Enzyme Activity Stoichiometry Suggests That Fertilization, Especially Nitrogen Fertilization, Alleviates Nutrient Limitation of Soil Microorganisms in Moso Bamboo Forests

Abstract

Rational application of N fertilizer is essential for maintaining the long-term productivity of Moso bamboo forests. Microbial activity is a crucial indicator of soil quality. Changes in soil nutrient resources due to N addition can lead to microbial nutrient limitations, thereby impeding the maintenance of soil quality. Currently, there is limited research on the effects of N application on microbial nutrient limitations in Moso bamboo forest soils. To examine the changes in extracellular enzyme activity and microbial nutrient limitations in Moso bamboo forest soils following N application, we conducted an N application experiment in northern Guizhou. The findings revealed that the N3 treatment (726 kg·N·hm⁻²·yr⁻¹) significantly reduced β-glucosidase (BG) activity by 27.61% compared to the control group (no fertilization). The N1 (242 kg·N·hm⁻²·yr⁻¹), N2 (484 kg·N·hm⁻²·yr⁻¹), and N3 treatments notably increased the activities of leucine aminopeptidase (LAP) and N-acetyl-β-D-glucosidase (NAG) by 11.45% to 15.79%. Acid phosphatase (ACP) activity remained unaffected by fertilization. N application treatments significantly decreased the C:Ne and C:Pe ratios, while the N:Pe ratio was less influenced by N fertilizer application. Scatter plots and vector characteristics of enzyme activity stoichiometry suggested that microorganisms in the study area were limited by C and N, and N fertilizer application reduced the vector length and increased the vector angle, indicating that N application alleviated the C and N limitation of microorganisms in Moso bamboo forests. Redundancy Analysis (RDA) demonstrated that microbial biomass phosphorus (MBP) was the most critical factor affecting extracellular enzyme activity and stoichiometry. Furthermore, Random Forest Regression analysis identified MBP and the N:Pm ratio as the most significant factors influencing microbial C and N limitation, respectively. The study demonstrated that N application modulates the microbial nutrient acquisition strategy by altering soil nutrient resources in Moso bamboo forests. Formulating fertilizer application strategies based on microbial nutrient requirements is more beneficial for maintaining soil quality and sustainably managing Moso bamboo forests. Additionally, our study offers a theoretical reference for understanding carbon cycling in bamboo forest ecosystems in the context of substantial N inputs.

1. Introduction

China boasts a rich history of bamboo cultivation and utilization, positioning it among nations with the most abundant bamboo resources globally [1]. Bamboo has many applications in food, furniture, papermaking, and biomass energy, and the C sequestration potential of bamboo forest ecosystems is considerable [2]. According to the latest data, the area of Moso bamboo forests in China is 527.76 ha as of 2021, accounting for 69.78% of the total area of bamboo forests, with a carbon stock of 23.1 billion tons, accounting for 2.3% of the country’s total carbon stock in forest vegetation [3,4]. Moso bamboo (Phyllostachys edulis (Carrière) J. Houz)—the most prevalent species in China—is distinguished by its rapid growth rate and robust regeneration capability [5,6]. N is the element with the highest demand and uptake during the growth and development of Moso bamboo [7,8]. The application of N fertilizer guarantees high yields of Moso bamboo, and rationally applied will not adversely influence soil quality [9]. In bamboo forest management, operators apply large amounts of N fertilizers to increase yield. However, this substantial N input can lead to soil organic carbon loss and soil acidification, potentially resulting in decreased microbial activity [10,11,12]. Microbial activity serves as a crucial indicator for assessing soil quality, as microorganisms contribute to the decomposition of soil organic matter by secreting extracellular enzymes [13,14]. This process not only supports their own metabolic needs but also generates additional nutrients that are readily accessible for plant growth [15]. Consequently, investigating the response of soil microbial communities to N application in Moso bamboo forests is an insightful endeavor.

