Response of Soil Enzyme and Plant Stoichiometry to Root Interactions: Insights from Mixed Plantings of Moso Bamboo

Abstract

Root interactions are crucial in regulating soil microbial metabolism and plant nutrient allocation strategies, especially in mixed plantings. However, the effects of mixed planting and direct root contact on soil properties and plant nutrient allocation remain unclear. Thus, we established potted plants with Moso bamboo (Phyllostachys edulis) and Phoebe chekiangensis and created a physical barrier to the root system without blocking chemical communication using four treatments: mixed planting with root segregation (MT), mixed planting without root segregation (MS), pure Moso bamboo with root segregation (BT), and pure Moso bamboo without root segregation (BS). We investigated changes in soil and Moso bamboo nutrient content, soil enzyme activity, and microbial metabolic limitation. The results show that mixed planting and root segregation significantly affected soil and plant nutrient content and soil enzyme activities. Compared to the two pure Moso bamboo treatments, mixed planting increased microbial carbon limitation but decreased microbial nitrogen limitation. Physical segregation between roots increased microbial carbon use efficiency (CUE) compared to no segregation. Random forest analyses revealed that the best predictors of soil C and N limitations and CUE were microbial biomass and dissolved organic nitrogen (DON), respectively. Partial least squares path modeling indicated that mixed planting and root separation, directly and indirectly, affected soil microbial metabolic limitation through their effects on soil nutrients, microbial biomass, and enzyme activities. Carbon limitation significantly increased plant nutrient contents. Our study provides further insights into factors influencing nutrient limitation, CUE, and plant nutrient allocation strategies in mixed Moso bamboo plantations.

1. Introduction

Interactions between plants play an important role in ecosystem regulation [1]. These interactions can significantly affect soil microorganisms, which are important biological components of ecosystems. In turn, changes in soil microorganisms significantly influence soil fertility and nutrient cycling through their metabolic activities as decomposers, including the decomposition and mineralization of organic matter [2,3]. Soil microorganisms release extracellular enzymes that directly mediate microbial metabolism [4]. They participate in the degradation of microbial and plant cell walls, depolymerize macromolecules, drive soil nutrient cycling, and serve as key regulators in modulating the speed of decomposition processes [5,6]. Soil microorganisms are often constrained by key nutrients and energy sources [7] and can be impacted by changes in soil physico-chemical properties, climate, and other environmental elements [2,8]. These factors prompt soil microorganisms to adapt their metabolic processes by changing the proportion of extracellular enzyme secretion [9]. Moorhead et al. [10] proposed a measure to describe microbial metabolic limitation by using the activities of C, N, and P acquisition enzymes to calculate the length and angle of the vector, providing an assessment tool for metabolic limitations in soil microorganisms [11]. Microbial metabolism can be measured by the microbial carbon use efficiency (CUE) indicator [12]. Ref. [13] demonstrates that it can be calculated from soil enzyme activities, microbial biomass, and soil nutrient stoichiometry. Changes in extracellular enzyme activities and their stoichiometric ratios can be used to investigate the nutrient status and requirements of soils and can help to describe microbial nutrient requirements and resource acquisition strategies [14,15].

Roots act as the bridge between the plant and the soil, establishing relationships and serving as the key organ for plant interactions [16]. Many studies have demonstrated the influence of mixed plantings on soil nutrients and microorganisms. For example, Yang et al. [17] demonstrated that different intercropping systems can regulate soil stoichiometric ratios and microbial metabolic limitations. In addition, changes in soil properties in mixed planting systems can result from root distribution patterns and root secretions, which can, in turn, affect the plants. Some researchers have reported that plants may be able to detect neighboring plants by resource and non-resource methods [18]. Many researchers have used mesh, which does not prevent soil chemical and microbiological communication, to explore the effects of root contact. In soybean–cotton intercropping systems, root distribution and direct contact are the main factors influencing nitrogen transfer and dry matter in the aboveground part of cotton [19]. Similarly, in the maize and soybean intercrop system, direct root contact influences the dry matter quality of the above and below-ground parts of both soybean and maize [20]. However, these previous studies primarily focused on the effects of root direct contact on plant dry matter and chemosensory substances; research on the effects of root contact on soil microbial metabolic limitation and plant nutrient allocation strategies is scarce.

