1. Introduction
Due to widespread N limitation in terrestrial ecosystems, an increase in N deposition caused by human activities like fertilizer and fossil burning [1] (Nitrogen Addition and Precipitation Reduction Alter Ecosystem Multifunctionality and Decrease Soil Nematode Abundance and Trophic Energy Fluxes in a Temperate Forest) can strongly alter the soil N availability, which inevitably affects the microorganisms and plant growth [2]. Likewise, previous studies reported that changes in the precipitation pattern, often coupled with increased N deposition [1], can affect belowground biochemical processes and soil nutrient availability. The interactive effect of precipitation reduction and N addition have significant implications for various ecosystem functions, including soil nutrient cycling and C dynamics [3,4]. Specifically, precipitation reduction directly decreases soil water availability, potentially disrupting the soil nutrient cycle by modulating soil enzyme activity [5], soil organic carbon (SOC) mineralization, and litter decomposition [6,7]. Moreover, the increased N addition has been shown to influence SOC decomposition [8], nutrient limitation [9], and enzymatic activities [10]. Excessive N addition can further disrupt the balance of N and P in ecosystems, limiting the availability of essential elements for microbes and plants and consequently affecting impact productivity and soil biological processes [11]. However, the mechanisms underlying nutrient imbalances between organisms and soil pools caused by precipitation reduction and N addition remain inadequately understood.
Soil microbial stoichiometry, which reflects the nutrient requirements of microbes, is a key factor in soil nutrient cycling [12]. When soil nutrients fail to meet microbial requirements (resulting in stoichiometric imbalances), specific nutrient limitations can restrict microbial activity [13]. For example, microbes may alter their C/N/P ratio to regulate biomass [14], adjust enzyme production [15], release excess elements, retain limiting elements [16], or shift the abundance and composition of microbial communities [17]. Although soil microbes possess adaptive mechanisms to cope with nutrient imbalances, their responses to soil nutrient imbalances imposed by N addition and precipitation reduction remain largely unexplored. The stoichiometric analysis of ecosystem components (plant tissues, litter, soil, and microbes) can offer new insights into how above- and belowground systems respond to global changes [12,13]. The current knowledge gap impedes a full understanding of how microbes adapt to projected nutrient limitations caused by increased N deposition and precipitation reduction, which involve various biotic and abiotic factors.
Previous studies reported that N additions can disrupt the balance between N and P inputs, altering the soil C/N ratio and decreasing bacterial and fungal diversity [18]. Consequently, the increased N availability in soil may enhance the microbial P limitations, leading to changes in microbial C/N/P stoichiometry. Furthermore, N addition can affect microbial enzyme production [10,19] and community composition [20] as microbes adapt to changes in soil nutrient status [21]. These findings illustrate that microbes adjust their metabolic processes to fulfill nutrient requirements in response to N addition [22]. Similarly, precipitation reduction directly decreases soil water availability, hindering nutrient accessibility and leading to reduced plant growth and nutrient uptake [23]. A comprehensive global analysis revealed that decreased precipitation significantly reduced microbial biomass N and N/P ratios due to limited nutrient availability [24]. In response to nutrient limitations, microorganisms increase their production of extracellular polysaccharides [25] and store osmotically active substances rich in C and N. This adaption promotes the conservation of limited nutrients [26], promoting the conservation of limited nutrients during periods of reduced precipitation [27]. Changes in microbial community structure [21] and enzymatic production [28] facilitate microbial adaptation to stress under reduced precipitation [29]. However, discrepancies exist in the effects of N addition and precipitation reduction on microbial properties with studies reporting positive, negative, and neutral effects [10,30]. These contrasting results highlight a large knowledge gap regarding the response of microbial activity to changes in soil nutrient status under N addition and precipitation reduction.