N application may change the content and relative concentration of soil nutrient elements. N application significantly reduced soil organic carbon (SOC)and total N (TN) contents, had no significant effect on the total phosphorus (TP) content, but increased the soil C:P ratio [16]. N application can lead to changes in soil nutrient resources. Such changes may lead to an imbalance between soil resources and microbial nutrient requirements, resulting in microbial nutrient limitations [17]. Microorganisms regulate the balance between their own nutrient requirements and soil nutrients by secreting extracellular enzymes, and enzyme activity stoichiometry is considered to be an effective indicator of microbial nutrient limitations [18,19,20]. Enzymes such as β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid phosphatase (ACP)—which are vital components of the C, N, and P cycles—have been extensively employed in investigations into the stoichiometry of extracellular enzyme activities [21,22,23,24]. Sinsabaugh et al. reported that the ecological enzyme stoichiometry of soils and sediments in geoecosystems is approximately 1:1:1 [25]. However, Luo et al. observed differences in enzyme C:N:P ratios in peatlands on the Ruoergai Plateau compared to the expected 1:1:1 ratio, with ratios closer to soil C:N:P ratios, indicating phosphorus (P) limitation in the soils of this region [26]. Moorhead et al. suggested using carrier properties of soil enzyme activities to characterize energy and nutrient limitations of soil microorganisms, and a large number of studies have been conducted to demonstrate that vector characteristics can be used to reflect the nutrient limitation of microorganisms [27,28,29,30]. Zhang et al. employed extracellular enzyme activity stoichiometry and vector features to characterize the nutrient limitations of soil microorganisms in Moso bamboo forests under varying management intensities [31]. The results indicated that intensive management (fertilization with N, P, and K) decreased BG, NAG, and ACP activity and alleviated the C, N limitation of soil microorganisms in Moso bamboo forests. Zeng et al., in studying the stoichiometry of enzyme activities in Moso bamboo forest soils after N application, also found that NAG activity decreased and ACP activity increased and that N application exacerbated the C and P limitation of Moso bamboo forest microorganisms [32]. These findings suggest that soil enzyme activities and enzyme stoichiometric characteristics can be used to characterize the nutrient acquisition strategies of soil microorganisms under N addition conditions.

Moso bamboo, with its extensive distribution in southern China, may exhibit varying soil microbial nutrient limitations across different regions. Despite this, there is a dearth of research investigating the impact of fertilization, particularly N fertilization, on the microbial nutrient limitation of Moso bamboo forest soils. In this study, we conducted a fertilization experiment (0, 242, 484, 726 kg·N·hm⁻²·yr⁻¹) in a pure Moso bamboo forest in northern Guizhou, China. We measured soil nutrients, microbial biomass, and extracellular enzyme activities post-fertilization. The enzyme activity stoichiometry and vector characterization were used to identify microbial nutrient limitations. Our objectives were twofold: firstly, to examine the changes in extracellular enzyme activities and microbial nutrient limitations in Moso bamboo forest soils following large-scale N fertilizer application, and secondly, to identify the key environmental factors influencing extracellular enzyme activities, enzyme activity stoichiometry, and vectorial characterization of Moso bamboo forest soils after such fertilization. To accomplish our objectives, we formulated the following hypotheses: (1) N application will modify extracellular enzyme activities and stoichiometry; (2) soil microorganisms in Moso bamboo forests within the study area are limited by C and N, and N application will either exacerbate or alleviate this nutrient limitation without altering the limiting nutrient elements; (3) the primary factors contributing to microbial nutrient limitation are alterations in both the biomass and stoichiometry of microorganisms.

2. Materials and Methods

2.1. Study Site

The research site was situated in the Hu Shi Forest Farm (28°23′~105°54′), Chishui City, Guizhou Province, China. This region is characterized by its medium to low mountains and is subject to a subtropical humid monsoon climate. The annual average temperature is 18.1 °C, with an average precipitation of 1195.7 mm. The forest farm was inaugurated in 1999 and encompasses 330 ha of pure Moso bamboo forest. Notably, there are no pests or diseases within the forest area, exhibiting a distinct size–year relationship. The average diameter at breast height for Moso bamboo is 9.86 cm, with an average height of 12.07 m and a density of 3866 plants/ha. The understory vegetation primarily comprises Rubus buergeri Miq, Tetrastigma hemsleyanum Diels et Gilg, Curculigo capitulata (Lour.) Kuntze, Nothapodytes pittosporoides (Oliv.) Sleum, and Machilus nanmu (Oliv.) Hemsl. According to the classification from the Chinese Soil Database, the soil in the study area is categorized as purple–yellow sandy loam. The average total carbon content, organic carbon content, and pH value (measured in distilled water) in the 0–30 cm soil layer are 19.8 g/kg, 16.94 g/kg, and 4.75, respectively [12]. The geographical location of the study area is shown in Figure 1.

