically along the plant strategy gradient. A strategy biased toward belowground allocation was expected to favor N-cycling and redox-related bacteria. A strategy biased toward aboveground allocation was expected to favor P-cycling fungi. Materials and Methods Study site and experimental design The study was conducted at the Honghe National Nature Reserve, Sanjiang Plain Wetland Ecological Research Station, Institute of Natural Resources and Ecology, Heilongjiang Academy of Sciences, Heilongjiang, China. The reserve is located in the northeastern Sanjiang Plain (47°42′-47°52′ N, 133°34′38″-133°46′29″ E). The region has a temperate monsoon climate, with a mean annual temperature of 1.9°C, a mean temperature of − 23.4°C in the coldest month, and 22.4°C in the warmest month. Mean annual precipitation is 585 mm and occurs mainly from July to September. Mean annual evaporation is 1166 mm. The main soil types are meadow soil, albic soil, and marsh soil. The vegetation type is wetland meadow, and the dominant plant species is Deyeuxia angustifolia . The experiment was established in 2009 and included three nitrogen-addition treatments: N0 (0 g N m⁻² yr⁻¹), N1 (4 g N m⁻² yr⁻¹), and N2 (8 g N m⁻² yr⁻¹). Each plot measured 5 m × 5 m. At the beginning of May each year, NH₄NO₃ was dissolved in 50 L of water and sprayed evenly once onto each treatment plot. Control plots received the same volume of water without nitrogen addition. Sample collection and measurements At peak biomass during the growing season (mid-July 2023), five 1 m × 1 m quadrats were randomly established in each nitrogen-treatment plot. All aboveground plant material within each quadrat was clipped at the soil surface. Stems and leaves were separated by tissue type and washed with distilled water. Samples were heated at 105°C for 15 min and then oven-dried at 65°C to constant mass. Three soil cores (0–20 cm depth) were randomly collected from each quadrat using a square soil corer with a side length of 10 cm. Roots were carefully separated and washed through a 0.5 mm mesh sieve to remove soil particles, and then oven-dried at 65°C to constant mass. Biomass was expressed as dry mass in g m⁻². Leaf samples of Deyeuxia angustifolia were ground and passed through a 100-mesh sieve. Total C and total N were measured using an Elementar Vario Macro Cube elemental analyzer (Elementar, Germany). Total P was determined by the molybdenum-antimony colorimetric method after H₂SO 4 -HClO 4 digestion. Each nitrogen treatment included five replicate plots. In each plot, soil samples were collected from 0–20 cm depth. Soil from five sampling points within each plot was thoroughly mixed by quartering to form one composite sample. Each composite sample was divided into two subsamples. One subsample was air-dried, ground, and passed through a 2 mm sieve for analysis of soil physicochemical properties. The other subsample was transported in an ice box and stored immediately at − 80°C for soil microbial analysis. A total of 10 soil physicochemical properties were measured, with five replicates for each treatment: soil pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available potassium (AK), available phosphorus (AP), water content (WC), and electrical conductivity (EC) (Table S1 ). Soil pH was measured potentiometrically. SOC and TN were measured using an Elementar Vario Macro Cube elemental analyzer. TP was determined colorimetrically after H₂SO₄-HClO₄ wet digestion. TK was measured by flame photometry after wet digestion. AN was extracted with 2 mol L⁻¹ KCl and measured colorimetrically. AP was determined by the Olsen method. AK was extracted with ammonium chloride and measured by flame photometry. WC was determined by oven drying at 105 ± 2°C. EC was measured with a conductivity meter at a soil:water ratio of 1:5. To summarize soil property data, principal component analysis (PCA) was performed on all measured soil variables. The first three PCA axes were independent and explained 88.28% of the total variation (Table S2). These axes were used as representative soil factors in subsequent analyses. Soil PC1 explained 72.8% of the total variation and was closely associated with soil nutrient status. It was driven mainly by SOC, TN, TP, TK, and available N, P, and K, and was also related to soil pH. Soil PC2 explained 8.91% of the total variation and was driven mainly by AP, WC, and EC. It represented a joint gradient of soil moisture and mineral nutrient status. Soil PC3 explained 6.56% of the total variation and was determined mainly by EC and WC. It represented independent variation in dissolved salts and soil water status. Soil samples were collected from the 0–10 cm layer. Total genomic DNA was extracted from 0.5 g fresh soil with the FastDNA® SPIN Kit for Soil (MP Biomedicals, USA) according to the manufacturer’s instructions. DNA concentration and purity were measured with a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). For bacteria, the V3-V4 region of the 16S rRNA gene was amplified with primers 27F/338R. For fungi, the ITS1 region was amplified with primers ITS5/ITS2. Purified PCR products were sequenced on an Illumina NovaSeq 6000 platform. Raw reads were processed with UPARSE for quality control and clustering. OTUs were defined at 97% sequence similarity. Bacterial OTUs were annotated against SILVA, and fungal OTUs against UNITE. All samples were rarefied to the same minimum sequencing depth before community analysis. Microbial niche breadth was calculated with the Levins index. Taxa with observed values above the upper 95% confidence limit of the null distribution were classified as cosmopolitan taxa. Taxa with values below the lower 95% confidence limit were classified as specialist taxa. OTUs with values within the 95% confidence interval were classified as neutral taxa (Pandit et al. 2009 ). Classification of functional groups was based on published lists of functional genes and taxa (Trivedi et al. 2017 ; Nelson et al. 2016 ; Lovley and Phillips 1988 ; Richardson and Simpson 2011 ) and on reported genus-function associations. Genus-level OTUs repeatedly linked to nitrification, denitrification, or Fe/Mn redox metabolism were classified as putative N-cycling taxa or putative redox-related taxa. OTUs assigned to genera such as Nitrosomonas , Nitrospira , Nitrobacter , Bradyrhizobium , and Pseudomonas were classified as putative N-cycling bacteria. OTUs assigned to genera such as Geobacter , Shewanella , Ferribacterium , and Anaeromyxobacter were classified as putative redox-related bacteria. Fungal OTUs assigned to genera such as Mortierella , Penicillium , and Aspergillus were classified as putative P-cycling fungi. Although amplicon-based inference reflects genetic potential rather than immediate activity, systematic shifts in these groups along the long-term nitrogen-addition gradient provide evidence for links between plant resource-allocation strategy and C-N-P coupling in wetland ecosystems. Statistical analysis All analyses were conducted in R v4.3.2 (R Development Core Team, 2023). The coefficient of variation (CV, %) of each plant trait was calculated across the 15 plots to quantify intraspecific variation under nitrogen addition. Effects of soil factors on individual plant traits were tested with multiple linear regression. Each plant trait was used as a response variable. Soil PC1, Soil PC2, and Soil PC3 were used as explanatory variables. The best model was selected by the minimum AICc with the dredge function in MuMIn (Bartoń 2019 ). To test H 1 , pairwise trait correlations were calculated with Pearson correlation coefficients. PCA was then performed on the plant trait matrix. All traits were standardized to zero mean and unit variance before analysis. The first two principal components were used to represent the whole-plant economic space. To test H 2 , NMDS was used to visualize bacterial and fungal community structure. The analysis was conducted with metaMDS in vegan based on Bray-Curtis dissimilarity. Plant traits and soil principal components were fitted onto the ordination with envfit. Significance was tested with 999 permutations. Variables with p < 0.05 (Tables S4-5) were retained for variation partitioning. This analysis was based on db-RDA and implemented with varpart to quantify the fractions of community variation explained by plant traits and soil factors. To test H 3 , multiple linear regression was used to relate microbial indicator groups to plant functional strategy. Plant PC1 and Plant PC2 were used as predictors. Response variables included cosmopolitan bacteria, specialist bacteria, putative N-cycling bacteria, putative redox-related bacteria, putative P-cycling fungi, cosmopolitan fungi, and specialist fungi. Soil PC1-3 were included as covariates. Relative abundance of putative P-cycling fungi was log-transformed before analysis. The best model was selected by the minimum AICc. Results were visualized with partial regression plots generated by avPlots in the car package (Fox and Weisberg 2019). These plots showed the independent relationships between explanatory variables and microbial indicator groups. Results Effects of nitrogen addition on the intraspecific economic space of the dominant plant Deyeuxia angustifolia Plant traits of Deyeuxia angustifolia varied significantly and unevenly among nitrogen treatments (Table 1 ). LC and RC showed the smallest variation (CV < 2%), whereas LN showed the largest (CV = 63.6%). LP, RP, and AGB/BGB showed intermediate intraspecific variation (CV = 31%-58%). Soil factors explained 5.6%-60.0% of trait variation. LC and LP were most strongly associated with Soil PC1-3 ( R ² = 54.4% and 60.0%, respectively). RN and RP were weakly associated with soil principal components ( R ² < 10%). Only a few plant traits were significantly correlated (Figure S1 ). Principal component analysis of six functional traits identified two major axes across the 15 plots. These axes explained 85.50% of total variation (Fig. 1 a; Table S3). Plant PC1 explained 56.88% of the variation and represented a gradient from acquisitive to conservative strategy. LCN showed a strong positive loading, whereas LNP showed a strong negative loading. This axis reflected a trade-off between resource acquisition and nutrient conservation. Plant PC2 explained 28.62% of the variation and was related mainly to biomass allocation. AGB/BGB and RNP showed strong negative loadings, whereas RCN showed a strong positive loading. This axis represented an aboveground-belowground allocation gradient (Fig. 1 a). Plant functional strategies differed significantly among nitrogen treatments (Fig. 1 b,c). N0 had the highest Plant PC1 score, whereas N1 and N2 had lower scores ( p < 0.05). N1 had the highest Plant PC2 score, whereas N0 and N2 had lower scores ( p < 0.05) (Fig. 1 c). Table 1 Trait ranges of Deyeuxia angustifolia under three nitrogen addition treatments (N0, N1, N2).Values represent the observed minimum-maximum range for each trait across plots. The units for leaf and root carbon (LC, RC), leaf and root nitrogen (LN, RN), and phosphorus (LP, RP) are all g\kg − 1 AGB/BGB is a dimensionless ratio. CV is the coefficient of variation (%) calculated across all 15 plots. "Soil explained" denotes the percentage of trait variation explained by soil PCA axes based on the best-fitting linear model. "Sig." indicates whether the trait differs significantly among nitrogen treatments (ANOVA; p < 0.05 shown as "*", p < 0.01 shown as "**", P < 0.001 shown as "***"). LN N0 N1 N2 CV Soil explained Sig. 0.49–0.86 0.66–2.17 0.6–2.37 63.6 48.7 RN 2.13–2.48 0.46–1.26 0.44–2.43 56.1 6.4 ** LC 43.37–45.27 43.53–45.62 43.38–44.43 1.6 54.4 RC 43.31–46.06 42.91–45.04 43.53–44.64 1.8 30.8 LP 0.08–0.15 0.1–0.22 0.1–0.21 33.4 60.0 RP 0.09–0.19 0.07–0.13 0.09–0.19 31.0 5.6 AGB/BGB 0.18–0.36 0.14–0.33 0.33–0.8 58.0 56.5 ** Changes in the taxonomic composition of soil microbial communities under nitrogen addition The bacterial community was dominated by Acidobacteriota (30.97%), Chloroflexi (21.12%), and Proteobacteria (14.81%) (Figure S2a). No bacterial genus exceeded 10% mean relative abundance. The most abundant genera were AD3 (8.86%), Subgroup 7 (6.28%), and A21b (3.52%) (Figure S2b). The fungal community was dominated by Ascomycota (73.92%) (Figure S2c). No fungal genus exceeded 10% mean relative abundance. The relatively abundant genera were Archaeorhizomyces (7.76%) and Dimorphospora (3.28%) (Figure S2d). Bacterial taxa that differed significantly among nitrogen treatments