Moso bamboo (Phyllostachys edulis) is a fast-growing plant widely distributed in China [21]. The long-term cultivation of Moso bamboo decreases the soil enzyme activity, pH, and microbial diversity [22]. Conversely, the introduction of other tree taxa is beneficial to carbon and nitrogen pools in Moso bamboo forest soil and can improve soil quality and ecological functions [23]. Meanwhile, Phoebe chekiangensis is a very valuable tree species, and many farmers plant it together with Moso bamboo to enhance the economic benefits [24]. However, Moso bamboo is known for its rapid clonal growth through its root system [25], and its robust underground system enables strong interactions with other plants in mixed plantings. Environmental factors, the species composition of plant communities, and root interactions can influence soil enzyme activities and CUE, altering the metabolic constraints on soil microorganisms [26,27].

Therefore, in the present study, we aimed to investigate the effects of root segregation and mixed planting on soil nutrients, microbial metabolism, and nutrient allocation strategies of Moso bamboo. To this end, we established a long-term pot experiment with a mixed planting of Moso bamboo and Phoebe chekiangensis. Based on this, barriers were established between the roots of different plants to investigate the influence of introducing other tree taxa into the Moso bamboo on soil nutrient limitations. We hypothesized that (1) mixed planting of Moso bamboo and Phoebe chekiangensis alters soil chemical properties and affects soil enzymes and their stoichiometric ratios; (2) mixed planting helps alleviate soil microbial nitrogen limitation but exacerbates microbial carbon limitation; (3) physical segregation between roots affects microbial CUE; and (4) soil microbial carbon limitation is an important factor influencing plant nutrient content. In this study, we aimed to (1) identify the effects of mixed planting of Moso bamboo and Phoebe chekiangensis on soil enzymes, their stoichiometry, and soil microbial metabolic limitation; (2) examine new factors influencing and methods to improve CUE; and (3) explore the pathways of mixed planting and root system effects on plant nutrient distribution strategies.

2. Materials and Methods

2.1. Experimental Site

The study site was located in Yuhang District, Hangzhou City, Zhejiang Province, China (30°18′ N, 119°53′ E). The study area has an average annual temperature of 18.2 °C; precipitation of 1632 mm; annual sunshine of 1611 h; and a frost-free period of 314 d. The climate is subtropical monsoon.

2.2. Experimental Design

The pots were set up for the experiments in May 2021. Pots with a diameter of 60 cm and a height of 50 cm were used for the experiment and were divided into two treatment groups. One of these had 30 μm mesh of nylon, which allowed for the interaction between chemicals and microbes; however, this prevented physical contact between roots inside the pot to separate the soil into a central area of 28 cm in diameter and an external area [28]. The others were not separated using mesh. In the center of each pot was a cluster of Moso bamboo. Four Phoebe chekiangensis or Moso bamboo trees were planted in the outer pots in a centrally symmetrical arrangement (Figure 1). This experiment comprised four treatments: using mesh segregation and planting Phoebe chekiangensis in the outer ring (MT); no mesh segregation and planting Phoebe chekiangensis in the outer ring (MS); using mesh segregation and planting Moso bamboo in the outer ring (BT); and no mesh segregation and planting Moso bamboo in the outer ring (BS). Each treatment was replicated five times, totaling twenty pots. The potting soil was collected from a Moso bamboo forest under conventional management conditions near the experimental site, which has montane red soil and is acidic, and thoroughly mixed after removing plant tissues. The pots consisted of black plastic root control containers designed with a grid-like structure, including internal vertical ridges and perforations. The Moso bamboo and Phoebe chekiangensis used in the experiment were all one-year-old seedlings that had undergone a hardening-off period of over 90 d. All the potted plants were completely randomized with regular weeding and watering, and no fertilizer was applied.