The stand structure and site conditions were consistently applied, with a plot selected consisting of a pest-free pure bamboo forest. The N fertilizer level in this investigation was ascertained based on the research team’s prior studies concerning Moso bamboo nutrient utilization and optimal scientific fertilization quantities [33]. Four distinct fertilization treatments were established in the experiment. Each plot received a consistent amount of phosphorus and potassium fertilizer as a base, while varying concentrations of N fertilizer were applied: N0 (0 kg·N·hm⁻²·yr⁻¹), N1 (242 kg·N·hm⁻²·yr⁻¹), N2 (484 kg·N·hm⁻²·yr⁻¹), and N3 (726 kg·N·hm⁻²·yr⁻¹). A no-fertilization treatment served as the control (CK). The experiment employed a randomized block design, with each plot measuring 15 m × 15 m. Each treatment was replicated three times, resulting in a total of 12 plots. A 5 m isolation zone was established between adjacent plots.

The N application experiment was executed in early October 2021 during the pre-shooting stage. The fertilizer used for this experiment comprised urea (46% N), superphosphate (178 kg/ha; 12% P₂O₅) as the base phosphorus fertilizer, and potassium chloride (147 kg/ha; 60% K₂O) as the potassium fertilizer. Based on the required proportions of fertilizers for different treatments, the prepared mixtures were dissolved in 50 L of distilled water and then applied through spraying.

2.2. Soil Sample Collection

Soil sampling was carried out in October 2022, utilizing the “S”-shaped sampling method to establish ten distinct points within each plot. Soil samples were collected from a depth of 0 to 10 cm using a soil corer with an inner diameter of 38 mm. Prior to collection, the litter layer at the sampling point was meticulously removed. The soil core was preserved in a portable thermal box and promptly transported to the laboratory for processing. From each plot, ten cores of soil were gathered. The cores from the same plot were then amalgamated to yield fifteen mixed soil samples, after which stones, roots, and other impurities were meticulously extracted. The soil mixture was divided using the quadrat method, and the excess soil was discarded. A portion of the fresh soil was passed through a 2 mm sieve to analyze the biomass of C, N, and P (MBC, MBN, MBP) and extracellular enzyme activity determination. Additionally, one portion of the soil underwent air-drying, grinding, and sieving to determine soil pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorous (TP), and available phosphorous (AP).

2.2.1. Soil Chemical Properties

SOC and TN were determined by an elemental analyzer (elementar vario El cube, Langenselbold, Germany) [34]. TP was determined by the NaOH alkali fusion molybdenum antimony colorimetric method [35]. AP content was extracted with sodium fluoride hydrochloric acid and then determined by the molybdenum antimony colorimetric method [35]. The air-dried soil samples were saturated with distilled water and the soil pH was measured using a glass electrode method (ST2100, Ohaus, Parsippany, NJ, USA). The stoichiometric ratios of soil nutrients C:N, C:P, and N:P were calculated using values of SOC, TN, and TP and were expressed as C:Ns, C:Ps, and N:Ps, respectively.

2.2.2. Microbial Biomass

The MBC and MBN were determined by chloroform fumigation, and the C and N content of the extracts were measured with C and N analyzer (TOC-LCSH/CPH, Shimadzu, Beijing, China) [36]. The MBP was determined through the chloroform fumigation extraction method, with the P content in the extract being evaluated using the molybdenum antimony colorimetric method [36]. In brief, fresh soil was subjected to fumigation with ethanol-free chloroform at a temperature of 25 °C in darkness for a duration of 24 h. An equivalent volume of fresh soil, which had not undergone fumigation, was also utilized. Extracts from both fumigated and non-fumigated soils, prepared using 0.5 M K₂SO₄, were employed for MBC and MBN determinations. Meanwhile, extracts from both fumigated and non-fumigated soils, prepared using 0.5 mol/L NaHCO₃, were used for MBP determination. The stoichiometric C:N, C:P, and N:P ratios of microbial biomass were expressed as C:Nm, C:Pm, and N:Pm, respectively.

2.2.3. Soil Extracellular Enzyme Activity

The activity of enzymes β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid phosphatase (ACP) was determined by enzyme-linked immunosorbent assay (ELISA) employing a double antibody one-step sandwich technique. Using BG as an example, 1 g of fresh soil was extracted with 9 milliliters of phosphate buffer (pH = 9.8), thoroughly mixed, centrifuged at 2500 revolutions per minute for 25 min at 4 degrees Celsius, and the supernatant was collected. The optical density (OD) value of each sample was measured using an enzyme marker to ascertain the sample activity. The methodology for determining soil enzyme activity was based on the protocol described by Xia et al. [37,38].