belonged mainly to Nitrospirota, Sva0485, Thermodesulfovibrionia, Burkholderiales, and Candidatus Solibacter (Figure S2e). Nitrospirota and Sva0485 were enriched under nitrogen addition, whereas Candidatus Solibacter was more abundant in the control. Fungal differences were concentrated in Ophiocordyceps , Endophragmiella , Umbelopsis , Mortierella , and Oidiodendron (Figure S2f). Mortierella and Umbelopsis were enriched under nitrogen addition, whereas Oidiodendron was more abundant in the control. The NMDS ordination (stress < 0.2) showed differences in microbial community composition among nitrogen treatments (Fig. 2 a,b). LC was the only plant trait significantly associated with the bacterial community ( R² = 0.53, p = 0.01) (Table S4). Plant traits, soil factors, and their shared effect explained 8.0%, 1.5%, and 2.3% of bacterial community variation, respectively (Fig. 2 a). For fungi, AGB/BGB was the only plant trait significantly associated with overall community composition ( R² = 0.49, p = 0.02) (Table S5). Soil factors explained 5.0% of fungal community variation (Fig. 2 b). Associations between soil microbial functional composition and the plant economic gradient under long-term nitrogen addition The relative abundances of cosmopolitan bacteria, specialist bacteria, cosmopolitan fungi, and specialist fungi differed among nitrogen treatments: N0 (43.7%-60.8%, 0.8%-16.6%, 5.6%-10.6%, and 18.9%-50.5%, respectively), N1 (60.5%-66.8%, 0.8%-2.1%, 5.5%-11.4%, and 36.8%-46.5%), and N2 (48.8%-62.6%, 1.3%-9.8%, 4.7%-9.8%, and 26.4%-59.0%) (Figure S3a-f). After controlling for soil factors, the relative abundances of both cosmopolitan and specialist bacteria were significantly associated with the second plant economic trait axis (Fig. 3 a,b; p < 0.05). Higher Plant PC2 was associated with enrichment of cosmopolitan bacteria, N-cycling bacteria, and redox-related bacteria, whereas lower Plant PC2 was associated with enrichment of specialist bacteria and P-cycling fungi (Fig. 4 a-c). No significant associations were detected between fungal lifestyle groups and either the plant economic gradient or soil physicochemical properties. Discussion Trait variation and intraspecific economic space of Deyeuxia angustifolia under long-term nitrogen addition Long-term nitrogen addition reshaped the two-dimensional economic space of Deyeuxia angustifolia . This pattern supported H1 and indicated a stable intraspecific economic strategy axis. The coefficients of variation of the six traits remained within the common intraspecific range for plants (9.8%-87%) (Sun 2021; Kong et al. 2014 ). Nitrogen addition changed the direction and strength of trait correlations rather than the mean of single traits. Plants therefore responded to resource enrichment mainly through trait coupling. This response may reflect constraints from organismal integration (Sweeney et al. 2021 ). The first PCA axis was dominated by LCN, LNP, and RCN. It represented a gradient from acquisitive to conservative resource use. This fast-slow economic axis indicates that nitrogen enrichment increased assimilatory activity and strengthened coordinated demand for N and P. A single species thus expressed a fast-slow strategy structure similar to that reported across species (Kong et al. 2014 ; de la Riva et al. 2021 ). This gradient further indicates intraspecific differentiation of resource-use strategy under long-term nutrient enrichment. The second PCA axis was defined by trade-offs between aboveground-belowground biomass allocation and root stoichiometry. This axis reflected regulation between aboveground resource acquisition and belowground carbon input under long-term nitrogen addition. Joint shifts in AGB/BGB, root C:N, and root N:P indicate adjustment of carbon and nutrient allocation among organs. This adjustment balanced rapid use of surface nitrogen with maintenance of long-term belowground carbon input. This axis was not equivalent to the cross-species decomposition economic spectrum, because it arose from intraspecific allocation strategy rather than species turnover (Freschet et al. 2012 ; Lin et al. 2019 ). This axis determined the quality and turnover of plant-derived inputs to soil. It thus created substrate conditions of different quality and temporal scale for microbial indicator groups. Long-term nitrogen addition therefore reshaped plant traits and made the intraspecific economic space a key expression of plant functional adjustment under resource enrichment. This framework provides a basis for understanding how nitrogen addition regulates microbial community structure across the plant-soil interface. Differential control of bacterial and fungal communities by plant traits and soil factors Under long-term nitrogen addition, bacterial and fungal communities showed contrasting control patterns. Bacterial communities varied mainly along the continuous gradient of plant traits, whereas fungal communities varied mainly with soil physicochemical properties. This pattern supported H 2 . In the NMDS ordination (Fig. 2 a), bacterial taxa were highly mixed among treatments. Their distribution followed the continuous gradient of plant traits rather than treatment categories. Soil physicochemical properties explained only a small fraction of bacterial community composition. In contrast, fungal communities showed clear treatment separation in the NMDS ordination (Fig. 2 b). Their distribution followed the gradients of TN, AP, WC, and EC generated by nitrogen addition. Fungal communities were therefore controlled mainly by soil environmental filtering rather than by plant trait shifts. Bacteria and fungi differed in nutrient demand, resource acquisition, and niche preference (He et al. 2023 ; Preusser et al. 2019 ; Wang and Kuzyakov 2024 ). Bacteria responded mainly to resource filtering associated with plant traits. Fungi responded mainly to environmental filtering associated with soil structure. This contrast reflects differences in metabolism and ecology. Bacteria usually have lower C:N:P stoichiometric ratios and depend more strongly on dissolved nutrients and rapidly cycling inorganic resources (Mouginot et al. 2014 ; Strickland and Rousk 2010 ). Most single traits did not differ consistently among treatments. The continuous plant economic spectrum still provided a detectable resource gradient for bacteria. Fungi depend more strongly on complex organic substrates and are more sensitive to soil structure and moisture (Fanin et al. 2013 ). Their distribution therefore aligned more closely with the soil PCA axis than with plant traits. Plant control over fungi was weaker in the absence of strong rhizosphere-scale root exudate inputs. Nitrogen addition produced more stable shifts in soil moisture and nutrient structure and thus imposed stronger environmental selection on fungi (Wang et al. 2023 ; Morrison et al. 2016 ). Plant traits and soil factors jointly contributed to microbial community shifts, but their explanatory power remained limited at the intraspecific scale. This result indicates additional control by finer-scale soil structure and short-term processes. Aggregate structure, pore connectivity, pulses of dissolved organic matter, and fluctuations in microsite moisture may all influence microbial competition, but these processes were not measured here. Thus, contrasting bacterial and fungal responses to nitrogen addition arose not only from broad gradients in plant traits and soil properties, but also from finer-scale physicochemical heterogeneity. This heterogeneity remains a key target for future studies of plant-soil-microbe interactions. Associations between plant economic strategy and soil microbial functional composition The results supported the third hypothesis (H 3 ): plant resource-allocation strategy (Plant PC2) was systematically linked to soil microbial functional groups. Trade-offs along the aboveground-belowground biomass allocation axis created two contrasting substrate environments in soil. One end corresponded to a belowground-input strategy, with higher root biomass and higher root residue C:N ratio. This combination formed a more recalcitrant carbon pool with slower turnover. The other end corresponded to an aboveground-input strategy, with faster aboveground growth and more rapidly turning over leaf litter. Under long-term nitrogen addition, this strategy was also associated with lower N:P ratio and stronger P limitation (Freschet et al. 2012 ). At the belowground-input end of the spectrum (N0 and N2), root residues with higher C:N ratio provided a persistent but less decomposable carbon source to the native soil. This slow-turnover, recalcitrant substrate environment favored cosmopolitan bacteria under environmental filtering. Cosmopolitan taxa usually possess broader substrate-use capacity and greater environmental tolerance than specialist taxa. They were therefore better able to exploit complex root-derived carbon (Pandit et al. 2009 ). Long-term root accumulation may also have increased moisture and redox heterogeneity in soil microsites. Such microsite conditions likely favored putative N-cycling and redox-related indicator groups (Strickland and Rousk 2010 ). At the aboveground-input end of the spectrum (N1), plants allocated more resources to aboveground growth. This shift increased pulse input of leaf litter into soil. These labile C inputs, characterized by fast turnover and high input flux, favored rapid proliferation of specialist bacteria. Specialist taxa usually have higher resource-use efficiency and faster growth rates. They can therefore respond rapidly to substrate pulses (Strickland and Rousk, 2010 ). Rapid aboveground growth also increased plant demand for soil P. As a result, litter returned to soil carried a stronger signature of P limitation, expressed as N:P imbalance. This plant-driven stoichiometric imbalance further strengthened P limitation in soil (Liu et al. 2013 ; Mori et al. 2018 ). Microorganisms maintain soil P availability by releasing phosphatases that mineralize organic P and by using high-affinity transport systems that acquire inorganic P (Richardson and Simpson 2011 ; Liu et al. 2025 ). Under low P availability, P limitation often constrains microbial activity and community function (Mori et al. 2018 ; Cui et al. 2022 ). Accordingly, enrichment of P-cycling fungi at this end of the gradient likely reflected a microbial response to the substrate environment created by long-term nitrogen addition, which was characterized by C enrichment but P scarcity. Conclusions Intraspecific trait shifts of Deyeuxia angustifolia along the long-term nitrogen gradient explained bacterial community composition. Soil physicochemical properties explained fungal community composition. These patterns indicate different pathways of environmental control for different microbial groups. Joint evaluation of plant traits and soil properties is therefore necessary to understand soil-plant-microbe interactions in wetlands. The main axis of intraspecific trait variation in Deyeuxia angustifolia was a whole-plant economic space. This space combined the fast-slow strategy axis with aboveground-belowground resource allocation. Cosmopolitan bacteria, N-cycling bacteria, redox-related bacteria, and P-cycling fungi shifted along this gradient. This pattern indicates tight coupling among plant resource allocation, soil substrate structure, and microbial resource use. At the intraspecific scale, plant resource-use strategy influenced soil carbon source structure through aboveground and belowground litter inputs. It also shaped the functional composition of microbial groups through substrate quality and nutrient limitation. Small trait shifts in plants were therefore sufficient to drive contrasting microbial responses. These shifts provide an empirical basis and a hypothesis framework for the response pathways of wetland carbon-nutrient coupling under nitrogen deposition. Declarations Funding This work was supported by the Scientific Research Fund for Heilongjiang Provincial Research Institutes (Grant No. CZBZ202507001 to Haixiu Zhong) and the Heilongjiang Academy of Sciences Institutional Capacity Enhancement Programme (Grant No. YSTS2025ZR01 to Qingyang Huang). Conflicts of interest The authors declare no conflicts of interest. Author contributions Haixiu Zhong and Zhaodong Cui conceived the ideas and designed the methodology. Zhaodong Cui, Mingyi Chen, Wenyu Wu, Jianyu Wang, and Yutong Ma collected the field data. Zhaodong Cui and Haixiu Zhong