Climate models projected a progressive reduction in precipitation over the next ten years [31] alongside an increase in N deposition rate in northeast China [32]. These changes have been shown to affect multiple ecosystem functions, including microbial diversity [33], nematode community, C and N cycles [34]. Here, we conducted a 10-year field experiment in a temperate Korean pine mixed forest in northeast China to understand how N addition and precipitation reduction affect microbial stoichiometry and the soil available nutrient status. Few studies have determined the long-term effects of soil N–water interactions on microbial C/N/P stoichiometry and soil resource imbalances as well as the subsequent impacts on microbial adaptive strategies. We hypothesized that (1) N addition and precipitation would increase C/P and N/P imbalances, causing microbial P limitation; and (2) the enhancement of P limitation would correlate with modifications in microbial and enzyme stoichiometry.
2. Methods and Materials
2.1. Site Description and Experimental Design
This experiment was carried out in a mixed Korean pine forest located within the Changbai Mountain Biosphere Reserve, Jilin Province, northeast China (128°05′ E, 42°24′ N, 766 m above sea level). The region experiences a temperate continental monsoon climate with an average annual temperature of 3.6 °C and annual rainfall of 740 mm. The dominant plant species in this area include Quercus mongolica (QM), Pinus koraiensis (PK), and Tilia amurensis (TA) with an average tree age of 200 years. The surface soil of the forest is rich in organic matter, containing ~200 g C kg−1, and is classified as eluviated soil according to the American soil classification system.
The experiment was conducted in a nearly flat (with a slope < 5°) forest using a split-plot design (Figure S1). The experiment included three replicates per treatment, comprising N addition and precipitation reduction treatments [35]. Three 120 m × 50 m blocks with buffer strip widths greater than 20 m were randomly established, and each block had a pair of 50 × 50 m plots, 10 m apart, used for the precipitation reduction treatment or control as the main treatment. Each plot was then divided into two 25 × 50 m subplots for N addition. Precipitation was reduced using high transmittance polycarbonate V-translucent panels, intercepting ~30% of the precipitation and positioned 1 m above the ground to ensure normal air movement. The panels would intercept about 30% of throughfall and direct it outside the plot. Iron plates were inserted to a soil depth of 50 cm around the subplots to prevent interference between processing. To simulate N deposition, an N addition level of 50 kg N ha−1 yr−1 was selected, which is approximately twice the historical N deposition rate in the study area (23 kg N ha−1 yr−1). From May to October in 2009, 40 L of ammonium nitrate (NH4NO3) aqueous solution was uniformly sprayed in the treatment plots every month during the growing season (May to October). To avoid any difference in water application, the same amount of water was applied to the control plot. In the non-growing season, all panels in precipitation reduction treatment were removed to allow litterfall to reach the forest floor directly. Consequently, there were four replications for each treatment: control treatment (CK), N addition treatment (N50), precipitation reduction (PREC), and precipitation reduction combined with N addition treatment (PREC-N50). A detail schematic diagram of the experimental design with split plot is listed in Figure S1.
2.2. Sample Collection and Chemical Analyses
After 10 years of field trial, 15 soil cores were randomly collected from each plot (total 12 plots) and divided into three soil layers (A: 0–5 cm, B: 5–10 cm, and C: 10–20 cm) using a 2.5 cm soil sampler in August 2019. The soil cores from each soil layer were then homogenized into the composite sample, resulting in a total of 36 samples. All samples were immediately transported to the laboratory, where plant debris, gravel, and large soil organisms were removed, and the soil was passed through a 2 mm sieve. Some samples were stored at 4 °C for analysis of soil microbial properties and dissolved nutrients. The remaining samples were dried and ground through a 0.149 mm sieve for analysis of soil C, N, and P content. Soil total C and N were measured using an elemental analyzer (Elementar vario EL III, Hanau, Germany). The soil total phosphorus (TP) was extracted using concentrated H2SO4-HClO4, and the available phosphorus (AP) was extracted with 0.5 mol L−1 NaHCO3. The P concentration in the solution was determined by the molybdate blue colorimetric method [36] using a Skalar SAN plus Segmented Flow Analyzer (SAN++, SKALAR, Breda, The Netherlands). The NH4+ and NO3− contents were measured using a continuous flow analyzer (SAN++, SKALAR, Breda, The Netherlands) after extraction with 0.5 M KCl. Dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) in unfumigated soil were extracted using a K2SO4 solution.