2.3. Sample Collection

In April 2024, the potted plants were dismantled, and the soil and plant samples were collected. For pots with a mesh, the outer shell was first removed, the outer soil was discarded, the central portion was transferred to a clean area, the mesh was removed, and the soil from the central portion was collected. For potted plants without mesh, the outer shell was removed first, followed by the separation of the outer roots and soil. The soil surrounding the central bamboo root was then collected. Soil from the same area in each pot was thoroughly mixed to form a single sample. Visible debris and plant and animal remains were removed from the collected soil by sieving through a 2 mm sieve. Soil for measuring microbial biomass was stored at 4 °C. The soil was air-dried before the determination of chemical properties and enzyme activity. Plant samples were collected during pot disassembly. First, the above-ground parts of the Moso bamboo were collected and cut at the stem bottom. The leaves were immediately removed from the stem to prevent nutrient transfer. The roots were collected during the soil collection process. The leaves, stems, and roots of each potted plant were separately placed in Ziplock bags and transported to the laboratory, where they were dried and used to measure the carbon, nitrogen, and phosphorus contents.

2.4. Analysis of Soil Biotic and Abiotic Indicators

Soil pH was determined using the glass electrode of pH meter using a 1:2.5 (w/v) soil–water suspension. The Kjeldahl method was used to determine the total soil nitrogen (TN) content [29]. Determination of microbial biomass carbon (MBC), microbial biomass phosphorus (MBP), and microbial biomass nitrogen (MBN) was carried out using chloroform fumigation and leaching. This method uses differential values after fumigation of fresh soil with chloroform [30,31]. MBC, MBN, dissolved organic carbon (DOC), and dissolved organic nitrogen (DON) were extracted using K₂SO₄ solution. A TOC analyzer (Multi N/C 3100; Analytik Jena, Germany) was used to determine the concentration of total carbon (TC) and the properties mentioned above [32]. C and N contents in plants were determined using an elemental analyzer (Elementar Scientific Instruments, Hanau, Germany). MBP and soil available phosphorus (AP) were extracted using NaHCO₃ solution. Soil total phosphorus (TP) and plant phosphorus were extracted using the HClO₄-H₂SO₄ digestion method. The molybdenum–antimony colorimetric method was used to measure the content of the aforementioned indicators [33]. The conversion factors for MBC, MBN, and MBP were set to 0.45, 0.45, and 0.4, respectively [34,35]. Five soil enzyme activities, including β-1,4-glucosidase (BG) and β-D-cellobiosidase (CBH), related to carbon acquisition; β-1,4-N-acetylglucosaminidase (NAG) and leucine aminopeptidase (LAP), related to nitrogen acquisition; and the phosphorus-acquiring enzyme acid phosphatase (ACP) were determined using the microtiter plate fluorescence method [36,37]. The unit of enzyme activity was defined in μmol·h⁻¹·g⁻¹ dry soil.

2.5. Calculation of Microbial Metabolic Limitation and CUE

Calculations based on the vector model using the relative ratios of C vs. N- and C vs. P-acquiring enzyme activities were used to analyze the resource limitations occurring during microbial metabolism [10,37,38]. The calculation process was as follows:

$$\mathrm{V}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r} \mathrm{l}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}={({\mathrm{x}}^2+{\mathrm{y}}^2)}^{0.5}$$

(1)

$$\mathrm{V}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{e}\mathrm{r} \mathrm{a}\mathrm{n}\mathrm{g}\mathrm{l}\mathrm{e}=\mathrm{D}\mathrm{e}\mathrm{g}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{s}\left\{\mathrm{A}\mathrm{T}\mathrm{A}\mathrm{N}2(\mathrm{x},\mathrm{y})\right\}$$

(2)

$$\mathrm{x}={\displaystyle \frac{\mathrm{C}-\mathrm{a}\mathrm{c}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{g}}{\mathrm{C}-\mathrm{a}\mathrm{c}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{g}+\mathrm{P}-\mathrm{a}\mathrm{c}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{g}}}$$

(3)

$$\mathrm{y}={\displaystyle \frac{\mathrm{C}-\mathrm{a}\mathrm{c}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{g}}{\mathrm{C}-\mathrm{a}\mathrm{c}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{g}+\mathrm{N}-\mathrm{a}\mathrm{c}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{g}}}$$

(4)

where C-acquiring enzyme activity is (CBH + BG), P-acquiring enzyme activity is ACP, and N-acquiring enzyme activity is (LAP + NAG). The values of x and y in the formula can be calculated using various methods. Following the method in [10], we used the original relative ratio of C- to N- or to P-acquiring enzyme to define it without further transformations. The vector length was related to carbon limitation, with severe carbon limitation being seen when the length was greater. The vector angle was related to microbial N/P limitation, with <45° representing nitrogen and >45° representing phosphorus limitations, and with values deviating more from 45° representing greater limitation.