analysed the data and interpreted the results. Zhaodong Cui led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Statement on inclusion: This study was conducted at a single site in northeastern China (Sanjiang Plain). The entire authorship team is based at a Chinese institution with direct expertise in the regional wetland ecosystem studied. Regional literature was cited where applicable, and all primary data were collected by authors with first-hand knowledge of the study system. Data availability statement The 16S rRNA and ITS amplicon processed datasets, along with plant functional traits and soil physicochemical data, will be deposited in the Dryad Digital Repository (datadryad.org) upon acceptance. All data used to support the findings and construct the figures/tables of this study will be made available. Accession numbers and DOIs will be provided at the proof stage. References Barberán A, McGuire KL, Wolf JA, Jones FA, Wright SJ, Turner BL, Essene A, Hubbell SP, Faircloth BC, Fierer N (2015) Relating belowground microbial composition to the taxonomic, phylogenetic, and functional trait distributions of trees in a tropical forest. Ecol Lett 18:1397–1405. https://doi.org/10.1111/ele.12536 Bardgett RD, Mommer L, De Vries FT (2014) Going underground: root traits as drivers of ecosystem processes. Trends Ecol Evol 29:692–699. https://doi.org/10.1016/j.tree.2014.10.006 Bartoń K (2019) MuMIn:Multi-model inference. https://CRAN.Rproject.org/package=MuMIn Brzostek ER, Greco A, Drake JE, Finzi AC (2013) Root carbon inputs to the rhizosphere stimulate extracellular enzyme activity and increase nitrogen availability in temperate forest soils. Biogeochemistry 115:65–76. https://doi.org/10.1007/s10533-012-9818-9 Cui Y, Moorhead DL, Wang X, Xu M, Wang X, Wei X, Zhu Z, Ge T, Peng S, Zhu B, Zhang X, Fang L (2022) Decreasing microbial phosphorus limitation increases soil carbon release. Geoderma 419:115868. https://doi.org/10.1016/j.geoderma.2022.115868 de la Riva EG, Prieto I, Marañón T, Pérez-Ramos IM, Olmo M, Villar R (2021) Root economics spectrum and construction costs in Mediterranean woody plants: The role of symbiotic associations and the environment. J Ecol 109:1873–1885. https://doi.org/10.1111/1365-2745.13612 Díaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Colin Prentice I, Garnier E, Bönisch G, Westoby M, Poorter H, Reich PB, Moles AT, Dickie J, Gillison AN, Zanne AE, Chave J, Joseph Wright S, Sheremet’ev SN, Jactel H, Baraloto C, Cerabolini B, Pierce S, Shipley B, Kirkup D, Casanoves F, Joswig JS, Günther A, Falczuk V, Rüger N, Mahecha MD, Gorné LD (2016) The global spectrum of plant form and function. Nature 529:167–171. https://doi.org/10.1038/nature16489 Falkowski PG, Fenchel T, Delong EF (2008) The Microbial Engines That Drive Earth's Biogeochemical Cycles. Science 320:1034–1039. https://doi.org/10.1126/science.1153213 Fanin N, Fromin N, Buatois B, Hättenschwiler S (2013) An experimental test of the hypothesis of non-homeostatic consumer stoichiometry in a plant litter-microbe system. Ecol Lett 16:764–772. https://doi.org/10.1111/ele.12108 Freschet GT, Aerts R, Cornelissen JHC (2012) A plant economics spectrum of litter decomposability. Funct Ecol 26:56–65. https://doi.org/10.1111/j.1365-2435.2011.01913.x Guo X, Liu H, Ngosong C, Li B, Wang Q, Zhou W, Nie M (2022) Response of plant functional traits to nitrogen enrichment under climate change: A meta-analysis. Sci Total Environ 834:155379. https://doi.org/10.1016/j.scitotenv.2022.155379 He L, Viovy N, Xu X (2023) Macroecology Differentiation Between Bacteria and Fungi in Topsoil Across the United States. Global Biogeochemical Cycles , 37, e2023GB007706. https://doi.org/10.1029/2023GB007706 Henneron L, Kardol P, Wardle DA, Cros C, Fontaine S (2020) Rhizosphere control of soil nitrogen cycling: a key component of plant economic strategies. New Phytol 228:1269–1282. https://doi.org/10.1111/nph.16760 Ho A, Di Lonardo DP, Bodelier PL (2017) Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol 93:fix006. https://doi.org/10.1093/femsec/fix006 Huang Y, Dai Z, Lin J, Li D, Ye H, Dahlgren RA, Xu J (2021) Labile carbon facilitated phosphorus solubilization as regulated by bacterial and fungal communities in Zea mays. Soil Biol Biochem 163:108465. https://doi.org/10.1016/j.soilbio.2021.108465 Isaac ME, Martin AR, de Melo Virginio Filho E, Rapidel B, Roupsard O, Van den Meersche K (2017) Intraspecific Trait Variation and Coordination: Root and Leaf Economics Spectra in Coffee across Environmental Gradients. Front Plant Sci 8. https://doi.org/10.3389/fpls.2017.01196 Knight CG, Nicolitch O, Griffiths RI, Goodall T, Jones B, Weser C, Langridge H, Davison J, Dellavalle A, Eisenhauer N, Gongalsky KB, Hector A, Jardine E, Kardol P, Maestre FT, Schädler M, Semchenko M, Stevens C, Tsiafouli MΑ, Vilhelmsson O, Wanek W, de Vries FT (2024) Soil microbiomes show consistent and predictable responses to extreme events. Nature 636:690–696. https://doi.org/10.1038/s41586-024-08185-3 Kong D, Ma C, Zhang Q, Li L, Chen X, Zeng H, Guo D (2014) Leading dimensions in absorptive root trait variation across 96 subtropical forest species. New Phytol 203:863–872. https://doi.org/https://doi.org/10.1111/nph.12842 Laliberté E (2017) Below-ground frontiers in trait-based plant ecology. New Phytol 213:1597–1603. https://doi.org/10.1111/nph.14247 Leff JW, Bardgett RD, Wilkinson A, Jackson BG, Pritchard WJ, De Long JR, Oakley S, Mason KE, Ostle NJ, Johnson D, Baggs EM, Fierer N (2018) Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits. ISME J 12:1794–1805. https://doi.org/10.1038/s41396-018-0089-x Li X, Su L, Jing M, Wang K, Song C, Song Y (2025) Nitrogen addition restricts key soil ecological enzymes and nutrients by reducing microbial abundance and diversity. Sci Rep 15:5560. https://doi.org/10.1038/s41598-025-87327-7 Lin D, Shen R, Lin J, Zhu G, Yang Y, Fanin N (2024) Relationships between rhizosphere microbial communities, soil abiotic properties and root trait variation within a pine species. J Ecol 112:1275–1286. https://doi.org/10.1111/1365-2745.14297 Lin D, Yang S, Dou P, Wang H, Wang F, Qian S, Yang G, Zhao L, Yang Y, Fanin N (2019) A plant economics spectrum of litter decomposition among coexisting fern species in a sub-tropical forest. Ann Botany 125:145–155. https://doi.org/10.1093/aob/mcz166 Liu C, Groff T, Anderson E, Brown C, Cahill Jr JF, Paulow L, Bennett JA (2023) Effects of the invasive leafy spurge (Euphorbia esula L.) on plant community structure are altered by management history. NeoBiota 81. https://doi.org/10.3897/neobiota.81.89450 Liu L, Zhang T, Gilliam FS, Gundersen P, Zhang W, Chen H, Mo J (2013) Interactive Effects of Nitrogen and Phosphorus on Soil Microbial Communities in a Tropical Forest. PLoS ONE 8:e61188. https://doi.org/10.1371/journal.pone.0061188 Liu S, Zhang X, Wang H, Kuzyakov Y, Pan J, Chen F, Wang F, Li D, Tang Y, Ma Z (2025) Phosphorus-transforming microbes enhance phosphatase catalytic efficiency to alleviate phosphorus limitation under nitrogen and phosphorus additions in subtropical forest soil. Soil Biol Biochem 209:109915. https://doi.org/10.1016/j.soilbio.2025.109915 Lovley DR, Phillips EJ (1988) Novel mode of microbial energy metabolism: organic carbon oxidation coupled to dissimilatory reduction of iron or manganese. Appl Environ Microbiol 54:1472–1480. https://doi.org/10.1128/aem.54.6.1472-1480.1988 Lu M, Yang Y, Luo Y, Fang C, Zhou X, Chen J, Yang X, Li B (2011) Responses of ecosystem nitrogen cycle to nitrogen addition: a meta-analysis. New Phytol 189:1040–1050. https://doi.org/10.1111/j.1469-8137.2010.03563.x Mori T, Lu X, Aoyagi R, Mo J (2018) Reconsidering the phosphorus limitation of soil microbial activity in tropical forests. Funct Ecol 32:1145–1154. https://doi.org/10.1111/1365-2435.13043 Morrison EW, Frey SD, Sadowsky JJ, van Diepen LTA, Thomas WK, Pringle A (2016) Chronic nitrogen additions fundamentally restructure the soil fungal community in a temperate forest. Fungal Ecol 23:48–57. https://doi.org/10.1016/j.funeco.2016.05.011 Mouginot C, Kawamura R, Matulich KL, Berlemont R, Allison SD, Amend AS, Martiny AC (2014) Elemental stoichiometry of Fungi and Bacteria strains from grassland leaf litter. Soil Biol Biochem 76:278–285. https://doi.org/10.1016/j.soilbio.2014.05.011 Nelson MB, Martiny AC, Martiny JBH (2016) Global biogeography of microbial nitrogen-cycling traits in soil. Proceedings of the National Academy of Sciences , 113, 8033–8040. https://doi.org/10.1073/pnas.1601070113 Niu G, Hasi M, Wang R, Wang Y, Geng Q, Hu S, Xu X, Yang J, Wang C, Han X, Huang J (2021) Soil microbial community responses to long-term nitrogen addition at different soil depths in a typical steppe. Appl Soil Ecol 167:104054. https://doi.org/10.1016/j.apsoil.2021.104054 Pandit SN, Kolasa J, Cottenie K (2009) Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology 90:2253–2262. https://doi.org/10.1890/08-0851.1 Preusser S, Poll C, Marhan S, Angst G, Mueller CW, Bachmann J, Kandeler E (2019) Fungi and bacteria respond differently to changing environmental conditions within a soil profile. Soil Biol Biochem 137:107543. https://doi.org/10.1016/j.soilbio.2019.107543 Reich PB (2014) The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. J Ecol 102:275–301. https://doi.org/10.1111/1365-2745.12211 Richardson AE, Simpson RJ (2011) Soil Microorganisms Mediating Phosphorus Availability Update on Microbial Phosphorus. Plant Physiol 156:989–996. https://doi.org/10.1104/pp.111.175448 Roumet C, Birouste M, Picon-Cochard C, Ghestem M, Osman N, Vrignon-Brenas S, Cao K-f, Stokes A (2016) Root structure-function relationships in 74 species: evidence of a root economics spectrum related to carbon economy. New Phytol 210:815–826. https://doi.org/10.1111/nph.13828 Strickland MS, Rousk J (2010) Considering fungal:bacterial dominance in soils - Methods, controls, and ecosystem implications. Soil Biol Biochem 42:1385–1395. https://doi.org/10.1016/j.soilbio.2010.05.007 Sun L, Ataka M, Han M, Han Y, Gan D, Xu T, Guo Y, Zhu B (2021) Root exudation as a major competitive fine-root functional trait of 18 coexisting species in a subtropical forest. New Phytol 229:259–271. https://doi.org/10.1111/nph.16865 Sweeney CJ, de Vries FT, van Dongen BE, Bardgett RD (2021) Root traits explain rhizosphere fungal community composition among temperate grassland plant species. New Phytol 229:1492–1507. https://doi.org/10.1111/nph.16976 Trivedi P, Delgado-Baquerizo M, Jeffries TC, Trivedi C, Anderson IC, Lai K, McNee M, Flower K, Singh P, Minkey B, D., Singh BK (2017) Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content. Environ Microbiol 19:3070–3086. https://doi.org/10.1111/1462-2920.13779 Wan X, Chen X, Huang Z, Chen HYH (2021) Contribution of root traits to variations in soil microbial biomass and community composition. Plant Soil 460:483–495. https://doi.org/10.1007/s11104-020-04788-7 Wang C, Kuzyakov Y (2024) Mechanisms and implications of bacterial-fungal competition for soil resources. Isme j 18. https://doi.org/10.1093/ismejo/wrae073 Wang X, Feng J, Ao G, Qin W, Han M, Shen Y, Liu M, Chen Y, Zhu B (2023) Globally nitrogen addition alters soil microbial community structure, but has minor effects on soil microbial diversity and richness. Soil Biol Biochem 179:108982. https://doi.org/10.1016/j.soilbio.2023.108982 Zheng Q, Hu Y, Zhang S, Noll L, Böckle T, Dietrich M, Herbold CW, Eichorst SA, Woebken D, Richter A, Wanek W (2019) Soil multifunctionality is affected by the soil environment and by microbial community composition and diversity. Soil Biol Biochem 136:107521. https://doi.org/10.1016/j.soilbio.2019.107521 Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9417578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628054865,"identity":"ab65f2c7-f32d-4d46-9e60-62d91d2f9208","order_by":0,"name":"Zhaodong Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYFACxgZmMAliPzCQ4+cHMRIKiNWSYGAsObMBzMBvD5IWBmPJDQdALDxa+GckN38uqLGR7Zduv/ghocBAwvj86sQPDwwY5PnFDmDVInEjscF4xrE045lzzhRLJBgYSJjdeLsZyGAwnDk7AasWA4nEhmQetsOJG27kpAG98KfO7MbZDSAtCQa3cWs5zPMPrgXosBlnN/8goKWxmbcNpCX9GFiLAX/vNry2SJx52MzM2wf0y4wcZrBfJG7wbrNIMJDA6Rf+9vTHn3m+AUNMIv3hhw9/DCT4+89uvvmjwkaeXxq7FiTAA40LCbBKCULKQYD9AdTiA8SoHgWjYBSMghEEAPBmYkHnjVRJAAAAAElFTkSuQmCC","orcid":"","institution":"Heilongjiang Academy of Sciences Institute of Nature and Ecology: Heilongjiang Academy of Sciences Institute of Natural Resources and