2.3. Analysis of Soil Microbial Biomass and Enzyme Activity
Soil microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and microbial biomass phosphorus (MBP) were determined using the chloroform fumigation-extraction method [37,38]. The potential activities of C-acquiring (β-1,4-glucosidase, BG), N-acquiring (β-1,4-acetylglucosaminidase, NAG), and P-acquiring enzymes (acid phosphatase, ACP) were measured using the following methodology. First, soil samples were air-dried and sieved to remove large particles, ensuring uniformity. A specific amount of soil was then mixed with a buffer solution, thoroughly shaken, and centrifuged to obtain the soil extract. Subsequently, specific enzyme substrates were added: 4-nitrophenyl-β-D-glucopyranoside for BG, 4-nitrophenyl-β-D-acetylglucosaminide for NAG, and 4-nitrophenyl phosphate for ACP. The mixtures were incubated under optimal pH and temperature conditions for a designated period to facilitate enzymatic reactions. After incubation, the release of 4-nitrophenol was quantified spectrophotometrically at a wavelength of 405 nm. Enzyme activities were expressed as micromoles of product formed per gram of soil per hour. All assays were performed in triplicate to ensure reproducibility, and blank controls were included to account for non-enzymatic hydrolysis. This standardized approach enabled the accurate assessment of the functional potential of soil microorganisms in the carbon, nitrogen, and phosphorus cycles.
2.4. Analysis of Microbial Phospholipid Fatty Acids
The soil microbial community composition and biomass were analyzed using phospholipid fatty acid (PLFA) analysis [39]. After freeze drying, phospholipids were extracted using a mixed solvent of chloroform, methanol, and phosphate buffer (1:2:0.8) at room temperature for 24 h. The extracted solvents were then esterified with methanol at 37 °C and subsequently extracted with hexane. Afterward, phospholipids were identified using gas chromatography (Hewlett Packard 5890GC, Temecula, CA, USA. equipped with a 6890 series injector) and the MIDI Sherlock Microbial Identification System (MIDI Inc., Newark, DE, USA), with 19:0-C as the internal standard. The sum of 16:1ω7c, 16:1ω9c, cy17:0, cy19:0, 18:1ω5c, and 18:1ω7c served as the relative marker for Gram-negative bacteria (GN), and a17:0 was the marker for Gram-positive bacteria (GP). Additionally, the sum of i14:0, i15:0, a15:0, i16:0, i17:0, and the 10Me16:0, 10Me17:0, and 10Me18:0 fatty acids was used as the marker for actinomycetes (ACT). The sum of 18:2ω6c and 18:1ω9c indicated fungal abundance. Furthermore, F/B represents the ratio of fungi to bacteria and GP/GN represents the ratio of GP to GN bacteria [18].
2.5. Data Calculation
The stoichiometric imbalances between soil microbes and their various substrates were calculated using the following Equations (1) and (2) [40]:
Labile C: N: P imbalance (dissolved soil) = DOC:AN:AP/MBC:MBN:MBP(1)
Bulk soil C: N: P imbalance (bulk soil) = TC:TN:TP/MBC:MBN:MBP(2)
Soil resources are divided into total soil resources (bulk soil) and soluble soil resources (dissolved soil). The latter is often considered a reliable indicator of C, N, and P availability in the soil [41]. Changes in the stoichiometric imbalance between microorganisms and substrates indicate that soil microorganisms may be limited by certain elements. For example, a decrease in the C:N ratio implies an increased microbial demand for C, whereas an increase in the C:N ratio indicates a higher microbial demand for N [42,43].