2.2.4. Enzyme Activity Stoichiometry

The stoichiometric characterization of the extracellular enzyme activities C:N, C:P, and N:P are denoted as C:Ne, C:Pe, and N:Pe, respectively. These ratios were calculated using the following equations [25]:

$$\mathrm{C}:\mathrm{N}\mathrm{e}=\ln \left(\mathrm{B}\mathrm{G}\right)/\ln (\mathrm{L}\mathrm{A}\mathrm{P}+\mathrm{N}\mathrm{A}\mathrm{G})$$

(1)

$$\mathrm{C}:\mathrm{P}\mathrm{e}=\ln \left(\mathrm{B}\mathrm{G}\right)/\ln \left(\mathrm{A}\mathrm{C}\mathrm{P}\right)$$

(2)

$$\mathrm{N}:\mathrm{P}\mathrm{e}=\ln (\mathrm{L}\mathrm{A}\mathrm{P}+\mathrm{N}\mathrm{A}\mathrm{G})/\mathrm{l} \left(\mathrm{A}\mathrm{C}\mathrm{P}\right)$$

(3)

2.2.5. Enzyme Activity Vector Characterization

Moorhead et al. proposed using extracellular enzyme activity vector features to characterize soil microbial growth limited by nutrients. Vector length (VL) is used to characterize soil microbial growth limited by C, while vector angle (VA) can be used to characterize soil microbial growth limited by N or P [30]. The equations for calculating these vector characterizations are as follows:

$$\mathrm{V}\mathrm{L}=\sqrt{{\left(\ln \left(\mathrm{B}\mathrm{G}\right)/\ln (\mathrm{L}\mathrm{A}\mathrm{P}+\mathrm{N}\mathrm{A}\mathrm{G})\right)}^2+{(\ln (\mathrm{B}\mathrm{G})/\ln (\mathrm{A}\mathrm{C}\mathrm{P}\left)\right)}^2}$$

(4)

$$\mathrm{V}\mathrm{A}=\mathrm{D}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{s}\left[\mathrm{A}\mathrm{T}\mathrm{A}\mathrm{N}2\right(\ln \left(\mathrm{B}\mathrm{G}\right)/\ln \left(\mathrm{A}\mathrm{C}\mathrm{P}\right),\ln (\mathrm{L}\mathrm{A}\mathrm{P}+\mathrm{N}\mathrm{A}\mathrm{G})/\ln \left(\mathrm{A}\mathrm{C}\mathrm{P}\right)]$$

(5)

The magnitude of the vector length serves as an indicator of the degree to which soil microbial growth is limited by C. Larger vector lengths denote a higher degree of C limitation, whereas smaller lengths indicate a lesser degree of C limitation. Using 45° as a reference, a vector angle exceeding 45° signifies that soil microbial growth is limited by P. Conversely, a vector angle below 45° indicates N limitation. The degree of limitation by either N or P increases as the vector angle deviates further from 45° and decreases as it approaches 45°.

2.3. Data Analysis

Variations in soil properties, extracellular enzyme activity, enzyme activity stoichiometry, and vector characteristics across different N application treatments were assessed using one-way analysis of variance (ANOVA). Post hoc comparisons were conducted using the Duncan test with a significance level set at p < 0.05. Relationships among soil properties, extracellular enzyme activity, enzyme activity stoichiometry, and vector characteristics were determined through Pearson’s correlation analysis. Redundancy Analysis (RDA) was performed with soil extracellular enzyme activity and stoichiometry as response variables and soil properties as explanatory variables to identify the primary environmental factors influencing these enzyme activities and stoichiometries. Random Forest Regression Analysis was employed to discern the principal environmental determinants of soil microbial carbon limitation and nitrogen/phosphorus limitation. Data management was executed in Excel 2021 (Microsoft Corp., Redmond, WA, USA), one-way ANOVA in SPSS 27.0 (SPSS, Chicago, IL, USA), Pearson’s correlation analysis in RStudio (version 3.4.2; R Foundation for Statistical Computing, Vienna, Austria) utilizing the “corrplot” package, RDA in Canoco5 (Canoco, Ithaca, NY, USA), and Random Forest Regression Analysis in RStudio (version 3.4.2) using the “rfPermute” package. All graphs presented in this study were generated in RStudio with the “ggplot2” package.

3. Results

3.1. Changes in Soil Properties

Table 1. N application effects on soil chemical properties of Moso bamboo forests.

