Next Article in Journal
TEB-YOLO: A Lightweight YOLOv5-Based Model for Bamboo Strip Defect Detection
Previous Article in Journal
Soil Hydrological Properties and Organic Matter Content in Douglas-Fir and Spruce Stands: Implications for Forest Resilience to Climate Change
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 16
Issue 8
10.3390/f16081216
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Elevational Patterns and Drivers of Soil Total, Microbial, and Enzymatic C:N:P Stoichiometry in Karst Peak-Cluster Depressions in Southwestern China

by
Siyu Chen
1,
Chaohao Xu
2,
Cong Hu
2,
Chaofang Zhong
2,
Zhonghua Zhang
1,2,* and
Gang Hu
1,2,*
1
Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530100, China
2
Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530100, China
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(8), 1216; https://doi.org/10.3390/f16081216
Submission received: 28 June 2025 / Revised: 21 July 2025 / Accepted: 22 July 2025 / Published: 24 July 2025
(This article belongs to the Section Forest Soil)
Browse Figures
Versions Notes

Abstract

Elevational gradients in temperature, moisture, and vegetation strongly influence soil nutrient content and stoichiometry in mountainous regions. However, exactly how total, microbial, and enzymatic carbon (C), nitrogen (N), and phosphorus (P) stoichiometry vary with elevation in karst peak-cluster depressions remains poorly understood. To address this, we studied soil total, microbial, and enzymatic C:N:P stoichiometry in seasonal rainforests within karst peak-cluster depressions in southwestern China at different elevations (200, 300, 400, and 500 m asl) and depths (0–20 and 20–40 cm). We found that soil organic carbon (SOC), total nitrogen (TN), and the C:P and N:P ratios increased significantly with elevation, whereas total phosphorus (TP) decreased. Microbial phosphorus (MBP) also declined with elevation, while the microbial N:P ratio rose. Activities of nitrogen- (β-N-acetylglucosaminidase and L-leucine aminopeptidase combined) and phosphorus-related enzymes (alkaline phosphatase) increased markedly with elevation, suggesting potential phosphorus limitation for plant growth at higher elevations. Our results suggest that total, microbial, and enzymatic soil stoichiometry are collectively shaped by topography and soil physicochemical properties, with elevation, pH, and exchangeable calcium (ECa) acting as the key drivers. Microbial stoichiometry exhibited positive interactions with soil stoichiometry, while enzymatic stoichiometry did not fully conform to the expectations of resource allocation theory, likely due to the functional specificity of phosphatase. Overall, these findings enhance our understanding of C–N–P biogeochemical coupling in karst ecosystems, highlight potential nutrient limitations, and provide a scientific basis for sustainable forest management in tropical karst regions.

1. Introduction

Ecological stoichiometry integrates the fundamental principles of biology, stoichiometry, and other relevant disciplines, providing a comprehensive approach to revealing elemental coupling relationships among ecological processes [1]. Carbon (C), nitrogen (N), and phosphorus (P) are essential constituents of cellular structures [2]. Within ecosystems, the cycling and transformation of matter create intricate links between the soil, microbes, and enzymes, forming an organic whole [3,4].
Soil serves as an important nutrient reservoir for C, N, and P [5]. The stoichiometric ratios of these elements serve as vital indicators of soil nutrient status and quality, playing a central role in regulating biogeochemical cycling [5]. Soil microbes convert complex organic substances into simple inorganic substances and significantly alter the soil C:N:P ratio, which reflects nutrient fixation and utilization levels [6,7]. Soil enzymes—biologically active compounds released by microbes and plant roots—are key drivers of soil biogeochemical cycling. Variations in the activities of enzymes responsible for mineralizing soil C, N, and P can indicate stoichiometric and energetic differences linked to nutrient limitation and microbial biomass growth [8]. Consequently, the relationships among soil enzyme activities provide valuable insights into the structural and functional dynamics of soil biogeochemical cycles [4,9]. Studies on soil C:N:P stoichiometry, microbes, and enzymes have typically focused on individual components [10,11,12] or on interactions between two aspects, such as soil and extracellular enzymes [13] and soil and microbes [5]. In contrast, research simultaneously addressing all three components is lacking, and their stoichiometric relationships remain unclear.
Soil nutrients, microbial biomass, and enzyme activities are strongly shaped by environmental factors, including topography, hydrothermal regimes, plant community composition, and soil physicochemical characteristics [5,11,14,15,16]. Elevation, as a fundamental geographic variable, influences environmental factors such as hydrothermal regimes, plant distribution, and soil physical properties. It can induce gradient effects in regional ecosystems, thereby directly or indirectly affecting the distribution of soil nutrients, microbial biomass, and enzyme activities [17]. Recent studies on karst region ecological stoichiometry have focused primarily on vegetation or land-use type [5,12,15,18], with fewer addressing the effects of elevation and, in particular, its effects on the soil and its microbes and enzymes [19,20]. For instance, Bin et al. (2022) [21] reported an increase in soil C, N, and P contents with elevation, whereas Liu et al. (2022) [20] reported that soil C and P initially increased, then declined, and finally increased again with elevation, with soil N showing a nonsignificant change.
As with soil nutrient content, soil microbial biomass and enzyme activity vary in response to elevation. Soil microbial P initially decreases and then increases with elevation [22], while microbial C shows the opposite trend [23] or no significant change [22]. Soil enzyme activity decreases or initially increases then decreases with elevation [20]. Soil C, N, and P stoichiometric ratios do not vary consistently with elevation, potentially owing to differences in geographical location, elevation range, climate characteristics, and community structure [20,21,22]. Elevational patterns of soil total, microbial, and enzymatic C:N:P stoichiometry thus vary between sources and remain open to debate [19]. For karst peak-cluster depression areas, the effects of elevation on these factors and the drivers of variability remain to be clarified.
Karst, a major landform type, covers approximately 15% of the Earth’s land surface and exhibits relatively sparse soil, low biodiversity, and fragile habitats [24]. China has the widest distribution and largest area of karst, covering 5.8% of its land area, mainly concentrated in Guangxi, Guizhou, and Yunnan Provinces [25]. Peak-cluster depressions, a unique karst landscape located in the southwestern karst regions of China, cover an area of approximately 97,000 km2 [26,27]. Unlike many other karst regions, tropical karst peak-cluster depressions are covered by seasonal rainforests with relatively high local biodiversity. Their forest community structure is complex, with marked vertical vegetation changes along elevation gradients. Tree species that are drought-tolerant, light-loving, and adapted to infertile conditions are primarily distributed at higher elevations, while flood- and shade-tolerant species dominate at lower elevations. These forests harbor numerous rare and endangered plant and animal species, making them highly valuable for conservation initiatives. Previous research has mainly explored plant community ecology, emphasizing community structure, biomass, productivity, and ecosystem functions [28,29,30], along with soil physicochemical characteristics and microbial communities [31,32,33]. Nonetheless, the elevational patterns of soil nutrients and their stoichiometry in karst seasonal rainforests remain to be elucidated. Understanding soil C:N:P stoichiometry in these forests is crucial for assessing nutrient limitations, predicting ecosystem responses to climate change, and informing reforestation and conservation strategies. Moreover, these seasonal rainforests provide vital ecological services and cultural value to local communities, making the study of their nutrient dynamics both environmentally and socioeconomically significant.
To address this, we collected soil samples at elevations of 200 to 500 m in tropical karst peak-cluster depressions of southwestern China and measured soil C, N, and P contents, microbial biomass C, N, and P content, and the activities of enzymes associated with the microbial acquisition of these elements at two soil depths (0–20 and 20–40 cm). Our objective was to reveal elevational patterns in soil total, microbial, and extracellular enzymatic C:N:P stoichiometry as well as the drivers affecting stoichiometry. We first tested the hypothesis that these stoichiometric patterns change significantly along an elevational gradient. Second, based on resource allocation theory [34,35], we tested the hypothesis that microbes adjust their stoichiometry and their enzymatic stoichiometry to adapt to variations in soil stoichiometry. This study reveals elevational variation in soil nutrient content, providing a scientific basis for vegetation restoration, sustainable development, and comprehensive rocky desertification management in karst peak-cluster depression areas.