The enzyme stoichiometry method was used to calculate the potential and relative degree of soil microbial demand for C or N with the two indicators (vector length and vector angle) calculated according to two Equations (3) and (4) [44]:
Angle (o) = DEGREES(ATAN2(x, y))(3)
(4)
where x and y represent the ratios of C-, N-, and P-acquiring enzymes, respectively. These ratios are calculated as BG/(BG + ACP) and BG/(BG + NAG). The vector length represented microbial C versus nutrient enzyme allocation, while vector angle represented microbial N versus P enzyme allocation. An increase in vector length indicates a higher microbial demand for C, indicating that the distribution of C-acquiring enzymes exceeds that of N- or P-acquiring enzymes. When the vector angle was >45°, with increasing vector angle, the microbial P-limiting enzyme increased relative to the N-limiting enzyme. Conversely, when the vector angle was <45°, the microbial N-limiting enzyme increased with decreasing vector angle.2.6. Statistical Analyses
The general linear mixed model (GLMM) for completed randomized split-plot design was used to assess the effects of increased N fertilizer and reduced precipitation on soil chemical properties, microbial characteristics, soil enzyme activities, stoichiometric imbalances, and microbial stoichiometry. The analysis was conducted using the R package “NLME.” Fixed effects included precipitation reduction (PREC), N addition (N50), soil layer (Layer), and their interactions (PREC-N50, PREC × Layer, Layer × N50, PREC-N50 × Layer), while blocks were treated as random effects. When significant effects were found at the 0.05 significance level, multiple comparisons between treatments were conducted using Tukey’s HSD test. To explore the microbial strategies related to stoichiometric imbalances, redundancy analysis (RDA) was performed using the R package “VEGAN” to examine explanatory factors such as soil and microbial chemical concentrations, enzyme activities, and microbial composition (PLFA). Variance partitioning analysis (VPA) was also carried out with the R package “RDACCA.HP” to assess the contribution of these factors in explaining microbial adaptation to nutrient imbalances [45].
3. Results
3.1. Stoichiometric Imbalances Between Soil Microbes and Their Resources
The C/N imbalance between microbes and their dissolved substrate increased under N50, while it remained unchanged under PREC and PREC-N50 treatment in the C layer, and it decreased under PREC and PREC-N50 treatment in the A layer (Figure 1). PREC and N50 alone significantly increased C/P and N/P imbalances in the A and B layers, while they decreased the N/P imbalance in the C layer (p < 0.05). The PREC-N50 treatment decreased the C/P imbalance in the C layer. Conversely, the response of the stoichiometric imbalance calculated from bulk soil to N50 and PREC showed an opposite trend in the A and B soil layers (Figure S3). Notably, the microbial stoichiometric ratio was significantly negatively correlated with the stoichiometric ratio of soil resources (p < 0.05).
3.2. Responses of Soil Enzyme Stoichiometry to N Addition and Precipitation Reduction
Soil enzyme stoichiometric characteristics exhibited a notable divergent response to N50 and PREC (Figure 2). The BG/NAG ratio significantly decreased under N50, PREC, and PREC-N50 treatments in both the A and B layers compared to the control (CK) (p < 0.05). Conversely, the NAG/ACP ratio increased under N50 and PREC treatments in the A and B layers, and it substantially increased under PREC-N50 in the B layer. The N50 treatment significantly reduced the BG/ACP ratio in all three layers (p < 0.05), while PREC had no effect on the BG/ACP ratio. The C/N imbalance was significantly negatively correlated with BG/NAG (p < 0.05), while the N/P imbalance was significantly positively correlated with NAG/ACP (p < 0.05). There was no significant correlation between the C/P imbalance and the BG/ACP ratio. The vector length decreased significantly after N50 and PREC treatments (p < 0.05), while the amplitude of this reduction was less pronounced in the PREC-N50 treatment across all three layers. Conversely, the vector angle (>45°) decreased under N50 and PREC treatments in the A and B layers (Figure 3), and it decreased under PREC-N50 in the B layer.