Treatment	SOC g/kg	TN g/kg	TP g/kg	AP mg/kg	pH	MBC mg/kg	MBN mg/kg	MBP mg/kg	C:Ns	C:Ps	N:Ps	C:Nm	C:Pm	N:Pm
CK	16.94 ± 0.85 ab	1.25 ± 0.11 a	0.39 ± 0.02 b	7.63 ± 0.53 bc	4.59 ± 0.03 a	376.29 ± 39.94 a	55.05 ± 1.64 a	3.51 ± 0.23 c	13.56 ± 0.09 a	43.77 ± 0.58 a	3.27 ± 0.46 a	6.89 ± 0.92 a	108.27 ± 14.58 a	15.81 ± 1.17 a
N0	19.23 ± 0.54 a	1.44 ± 0.08 a	0.67 ± 0.05 a	8.8 ± 1.01 b	4.57 ± 0.03 a	415.3 ± 3.97 ab	61.59 ± 2.28 a	3.59 ± 0.33 c	13.4 ± 0.94 a	29.33 ± 2.75 b	2.21 ± 0.28 b	6.76 ± 0.26 a	117.84 ± 11.53 a	17.37 ± 1.2 a
N1	13.62 ± 1.58 b	1.13 ± 0.04 a	0.74 ± 0.07 a	5.81 ± 0.46 c	4.58 ± 0.03 a	117.58 ± 4.01 b	21.53 ± 0.48 b	2.64 ± 0.03 d	12.12 ± 1.63 a	18.94 ± 3.49 d	1.55 ± 0.1 b	5.47 ± 0.3 b	44.57 ± 1.86 b	8.16 ± 0.19 b
N2	14.48 ± 1.06 b	1.23 ± 0.14 a	0.71 ± 0.01 a	9.48 ± 0.83 b	4.68 ± 0.07 a	162.14 ± 10.51 c	33.98 ± 0.25 c	4.41 ± 0.06 b	11.98 ± 0.9 a	20.28 ± 1.11 cd	1.71 ± 0.16 b	4.77 ± 0.31 b	36.69 ± 1.97 b	7.7 ± 0.09 b
N3	17.68 ± 2.29 ab	1.41 ± 0.1 a	0.64 ± 0.01 a	14.34 ± 0.99 a	4.61 ± 0.09 a	332.72 ± 8.81 c	59.45 ± 1.51 a	6.04 ± 0.2 a	12.47 ± 1 a	27.55 ± 3.26 bc	2.2 ± 0.17 b	5.6 ± 0.01 b	55.12 ± 0.69 b	9.85 ± 0.14 b

Lowercase letters in each column indicate significant p < 0.05 differences between different N application treatments. Note: Values are means ± standard error (n = 3).

shows that, compared to the control, the N0 treatment significantly increased TP content to 0.67 g/kg and reduced the C:Ps and N:Ps ratios by 32.99% and 32.24%, respectively. However, the N0 treatment did not significantly impact other soil properties (p > 0.05). The N1, N2, and N3 treatments all significantly increased TP content, ranging from 64.1% to 89.74%. Additionally, the N3 treatment significantly increased AP content to 14.34 g/kg. N fertilizer treatment decreased MBC and MBN contents. Specifically, the N1, N2, and N3 treatments significantly reduced MBC content, while the N1 and N2 treatments significantly decreased MBN content (p < 0.05). The response of MBP to fertilization is correlated with N fertilizer dosage. The N1 treatment significantly reduced MBP content to 2.64 mg/kg, while the N2 and N3 treatments significantly increased MBP content to 4.41 mg/kg and 6.04 mg/kg, respectively (p < 0.05).

Compared to the no N fertilizer treatment (N0), both N1 and N2 treatments significantly decreased SOC content by 29.17% and 24.7%, respectively (p < 0.05). The N1 treatment notably reduced AP content to 5.81 mg/kg, whereas the N3 treatment significantly increased AP content to 14.34 mg/kg (p < 0.05). N2 and N3 treatments significantly diminished MBC content by 60.96% and 19.88%, respectively. The intergroup differences revealed that variations in MBN content, MBP content, soil nutrient stoichiometry (C:Ns, C:Ps, and N:Ps), and microbial biomass stoichiometry (C:Nm, C:Pm, and N:Pm) between the N fertilizer treatment and the N0 treatment were consistent with those observed between the N fertilizer treatment and the control. This suggests that the alterations in these soil properties are primarily attributable to the addition of N fertilizer.

3.2. Changes in Extracellular Enzyme Activity and Stoichiometry

Figure 2A,B shows that, compared to the control, the N0 treatment had no significant effect on BG activity (p > 0.05) but significantly increased LAP + NAG activity by 6.11% (p < 0.05). Compared to N0 and CK, the N3 treatment significantly reduced BG activity to 217.72 U/L. In comparison to the control, the N1, N2, and N3 treatments significantly increased LAP + NAG activity, with increases ranging from 11.45% to 15.79%. Compared to the no-nitrogen treatment (N0), the application of N fertilizer increased LAP + NAG activity to varying degrees. Specifically, the N1 and N3 treatments significantly increased LAP + NAG activity by 6.5% and 9.12%, respectively. There were no significant differences in ACP activity among the treatments (p > 0.05) (Figure 2C).