2. Materials and Methods

2.1. Study Site

The study area was located in the Nonggang National Nature Reserve in southern Guangxi Province, China (106°42′28″–107°04′54″ E, 22°13′56″–22°39′09″ N, Figure 1). The reserve covers an area of 10,077 hectares and features a karst peak-cluster depression landform comprising numerous stone peaks surrounding enclosed depressions (Figure 1). The average peak elevation is 400–500 m asl, while the average elevation of the depressions is 150–200 m asl. The peak density is 10–30 peaks/km2, and the maximum depression width is 450 m. The proportion of exposed rock is 10%–95%. The soil is developed from calcareous limestone, which is rich in calcium and slightly alkaline. The vegetation type is northern tropical karst seasonal rainforest, dominated primarily by evergreen broadleaved trees, accounting for >80% of the plant species in this forest type. The study area exhibited strong habitat heterogeneity and high species richness. The reserve harbors one of the rarest and most well-preserved karst tropical seasonal rainforests, making it one of the 14 key areas of international significance for terrestrial biodiversity in China [36]. It is home to 1752 vascular plant species in 810 genera and 184 families, with conservation targets including the northern tropical seasonal rainforest ecosystem, as well as rare animals such as Trachypithecus poliocephalus (Trouessart, 1911) and Trachypithecus francoisi (Pousargues, 1898) and rare plants such as Excentrodendron tonkinense (A.Chev.) Hung T. Chang & R. H. Miau, Garcinia paucinervis Chun & How, and Camellia petelotii (Merr.) Sealy. The region has a northern tropical monsoon climate, strongly influenced by the Pacific monsoon, characterized by distinct wet and dry seasons. The mean annual temperature is approximately 22 °C, with the coldest month averaging above 13 °C and seven months exceeding 22 °C. Annual sunshine duration ranges from 1500 to 1800 h, while annual precipitation varies between 1150 and 1550 mm, reaching a maximum of 2043 mm and a minimum of 890 mm [27,36].

2.2. Soil Sampling

Soil sampling was conducted in July 2022 at the peak of the rainy season, when microbial activity and nutrient fluxes are generally most dynamic in subtropical monsoonal climates. Four sampling sites were established along an elevational gradient (200, 300, 400, and 500 m asl) within a peak-cluster depression in the study area. At each elevation, three 20 m × 20 m plots with minimal human disturbance were randomly established, each spaced over 30 m apart. Within each plot, soil samples were collected using a five-point composite method from two depth intervals: 0–20 cm and 20–40 cm. The chosen depths reflected the active rooting zone and the soil layers with the highest microbial activity and nutrient turnover. Although deeper horizons (e.g., argillic or petrocalcic layers) may exist below 40–50 cm in some karst soils, our preliminary field survey indicated high heterogeneity and frequent lithic contacts below 50 cm, with limited continuous soil profiles. Therefore, to ensure consistency across all plots and to focus on biologically active layers, we limited sampling to the upper 40 cm. Soil samples for water content (SWC) analysis were collected with a 100 cm3 ring cutter. Samples were kept in a thermostatic container during transport to the laboratory, where stones and roots were removed, and the soil was passed through a 2 mm sieve. Each sample was then split into two subsamples: one was refrigerated at 4 °C for microbial biomass (C, N, P) and enzyme activity assays, while the other was air-dried, ground, and analyzed for chemical properties. In total, 24 soil samples (4 elevations × 2 soil layers × 3 plots) were collected. Latitude, longitude, slope, aspect, and tree diameter at breast height (for diameters > 1 cm) were recorded at each elevation (Table A1).