3.3. The Relationships Between Stoichiometric Imbalances and Environmental Factors
RDA results demonstrated that the joint effects of microbial stoichiometry, composition, soil properties, and enzyme stoichiometry accounted for 91.8% of the total variation (Figure 4). Specifically, RDA1 and RDA2 explained 82.9% and 8.9% of the variation, respectively. PLFA abundance and the enzyme stoichiometric ratio were significantly positively correlated with bulk soil stoichiometric BS-C/P and BS-N/P imbalances (p < 0.05). Conversely, these factors showed significant negative correlations with the C/N imbalance, particularly in the A and B soil layers. Additionally, the GP/GN ratio was positively correlated with the C/N imbalance. MBN and MBP were negatively correlated with the C/N imbalance. The variance partitioning results indicated that each of the following factors individually explained a significant portion of the total variation: microbial elemental composition (17.4%), soil properties (34.1%), microbial composition (20.5%), and enzyme ratios (25.28%).
4. Discussion
4.1. Responses of Microbial Element Limitation to N Addition and Precipitation Reduction
In this study, the stoichiometric imbalance between soil microbes and the dissolved soil matrix was more sensitive to environmental changes than the stoichiometric imbalance between soil microbes and total soil nutrients (Figure 1 and Figure S3). This finding suggests that the dissolved nutrient stoichiometry in soil can serve as an effective proxy for assessing stoichiometric imbalances, which is supported by several studies [46,47,48]. Regarding nutrient stoichiometry, the C/P imbalance increased significantly under N addition and PREC treatments compared to CK (p < 0.05), indicating a shift toward soil nutrient limitation relative to C limitation. The increase in the N/P imbalance further suggests heightened soil P limitation over N limitation (Figure 1). These results imply that N addition may alleviate soil N limitation [49] while simultaneously increasing microbial demand for P from the soil’s extractable fraction. Additionally, N addition and precipitation reduction contribute to a decrease in AP, which restricts microbial P acquisition. We observed that these treatments did not alter the quantity or quality of active soil nutrients (DOC), yet they resulted in a decreased DOC/AP ratio, shifting the microbial environment from N limited to P limited. Although reductions in the MBC/MBN and MBN/MBP ratios indicated an increased microbial demand for N [41], the availability of N and stable DOC levels appear sufficient to meet microbial needs. Enzyme stoichiometry results (Figure 2 and Figure 3) suggest that N addition and precipitation reduction partially alleviate P limitation. However, microbial stoichiometry alone may not fully reflect microbial nutrient limitations. This could be due to microbial adaptation, where microorganisms increase their internal P storage in response to precipitation reduction, supported by N addition and increased MBP (Figure S2), to counter low P availability. Previous studies have reported that N addition and precipitation reduction [18,50,51] can increase P limitation in temperate forests, which was consistent with our results. The common understanding is that in N-limited ecosystems, additional N increases soil N availability, which stimulates plant growth and in turn enhances the limitation of P or other nutrients [52,53]. The increased plant P content was found in our study (Figure S4), suggesting an increased P uptake of the plant. Furthermore, N addition leads to soil acidification, which activates Fe/Al oxides, promoting the binding of dissolved P to mineral surfaces [54,55], thereby increasing P limitation. Precipitation reduction directly lowers the soil water content, which impairs substrate mobility and accessibility [56]. Consequently, this slows the absorption of N and P by microorganisms [57]. The reduced mobility of soil P due to lower precipitation can also lead to its precipitation with carbonates or Fe/Al oxides [58]. In our study, soil pH and the available P were reduced under N addition, alongside the reduction in available P in PREC treatment (Table S3), supporting with above explanation. Together, these mechanisms along with our data (Figure S4) provide clear evidence that shifts in soil stoichiometric imbalances indicate increased P limitation under N addition and precipitation reduction.
Unlike the individual effects of N addition and precipitation reduction, the effect of N addition on stoichiometric imbalances was offset by precipitation reduction; thus, no difference was observed between CK and PREC-N50 treatments. Under PREC-N50 treatment, the levels of available N increased (see Table S3), supporting the observed changes in stoichiometric imbalances. Additionally, the enzymatic ratios of BG/NAG and NAG/ACP were significantly correlated with stoichiometric imbalances (Figure 2), aligning with previous findings showing inverse effects on soil enzyme activities [8]. While N addition alone promoted P uptake by plants, PREC increased litter production, thereby enhancing soil nutrient supply and soil enzyme activity (Table S3, Figure S4), which could further support soil organic matter mineralization. Therefore, while N addition and precipitation reduction individually heightened the microbial P limitation through increased P uptake or decreased P availability, their interaction did not affect stoichiometric imbalances, as litter production maintained N and P availability. These results are consistent with our initial hypothesis.