Based on the method in [39], soil microbial CUE can be calculated as follows:

$$\mathrm{C}\mathrm{U}\mathrm{E}={\mathrm{C}\mathrm{U}\mathrm{E}}_{\mathrm{m}\mathrm{a}\mathrm{x}}\times \sqrt{({\mathrm{S}}_{\mathrm{C}:\mathrm{N}}\times {\mathrm{S}}_{\mathrm{C}:\mathrm{P}})/\left[({\mathrm{K}}_{\mathrm{C}:\mathrm{N}}+{\mathrm{S}}_{\mathrm{C}:\mathrm{N}})\times ({\mathrm{K}}_{\mathrm{C}:\mathrm{P}}+{\mathrm{S}}_{\mathrm{C}:\mathrm{P}})\right]},$$

(5)

$${\mathrm{S}}_{\mathrm{C}:\mathrm{N}}={\mathrm{M}}_{\mathrm{C}:\mathrm{N}}\times \left[1/{(\mathrm{L}}_{\mathrm{C}:\mathrm{N}}\times \mathrm{E}\mathrm{E}{\mathrm{A}}_{\mathrm{C}:\mathrm{N}})\right],$$

(6)

$${\mathrm{S}}_{\mathrm{C}:\mathrm{P}}={\mathrm{M}}_{\mathrm{C}:\mathrm{P}}\times \left[1/{(\mathrm{L}}_{\mathrm{C}:\mathrm{P}}\times \mathrm{E}\mathrm{E}{\mathrm{A}}_{\mathrm{C}:\mathrm{P}})\right]$$

(7)

KC:N and KC:P are the half-saturation constants of CUE based on the availability of C, N, and P, both considering the value of 0.5.

2.6. Statistical Analysis

Excel 2016 was used to collate all the data; a one-way analysis of variance (ANOVA) was performed on the data, such as soil properties, enzyme activities, and microbial variables, using SPSS 26.0. Relationships between data were determined using Pearson’s correlation coefficients. The ‘Vegan’ package in R4.3.3 was used to perform redundancy analysis (RDA), the ‘randomForest’ and ‘rfPermute’ packages in R4.3.3 were used to perform random forest analysis, and the ‘plspm’ package in R4.3.3 was used to perform partial least squares path modeling (PLS-PM). Visualizations were created using the ‘ggplot2’ package in R and Origin 2025 software.

3. Results

3.1. Soil Chemistry and Microbial Biomass

Mixed planting considerably affected the soil properties and microbial biomass (Figure 2). Compared to BT and BS, soil TC, TN, DOC, and DON were significantly (p < 0.05) reduced in MT and MS. However, the soil TP and AP contents were significantly (p < 0.05) enhanced. The soil TC, TN, and DON contents reached their maximum in BT. Meanwhile, BS had the lowest MBC and AP contents. The contents of MBP, MBN, DON, and TN were all significantly influenced by the mesh type, with MT > MS and BT > BS (p < 0.05). Soil pH was significantly (p < 0.05) lower in the BT than in the others (Figure S1a).

The four treatments significantly affected the soil stoichiometric ratios (Figure 2). Mixed planting (MT and MS) significantly (p < 0.05) reduced Soil_C:P, Soil_N:P, L_C:P, and L_N:P, and all the above indexes were minimized under the MS treatment at 14.11, 1.35, 1.26, and 1.93, respectively. M_N:P was significantly (p < 0.05) higher in the MT and MS treatments than in the BT and BS treatments. The mesh influenced the values of Soil_C:P, Soil_N:P, L_N:P, M_C:N, M_C:P, and M_N:P, except M_N:P showed MT > MS, BT > BS. And M_N:P ordered from largest to smallest is MS > MT > BS > BT.