2.3. Soil Measurements

Soil physical and chemical properties measured included SWC, soil pH, and elemental content. SWC was measured via oven-drying (DHG-9070A, Shanghai Yiheng Scientific Instrument Co., Ltd., Shanghai, China), and soil pH was measured via potentiometry using a pH meter (CS-93003, HORIBA Advanced Techno Co., Ltd., Kyoto, Japan). Soil organic carbon (SOC) was measured via the Walkley–Black method (K2Cr2O7–H2SO4 oxidation), which quantifies organic carbon but does not account for inorganic carbon fractions. Given the shallow and organic-rich nature of the surface soils (0–40 cm), the potential interference from CaCO3 was considered negligible. Total N and P (TN and TP, respectively) were determined using the semimicro Kjeldahl method and NaOH digestion, respectively. The ammonium acetate exchange–EDTA complex titration method was used to determine exchangeable Ca (ECa), and ammonium acetate exchange–atomic absorption spectrophotometry was used to determine exchangeable magnesium (EMg) using an atomic absorption spectrophotometer (AA-7000, Shimadzu Corporation, Kyoto, Japan) [37] (Bao 2013). Soil microbial biomass C, N, and P were measured using the CHCl3 fumigation method [38]. The activities of C-acquiring β-1, 4-glucosidase (BG), N-acquiring β-1, 4-N-acetylglucosaminidase (NAG) and L-leucine aminopeptidase (LAP), and P-acquiring alkaline phosphatase (AP) were quantified fluorometrically using a microplate reader (SpectraMax i3x, Molecular Devices LLC., San Jose, CA, USA) [39]. The soil C:N (SCN), C:P (SCP), and N:P (SNP) ratios were derived from SOC:TN, SOC:TP, and TN:TP, respectively. Microbial C:N (MCN), C:P (MCP), and N:P (MNP) ratios were determined as MBC:MBN, MBC:MBP, and MBN:MBP, respectively (with ‘MB’ denoting ‘microbial’). Enzymatic C:N (ECN), C:P (ECP), and N:P (ENP) ratios were calculated as BG:[NAG + LAP], BG:AP, and [NAG + LAP]:AP, respectively.

2.4. Statistical Analysis

All variables were tested for normality and homogeneity of variance prior to analysis. One-way ANOVA was then applied to examine differences in soil total, microbial, and enzymatic C–N–P contents and stoichiometric ratios across elevations and soil depths. Multiple comparisons were performed using the Duncan test. Two-way ANOVA was used to examine the effects of elevation and soil depth, as well as their interactions, with a significance level of 0.05. All statistical analyses were performed using SPSS version 24.0 (SPSS Inc., Chicago, IL, USA).
Pearson correlation analyses were conducted to assess the relationships between soil total, microbial, and enzymatic C–N–P contents, their stoichiometric ratios, and associated environmental factors, using the ‘psych’ package in R (v4.1.3, R Foundation for Statistical Computing, Vienna, Austria). To identify and quantify the relative importance of key environmental drivers influencing total, microbial, and enzymatic C:N:P stoichiometry, redundancy analyses (RDA) were performed with the ‘vegan’ and ‘rdacca.hp’ packages.
To further clarify the effects of major drivers on stoichiometry, we developed a structural equation model (SEM) using the ‘piecewiseSEM’ package. The model structure was designed based on prior knowledge. Given the significant correlations among variables within the C:N:P ratios, the first principal component (PC1) scores from a principal component analysis (PCA) were used to replace the original variables, employing the ‘FactoMineR’ package (Table A2). Model fit was evaluated using chi-square tests, p-values, degrees of freedom (df), and Fisher’s C tests, with p > 0.05 considered an acceptable fit [40].

3. Results

3.1. Soil Total C, N, and P Contents and Stoichiometry

With increasing elevation, SOC increased significantly, TN initially increased, and TP decreased significantly (p < 0.05). SOC, TN, and TP declined with soil depth at all elevations, but the decrease was statistically significant only for SOC and TN (Figure 2a–c; Table 1; p < 0.01). SCP and SNP, but not SCN, increased significantly with elevation (p < 0.01). The stoichiometric ratios decreased significantly with soil depth (Figure 2d–f; Table 1; p < 0.05).

3.2. Soil Microbial C, N, and P Contents and Stoichiometry

Soil MBP decreased significantly with elevation (p < 0.01), while MBC and MBN showed no significant changes. Microbial biomass declined with soil depth across all elevations, with MBC and MBN varying significantly by depth (Figure 3a–c; Table 1; p < 0.01). Among the stoichiometric ratios, only MNP was significantly affected by both elevation and depth, increasing with elevation but decreasing with depth (Figure 3d–f; Table 1; p < 0.05).

3.3. Soil Enzymatic Activity and Stoichiometry

Soil BG, NAG + LAP, and AP activities increased with elevation, but the increase was significant only for NAG + LAP and AP (p < 0.05). In contrast, soil enzyme activities declined significantly with depth (Figure 4a–c; Table 1; p < 0.05). ECN, ECP, and ENP showed no significant variation with elevation. With increasing soil depth, ECN and ENP increased significantly (Figure 4d–f; Table 1; p < 0.05), while ECP remained unchanged. The enzyme stoichiometric ratio of ln(BG):ln(NAG + LAP):ln(AP) was 1:1:1.1.