4.2. Adaptation Mechanisms of the Soil Microbial Community to N Addition and Precipitation Reduction
Microorganisms adjust their physiology and structure in response to elemental imbalances, thriving in substrates where organic matter is released [42]. Our results showed that microbial C/N, C/P, and N/P ratios showed a stronger balance than dissolved soil stoichiometry in response to N addition and precipitation reduction results in significant P limitation in our study site (Figure 1). To cope with P limitation, a decrease in MBC and an increase in MBP were found in response to N addition and precipitation reduction, indicating the non-steady-state reaction of microorganisms to meet P demands when available P is reduced. Additionally, previous studies found that N addition and precipitation reduction significantly increases microbial respiration, as evidenced by higher N2O and CO2 emissions [35,38]. Together, these findings suggest that microbial communities can mineralize substrates, excrete excess elements (C or N), and conserve deficient elements (P) for growth. The adjustment of soil microbial element content to resource imbalances helps maintain the stoichiometric balance (Figure S2) required for the elemental composition of substrates [59]. These physiological adjustments contrast with results from several experiments [8,60], demonstrating that the homeostasis of soil microbial stoichiometry is influenced by biological and environmental changes, such as nutrient limitations [61] or varying microbial community stoichiometry [14].
To further the adaption of nutrient limitations, soil microorganisms appear to increase their synthesis of extracellular enzymes targeting limiting nutrients, addressing nutrient limitations caused by stoichiometric imbalances [15]. Previous global data show that the logarithmic ratio of C-, N-, and P-acquiring enzymes approximates 1:1:1 [62]. In this study, the C:N:P acquisition enzyme ratio of ln(BG):ln(NAG):ln(ACP) across all treatments was 0.84:0.72:1 (Figure S4). This suggests that soil microorganisms invest more energy in producing P-acquiring enzymes to address P limitations rather than focusing on C- or N-acquiring enzymes. This aligns with widespread P-deficient forest soils affected by N addition and precipitation reduction [3,51]. The change in allocation of microbial enzyme synthesis supports the resource allocation theory, suggesting that microorganisms reduce soil resource stochiometric imbalances by increasing the secretion of enzymes associated with the acquisition of scarce elements [15].
Another adaptation mechanism by which microbial communities address stoichiometric imbalances is through adjustments in community composition and diversity [17]. In our study, the amplitude of the reduction in fungal PLFA (−43%) was higher than bacterial PLFA (−17%), showing a significant negative feedback in response to N addition and precipitation reduction (Table S3). Previous studies suggested that the stoichiometric ratio in fungi was significantly higher than that in bacteria [63,64]. Thus, the reduction in fungal biomass relative to bacterial biomass suggested that microbes maintain stoichiometric homeostasis (lowering C to nutrient ratio) under N addition and PREC by favoring bacteria growth over fungal growth, offsetting the negative effect of N on microbial stoichiometry [8]. This explanation was supported by the positive correlation between fungal or bacteria biomass and stoichiometric imbalances (Figure 4), which is consistent with previous studies [65,66]. This finding implies that shifts in microbial composition in response to N addition and PREC may also help reduce microbial stoichiometric imbalances (Figure S2). Additionally, soil microbes may use various strategies to overcome stoichiometric imbalances due to precipitation reduction, such as accumulating osmotic solutes or developing structures that resist osmotic stress. Under water stress, soil microorganisms can increase their cytoplasmic components [67]. Furthermore, increased hyphal length may also help microbes access nutrients over larger areas and solubilize nutrients through the secretion of organic acids or enzymes [51].