3.2. Soil Enzyme Activities and Stoichiometric Ratios

BS had the lowest and MT had the highest BG activities, respectively, with the order from highest to lowest being MT > BT > MS > BS (Figure S1c). CBH activity was significantly (p < 0.05) reduced at MT than at MS and BT (Figure S1b). Overall, C-acquiring enzyme activity was significantly (p < 0.05) reduced in BS than under the other three treatments. No difference was observed in C-acquiring enzyme activity among MT, MS, and BT (Figure 3a). Mixing significantly (p < 0.05) affected soil LAP activity, with LAP activity being significantly (p < 0.05) higher in BT and BS than in MT and MS (Figure S1d). NAG activity was significantly (p < 0.05) higher in the BT than in the other three treatments (Figure S1e). Overall, BT had the highest soil N-acquiring enzyme activity, with no significant (p < 0.05) differences between the remaining three treatments (Figure 3b). Soil P-acquiring enzyme activity reached a maximum at BT (Figure 3c). Compared with pure Moso bamboo, mixed planting increased EES_{C: N} and EES_{C: P}, both of which had minimum values in BT and maximum values in MS and MT (Figure 3d,e). No considerable differences were observed in the EES_{N: P} for MT, MS, BT, and BS (Figure 3f).

3.3. Plant Nutrient Content and Stoichiometry

Mixed planting significantly (p < 0.05) increased the carbon content of the stems. Compared to the other three treatments, BT exhibited the lowest leaf carbon content, while MT had the highest root carbon content. The nitrogen content both the in leaves and stems was the highest in BT, followed by BS, MS, and MT. However, in the roots, the nitrogen content was the highest in BS, followed by BT, MS, and MT. Notably, mixed planting significantly (p < 0.05) reduced the nitrogen content in all parts of the plant. In pure Moso bamboo pots, the phosphorus content was high in the leaves and roots. In contrast, the phosphorus content was affected by the mesh in the stems and roots and was higher in MT and BT than in MS and BS, respectively. Notably, mixed planting increased the C:N ratio in all parts of the Moso bamboo and the C:P ratio in the leaves and roots. The mesh significantly (p < 0.05) affected the nutrient stoichiometry ratios of both the stems and roots. In particular, except for the C:N ratio in the stems, both stem and root C:P ratios and the root N:P ratio were higher in MS and BS than in MT and BT, respectively (Figure 4).

3.4. Microbial Metabolic Limitation and Carbon Use Efficiency

Mixing significantly (p < 0.05) affected microbial C and N limitation (Figure 5). Mixing increased the vector length (Figure 5a), indicating enhanced soil microbial carbon limitation. All four treatments exhibited N limitation with vector angles less than 45°. However, mixed planting increased the vector angle, suggesting a reduction in soil microbial nitrogen limitation. The MS and MT treatments reduced soil N limitation. A positive and significant (p < 0.001) correlation was observed between C and N limitation (Figure 5d). Root separation significantly (p < 0.05) influenced CUE (Figure 5c). Microbial CUE was the highest under BT and the lowest under BS, and both showed MT > MS and BT > BS.

3.5. Relationship Between Soil Enzyme Activity and Chemical Properties

Soil enzyme activities for N and P acquisition were significantly and negatively correlated with pH, TP, MBN, L_C:N, and M_N:P (p < 0.05) and correlated positively with soil TC, TN, DON, Soil_C:P, Soil_N:P, L_N:P, and M_C:N (Figure 6). A significant (p < 0.05) negative correlation between soil C-acquiring enzyme activity and DOC and L_C:P and a significant (p < 0.05) positive correlation between C-acquiring enzyme activity and CUE, MBC, and M_C:P (Figure 6) were observed. The Pearson correlation between soil carbon limitation and soil chemical properties was similar to that between soil nitrogen limitation and soil chemical properties. There positive correlations were observed between soil pH, TP, AP, MBN, Soil_C:N, L_C:N, and M_N:P with soil C and N limitation (p < 0.05). Moreover, soil TC, TN, Soil_C:P, Soil_N:P, DOC, DON, L_C:P, L_N:P, and M_C:N exhibited significant (p < 0.05) negative correlations with soil C and N limitation. The nitrogen and phosphorus contents in the plant parts were significantly and positively correlated with TC, TN, DOC, and DON and negatively correlated with pH, TP, and AP (p < 0.05). In contrast, the carbon contents of plant leaves and stems exhibited positive correlations with pH, TP, and AP, and negative correlations with TC, TN, DOC, and DON. A redundancy analysis (RDA, Figure 7) indicated that environmental factors explained 93.62% of the changes in soil C and N limitation and CUE (RDA1, 58.24%; RDA2, 35.58%) and 86.71% of the changes in plant carbon, nitrogen, and phosphorus contents (RDA1, 62.43%; RDA2, 24.28%).