3.4. Drivers of Soil Total, Microbial, and Enzymatic C-N-P Contents and Stoichiometry

SOC was significantly and positively correlated with TN, and both were strongly associated with SWC and EMg. SOC was also positively correlated with ECa. Conversely, TP exhibited negative correlations with elevation and slope but was positively associated with SWC and pH. MBC was positively correlated with MBN, and both were related to SOC, TN, and SWC. MBP exhibited positive correlations with TP, pH, and SWC, but it had negative correlations with elevation and slope. Soil BG correlated positively with SOC, TN, EMg, MBN, and SWC. NAG + LAP was positively associated with MBC, slope, elevation, and species richness, but negatively associated with pH and TP. Soil AP was positively correlated with SOC, TN, BG, EMg, MBC, MBN, slope, and SWC, and negatively correlated with pH (p < 0.05, Figure 5a).
SCN and SCP were significantly positively correlated with SCP and SNP, respectively. SCN also showed significant positive correlations with SNP and EMg. Both SCP and SNP were significantly positively correlated with EMg, elevation, slope, and ECa, but negatively correlated with DBH and pH. SNP exhibited a significant positive correlation with species richness. MCN was significantly negatively correlated with slope and EMg, whereas MCP was positively correlated with SCP and SNP and negatively correlated with pH. MNP showed significant positive correlations with SNP, elevation, slope, EMg, SCN, SCP, and MCP, but a negative correlation with pH. ECN was positively correlated with SCN, ECP, SWC, and EMg, while ENP was significantly negatively correlated with SCN, ECN, SWC, and EMg (p < 0.05, Figure 5b).
RDA1 and RDA2 accounted for 35.48% and 15.06% of the total variance, respectively. Collectively, the environmental factors explained 68.07% of the variation in soil total, microbial, and enzymatic C:N:P stoichiometric ratios. Among these factors, soil pH, elevation, and ECa were the dominant drivers, explaining 33.86%, 24.52%, and 13.14% of the variance, respectively (Figure 6, Table 2).
SEM indicated a good model fit, demonstrating strong relationships among soil total, microbial, and enzymatic C:N:P stoichiometric ratios and the key drivers (elevation, ECa, and pH). These factors collectively explained 75% of the variance in soil C:N:P ratios, as well as 41% and 21% of the variance in microbial and enzymatic C:N:P ratios, respectively. The primary drivers directly influenced soil C:N:P ratios and indirectly affected microbial and enzymatic ratios by regulating soil and microbial stoichiometry (Figure 7).

4. Discussion

4.1. Elevational Patterns of Soil Total C, N, and P Contents and Stoichiometry

Soil properties are shaped by elevation-driven climatic and environmental factors, leading to pronounced spatial heterogeneity [21]. In this study, SOC and TN increased significantly with elevation, whereas TP decreased (Figure 2a–c; Table 1), which is consistent with previous findings [31]. These elevational patterns may reflect a combination of abiotic and biotic processes rather than a single controlling mechanism. From an abiotic perspective, higher exchangeable calcium (ECa) and magnesium (Mg) levels observed in karst soils are often associated with the formation of organo–mineral complexes and soil aggregation, which can reduce the physical accessibility of SOM to decomposers [12,41]. From a biotic perspective, upper-slope areas with greater rock exposure, reduced canopy cover, and lower soil moisture [31] tend to experience slower microbial turnover and reduced enzymatic activity, indirectly slowing SOM decomposition and promoting SOC and TN accumulation [42]. Both mechanisms likely operate simultaneously but with different relative importance along the elevational gradient. Future studies should integrate soil respiration measurements and lignin-depolymerizing enzyme activity data to validate these proposed pathways. Soil P, a sedimentary element derived primarily from rock weathering, exhibits low mobility [41]. In high-elevation areas, the combination of high rock exposure rates, rock fragmentation, and steep slopes can enhance the downslope transfer of P through rainfall-induced erosion [43], leading to P enrichment at lower elevations. This interpretation is supported by the significant positive correlations between SOC and TN and between soil ECa and EMg, as well as the negative correlations between soil TP, elevation, and slope (Figure 5a).
Soil C, N, and P stoichiometry is an effective indicator of nutrient limitation and saturation [44]. SCN is inversely related to the SOM decomposition rate; SCP reflects the potential of SOM to release or immobilize phosphorus through mineralization and is inversely associated with the availability of P as a nutrient. Similarly, SNP indicates the soil’s capacity to supply nutrients for plant growth [16,18]. Here, the mean surface soil SCN, SCP, and SNP values were 10.44 ± 0.56, 53.61 ± 7.33, and 4.8 ± 0.52, respectively (Table A3), below global values (14.3, 76, and 5.9, respectively) [45,46], below the mean values for terrestrial soils in China (11.9, 61, and 5.2, respectively) [47], but higher than the values reported for karst areas by Wang et al. (2018) (9.22, 40.36, and 4.35, respectively) and Lu et al. (2022) (10.07, 23.78, and 2.35, respectively) [48,49]. This reflects a certain degree of C and P limitation and N saturation in the study area, consistent with the results of Chen et al. (2018) [50]. SCP and SNP increased significantly with elevation (Figure 2e,f), reaching maximum values at 500 m (93.1 ± 10.01 and 8.72 ± 0.39, respectively), far above the global and Chinese means, reflecting P limitation at high elevations. This is consistent with prior results [51,52,53]. In alkaline soils, P is prone to forming insoluble phosphates with Ca and Mg ions, reducing the effectiveness of soil P [54,55]. However, in our study, soil TP was negatively correlated with ECa and EMg (Figure 5a), suggesting that other mechanisms may influence P availability, which warrants further investigation.
Here, soil C, N, and P contents and their ratios declined with increasing soil depth (Figure 2), which aligns with observations from subtropical karst regions [49,56] as well as non-karst regions [57,58]. This pattern can be attributed to the accumulation of surface litter and the microbial decomposition of plant and animal residues, which return nutrients to the topsoil and enhance nutrient availability in surface layers [59]. Due to the low mobility and inherent stability of soil P, its vertical distribution remains relatively uniform, resulting in a weaker influence of soil depth on TP [53].