Figure 2D,E shows that fertilization reduced the C:Ne and C:Pe ratios to varying degrees. Compared to the control, the N0 treatment significantly reduced the C:Ne and C:Pe ratios by 3.27% and 3.5%, respectively. Among the different fertilization treatments, the C:Ne ratio in the N3 treatment was significantly lower than that in the N0, N1, and N2 treatments, with reductions ranging from 5.51% to 6.35%. There were no significant differences in the C:Pe ratio among the different fertilization treatments (p > 0.05). Figure 2F shows that there was no significant difference in the N:Pe ratio between the N0 treatment and the control. However, the N1 treatment significantly increased the N:Pe ratio compared to the control and other fertilization treatments, with increases ranging from 3.33% to 4.58%.

3.3. Changes in Microbial Nutrient Limitation

A scatter plot with the ratio of (LAP + NAG)/AP on the x-axis and the ratio of BG/ACP on the y-axis can be used to represent the nutrient limitation of microbial growth. The enzymatic stoichiometry scatter plot (Figure 3A) indicates that soil microbial growth in Moso bamboo forests is limited by both carbon and N under different treatments. Figure 3B,C depicts the characteristics of changes in vector length and angle under different treatments. Fertilization significantly reduced the vector length (VL), with decreases ranging from 3.53% to 8.3%. Among the different fertilization treatments, N input reduced the vector length to varying degrees, with the N3 treatment showing a significantly lower vector length compared to the N0 treatment. The vector angles under different treatments were all below 45°, with no significant difference between the N0 treatment and the control (p > 0.05). Among the different fertilization treatments, N input increased the vector angle to varying extents. The vector angles of extracellular enzyme activity in the N1 and N3 treatments were significantly higher than those in the N0 treatment (p < 0.05), with increases of 4.94% and 2.58%, respectively. Figure 3D illustrates the correlation between vector length and angle under different treatments, showing a significant negative correlation between these two parameters.

3.4. Environmental Factors Affecting Extracellular Enzyme Activity and Microbial Nutrient Limitation

Figure 4 illustrates the correlations between soil properties, extracellular enzyme activities, enzyme stoichiometry, and vector characteristics. BG shows a significant negative correlation with AP and MBP, while ACP shows a significant positive correlation with AP and MBP. (LAP + NAG) is significantly positively correlated with TP but significantly negatively correlated with N:Ps, MBC, and C:Nm. C:Ne exhibits a significant negative correlation with MBP. C:Pe is significantly positively correlated with C:Ps, N:Ps, C:Pm, and N:Pm but significantly negatively correlated with TP and MBP. N:Pe shows a significant negative correlation with SOC, MBC, and N:Pm. Vector length is significantly positively correlated with C:Ps, N:Ps, C:Nm, and N:Pm but significantly negatively correlated with TP, AP, and MBP. Vector angle is significantly positively correlated with TP but significantly negatively correlated with C:Ps, N:Ps, MBC, MBN, C:Nm, C:Pm, and N:Pm.

RDA analysis was conducted with extracellular enzyme activity and stoichiometry as response variables and soil properties as explanatory variables to identify key environmental factors influencing enzyme activity and stoichiometry. Figure 5A shows that the first and second axes explain 93.7% and 1.61% of the variation in extracellular enzyme activity and stoichiometry under different treatments, respectively, accounting for a total of 95.31%. Figure 5B indicates that MBP, C:Nm, and TP have explanatory rates of 66.7%, 10.7%, and 10.1%, respectively, all reaching significant levels, making them the key environmental factors affecting enzyme activity and stoichiometry under different treatments. Random forest regression analysis was used to explore key environmental factors affecting vector length and vector angle. Soil property variations explain 75.95% and 65.68% of the changes in vector length and vector angle, respectively, with MBP and N:Pm identified as the most critical factors influencing vector length and vector angle (Figure 5C,D).