Overall, our results suggested that soil microorganisms can adjust their nutrient status, composition, and energy investment to enzyme production to cope with nutrient limitation. These adjustments of microbial properties subsequently impact on soil C dynamic and nutrient cycles. Further experiments are needed to examine the effect of P limitation on plant and soil microbial behaviors and their implication for ecological processes by manipulating P addition.
5. Conclusions
In our study, N addition and precipitation reduction in a forest ecosystem significantly affected nutrient cycling in microbes and plants. Here, N addition and precipitation reduction increased the C/P and N/P imbalances, suggesting enhanced microbial P limitation due to the increased P demand of plants. Precipitation reduction heightened microbial P limitation by decreasing nutrient availability through lowering soil water content. To cope with P limitation, the composition, enzyme production, and nutrient composition of soil microbes had significantly shifted under N addition and precipitation reduction. However, the combined effect of N addition and precipitation reduction showed no effect on microbial nutrient limitation. These results highlight that nutrient imbalances are more robust than soil microbial stoichiometry to predict microbial nutrient limitation, and the effects of multiple global change factors can offset each other. Therefore, further research regarding P application is needed to examine whether P is really limited, subsequently guiding forest management in response to increased N deposition and reduced precipitation.
All authors have contributed to the research conception and design. Material preparation, data collection and analysis were completed by Y.X., X.D., Z.C. and S.H. The first draft of the manuscript was written by Y.X., X.D. and all authors commented on the first several editions of the manuscript. All authors have read and agreed to the published version of the manuscript.
The data of this study are available from the corresponding author upon reasonable request.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Stoichiometric imbalances between microbes and soil labile resources, and the relationships between microbial stoichiometry and soil labile resource stoichiometry in three soil layers (A: 0–5 cm depth, B: 5–10 cm, C: 10–20 cm) in response to four treatments: control (CK), N addition (N50), precipitation reduction (PREC) and precipitation reduction combined with nitrogen addition (PREC-N50). Values are means ± SE (n = 3). Different lowercase letters indicate significant differences between treatments in the same soil layer.
Enlarge this image.Figure 2. Soil enzymatic stoichiometry and the relationships between enzymatic stoichiometry and stoichiometric imbalances between microbes and soil labile resources in three soil layers (A: 0–5 cm, B: 5–10 cm, C: 10–20 cm depth) in response to four treatments: control (CK), N addition (N50), precipitation reduction (PREC), and precipitation reduction combined with nitrogen addition (PREC-N50). Values are means ± SE (n = 3). Different lowercase letters indicate significant differences between treatments in the same soil layer.
Enlarge this image.Figure 3. The enzymatic vector length and vector angle in response to four treatments: control (CK), N addition (N50), precipitation reduction (PREC), and precipitation reduction combined with nitrogen addition (PREC-N50). Values are means ± SE (n = 3). Different lowercase letters indicate significant differences between treatments in the same soil layer. Different capital letters A, B, and C represent the soil depth of 0–5cm, 5–10cm, and 10–20cm, respectively.
Enlarge this image.Figure 4. Redundancy analysis (RDA) of the relationships of stoichiometric imbalances between microbes and soil resources with soil abiotic and microbial parameters. The contribution of variables to the variation in stoichiometric imbalances obtained from RDA ordination via variance partitioning. Nitrate nitrogen (NO3−), ammonia nitrogen (NH4+), ratio of fungi to bacteria (F/B), microbial biomass phosphorus (MBP), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), Gram-negative bacteria (GN), Gram-positive bacteria (GP), actinomycetes (ACT), ratio of Gram-positive to Gram-negative bacteria (GP/GN), available phosphorus (AP), soil water content (SWC), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), bulk soil carbon:nitrogen imbalance (BS_C/Nim), bulk soil phosphorus:nitrogen imbalance (BS_P/Nim), bulk soil nitrogen:phosphorus imbalance (BS_N/Pim), labile soil carbon:nitrogen imbalance (LS_C/Nim), labile soil phosphorus:nitrogen imbalance (LS_P/Nim), and labile soil nitrogen:phosphorus imbalance (LS_N/Pim).
Supplementary Materials
The following supporting information can be downloaded at
References
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