4.2. Elevational Patterns of Soil Microbial Biomass Stoichiometry

Soil MBC and MBN did not exhibit significant variation with elevation, while MBP decreased markedly as elevation increased (Figure 3a–c; Table 1). These findings differ from those of He et al. (2016) [43], who observed increasing trends in MBC and MBN with elevation and a nonlinear pattern in MBP, likely driven by habitat-type shifts. In our study, differences in vegetation community composition and species diversity along the elevational gradient (Table A1) likely influenced litter quantity and quality, as well as root exudation patterns, thereby affecting microbial biomass composition and abundance [60,61]. The absence of significant elevational trends in MBC and MBN may reflect a balance between microbial nutrient demand and organic matter inputs, rather than indicating stoichiometric homeostasis per se. The decline in MBP with elevation appears to be closely related to its positive association with TP (Figure 5a). Variations in soil pH can modify soil structure, nutrient availability, and plant community dynamics, thereby directly or indirectly shaping the composition and structure of soil microbial communities [62,63]. Here, MBP was highly positively correlated with soil pH, indicating that the alkaline karst environment may affect soil microbial community structure and promote soil microbial growth [25]. However, our finding contradicts that of Pan et al. (2018) [64], who noted that in Ca-rich alkaline karst areas, high soil pH enhances soil nutrient stability, which is not conducive to the conversion of available nutrients required for microbial growth.
MCN reflects the soil’s capacity to supply N; MCP reflects the potential of microbes to mineralize SOM, thus releasing or absorbing soil P; and MNP reflects the demands for N and P owing to plant growth [65,66]. Here, the mean surface MCN, MCP, and MNP values were 3.84 ± 1.22, 47.34 ± 16.28, and 12.23 ± 3.12, respectively (Table A3). These values are substantially lower than the average MCN and MCP reported for Chinese terrestrial ecosystems (13.7 and 85.15) [67] and global forests (8.2 and 74) [46]. In contrast, MNP exceeded the reported averages for China (9.23) [67] and global forests (8.9) [46]. These findings indicate that the study area exhibited relatively low overall levels of soil-available C, and that the microbes in this region have greater potential to absorb and immobilize P from the environment. MCN and MCP did not vary significantly with elevation (Figure 3d,e; Table 1), indicating that elevation had a weak synergistic effect on MBC, MBN, and MBP and that the potential for soil microbes to release N and P was relatively consistent along the elevational gradient. This is consistent with prior findings that MCN and MCP have homeostatic characteristics [68].
MNP increased significantly with elevation (Figure 3f; Table 1), indicating that plants at higher elevations have a greater demand for P than for N. Owing to the P-limitation at high elevations, the microbes at high elevations are P-deficient, causing them to compete intensely with plants for soil available P [65,69]. Therefore, adding suitable levels of P fertilizers in high-elevation forests may help to alleviate the effects of soil P limitation, helping to achieve the effective nutrient circulation among the plants, soil, and soil microbes and to promote ecological restoration in the study area.
Unlike MCN, MCP and MNP decreased with soil depth (Figure 3), consistent with the variability in soil nutrient content and ratios observed here and with previous research findings [67,70]. This may be because animal and plant residue decomposition and root exudation provide a substrate for soil microbial biomass formation. Subsoil microbial biomass is influenced by the accumulation of soil nutrients and the transport of soil O2 by roots, causing microbial habitat conditions to deteriorate with increasing soil depth, further affecting the distribution of soil microbial biomass [71].

4.3. Elevation Patterns of Soil Enzymatic Activity and Stoichiometry

Soil BG, NAG + LAP, and AP activities tended to increase with elevation, though the rise in BG was not statistically significant (Figure 4a–c; Table 1), aligning with findings from previous studies [72,73,74]. There are two possible reasons for this finding. First, soil nutrients, as the primary source for enzyme substrates in soil, improve enzyme activity [75]. In this study, soil enzyme activities were positively correlated with SOC and TN (Figure 5a), and their variations with elevation mirrored those of SOC and TN. Additionally, soil SWC influences enzyme activity by affecting the diffusion rate of substrates and by providing essential conditions and sites for enzyme-catalyzed reactions [76,77]. Soil BG and AP were significantly positively correlated with SWC, indicating that the elevated SWC helped to enhance soil enzyme activity (Figure 5a). Zuccarini et al. (2020) [78] reported that adequate water conditions need to be accompanied by warming to significantly promote enzyme activity. In the studied area, the distance from the depression to the hilltop was only 300 m with little temperature difference, although the depression was dark and humid, whereas the hilltop was hot and dry. Therefore, soil SWC and temperature do not increase synchronously with increasing elevation, leading to a decline in enzyme activity in high-elevation areas.
Here, the ln(BG): ln(NAG + LAP): ln(AP) ratio was 1:1:1.1, closely matching the global reference ratio of 1:1:1. ECN, ECP, and ENP showed no significant variation with elevation (Figure 4d–f), suggesting that the microbial communities exhibit strong homeostatic properties. This finding also implies that enzyme stoichiometry is influenced more by the soil microenvironment than by elevation [79].
ENP, soil enzyme activity, ECN, and ECP all decreased with soil depth (Figure 4; Table 1), consistent with the depth-related changes observed in soil and microbial nutrient levels. In surface soils, enzyme activity is higher due to the abundance of surface litter serving as substrates for enzymatic reactions, as well as the presence of favorable conditions for microbial metabolism, including optimal temperature, moisture, and oxygen availability. As soil depth increases, reduced nutrient content and limited microbial activity lead to a gradual decline in enzyme activity [53,80].