4. Discussion

4.1. Extracellular Enzyme Activity and Stoichiometry in Response to N Application

Extracellular enzyme activity is related to microbial growth and metabolism. Microorganisms can adjust extracellular enzyme activity to adapt to changes in soil nutrient stoichiometry induced by N application [39]. In this study, there was no significant difference in BG activity between the N0 treatment and the control, while the N3 treatment significantly reduced BG activity (Figure 2A). This indicates that changes in BG activity are primarily influenced by N fertilizer concentration. Some meta-analyses have shown that N fertilization increases BG activity, and these changes in BG activity are closely related to microbial biomass [40,41]. As the dosage increases, the inhibitory effect of N fertilizer on BG activity becomes more pronounced. Chen et al.’s study on Larix mastersiana forests also observed this phenomenon (application of NH₄NO₃, 10 g m⁻² a⁻¹), where high concentrations of N fertilizer significantly suppressed microbial activity, thereby affecting BG activity [42]. The inhibition of BG activity by N application may be related to changes in bacterial community structure [43,44]. Correlation analysis reveals a significant negative correlation between the decrease in BG activity and alterations in AP and MBP content (Figure 4). This suggests that the reduction in BG activity is associated with changes in soil nutrients and microbial biomass. The N0 treatment resulted in an increase in the activity of (LAP + NAG), indicating that the application of P and K fertilizers influences the acquisition of soil microbial N. This, in turn, promotes the activity of N acquisition enzymes [45,46]. Among the various fertilization treatments, an increase in LAP + NAG activity was observed with a rise in N fertilizer concentration (Figure 2B). This suggests that N fertilizer significantly enhances LAP + NAG activity, a finding that aligns with previous studies [47,48,49]. Conversely, we also found that N application had no significant effect on ACP activity, whereas Tu et al. found that N application (application of NH₄NO₃) increased ACP activity in hybrid bamboo (Bambusa pervariabilis McClure × Bambusa grandis (Q. H. Dai & X. L. Tao) Ohrnb ) stands [50]. When there is a change in soil nutrient stoichiometry, microorganisms adjust their strategy of producing extracellular enzymes to obtain more of the missing nutrients to alleviate the deficient nutrient limitations [47,51,52].

The stoichiometry of extracellular enzyme activity can serve as an indicator of the dynamic equilibrium between nutrient availability [53,54]. In a meta-analysis conducted by Sinsabaugh, it was discovered that the stoichiometry of C, N, and P acquisition enzyme activities at a global scale, as represented by ln(BG):ln(LAP + NAG):ln(ACP) ratios, was approximately 1:1:1 [25]. Peng et al. found this pattern in tropical ecosystems [55]. However, this ratio may fluctuate depending on the ecosystem type and regional environmental conditions [26,56]. In this context, the ratios of C:Ne, C:Pe, and N:Pe were all greater than 1. Consequently, the ratio of lnBG:ln(LAP + NAG): lnACP also diverged from the expected 1:1:1 ratio. This observation indicates that fertilization, especially N application, induced changes in soil resource availability within our study area, prompting microbes to adapt their production strategies for C, N, and P-acquiring enzymes [57,58].

4.2. Effects of Soil Microbial Nutrient Limitation in Fertilized Moso Bamboo Forests

We employed enzyme activity stoichiometric scatter plots and enzyme activity vector features to jointly characterize the nutrient limitations of soil microbial growth in Moso bamboo forests under various treatments [59]. The findings revealed that the growth of soil microorganisms in these forests was primarily constrained by C and N. The vector angle results (Figure 3C) indicated that the vector angle remained below 45° across all treatments, suggesting that N limitation influenced the soil microorganisms of the Moso bamboo forests [22,60]. Scatter plots of enzyme activity stoichiometry further demonstrated (Figure 3A) that C and N limited the growth of soil microorganisms in Moso bamboo forests under all treatments. Additionally, we observed that the application of N fertilizer reduced the vector length and increased the vector angle, implying that N fertilization could mitigate the C and N limitations faced by soil microorganisms in the study area. Sinsabaugh et al. reported global mean values of C:Ne and N:Pe ratios at 0.62 and 0.44, respectively [17]. Our study’s C:Ne and N:Pe ratios were higher than these global averages, indicating enhanced activities of BG and LAP + NAG. In accordance with the “resource allocation theory” and the “optimal allocation principle”, nutrient addition alters soil enzyme activities, prompting microorganisms to secrete extracellular enzymes to address resource scarcities, thereby alleviating their own nutrient limitations [61,62]. Therefore, higher activities of BG and LAP + NAG indicate a greater microbial demand for C and N.