3.6. Drivers of Soil Microbial and Plant Indicators

The random forest model demonstrated that DON, L_N:P, pH, Soil_C:P, Soil_N:P, and TN were significant (p < 0.05) factors showing microbial carbon limitation (Figure 8a). DON, MBN, and L_C:P were the most crucial factors affecting microbial nitrogen limitation (Figure 8b). M_C:P was the most important factor affecting CUE, with MBC, DOC, TN, and L_C:P also exhibiting a significant (p < 0.05) effect. M_C:N and AP had a highly significant (p < 0.01) effect on CUE (Figure 8c). Partial least squares path modeling (PLS-PM; Figure 9) revealed direct and indirect effects of mixed planting and root segregation on total soil nutrients, available nutrients, microbial variables, soil enzyme activities, microbial carbon limitation, nitrogen limitation, and plant nutrient content. Mixed planting exhibited a significant (p < 0.001) negative effect on total and available soil nutrients (path coefficients = −0.930 and −0.961), while root segregation had a positive effect on total and available soil nutrients (path coefficients = 0.286 and 0.167). Soil available nutrients directly influenced microbial biomass (path coefficient = −0.926), while soil total nutrients exerted a direct positive effect on soil enzyme activity (path coefficient = 0.734). Soil carbon limitation was directly influenced by soil microbial biomass and enzyme activity (path coefficients = 0.211 and −0.813) and had a direct positive effect on plant nutrients (path coefficient = 0.993). Soil nitrogen limitation was directly and negatively affected by enzyme activity (path coefficient = −0.833). Soil nitrogen limitation was directly and negatively affected by enzyme activity (path coefficient = −0.833).

4. Discussion

4.1. Factors Influencing Soil Chemistry, Microbial Biomass, and Enzyme Activity

Plant interactions can change soil enzyme activity and nutrient content through root interactions and secretions [27]. Here, we investigated the effects of mixed planting of Moso bamboo and Phoebe chekiangensis and physical root segregation on the soil nutrient content and enzyme activity through pot experiments. Mixed planting was the primary factor influencing the soil properties. Mixed planting significantly (p < 0.05) changed the total and available nutrient contents of the soil and their stoichiometric ratios (Figure 2). Litter, root secretions, and detritus nutrient return are the primary ways through which plants provide fresh carbon for microorganisms [40,41]. Moso bamboo has a more developed and robust root system than that of trees [42], which can explain the higher TC and DOC in the BS and BT. Nutrient changes induced by plant interactions are also regulated by competition intensity [43]. Excessive competition intensity has negative effects and counteracts increases in the soil nutrient content [44]. Excessive competition intensity may explain the significantly (p < 0.05) lower TC, TN, DON, and AP values under the BS treatment than those under the BT treatment. Differences in the stoichiometric ratios of the soluble fractions of different substrates can be reflected in the microbial community [45]. The stoichiometric ratios of microbial biomass may be altered by nutrient limitations, such as N and P [46]. In this study, the stoichiometric ratios of MBN, MBP, and MBC were less than those of microbial biomass on a global scale, as shown in [47]. This may be because of the effects of mixed planting and root segregation on soil nutrient supply, which, in turn, affects the soil microbial community.