Ecology","correspondingAuthor":true,"prefix":"","firstName":"Zhaodong","middleName":"","lastName":"Cui","suffix":""},{"id":628054866,"identity":"c0e42269-925b-4916-b4b3-8947dd8cf9d2","order_by":1,"name":"Mingyi Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mingyi","middleName":"","lastName":"Chen","suffix":""},{"id":628054867,"identity":"6eeacec2-6f57-4e96-b1dd-33127044e658","order_by":2,"name":"Wenyu Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenyu","middleName":"","lastName":"Wu","suffix":""},{"id":628054868,"identity":"6a0f0ffc-3c93-4939-9caf-d51d7c584214","order_by":3,"name":"Jianyu Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jianyu","middleName":"","lastName":"Wang","suffix":""},{"id":628054869,"identity":"b9150497-3489-411b-87a7-e4ef226a5e94","order_by":4,"name":"Yutong Ma","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Ma","suffix":""},{"id":628054870,"identity":"dbd540d5-3de9-41d5-aad6-473a5d49b687","order_by":5,"name":"Haixiu Zhong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Haixiu","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2026-04-14 15:45:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9417578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9417578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108459936,"identity":"9a77ed6a-cd31-4131-9fe6-21e5baefd5e4","added_by":"auto","created_at":"2026-05-05 00:29:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253251,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Multivariate trait space of the dominant sedge species along two orthogonal axes. PC1 (56.88%) captures the conservation gradient, separating fast-acquisitive vs. slow-conservative strategies determined mainly by leaf and root C:N. PC2 (28.62%) captures the allocation gradient, reflecting coordinated shifts in nutrient distribution between above- and belowground organs. (b, c) Nitrogen addition significantly altered species’ positions in trait space, with N1 and N2 shifting toward more conservative (higher PC1) and belowground-oriented (lower PC2) strategies. Letters denote Tukey post-hoc groupings.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9417578/v1/9e192f3078e8ab75e78e2bd1.png"},{"id":108494124,"identity":"69c0b7a4-3b4e-45bf-8030-a7f95ae6ce13","added_by":"auto","created_at":"2026-05-05 10:02:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209847,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric multidimensional scaling (NMDS) ordination showing the responses of (a) bacterial and (b) fungal communities to plant traits and soil physicochemical gradients under long-term nitrogen enrichment. Arrows indicate the environmental vectors fitted using envfit, with the top predictors selected according to their explanatory strength (highest R² values). For bacteria, LC (leaf carbon), Soil PC1 and AGB/BGB showed the highest explanatory power; for fungi, AGB/BGB and Soil PC1-PC2 were the strongest predictors. The inset diagrams show the fractions of community variation independently explained by plant traits and soil factors. Stress values are provided for each NMDS solution.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9417578/v1/c54865bc97ad4d1b7d5caf3e.png"},{"id":108459938,"identity":"997cf0e8-f5e9-486e-869f-4f4f85ea66e2","added_by":"auto","created_at":"2026-05-05 00:29:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":205314,"visible":true,"origin":"","legend":"\u003cp\u003e(a-f) Relative proportions of generalist, specialist, and non-significant OTUs within bacterial (a-c) and fungal (d-f) communities under N0, N1 and N2 treatments. Generalists and specialists were identified using Levin’s niche-breadth index based on the 95% confidence intervals of the null distribution.(g-h) Partial regression plots showing the significant relationships between the above-below allocation gradient (Plant PC2) and the relative abundance of generalist bacteria (g) and specialist bacteria (h), estimated by multiple regression models (P \u0026lt; 0.05; Table S6). Solid blue lines represent the partial regression lines. Plant PC2 is the second axis of the principal component analysis on plant traits, serving as an index of the “above-below allocation strategy” gradient.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9417578/v1/6f8af6fdf44aa9159ead9565.png"},{"id":108494187,"identity":"e1403444-5b90-406e-a7f3-f646be034a8a","added_by":"auto","created_at":"2026-05-05 10:02:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269214,"visible":true,"origin":"","legend":"\u003cp\u003ePartial regression plots showing the significant relationships between the above-below allocation gradient (Plant PC2) and the relative abundance of redox bacteria (a), N-cycling bacteria (b), and P-cycling fungi (c) estimated by multiple regression models (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Table S6). The relative abundance of P-cycling fungi was natural log-transformed prior to the regression analysis. Solid blue lines represent the partial regression lines. Plant PC2 is the ordination score of the second axis of the principal component analysis on plant traits, used as a proxy for the above-below allocation strategy gradient.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9417578/v1/789be6a9e7815103bf3802a4.png"},{"id":109293964,"identity":"84a6f522-f000-4715-992e-c22c136d6c23","added_by":"auto","created_at":"2026-05-15 08:13:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1194368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9417578/v1/029d800a-81a0-4ae3-8471-4b00864e7593.pdf"},{"id":108493618,"identity":"21b02bb3-f049-4d6c-847f-ebd1e27dffde","added_by":"auto","created_at":"2026-05-05 10:01:04","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":755252,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9417578/v1/5b44cf367c5f6cb3b843b7ea.docx"}],"financialInterests":"","formattedTitle":"Intraspecific plant trait variation drives bacterial community assembly while soil properties govern fungal communities under long-term nitrogen enrichment in a wetland","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe plant economics spectrum describes plant strategies of resource acquisition and allocation. These strategies influence microbial community structure and function through nutrient inputs to soil (Isaac et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Reich, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Roumet et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Falkowski et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Knight et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; D\u0026iacute;az et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Most studies on plant traits and microbial communities have focused on interspecific differences. They usually compare root traits, leaf traits, or mycorrhizal types among species to explain microbial functional differentiation (Lin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Brzostek et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Henneron et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This approach shows the ecological importance of plant traits. It does not separate intraspecific trait adjustment from species replacement.\u003c/p\u003e \u003cp\u003eRecent studies have started to address intraspecific trait variation (ITV). Lin et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that root traits at the intraspecific scale explained shifts in microbial functional groups. Most of these studies, however, were based on spatial gradients or short-term treatments. Most previous studies also examined either bacteria or fungi alone, or focused on one microbial function (Ho et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mouginot et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Direct comparison of bacterial and fungal responses within one ecological framework remains rare (Leff et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Barber\u0026aacute;n et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wan et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Plant strategy under long-term environmental change is therefore still poorly linked to niche differentiation across multiple microbial groups.\u003c/p\u003e \u003cp\u003eLong-term nitrogen addition provides a suitable system to test this link. Nitrogen addition changes microbial diversity, functional gene abundance, and key taxa related to N cycling, redox processes, and litter decomposition (Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Niu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Most studies, however, used a simple soil-microbe framework. They did not include the joint effects of plant traits and soil environment. Other studies showed that nitrogen addition changed plant resource-use strategy (Guo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Few studies directly linked these trait shifts to responses of specific microbial functional groups. Two questions therefore remain open. Can soil change reshape microbial community structure through plant trait reorganization? Do different microbial groups respond along the same plant strategy axis?\u003c/p\u003e \u003cp\u003eTrait-based ecology provides a framework to study plant-microbe coupling (Lalibert\u0026eacute; \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bardgett et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Most evidence, however, comes from interspecific comparisons or short-term experiments. Intraspecific analysis requires a stable field system with a clear external driver. The long-term nitrogen addition experiment on the Sanjiang Plain provides such a system. The background environment is relatively uniform. The nitrogen gradient is clear. This system allows analysis of coordinated responses among plant traits, soil environment, and multiple microbial groups.\u003c/p\u003e \u003cp\u003eThis study aimed to clarify links between intraspecific trait variation and soil microbial communities. \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e was expected to show systematic intraspecific trait differentiation under different nitrogen treatments. Based on this expectation, H\u003csub\u003e1\u003c/sub\u003e proposed that intraspecific trait variation in \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e would form a two-dimensional plant economic space defined by a fast-slow strategy axis and an aboveground-belowground allocation axis, consistent with patterns reported in other plant species (Liu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; D\u0026iacute;az et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). H\u003csub\u003e2\u003c/sub\u003e proposed that fungal community structure would follow soil physicochemical properties more closely, whereas bacterial community structure would follow plant trait variation more closely, because fungi are usually more sensitive to soil environmental gradients and bacteria are more closely linked to plant traits (Fanin et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Nitrogen addition in this ecosystem usually causes relative phosphorus (P) limitation. Based on this pattern, H\u003csub\u003e3\u003c/sub\u003e proposed that the relative abundances of ecological indicator groups would vary systematically along the plant strategy gradient. A strategy biased toward belowground allocation was expected to favor N-cycling and redox-related bacteria. A strategy biased toward aboveground allocation was expected to favor P-cycling fungi.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site and experimental design\u003c/h2\u003e \u003cp\u003eThe study was conducted at the Honghe National Nature Reserve, Sanjiang Plain Wetland Ecological Research Station, Institute of Natural Resources and Ecology, Heilongjiang Academy of Sciences, Heilongjiang, China. The reserve is located in the northeastern Sanjiang Plain (47\u0026deg;42\u0026prime;-47\u0026deg;52\u0026prime; N, 133\u0026deg;34\u0026prime;38\u0026Prime;-133\u0026deg;46\u0026prime;29\u0026Prime; E). The region has a temperate monsoon climate, with a mean annual temperature of 1.9\u0026deg;C, a mean temperature of \u0026minus;\u0026thinsp;23.4\u0026deg;C in the coldest month, and 22.4\u0026deg;C in the warmest month. Mean annual precipitation is 585 mm and occurs mainly from July to September. Mean annual evaporation is 1166 mm. The main soil types are meadow soil, albic soil, and marsh soil. The vegetation type is wetland meadow, and the dominant plant species is \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe experiment was established in 2009 and included three nitrogen-addition treatments: N0 (0 g N m⁻\u0026sup2; yr⁻\u0026sup1;), N1 (4 g N m⁻\u0026sup2; yr⁻\u0026sup1;), and N2 (8 g N m⁻\u0026sup2; yr⁻\u0026sup1;). Each plot measured 5 m \u0026times; 5 m. At the beginning of May each year, NH₄NO₃ was dissolved in 50 L of water and sprayed evenly once onto each treatment plot. Control plots received the same volume of water without nitrogen addition.