4.4. Total, Microbial, and Enzymatic C, N, and P Contents and Stoichiometry, and Environmental Associations

SEM revealed that the primary environmental factors—elevation, exchangeable calcium (ECa), and soil pH—exerted significant effects on microbial and enzymatic C:N:P stoichiometry indirectly through their direct regulation of soil C:N:P ratios (Figure 7). These soil stoichiometric ratios served as mediating variables, linking environmental gradients with microbial and enzymatic responses. Specifically, ECa and pH had positive direct effects on soil C:N:P ratios, consistent with the stabilizing influence of alkaline and calcium-rich karst conditions [12,81], and the total model explained 75%, 41%, and 21% of the variation in soil, microbial, and enzymatic C:N:P ratios, respectively. The soil C:N:P ratio significantly positively affected the microbial C:N:P ratio. There were significant positive correlations between MBC and SOC; MBN and TN; MBP and TP; MCP and SCP; and MNP and SNP (Figure 5; Figure 6). These results indicate a positive feedback between soil microbes and soil resources, with microbes serving an essential role in maintaining soil nutrient balance and ecological functions [82]. However, no significant differences were observed between microbial and enzymatic C:N:P ratios, which may be attributed to the dependence of enzymatic stoichiometry on microbial community structure. Different microbial taxa exhibit distinct roles in shaping enzyme activity patterns; for example, ectomycorrhizal fungi can influence oxidative enzyme activity [83]. Moreover, plant roots are capable of producing enzymes that regulate nutrient cycling between plants and soils [8]. According to resource allocation theory [34,35], when soil nutrients are scarce, microbes tend to secrete more hydrolases to acquire the limiting elements and maintain a nutrient balance. However, our findings were not fully consistent with this prediction. In this study, the soil C:N:P ratio had a significant positive effect on the enzymatic C:N:P ratio. Soil AP was significantly correlated with SOC and TN, and ENP was strongly associated with SCN (Figure 5; Figure 6), which is consistent with the findings of Wei et al. (2024) [84]. This suggests that the relationships between soil and enzymatic stoichiometry are influenced by the functional diversity of enzymes. For example, research in the Baltic Sea (a low-carbon ecosystem) demonstrated that phosphatase primarily mobilizes organic P, which may indirectly alleviate microbial C limitation by enhancing microbial nutrient acquisition and growth [85]. Experimental studies have also shown that the addition of C and N can stimulate microbial biomass growth and increase phosphatase activity [86,87,88]. Therefore, our current results suggest that enzymatic stoichiometry in this system is not strictly governed by resource allocation theory.
These results highlight the complexity of enzyme regulation in nutrient-limited karst soils, where enzymatic stoichiometry is shaped by both nutrient availability and the activity of enzymes such as phosphatase, whose primary biochemical role is P mobilization, but which may indirectly influence C and N cycling. From a practical perspective, understanding these patterns can inform nutrient management and forest restoration strategies in tropical karst regions, particularly where P limitations constrain productivity. Future studies should examine seasonal dynamics and plant–soil–microbe interactions to validate and extend these findings.

5. Conclusions

These findings describe clear patterns of total, microbial, and enzymatic soil C:N:P stoichiometry along an elevational gradient in tropical karst peak-cluster depression areas. SOC, TN, SCP, SNP, MNP, NAG + LAP, and AP increased significantly with elevation, while soil TP and MBP decreased significantly with elevation, which may suggest potential P limitation in the ecosystem at higher elevations; however, direct evidence (e.g., plant tissue N:P ratios) is required to verify this interpretation. With increasing soil depth, the total, microbial, and enzymatic C-N-P contents and ratios decreased. Relative to global and Chinese average levels, the study location exhibited somewhat limited C and P contents. Soil nutrient, microbial biomass, enzyme activity, stoichiometric ratios, and environmental factors exhibited significant correlations, with elevation, pH, and ECa being the primary drivers of soil stoichiometry. Microbial stoichiometry positively influenced soil stoichiometry. According to resource allocation theory, the activities of C-, N-, and P-acquiring enzymes (e.g., BG, NAG + LAP, and AP) are expected to follow a near 1:1:1 ratio on a log-transformed basis when microbial nutrient acquisition is balanced. However, the enzymatic stoichiometry observed in this study did not fully align with this theoretical expectation, largely due to elevated AP activity relative to BG and NAG + LAP, which may indicate stronger P acquisition efforts under P-limited conditions. This divergence may also be partly attributed to the functional characteristics of phosphatase, which primarily mobilizes organic P but may indirectly influence C and N dynamics. Nevertheless, this association should be interpreted cautiously given the cross-sectional nature of the SEM results (n = 12). These findings describe the soil total, microbial, and enzymatic stoichiometry at these karst sites, offering insights into nutrient cycling mechanisms and the coupling of C, N, and P in tropical karst forest ecosystems. Nevertheless, this study is limited by its single-season sampling, the absence of plant tissue stoichiometry data, and the lack of multi-year monitoring, which constrain our ability to fully capture seasonal dynamics and nutrient limitation patterns. Future studies integrating seasonal comparisons, plant nutrient measurements, and functional microbial analyses are needed to validate and extend these findings.

Author Contributions

Conceptualization, Z.Z. and G.H.; Methodology, C.X., S.C. and C.Z.; Formal analysis, G.H., C.H., Z.Z. and S.C.; Investigation, G.H., S.C., Z.Z., C.Z. and C.X.; Funding acquisition, S.C., G.H., C.X., Z.Z., C.Z. and C.H.; Writing—original draft, S.C., G.H., C.H. and Z.Z.; Writing—review and editing, Z.Z., G.H., C.X., C.Z. and S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Guangxi Natural Science Foundation (grant numbers 2022GXNSFBA035461, 2021GXNSFFA196005, and 2022GXNSFBA035633), the National Natural Science Foundation of China (grant numbers 32001172, 31960275, and 42467008), and the Special Funding for Guangxi Bagui Young Top Talents Program (to Zhang Zhonghua). The article processing charge (APC) was also funded by the Guangxi Natural Science Foundation (grant number 2022GXNSFBA035461).

Data Availability Statement

The data that support the findings of this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
APAlkaline Phosphatase
BGβ-1,4-glucosidase
DBHDiameter at Breast Height
ECaSoil Exchangeable Calcium
EMgSoil Exchangeable Magnesium
EleElevation
ECN, ECP, ENPEnzymatic C:N, C:P, and N:P Ratios, respectively
MBCMicrobial Biomass Carbon
MBNMicrobial Biomass Nitrogen
MBPMicrobial Biomass Phosphorus
MCN, MCP, MNPMicrobial Biomass C:N, C:P, and N:P Ratios, respectively
NAG + LAPβ-1,4-N-acetylglucosaminidase and L-leucine aminopeptidase
RDARedundancy Analysis
SOCSoil Organic Carbon
SWCSoil Water Content
SRSpecies Richness
SDSlope Degree
SEMStructural Equation Modeling
SCN, SCP, SNPSoil C:N, C:P, and N:P Ratios, respectively
TNTotal Nitrogen
TPTotal Phosphorus