A global meta-analysis of enzymes that acquire C, N, and P [48] showed that the global ratio of these three types of enzymes was nearly 1:1:1. In our study, EES_C:N, EES_C:P, and EES_N:P deviated from 1. This indicates that the microbial strategies for investing in C-, N-, and P-acquiring enzymes changed with alterations in resources [49]. Microorganisms acquire resources through the secretion of enzymes. When resources are readily available, they follow the ‘economic rules’ by reducing enzyme yield [50,51]. However, under resource limitations, enzyme activities will be consistent with the availability of the corresponding resource [2,7]. Our study’s soils exhibited N and slight C limitations. A positive relationship was observed between N-acquiring enzymes and DON and a negative relationship between C-acquiring enzymes and DOC. Microorganisms increase soil nitrogen availability by increasing the secretion of nitrogen-acquiring enzymes to reduce nitrogen limitation. In this experiment, phosphorus resources were relatively abundant and easily available. Meanwhile, P-acquiring enzyme activities showed a negative correlation with TP and AP (Figure 6). Soil enzymes are nitrogen-rich compounds, and available nitrogen can enhance the synthesis and secretion of enzymes [52]. This explains the positive correlation between P-acquiring enzymes, TN, and DON.

4.2. Factors Influencing Microbial Metabolic Limitation and CUE

In the vector model, the vector lengths of MS and MT were significantly (p < 0.05) larger than those of BS and BT. This indicates that mixing had more severe carbon limitation [10]. The spatial expansion and stratification of plants above and below-ground can lead to increased competition for resources between plants [53]. As forests fix more carbon through photosynthesis, excess carbon is provided to the soil through the roots and litter [41]. In this study, the mixed Phoebe chekiangensis was located in the lower space and was strongly affected by the shadows cast by the Moso bamboo canopy. This weakened the photosynthesis of Phoebe chekiangensis, intensified resource competition, and reduced the carbon source obtained by soil microorganisms through root inputs, exacerbating the carbon limitation in the mixed pot plantation. Conversely, the exacerbation of soil carbon limitation by hybridization has not been observed in natural bamboo–broadleaf mixed forests [54]. This may also be due to the fact that potting soil only provides a very limited amount of total nutrients. In the present study, nitrogen limitation, rather than phosphorus limitation, was observed in all four treatments because the vector angles were less than 45° in all treatments. Simultaneously, we observed that the vector angles of the mixed plantings were closer to 45° than those of the pure Moso bamboo treatments. This indicates that nitrogen limitation was reduced. This may be due to greater plant diversity and increased root secretion in mixed planting, which increased nitrogen availability.

CUE reflects the microbial use of carbon [12]. In our study, the physical separation of roots was the dominant factor contributing to changes in CUE. In both the mixed and unmixed pot experiments, the MT and BT plants with physical root segregation exhibited higher CUE. A higher CUE means that less energy is allocated by organisms to respiration and more energy is available for biomass [55]. The physical segregation of roots can alleviate the intense competition between roots, allowing more nutrients to be available to soil microorganisms. Adequate mineral nutrients reduce the cost of nutrient acquisition for microbes, allowing more carbon to be allocated for growth, which manifests as a higher CUE. Soil microbial metabolic limitation and CUE were mainly affected by the soil pH, nutrient content, and stoichiometric ratios [14,56]. In the RDA, environmental factors explained 93.82% of microbial metabolic limitations and CUE. pH is a key factor influencing microbial community activity [57]. Random forest analysis has shown that pH is a key determinant of soil carbon limitation. The ratio of nitrogen to phosphorus is an important ecological stoichiometry that affects plant and microbial growth [58,59]. In our study, the L_N:P ratio was an extremely important predictor of microbial C and N limitation. Carbon and nutrients are important components of microbial metabolism that directly regulate CUE [60]. Duan et al. [61] proposed that changes in the microbial community biomass and specific soil properties could explain changes in CUE. The positive relationship between microbial biomass and carbon limitation may be due to the ‘burial effect.’ Here, soil microorganisms assimilate plant residues into their biomass and regulate soil nutrient pools through the production and deposition of microbial necromass, thereby changing microbial metabolism [62,63]. A higher CUE is associated with a higher microbial product yield, which benefits soil carbon stabilization and alleviates carbon limitation [64].