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection and measurements\u003c/h3\u003e\n\u003cp\u003eAt peak biomass during the growing season (mid-July 2023), five 1 m \u0026times; 1 m quadrats were randomly established in each nitrogen-treatment plot. All aboveground plant material within each quadrat was clipped at the soil surface. Stems and leaves were separated by tissue type and washed with distilled water. Samples were heated at 105\u0026deg;C for 15 min and then oven-dried at 65\u0026deg;C to constant mass.\u003c/p\u003e \u003cp\u003eThree soil cores (0\u0026ndash;20 cm depth) were randomly collected from each quadrat using a square soil corer with a side length of 10 cm. Roots were carefully separated and washed through a 0.5 mm mesh sieve to remove soil particles, and then oven-dried at 65\u0026deg;C to constant mass. Biomass was expressed as dry mass in g m⁻\u0026sup2;.\u003c/p\u003e \u003cp\u003eLeaf samples of \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e were ground and passed through a 100-mesh sieve. Total C and total N were measured using an Elementar Vario Macro Cube elemental analyzer (Elementar, Germany). Total P was determined by the molybdenum-antimony colorimetric method after H₂SO\u003csub\u003e4\u003c/sub\u003e-HClO\u003csub\u003e4\u003c/sub\u003e digestion.\u003c/p\u003e \u003cp\u003eEach nitrogen treatment included five replicate plots. In each plot, soil samples were collected from 0\u0026ndash;20 cm depth. Soil from five sampling points within each plot was thoroughly mixed by quartering to form one composite sample. Each composite sample was divided into two subsamples. One subsample was air-dried, ground, and passed through a 2 mm sieve for analysis of soil physicochemical properties. The other subsample was transported in an ice box and stored immediately at \u0026minus;\u0026thinsp;80\u0026deg;C for soil microbial analysis.\u003c/p\u003e \u003cp\u003eA total of 10 soil physicochemical properties were measured, with five replicates for each treatment: soil pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available potassium (AK), available phosphorus (AP), water content (WC), and electrical conductivity (EC) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Soil pH was measured potentiometrically. SOC and TN were measured using an Elementar Vario Macro Cube elemental analyzer. TP was determined colorimetrically after H₂SO₄-HClO₄ wet digestion. TK was measured by flame photometry after wet digestion. AN was extracted with 2 mol L⁻\u0026sup1; KCl and measured colorimetrically. AP was determined by the Olsen method. AK was extracted with ammonium chloride and measured by flame photometry. WC was determined by oven drying at 105\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C. EC was measured with a conductivity meter at a soil:water ratio of 1:5.\u003c/p\u003e \u003cp\u003eTo summarize soil property data, principal component analysis (PCA) was performed on all measured soil variables. The first three PCA axes were independent and explained 88.28% of the total variation (Table S2). These axes were used as representative soil factors in subsequent analyses. Soil PC1 explained 72.8% of the total variation and was closely associated with soil nutrient status. It was driven mainly by SOC, TN, TP, TK, and available N, P, and K, and was also related to soil pH. Soil PC2 explained 8.91% of the total variation and was driven mainly by AP, WC, and EC. It represented a joint gradient of soil moisture and mineral nutrient status. Soil PC3 explained 6.56% of the total variation and was determined mainly by EC and WC. It represented independent variation in dissolved salts and soil water status.\u003c/p\u003e \u003cp\u003eSoil samples were collected from the 0\u0026ndash;10 cm layer. Total genomic DNA was extracted from 0.5 g fresh soil with the FastDNA\u0026reg; SPIN Kit for Soil (MP Biomedicals, USA) according to the manufacturer\u0026rsquo;s instructions. DNA concentration and purity were measured with a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). For bacteria, the V3-V4 region of the 16S rRNA gene was amplified with primers 27F/338R. For fungi, the ITS1 region was amplified with primers ITS5/ITS2. Purified PCR products were sequenced on an Illumina NovaSeq 6000 platform. Raw reads were processed with UPARSE for quality control and clustering. OTUs were defined at 97% sequence similarity. Bacterial OTUs were annotated against SILVA, and fungal OTUs against UNITE. All samples were rarefied to the same minimum sequencing depth before community analysis.\u003c/p\u003e \u003cp\u003eMicrobial niche breadth was calculated with the Levins index. Taxa with observed values above the upper 95% confidence limit of the null distribution were classified as cosmopolitan taxa. Taxa with values below the lower 95% confidence limit were classified as specialist taxa. OTUs with values within the 95% confidence interval were classified as neutral taxa (Pandit et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClassification of functional groups was based on published lists of functional genes and taxa (Trivedi et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nelson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lovley and Phillips \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Richardson and Simpson \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and on reported genus-function associations. Genus-level OTUs repeatedly linked to nitrification, denitrification, or Fe/Mn redox metabolism were classified as putative N-cycling taxa or putative redox-related taxa.\u003c/p\u003e \u003cp\u003eOTUs assigned to genera such as \u003cem\u003eNitrosomonas\u003c/em\u003e, \u003cem\u003eNitrospira\u003c/em\u003e, \u003cem\u003eNitrobacter\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e were classified as putative N-cycling bacteria. OTUs assigned to genera such as \u003cem\u003eGeobacter\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, \u003cem\u003eFerribacterium\u003c/em\u003e, and \u003cem\u003eAnaeromyxobacter\u003c/em\u003e were classified as putative redox-related bacteria. Fungal OTUs assigned to genera such as \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, and \u003cem\u003eAspergillus\u003c/em\u003e were classified as putative P-cycling fungi.\u003c/p\u003e \u003cp\u003eAlthough amplicon-based inference reflects genetic potential rather than immediate activity, systematic shifts in these groups along the long-term nitrogen-addition gradient provide evidence for links between plant resource-allocation strategy and C-N-P coupling in wetland ecosystems.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R v4.3.2 (R Development Core Team, 2023). The coefficient of variation (CV, %) of each plant trait was calculated across the 15 plots to quantify intraspecific variation under nitrogen addition. Effects of soil factors on individual plant traits were tested with multiple linear regression. Each plant trait was used as a response variable. Soil PC1, Soil PC2, and Soil PC3 were used as explanatory variables. The best model was selected by the minimum AICc with the dredge function in MuMIn (Bartoń \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo test H\u003csub\u003e1\u003c/sub\u003e, pairwise trait correlations were calculated with Pearson correlation coefficients. PCA was then performed on the plant trait matrix. All traits were standardized to zero mean and unit variance before analysis. The first two principal components were used to represent the whole-plant economic space.\u003c/p\u003e \u003cp\u003eTo test H\u003csub\u003e2\u003c/sub\u003e, NMDS was used to visualize bacterial and fungal community structure. The analysis was conducted with metaMDS in vegan based on Bray-Curtis dissimilarity. Plant traits and soil principal components were fitted onto the ordination with envfit. Significance was tested with 999 permutations. Variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Tables S4-5) were retained for variation partitioning. This analysis was based on db-RDA and implemented with varpart to quantify the fractions of community variation explained by plant traits and soil factors.\u003c/p\u003e \u003cp\u003eTo test H\u003csub\u003e3\u003c/sub\u003e, multiple linear regression was used to relate microbial indicator groups to plant functional strategy. Plant PC1 and Plant PC2 were used as predictors. Response variables included cosmopolitan bacteria, specialist bacteria, putative N-cycling bacteria, putative redox-related bacteria, putative P-cycling fungi, cosmopolitan fungi, and specialist fungi. Soil PC1-3 were included as covariates. Relative abundance of putative P-cycling fungi was log-transformed before analysis. The best model was selected by the minimum AICc.\u003c/p\u003e \u003cp\u003eResults were visualized with partial regression plots generated by avPlots in the car package (Fox and Weisberg 2019). These plots showed the independent relationships between explanatory variables and microbial indicator groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eEffects of nitrogen addition on the intraspecific economic space of the dominant plant\u003c/b\u003e \u003cb\u003eDeyeuxia angustifolia\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePlant traits of \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e varied significantly and unevenly among nitrogen treatments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). LC and RC showed the smallest variation (CV\u0026thinsp;\u0026lt;\u0026thinsp;2%), whereas LN showed the largest (CV\u0026thinsp;=\u0026thinsp;63.6%). LP, RP, and AGB/BGB showed intermediate intraspecific variation (CV\u0026thinsp;=\u0026thinsp;31%-58%). Soil factors explained 5.6%-60.0% of trait variation. LC and LP were most strongly associated with Soil PC1-3 (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 54.4% and 60.0%, respectively). RN and RP were weakly associated with soil principal components (\u003cem\u003eR\u003c/em\u003e\u0026sup2; \u0026lt; 10%).\u003c/p\u003e \u003cp\u003eOnly a few plant traits were significantly correlated (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Principal component analysis of six functional traits identified two major axes across the 15 plots. These axes explained 85.50% of total variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea; Table S3). Plant PC1 explained 56.88% of the variation and represented a gradient from acquisitive to conservative strategy. LCN showed a strong positive loading, whereas LNP showed a strong negative loading. This axis reflected a trade-off between resource acquisition and nutrient conservation. Plant PC2 explained 28.62% of the variation and was related mainly to biomass allocation. AGB/BGB and RNP showed strong negative loadings, whereas RCN showed a strong positive loading. This axis represented an aboveground-belowground allocation gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Plant functional strategies differed significantly among nitrogen treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb,c). N0 had the highest Plant PC1 score, whereas N1 and N2 had lower scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). N1 had the highest Plant PC2 score, whereas N0 and N2 had lower scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\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\u003eTrait ranges of \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e under three nitrogen addition treatments (N0, N1, N2).Values represent the observed minimum-maximum range for each trait across plots. The units for leaf and root carbon (LC, RC), leaf and root nitrogen (LN, RN), and phosphorus (LP, RP) are all g\\kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e AGB/BGB is a dimensionless ratio. CV is the coefficient of variation (%) calculated across all 15 plots. \"Soil explained\" denotes the percentage of trait variation explained by soil PCA axes based on the best-fitting linear model. \"Sig.