Appendix A

Table A1. Basic information about plots at different elevations in karst peak-cluster depressions.
Table A1. Basic information about plots at different elevations in karst peak-cluster depressions.
Elevation (m)Longitude (E)Latitude (N)Slope Degree (◦)Slope PositionCanopy Cover (%)Bare Rock (%)Diameter at Breast Height (cm)Species RichnessDominant Species
200106°49′05.41″~106°56′28.95″22°21′10.09″~22°31′10.40″5.67 ± 0.92Depression60.0040.005.60 ± 0.3731.67 ± 5.13Litsea variabilis var. oblonga;
Catunaregam spinosa;
Antidesma bunius;
Ficus hispida;
Leea indica;
Strophioblachia fimbricalyx
300106°56′53.81″~106°57′12.49″22°27′05.32″~22°27′26.82″19.33 ± 1.48Downslope75.0076.679.01 ± 1.2519.00 ± 2.28Cephalomappa sinensis;
Cleistanthus petelotii;
Hydnocarpus hainanensis;
Excentrodendron tonkinense;
Ardisia thyrsiflora
400106°57′00.90″~106°57′04.08″22°27′13.15″~22°27′21.11″34.00 ± 1.32Mid-slope85.0073.335.39 ± 0.4924.67 ± 1.65Hydnocarpus hainanensis;
Cephalomappa sinensis;
Orophea polycarpa;
Cleistanthus sumatranus;
Pterospermum truncatolobatum;
Cleistanthus petelotii
500106°58′06.29″~106°58′10.03″22°27′12.82″~22°27′18.38″27.67 ± 0.92Upper slope65.0058.333.89 ± 0.0938.67 ± 2.57Boniodendron minus;
Cephalomappa sinensis;
Diospyros siderophylla;
Lysidice rhodostegia;
Tirpitzia sinensis;
Viburnum triplinerve
Table A2. Results of principal component analysis (PCA) of soil total, microbial, and enzymatic C:N:P stoichiometric ratios.
Table A2. Results of principal component analysis (PCA) of soil total, microbial, and enzymatic C:N:P stoichiometric ratios.
Response FactorPredictorPCA1
Soil C:N:P ratiosSCP0.986 ***
SNP0.917 ***
SCN0.778 ***
Cumulative (%)80.583
Microbial C:N:P ratiosMNP0.938 ***
MCP0.797 ***
MCN−0.486 ***
Cumulative (%)58.335
Enzymatic C:N:P ratiosECN0.999 ***
ECP0.815 ***
ENP−0.649 ***
Cumulative (%)69.423
Note: Correlations (r values) and significance (p values) of the contributions of individual parameters toward the first principal components (PCA1) of different variable groups. *** p < 0.001.
Table A3. The mean soil total, microbial, and enzymatic C:N:P stoichiometric ratios at soil depths of 0–20 and 20–40 cm.
Table A3. The mean soil total, microbial, and enzymatic C:N:P stoichiometric ratios at soil depths of 0–20 and 20–40 cm.
CategoryFactor0–20 cm20–40 cm
SoilSCN10.44 ± 0.567.92 ± 0.98
SCP53.61 ± 7.3332.12 ± 4.25
SNP4.80 ± 0.523.94 ± 0.41
MicrobeMCN3.84 ± 1.228.16 ± 3.24
MCP47.34 ± 16.2827.42 ± 12.57
MNP12.23 ± 3.126.42 ± 2.17
EnzymeECN1.03 ± 0.050.91 ± 0.06
ECP0.87 ± 0.020.85 ± 0.06
ENP0.85 ± 0.020.94 ± 0.03
Table A4. Soil physical and chemical properties along an elevation gradient in a karst peak-cluster depression.
Table A4. Soil physical and chemical properties along an elevation gradient in a karst peak-cluster depression.
Elevation (m)SWC (%)pHECa (mg·kg−1)EMg (mg·kg−1)
20043.29 ± 3.13 a7.59 ± 0.10 a22.09 ± 0.83 b1.05 ± 0.07 b
30039.06 ± 2.61 a7.16 ± 0.03 b22.58 ± 1.34 b1.16 ± 0.02 ab
40037.83 ± 2.66 a7.09 ± 0.06 b24.62 ± 0.97 b1.21 ± 0.04 a
50036.50 ± 1.53 a6.86 ± 0.22 b31.78 ± 3.91 a1.21 ± 0.04 a
Note: Lowercase letters indicate significant differences (p < 0.05) among the same soil physical and chemical properties at different elevations at 0–40 cm soil depth. The data are presented as the means ± standard errors.