Zeng et al. employed enzyme stoichiometric vector characterization to examine soil microbial nutrient limitation subsequent to N application (specifically NH₄NO₃ application) in Moso bamboo forests located in Fujian, China. Their findings revealed that both C and P limitations were experienced by microbes in Moso bamboo forests, with the limitations exacerbated by the application of N [32]. Zhang et al. found that soil microorganisms in intensively managed (N, P, and K fertilization) bamboo forests in Zhejiang, China, and in control (non-managed) bamboo forests were both C and N limited [31]. A comparison of the above studies reveals that nutrient addition did not change the soil microbial nutrient limitation pattern compared to the control but rather exacerbated or reduced the limitation of a nutrient element on the basis of the control. Regarding bamboo growing regions, the nutrient limitations affecting soil microorganisms in Moso bamboo forests differ across areas. These variations can primarily be attributed to disparities in soil and climatic conditions between these regions. Jing et al. suggested that soil microbial nutrient limitation may be the result of the long-term acclimatization to soil nutrient environments and climates and that short-term effects of nutrient additions on soil stoichiometry may have a microbial nutrient limitation to a lesser extent, a view that could provide a plausible explanation for our findings [63]. Our study found the nutrient limitation status of soil microorganisms in Moso bamboo forests and their response to N application in northern Guizhou. Our results have some limitations in extrapolation due to soil and climatic conditions but provide some theoretical references for the study of microbial nutrient limitation in Moso bamboo forests in different regions.

Random forest regression analysis was used to explore the key environmental factors influencing microbial nutrient limitation. The results showed that MBP is the most critical factor affecting microbial carbon limitation, while N:Ps, AP, C:Ps, N:Pm, TP, and C:Pm also play significant roles in regulating carbon limitation. N:Pm is identified as the most crucial factor influencing microbial N limitation, with C:Pm and C:Ps also playing important roles. This indicates that changes in soil nutrient stoichiometry and microbial biomass stoichiometry caused by fertilization, especially N fertilization, are the main reasons for alleviating microbial carbon and N limitation.

Ecological stoichiometry theory posits that microbial metabolism necessitates the preservation of a balanced C, N, and P stoichiometry within an organism [64,65]. Disruptions in this stoichiometric equilibrium between microbes and their soil environment can prompt microbial responses [66,67]. These responses may include adjustments to the efficiency of elemental utilization of C, N, and P, alterations in the secretion of extracellular enzymes, or modifications in their elemental composition through shifts in community structure [68,69]. Changes in microbial biomass stoichiometry can serve as indicators of changes in microbial nutrient acquisition strategies when there is an imbalance between soil nutrient resources and the microbes’ own nutrient requirements [70,71].

4.3. Shortcomings of the Study and Future Prospects

Our findings illustrate the impact of N input on nutrient limitations for soil microbes in Moso bamboo forests. However, these results were obtained with the addition of phosphorus (P) and potassium (K) fertilizers. Although the no-nitrogen treatment showed minimal differences in soil properties compared to the control, the input of P and K might obscure the effects of N on microbial activity and extracellular enzyme secretion. This makes it challenging to precisely investigate the impact of N fertilization on microbial nutrient limitations in Moso bamboo forest soils.

In routine practices, N fertilizer is typically applied alongside P and K fertilizers. Therefore, precisely exploring the effects of fertilization on microbial activity is crucial for understanding soil carbon turnover and maintaining soil quality. Further research is needed to investigate the mechanisms by which different nutrient elements, either independently or in combination, influence soil microbial metabolism.

5. Conclusions

The results indicate that N fertilization decreased BG activity, increased LAP + NAG activity, and had no significant effect on ACP activity. It also reduced enzyme stoichiometry (C:Ne, C:Pe, and N:Pe). In terms of microbial nutrient limitation, microbial growth was constrained by both carbon and N, and the addition of N alleviated these nutrient limitations. Changes in soil properties induced by fertilization were the drivers of variations in extracellular enzyme activity, enzyme stoichiometry, and vector characteristics. MBP was identified as the most critical driving factor affecting extracellular enzyme activity, enzyme stoichiometry, and microbial carbon limitation, while N:Pm was the most crucial factor influencing microbial N limitation. Nutrient addition, especially N fertilization, altered the nutrient acquisition strategies of soil microbes in Moso bamboo forests. This may change the microbial decomposition process of organic carbon, affecting the sequestration of organic carbon and the maintenance of soil quality in these forests. Our study contributes to understanding the nutrient requirements of microbes in bamboo forest ecosystems, providing a theoretical basis for the efficient use of N fertilizer and sustainable management of Moso bamboo forests. It also offers theoretical insights into the microbial mechanisms of carbon cycling in bamboo forest ecosystems under conditions of high N input.