\" indicates whether the trait differs significantly among nitrogen treatments (ANOVA; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 shown as \"*\", \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 shown as \"**\", P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 shown as \"***\").\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSoil explained\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSig.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u0026ndash;0.86\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u0026ndash;2.17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;2.37\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u0026ndash;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.37\u0026ndash;45.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.53\u0026ndash;45.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.38\u0026ndash;44.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.31\u0026ndash;46.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.91\u0026ndash;45.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.53\u0026ndash;44.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u0026ndash;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026ndash;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u0026ndash;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u0026ndash;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGB/BGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u0026ndash;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026ndash;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eChanges in the taxonomic composition of soil microbial communities under nitrogen addition\u003c/h3\u003e\n\u003cp\u003eThe bacterial community was dominated by Acidobacteriota (30.97%), Chloroflexi (21.12%), and Proteobacteria (14.81%) (Figure S2a). No bacterial genus exceeded 10% mean relative abundance. The most abundant genera were AD3 (8.86%), Subgroup 7 (6.28%), and A21b (3.52%) (Figure S2b).\u003c/p\u003e \u003cp\u003eThe fungal community was dominated by Ascomycota (73.92%) (Figure S2c). No fungal genus exceeded 10% mean relative abundance. The relatively abundant genera were \u003cem\u003eArchaeorhizomyces\u003c/em\u003e (7.76%) and \u003cem\u003eDimorphospora\u003c/em\u003e (3.28%) (Figure S2d).\u003c/p\u003e \u003cp\u003eBacterial taxa that differed significantly among nitrogen treatments belonged mainly to Nitrospirota, Sva0485, Thermodesulfovibrionia, Burkholderiales, and \u003cem\u003eCandidatus Solibacter\u003c/em\u003e (Figure S2e). Nitrospirota and Sva0485 were enriched under nitrogen addition, whereas \u003cem\u003eCandidatus Solibacter\u003c/em\u003e was more abundant in the control. Fungal differences were concentrated in \u003cem\u003eOphiocordyceps\u003c/em\u003e, \u003cem\u003eEndophragmiella\u003c/em\u003e, \u003cem\u003eUmbelopsis\u003c/em\u003e, \u003cem\u003eMortierella\u003c/em\u003e, and \u003cem\u003eOidiodendron\u003c/em\u003e (Figure S2f). \u003cem\u003eMortierella\u003c/em\u003e and \u003cem\u003eUmbelopsis\u003c/em\u003e were enriched under nitrogen addition, whereas \u003cem\u003eOidiodendron\u003c/em\u003e was more abundant in the control.\u003c/p\u003e \u003cp\u003eThe NMDS ordination (stress\u0026thinsp;\u0026lt;\u0026thinsp;0.2) showed differences in microbial community composition among nitrogen treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,b). LC was the only plant trait significantly associated with the bacterial community (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Table S4). Plant traits, soil factors, and their shared effect explained 8.0%, 1.5%, and 2.3% of bacterial community variation, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). For fungi, AGB/BGB was the only plant trait significantly associated with overall community composition (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) (Table S5). Soil factors explained 5.0% of fungal community variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between soil microbial functional composition and the plant economic gradient under long-term nitrogen addition\u003c/h2\u003e \u003cp\u003eThe relative abundances of cosmopolitan bacteria, specialist bacteria, cosmopolitan fungi, and specialist fungi differed among nitrogen treatments: N0 (43.7%-60.8%, 0.8%-16.6%, 5.6%-10.6%, and 18.9%-50.5%, respectively), N1 (60.5%-66.8%, 0.8%-2.1%, 5.5%-11.4%, and 36.8%-46.5%), and N2 (48.8%-62.6%, 1.3%-9.8%, 4.7%-9.8%, and 26.4%-59.0%) (Figure S3a-f). After controlling for soil factors, the relative abundances of both cosmopolitan and specialist bacteria were significantly associated with the second plant economic trait axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea,b; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Higher Plant PC2 was associated with enrichment of cosmopolitan bacteria, N-cycling bacteria, and redox-related bacteria, whereas lower Plant PC2 was associated with enrichment of specialist bacteria and P-cycling fungi (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). No significant associations were detected between fungal lifestyle groups and either the plant economic gradient or soil physicochemical properties.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eTrait variation and intraspecific economic space of\u003c/b\u003e \u003cb\u003eDeyeuxia angustifolia\u003c/b\u003e \u003cb\u003eunder long-term nitrogen addition\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLong-term nitrogen addition reshaped the two-dimensional economic space of \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e. This pattern supported H1 and indicated a stable intraspecific economic strategy axis. The coefficients of variation of the six traits remained within the common intraspecific range for plants (9.8%-87%) (Sun 2021; Kong et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Nitrogen addition changed the direction and strength of trait correlations rather than the mean of single traits. Plants therefore responded to resource enrichment mainly through trait coupling. This response may reflect constraints from organismal integration (Sweeney et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first PCA axis was dominated by LCN, LNP, and RCN. It represented a gradient from acquisitive to conservative resource use. This fast-slow economic axis indicates that nitrogen enrichment increased assimilatory activity and strengthened coordinated demand for N and P. A single species thus expressed a fast-slow strategy structure similar to that reported across species (Kong et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; de la Riva et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This gradient further indicates intraspecific differentiation of resource-use strategy under long-term nutrient enrichment. The second PCA axis was defined by trade-offs between aboveground-belowground biomass allocation and root stoichiometry. This axis reflected regulation between aboveground resource acquisition and belowground carbon input under long-term nitrogen addition. Joint shifts in AGB/BGB, root C:N, and root N:P indicate adjustment of carbon and nutrient allocation among organs. This adjustment balanced rapid use of surface nitrogen with maintenance of long-term belowground carbon input. This axis was not equivalent to the cross-species decomposition economic spectrum, because it arose from intraspecific allocation strategy rather than species turnover (Freschet et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This axis determined the quality and turnover of plant-derived inputs to soil. It thus created substrate conditions of different quality and temporal scale for microbial indicator groups. Long-term nitrogen addition therefore reshaped plant traits and made the intraspecific economic space a key expression of plant functional adjustment under resource enrichment. This framework provides a basis for understanding how nitrogen addition regulates microbial community structure across the plant-soil interface.\u003c/p\u003e\n\u003ch3\u003eDifferential control of bacterial and fungal communities by plant traits and soil factors\u003c/h3\u003e\n\u003cp\u003eUnder long-term nitrogen addition, bacterial and fungal communities showed contrasting control patterns. Bacterial communities varied mainly along the continuous gradient of plant traits, whereas fungal communities varied mainly with soil physicochemical properties. This pattern supported H\u003csub\u003e2\u003c/sub\u003e. In the NMDS ordination (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), bacterial taxa were highly mixed among treatments. Their distribution followed the continuous gradient of plant traits rather than treatment categories. Soil physicochemical properties explained only a small fraction of bacterial community composition. In contrast, fungal communities showed clear treatment separation in the NMDS ordination (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Their distribution followed the gradients of TN, AP, WC, and EC generated by nitrogen addition. Fungal communities were therefore controlled mainly by soil environmental filtering rather than by plant trait shifts.\u003c/p\u003e \u003cp\u003eBacteria and fungi differed in nutrient demand, resource acquisition, and niche preference (He et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Preusser et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang and Kuzyakov \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Bacteria responded mainly to resource filtering associated with plant traits. Fungi responded mainly to environmental filtering associated with soil structure. This contrast reflects differences in metabolism and ecology. Bacteria usually have lower C:N:P stoichiometric ratios and depend more strongly on dissolved nutrients and rapidly cycling inorganic resources (Mouginot et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Strickland and Rousk \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Most single traits did not differ consistently among treatments. The continuous plant economic spectrum still provided a detectable resource gradient for bacteria. Fungi depend more strongly on complex organic substrates and are more sensitive to soil structure and moisture (Fanin et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Their distribution therefore aligned more closely with the soil PCA axis than with plant traits. Plant control over fungi was weaker in the absence of strong rhizosphere-scale root exudate inputs. Nitrogen addition produced more stable shifts in soil moisture and nutrient structure and thus imposed stronger environmental selection on fungi (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Morrison et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlant traits and soil factors jointly contributed to microbial community shifts, but their explanatory power remained limited at the intraspecific scale. This result indicates additional control by finer-scale soil structure and short-term processes. Aggregate structure, pore connectivity, pulses of dissolved organic matter, and fluctuations in microsite moisture may all influence microbial competition, but these processes were not measured here. Thus, contrasting bacterial and fungal responses to nitrogen addition arose not only from broad gradients in plant traits and soil properties, but also from finer-scale physicochemical heterogeneity. This heterogeneity remains a key target for future studies of plant-soil-microbe interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between plant economic strategy and soil microbial functional composition\u003c/h2\u003e \u003cp\u003eThe results supported the third hypothesis (H\u003csub\u003e3\u003c/sub\u003e): plant resource-allocation strategy (Plant PC2) was systematically linked to soil microbial functional groups. Trade-offs along the aboveground-belowground biomass allocation axis created two contrasting substrate environments in soil. One end corresponded to a belowground-input strategy, with higher root biomass and higher root residue C:N ratio. This combination formed a more recalcitrant carbon pool with slower turnover. The other end corresponded to an aboveground-input strategy, with faster aboveground growth and more rapidly turning over leaf litter. Under long-term nitrogen addition, this strategy was also associated with lower N:P ratio and stronger P limitation (Freschet et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the belowground-input end of the spectrum (N0 and N2), root residues with higher C:N ratio provided a persistent