4.3. Factors Driving Plant Nutrient Distribution Strategies

Plants adopt different strategies for distributing nutrients under different nutrient conditions and adjust their morphology accordingly [65]. In the present study, RDA revealed that the soil nutrient content explained 86.71% of the variation in the plant part nutrient content. Changes in the soil nutrient content are the main factor influencing the plant nutrient content. Moreover, soil TN and DON contents and plant phosphorus contents were significantly (p < 0.05) increased in the BS and BT treatments than in MT and MS. This result may be attributed to increased soil nitrogen promoting fine root growth, which enhances plants’ uptake of phosphorus [66]. This conclusion is further supported by the reduction in the soil phosphorus content under BS and BT, as well as the significant (p < 0.05) positive correlation between soil DON, TN, and plant phosphorus content. In mixed planting, P. chekiangensis occupied the lower space, reducing the competition for light with Moso bamboo leaves compared to pure Moso bamboo potted plants. Competition for sunlight forces leaves to improve their photosynthetic capacity. As N is an important component of various photosynthesis-related enzymes [67], this caused a significant (p < 0.05) increase in the leaf nitrogen content and a significant (p < 0.05) decrease in the C:N ratio in pure Moso bamboo pot plants. According to the adaptive growth hypothesis, an elevated C:N ratio in plants enhances survivability under nitrogen-limited conditions [68]. This provides a competitive advantage for MT and MS Moso bamboo with P. chekiangensis in mixed plantings. Moreover, the higher TC content of MT and MS stems reflects their stronger competitiveness. In this study, the TC content of the stem was negatively correlated with the soil N content, possibly because reduced soil N promotes lignin synthesis in Moso bamboo [69]. This may also lead to reduced carbon input from the roots to the soil, thus decreasing the soil carbon content. The partial least squares pathway model revealed that carbon limitation had a highly significant (p < 0.001) positive effect on plant nutrients. In contrast, mixed planting had an indirect negative effect on plant nutrients through soil nutrients, enzyme activities, and microorganisms. Root segregation exhibited an indirect positive effect on plant nutrients through soil nutrients, enzyme activities, and microorganisms. These findings suggest that microbial limitation is also an important environmental factor influencing plant nutrient allocation strategies.

In this experiment, we used potted plants to simulate mixed cultivation of Moso bamboo and Phoebe chekiangensis and investigated the effects on the soil and plant allocation strategies resulting from mixed planting after using mesh to limit direct contact between plant roots. This experiment provided a better simulation of the initial planting of Phoebe chekiangensis in Moso bamboo forests. However, the small space and limited soil nutrient supply of the pot are not sufficiently accurate to model mature bamboo width in mixed forests. We also lacked an investigation of soil nutrient content changes before and after the experiment. Moreover, while litter is often considered an important input of soil nutrients, its production and accumulation were lacking in this study. The actual occurrence is often more complex under real conditions. Therefore, future research on long-term bamboo–broadleaf mixed forests is important. We also recommend future research on how direct root contact affects root secretion, soil microbial community structure, and microbial necromass turnover.

5. Conclusions

In this study, mixed planting and root segregation significantly (p < 0.05) influenced most soil nutrients and enzyme activities. Mixed planting reduced soil TC, TN, DOC, and DON contents and P-acquiring enzyme activity and increased soil total and available P contents and EES_C:N and EES_C:N ratios. Mixed planting of Moso bamboo and Phoebe chekiangensis exacerbated soil carbon limitation but alleviated soil nitrogen limitation; root segregation contributed to microbial CUE. DON, L_N:P, pH, and Soil_C:P were significant (p < 0.05) predictors of soil carbon limitation, whereas DON, L_C:P, L_N:P, and MBN were significant (p < 0.05) predictors of soil nitrogen limitation. In addition, carbon limitation, soil total nutrient content, and mixed planting were found to be important environmental factors influencing plant nutrient allocation strategies. Our results suggest that appropriate management measures are needed to address potential carbon limitations in mixed bamboo–broadleaf forests, such as increasing carbon sources at the beginning of mixed planting. At the same time, physical root segregation is an effective way to reduce below-ground competition for plants, which provides new insights into increasing CUE. However, studies of long-term mature Moso bamboo mixed forests are needed to clarify the specific mechanisms of how mixed planting affects plant nutrient allocation strategies.