but less decomposable carbon source to the native soil. This slow-turnover, recalcitrant substrate environment favored cosmopolitan bacteria under environmental filtering. Cosmopolitan taxa usually possess broader substrate-use capacity and greater environmental tolerance than specialist taxa. They were therefore better able to exploit complex root-derived carbon (Pandit et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Long-term root accumulation may also have increased moisture and redox heterogeneity in soil microsites. Such microsite conditions likely favored putative N-cycling and redox-related indicator groups (Strickland and Rousk \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the aboveground-input end of the spectrum (N1), plants allocated more resources to aboveground growth. This shift increased pulse input of leaf litter into soil. These labile C inputs, characterized by fast turnover and high input flux, favored rapid proliferation of specialist bacteria. Specialist taxa usually have higher resource-use efficiency and faster growth rates. They can therefore respond rapidly to substrate pulses (Strickland and Rousk, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Rapid aboveground growth also increased plant demand for soil P. As a result, litter returned to soil carried a stronger signature of P limitation, expressed as N:P imbalance. This plant-driven stoichiometric imbalance further strengthened P limitation in soil (Liu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mori et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Microorganisms maintain soil P availability by releasing phosphatases that mineralize organic P and by using high-affinity transport systems that acquire inorganic P (Richardson and Simpson \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Under low P availability, P limitation often constrains microbial activity and community function (Mori et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Accordingly, enrichment of P-cycling fungi at this end of the gradient likely reflected a microbial response to the substrate environment created by long-term nitrogen addition, which was characterized by C enrichment but P scarcity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIntraspecific trait shifts of \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e along the long-term nitrogen gradient explained bacterial community composition. Soil physicochemical properties explained fungal community composition. These patterns indicate different pathways of environmental control for different microbial groups. Joint evaluation of plant traits and soil properties is therefore necessary to understand soil-plant-microbe interactions in wetlands. The main axis of intraspecific trait variation in \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e was a whole-plant economic space. This space combined the fast-slow strategy axis with aboveground-belowground resource allocation. Cosmopolitan bacteria, N-cycling bacteria, redox-related bacteria, and P-cycling fungi shifted along this gradient. This pattern indicates tight coupling among plant resource allocation, soil substrate structure, and microbial resource use. At the intraspecific scale, plant resource-use strategy influenced soil carbon source structure through aboveground and belowground litter inputs. It also shaped the functional composition of microbial groups through substrate quality and nutrient limitation. Small trait shifts in plants were therefore sufficient to drive contrasting microbial responses. These shifts provide an empirical basis and a hypothesis framework for the response pathways of wetland carbon-nutrient coupling under nitrogen deposition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was supported by the Scientific Research Fund for Heilongjiang Provincial Research Institutes (Grant No. CZBZ202507001 to Haixiu Zhong) and the Heilongjiang Academy of Sciences Institutional Capacity Enhancement Programme (Grant No. YSTS2025ZR01 to Qingyang Huang).\u003c/p\u003e\n\u003ch3 id=\"_Toc227278642\"\u003eConflicts of interest\u003c/h3\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch3 id=\"_Toc227278643\"\u003eAuthor contributions\u003c/h3\u003e\n\u003cp\u003eHaixiu Zhong and Zhaodong Cui conceived the ideas and designed the methodology. Zhaodong Cui, Mingyi Chen, Wenyu Wu, Jianyu Wang, and Yutong Ma collected the field data. Zhaodong Cui and Haixiu Zhong analysed the data and interpreted the results. Zhaodong Cui led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003eStatement on inclusion: This study was conducted at a single site in northeastern China (Sanjiang Plain). The entire authorship team is based at a Chinese institution with direct expertise in the regional wetland ecosystem studied. Regional literature was cited where applicable, and all primary data were collected by authors with first-hand knowledge of the study system.\u003c/p\u003e\n\u003ch3 id=\"_Toc227278644\"\u003eData availability statement\u003c/h3\u003e\n\u003cp\u003eThe 16S rRNA and ITS amplicon processed datasets, along with plant functional traits and soil physicochemical data, will be deposited in the Dryad Digital Repository (datadryad.org) upon acceptance. All data used to support the findings and construct the figures/tables of this study will be made available. Accession numbers and DOIs will be provided at the proof stage.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarber\u0026aacute;n A, McGuire KL, Wolf JA, Jones FA, Wright SJ, Turner BL, Essene A, Hubbell SP, Faircloth BC, Fierer N (2015) Relating belowground microbial composition to the taxonomic, phylogenetic, and functional trait distributions of trees in a tropical forest. Ecol Lett 18:1397\u0026ndash;1405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12536\u003c/span\u003e\u003cspan address=\"10.1111/ele.12536\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBardgett RD, Mommer L, De Vries FT (2014) Going underground: root traits as drivers of ecosystem processes. Trends Ecol Evol 29:692\u0026ndash;699. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2014.10.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2014.10.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartoń K (2019) MuMIn:Multi-model inference. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.Rproject.org/package=MuMIn\u003c/span\u003e\u003cspan address=\"https://CRAN.Rproject.org/package=MuMIn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrzostek ER, Greco A, Drake JE, Finzi AC (2013) Root carbon inputs to the rhizosphere stimulate extracellular enzyme activity and increase nitrogen availability in temperate forest soils. Biogeochemistry 115:65\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10533-012-9818-9\u003c/span\u003e\u003cspan address=\"10.1007/s10533-012-9818-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui Y, Moorhead DL, Wang X, Xu M, Wang X, Wei X, Zhu Z, Ge T, Peng S, Zhu B, Zhang X, Fang L (2022) Decreasing microbial phosphorus limitation increases soil carbon release. Geoderma 419:115868. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2022.115868\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2022.115868\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede la Riva EG, Prieto I, Mara\u0026ntilde;\u0026oacute;n T, P\u0026eacute;rez-Ramos IM, Olmo M, Villar R (2021) Root economics spectrum and construction costs in Mediterranean woody plants: The role of symbiotic associations and the environment. J Ecol 109:1873\u0026ndash;1885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.13612\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.13612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Colin Prentice I, Garnier E, B\u0026ouml;nisch G, Westoby M, Poorter H, Reich PB, Moles AT, Dickie J, Gillison AN, Zanne AE, Chave J, Joseph Wright S, Sheremet\u0026rsquo;ev SN, Jactel H, Baraloto C, Cerabolini B, Pierce S, Shipley B, Kirkup D, Casanoves F, Joswig JS, G\u0026uuml;nther A, Falczuk V, R\u0026uuml;ger N, Mahecha MD, Gorn\u0026eacute; LD (2016) The global spectrum of plant form and function. Nature 529:167\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature16489\u003c/span\u003e\u003cspan address=\"10.1038/nature16489\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalkowski PG, Fenchel T, Delong EF (2008) The Microbial Engines That Drive Earth's Biogeochemical Cycles. Science 320:1034\u0026ndash;1039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1153213\u003c/span\u003e\u003cspan address=\"10.1126/science.1153213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFanin N, Fromin N, Buatois B, H\u0026auml;ttenschwiler S (2013) An experimental test of the hypothesis of non-homeostatic consumer stoichiometry in a plant litter-microbe system. Ecol Lett 16:764\u0026ndash;772. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12108\u003c/span\u003e\u003cspan address=\"10.1111/ele.12108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreschet GT, Aerts R, Cornelissen JHC (2012) A plant economics spectrum of litter decomposability. Funct Ecol 26:56\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2435.2011.01913.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2435.2011.01913.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo X, Liu H, Ngosong C, Li B, Wang Q, Zhou W, Nie M (2022) Response of plant functional traits to nitrogen enrichment under climate change: A meta-analysis. Sci Total Environ 834:155379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2022.155379\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2022.155379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe L, Viovy N, Xu X (2023) Macroecology Differentiation Between Bacteria and Fungi in Topsoil Across the United States. \u003cem\u003eGlobal Biogeochemical Cycles\u003c/em\u003e, 37, e2023GB007706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2023GB007706\u003c/span\u003e\u003cspan address=\"10.1029/2023GB007706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenneron L, Kardol P, Wardle DA, Cros C, Fontaine S (2020) Rhizosphere control of soil nitrogen cycling: a key component of plant economic strategies. New Phytol 228:1269\u0026ndash;1282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.16760\u003c/span\u003e\u003cspan address=\"10.1111/nph.16760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo A, Di Lonardo DP, Bodelier PL (2017) Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol 93:fix006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsec/fix006\u003c/span\u003e\u003cspan address=\"10.1093/femsec/fix006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Dai Z, Lin J, Li D, Ye H, Dahlgren RA, Xu J (2021) Labile carbon facilitated phosphorus solubilization as regulated by bacterial and fungal communities in Zea mays. Soil Biol Biochem 163:108465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2021.108465\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2021.108465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaac ME, Martin AR, de Melo Virginio Filho E, Rapidel B, Roupsard O, Van den Meersche K (2017) Intraspecific Trait Variation and Coordination: Root and Leaf Economics Spectra in Coffee across Environmental Gradients. Front Plant Sci 8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2017.01196\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.01196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnight CG, Nicolitch O, Griffiths RI, Goodall T, Jones B, Weser C, Langridge H, Davison J, Dellavalle A, Eisenhauer N, Gongalsky KB, Hector A, Jardine E, Kardol P, Maestre FT, Sch\u0026auml;dler M, Semchenko M, Stevens C, Tsiafouli MΑ, Vilhelmsson O, Wanek W, de Vries FT (2024) Soil microbiomes show consistent and predictable responses to extreme events. Nature 636:690\u0026ndash;696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-024-08185-3\u003c/span\u003e\u003cspan address=\"10.1038/s41586-024-08185-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong D, Ma C, Zhang Q, Li L, Chen X, Zeng H, Guo D (2014) Leading dimensions in absorptive root trait variation across 96 subtropical forest species. New Phytol 203:863\u0026ndash;872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1111/nph.12842\u003c/span\u003e\u003cspan address=\"10.1111/nph.12842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLalibert\u0026eacute; E (2017) Below-ground frontiers in trait-based plant ecology. New Phytol 213:1597\u0026ndash;1603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.14247\u003c/span\u003e\u003cspan address=\"10.1111/nph.14247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeff JW, Bardgett RD, Wilkinson A, Jackson BG, Pritchard WJ, De Long JR, Oakley S, Mason KE, Ostle NJ, Johnson D, Baggs EM, Fierer N (2018) Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits. ISME J 12:1794\u0026ndash;1805. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41396-018-0089-x\u003c/span\u003e\u003cspan address=\"10.1038/s41396-018-0089-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Su L, Jing M, Wang K, Song C, Song Y (2025) Nitrogen addition restricts key soil ecological enzymes and nutrients by reducing microbial abundance and diversity. Sci Rep 15:5560. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-87327-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-87327-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin D, Shen R, Lin J, Zhu G, Yang Y, Fanin N (2024) Relationships between rhizosphere microbial communities, soil abiotic properties and root trait variation within a pine species. J Ecol 112:1275\u0026ndash;1286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.14297\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.14297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin D, Yang S, Dou P, Wang H, Wang F, Qian S, Yang G, Zhao L, Yang Y, Fanin N (2019) A plant economics spectrum of litter decomposition among coexisting fern species in a sub-tropical forest. Ann Botany 125:145\u0026ndash;155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcz166\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcz166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu C, Groff T, Anderson E, Brown C, Cahill Jr JF, Paulow L, Bennett JA (2023) Effects of the invasive leafy spurge (Euphorbia esula L.) on plant community structure are altered by management history. NeoBiota 81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3897/neobiota.81.89450\u003c/span\u003e\u003cspan address=\"10.3897/neobiota.81.89450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Zhang T, Gilliam FS, Gundersen P, Zhang W, Chen H, Mo J (2013) Interactive Effects of Nitrogen and Phosphorus on Soil Microbial Communities in a Tropical Forest. PLoS ONE 8:e61188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0061188\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0061188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Zhang X, Wang H, Kuzyakov Y, Pan J, Chen F, Wang F, Li D, Tang Y, Ma Z (2025) Phosphorus-transforming microbes enhance phosphatase catalytic efficiency to alleviate phosphorus limitation under nitrogen and phosphorus additions in subtropical forest soil. Soil Biol Biochem 209:109915. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2025.109915\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2025.109915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLovley DR, Phillips EJ (1988) Novel mode of microbial energy metabolism: organic carbon oxidation coupled to dissimilatory reduction of iron or manganese. Appl Environ Microbiol 54:1472\u0026ndash;1480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/aem.54.6.1472-1480.1988\u003c/span\u003e\u003cspan address=\"10.1128/aem.54.6.1472-1480.1988\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu M, Yang Y, Luo Y, Fang C, Zhou X, Chen J, Yang X, Li B (2011) Responses of ecosystem nitrogen cycle to nitrogen addition: a meta-analysis. New Phytol 189:1040\u0026ndash;1050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.2010.03563.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.2010.03563.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMori T, Lu X, Aoyagi R, Mo J (2018) Reconsidering the phosphorus limitation of soil microbial activity in tropical forests. Funct Ecol 32:1145\u0026ndash;1154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2435.13043\u003c/span\u003e\u003cspan address=\"10.1111/1365-2435.13043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrison EW, Frey SD, Sadowsky JJ, van Diepen LTA, Thomas WK, Pringle A (2016) Chronic nitrogen additions fundamentally restructure the soil fungal community in a temperate forest. Fungal Ecol 23:48\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.funeco.2016.05.011\u003c/span\u003e\u003cspan address=\"10.1016/j.funeco.2016.05.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouginot C, Kawamura R, Matulich KL, Berlemont R, Allison SD, Amend AS, Martiny AC (2014) Elemental stoichiometry of Fungi and Bacteria strains from grassland leaf litter. Soil Biol Biochem 76:278\u0026ndash;285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2014.05.011\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2014.05.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson MB, Martiny AC, Martiny JBH (2016) Global biogeography of microbial nitrogen-cycling traits in soil. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, 113, 8033\u0026ndash;8040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1601070113\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1601070113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu G, Hasi M, Wang R, Wang Y, Geng Q, Hu S, Xu X, Yang J, Wang C, Han X, Huang J (2021) Soil microbial community responses to long-term nitrogen addition at different soil depths in a typical steppe. Appl Soil Ecol 167:104054. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apsoil.2021.104054\u003c/span\u003e\u003cspan address=\"10.1016/j.apsoil.2021.104054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandit SN, Kolasa J, Cottenie K (2009) Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology 90:2253\u0026ndash;2262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/08-0851.1\u003c/span\u003e\u003cspan address=\"10.1890/08-0851.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreusser S, Poll C, Marhan S, Angst G, Mueller CW, Bachmann J, Kandeler E (2019) Fungi and bacteria respond differently to changing environmental conditions within a soil profile. Soil Biol Biochem 137:107543. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2019.107543\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2019.107543\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReich PB (2014) The world-wide \u0026lsquo;fast-slow\u0026rsquo; plant economics spectrum: a traits manifesto. J Ecol 102:275\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2745.12211\u003c/span\u003e\u003cspan address=\"10.1111/1365-2745.12211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson AE, Simpson RJ (2011) Soil Microorganisms Mediating Phosphorus Availability Update on Microbial Phosphorus. Plant Physiol 156:989\u0026ndash;996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.111.175448\u003c/span\u003e\u003cspan address=\"10.1104/pp.111.175448\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoumet C, Birouste M, Picon-Cochard C, Ghestem M, Osman N, Vrignon-Brenas S, Cao K-f, Stokes A (2016) Root structure-function relationships in 74 species: evidence of a root economics spectrum related to carbon economy. New Phytol 210:815\u0026ndash;826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.13828\u003c/span\u003e\u003cspan address=\"10.1111/nph.13828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrickland MS, Rousk J (2010) Considering fungal:bacterial dominance in soils - Methods, controls, and ecosystem implications. Soil Biol Biochem 42:1385\u0026ndash;1395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2010.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2010.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun L, Ataka M, Han M, Han Y, Gan D, Xu T, Guo Y, Zhu B (2021) Root exudation as a major competitive fine-root functional trait of 18 coexisting species in a subtropical forest. New Phytol 229:259\u0026ndash;271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.16865\u003c/span\u003e\u003cspan address=\"10.1111/nph.16865\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweeney CJ, de Vries FT, van Dongen BE, Bardgett RD (2021) Root traits explain rhizosphere fungal community composition among temperate grassland plant species. New Phytol 229:1492\u0026ndash;1507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.16976\u003c/span\u003e\u003cspan address=\"10.1111/nph.16976\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrivedi P, Delgado-Baquerizo M, Jeffries TC, Trivedi C, Anderson IC, Lai K, McNee M, Flower K, Singh P, Minkey B, D., Singh BK (2017) Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content. Environ Microbiol 19:3070\u0026ndash;3086. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1462-2920.13779\u003c/span\u003e\u003cspan address=\"10.1111/1462-2920.13779\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan X, Chen X, Huang Z, Chen HYH (2021) Contribution of root traits to variations in soil microbial biomass and community composition. Plant Soil 460:483\u0026ndash;495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-020-04788-7\u003c/span\u003e\u003cspan address=\"10.1007/s11104-020-04788-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Kuzyakov Y (2024) Mechanisms and implications of bacterial-fungal competition for soil resources. Isme j 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ismejo/wrae073\u003c/span\u003e\u003cspan address=\"10.1093/ismejo/wrae073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Feng J, Ao G, Qin W, Han M, Shen Y, Liu M, Chen Y, Zhu B (2023) Globally nitrogen addition alters soil microbial community structure, but has minor effects on soil microbial diversity and richness. Soil Biol Biochem 179:108982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2023.108982\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2023.108982\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Q, Hu Y, Zhang S, Noll L, B\u0026ouml;ckle T, Dietrich M, Herbold CW, Eichorst SA, Woebken D, Richter A, Wanek W (2019) Soil multifunctionality is affected by the soil environment and by microbial community composition and diversity. Soil Biol Biochem 136:107521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2019.107521\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2019.107521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"intraspecific trait variation, nitrogen deposition, plant economic spectrum, trait plasticity","lastPublishedDoi":"10.21203/rs.3.rs-9417578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9417578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eLong-term nitrogen addition alters plant economic strategy and soil environment, yet whether bacteria and fungi respond through the same plant trait axis remains unresolved.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing a 16-year nitrogen addition experiment in a \u003cem\u003eDeyeuxia angustifolia\u003c/em\u003e-dominated wetland, we integrated plant functional traits, soil properties, and microbial sequencing data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIntraspecific trait variation formed a two-dimensional economic space: a fast-slow resource-use axis and an aboveground-belowground allocation axis. Bacterial community composition tracked plant trait gradients (explaining 8.0% of variation), whereas fungal communities responded more strongly to soil nutrient-moisture gradients (5.0%). Belowground-biased allocation enriched cosmopolitan bacteria and putative N-cycling and redox-related taxa, while aboveground-biased allocation favored specialist bacteria and putative P-cycling fungi.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings demonstrate that subtle intraspecific trait shifts drive divergent microbial filtering pathways, with implications for carbon-nutrient cycling in wetlands under nitrogen deposition.\u003c/p\u003e","manuscriptTitle":"Intraspecific plant trait variation drives bacterial community assembly while soil properties govern fungal communities under long-term nitrogen enrichment in a wetland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 00:29:20","doi":"10.21203/rs.3.rs-9417578/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e745386d-d909-4a4a-9aaf-846524591775","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject after review","date":"2026-05-14T22:25:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T08:13:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 00:29:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9417578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9417578","identity":"rs-9417578","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}