References

  1. Elser, J.J.; Sterner, R.W.; Gorokhova, E.; Fagan, W.F.; Markow, T.A.; Cotner, J.B.; Harrison, J.F.; Hobbie, S.E.; Odell, G.M.; Weider, L.W. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 2000, 3, 540–550. [Google Scholar] [CrossRef]
  2. Schindler, D.W. Ecological stoichiometry: The biology of elements from molecules to the biosphere. Nature 2003, 423, 225–226. [Google Scholar] [CrossRef]
  3. Elser, J.J.; Fagan, W.F.; Kerkhoff, A.J.; Swenson, N.G.; Enquist, B.J. Biological stoichiometry of plant production: Metabolism, scaling and ecological response to global change. New Phytol. 2010, 186, 593–608. [Google Scholar] [CrossRef]
  4. Wang, X.; Li, Y.; Wang, L.; Duan, Y.; Yao, B.; Chen, Y.; Cao, W. Soil extracellular enzyme stoichiometry reflects microbial metabolic limitations in different desert types of northwestern China. Sci. Total Environ. 2023, 874, 161929. [Google Scholar] [CrossRef]
  5. Song, M.; Peng, W.; Du, H.; Xu, Q. Responses of soil and microbial C:N:P stoichiometry to vegetation succession in a karst region of Southwest China. Forests 2019, 10, 781. [Google Scholar] [CrossRef]
  6. Zhao, H.; Sun, J.; Xu, X.; Qin, X. Stoichiometry of soil microbial biomass carbon and microbial biomass nitrogen in China′s temperate and alpine grasslands. Eur. J. Soil Biol. 2017, 83, 1–8. [Google Scholar] [CrossRef]
  7. He, X.; Hou, E.; Veen, G.F.; Ellwood, M.D.F.; Dijkstra, P.; Sui, X.; Zhang, S.; Wen, D.; Chu, C. Soil microbial biomass increases along elevational gradients in the tropics and subtropics but not elsewhere. Glob. Ecol. Biogeogr. 2020, 29, 345–354. [Google Scholar] [CrossRef]
  8. Luo, L.; Meng, H.; Gu, J.-D. Microbial extracellular enzymes in biogeochemical cycling of ecosystems. J. Environ. Manag. 2017, 197, 539–549. [Google Scholar] [CrossRef] [PubMed]
  9. Xu, Z.; Yu, G.; Zhang, X.; He, N.; Wang, Q.; Wang, S.; Wang, R.; Zhao, N.; Jia, Y.; Wang, C. Soil enzyme activity and stoichiometry in forest ecosystems along the North–South Transect in eastern China (NSTEC). Soil Biol. Biochem. 2017, 104, 152–163. [Google Scholar] [CrossRef]
  10. Chen, H.; Li, D.; Mao, Q.; Xiao, K.; Wang, K. Resource limitation of soil microbes in karst ecosystems. Sci. Total Environ. 2019, 650, 241–248. [Google Scholar] [CrossRef]
  11. Jiang, C.; Zhu, B.; Zeng, H. Soil extracellular enzyme stoichiometry reflects the unique habitat of karst tiankeng and helps to alleviate the P-limitation of soil microbes. Ecol. Indic. 2022, 144, 109534. [Google Scholar] [CrossRef]
  12. Ma, Y.; Zhang, C.; Yang, H.; Xu, Y.; Chen, Y.; Ning, J. The characteristics of soil C, N and P and stoichiometric ratios as affected by land-use in a karst area, Southwest China. Land 2023, 12, 1345. [Google Scholar] [CrossRef]
  13. Ding, L.; Wang, P.; Zhang, W.; Zhang, Y.; Li, S.; Wei, X.; Chen, X.; Yang, F. Soil stoichiometry modulates effects of shrub encroachment on soil carbon concentration and stock in a subalpine grassland. Iforest-Biogeosciences For. 2020, 13, 65–72. [Google Scholar] [CrossRef]
  14. Wu, P.; Zhou, H.; Cui, Y.; Zhao, W.; Hou, Y.; Tan, C.; Yang, G.; Ding, F. Stoichiometric characteristics of leaf, litter and soil during vegetation succession in Maolan National Nature Reserve, Guizhou, China. Sustainability 2022, 14, 16195. [Google Scholar] [CrossRef]
  15. Wang, L.; Sheng, M. Responses of soil C, N, and P stoichiometrical characteristics, enzyme activities and microbes to land use changes in Southwest China Karst. Forests 2023, 14, 971. [Google Scholar] [CrossRef]
  16. Sun, M.; Yang, R.; Tang, Y.; Xiao, D.; Zhang, W.; Xu, Z.; Shi, Z.; Hu, P.; Wu, H.; Wang, K. Lithologic control of soil C:N:P stoichiometry across a climatic gradient in southwest China. J. Soils Sediments 2023, 23, 1662–1673. [Google Scholar] [CrossRef]
  17. De Feudis, M.; Cardelli, V.; Massaccesi, L.; Lagomarsino, A.; Fornasier, F.; Westphalen, D.J.; Cocco, S.; Corti, G.; Agnelli, A. Influence of altitude on biochemical properties of European beech (Fagus sylvatica L.) forest soils. Forests 2017, 8, 237. [Google Scholar] [CrossRef]
  18. Pan, G.; Song, S.; Wang, X.; Yongkuan, C. Using ecological stoichiometric characteristics to inform grassland management in the karst desertification area. Agronomy 2023, 13, 1123. [Google Scholar] [CrossRef]
  19. Hu, Q.; Sheng, M.; Bai, Y.; Jie, Y.; Xiao, H. Response of C, N, and P stoichiometry characteristics of Broussonetia papyrifera to altitude gradients and soil nutrients in the karst rocky ecosystem, SW China. Plant Soil 2022, 475, 123–136. [Google Scholar] [CrossRef]
  20. Liu, J.; Li, J.; Long, J.; Li, H. Altitude dependence of soil ecological stoichiometry and enzyme activities in a karst region of Southwest China. J. Forest Environ. 2022, 42, 113–122. [Google Scholar]
  21. Bin, H.; Li, Q.; Zhang, P.; Li, W.; Xue, X.; Zou, S.; Zhang, Q. Effects of elevation on ecological stoichiometry of plant leaves, litter, and soils in Pseudotsuga sinensis forest in the karst mountain region, Southwest China. J. Soil Sci. Plant Nutr. 2022, 22, 3582–3597. [Google Scholar] [CrossRef]
  22. Wu, Y.G.; Yu, Y.H. Soil carbon, nitrogen and phosphorus fractions and response to microorganisms and mineral elements in Zanthoxylum planispinum ‘Dintanensis’ plantations at different altitudes. Agronomy 2023, 13, 1324. [Google Scholar] [CrossRef]
  23. Jin, Z.; Liu, G.; Zhou, M.; Xu, N. Soil enzyme activity and microbial carbon metabolism along an altitudinal gradient in grasslands of karst mountain. Res. Soil Water Conserv. 2020, 27, 37–44. [Google Scholar]
  24. Wang, K.; Zhang, C.; Chen, H.; Yue, Y.; Zhang, W.; Zhang, M.; Qi, X.; Fu, Z. Karst landscapes of China: Patterns, ecosystem processes and services. Landsc. Ecol. 2019, 34, 2743–2763. [Google Scholar] [CrossRef]
  25. Xiao, D.; He, X.; Zhang, W.; Hu, P.; Sun, M.; Wang, K. Comparison of bacterial and fungal diversity and network connectivity in karst and non-karst forests in southwest China. Sci. Total Environ. 2022, 822, 153511. [Google Scholar] [CrossRef]
  26. Wang, S.-J.; Liu, Q.-M.; Zhang, D.-F. Karst rocky desertification in southwestern China: Geomorphology, land use, impact and rehabilitation. Land Degrad. Dev. 2004, 15, 115–121. [Google Scholar] [CrossRef]
  27. Li, Y.B.; Li, Q.Y.; Luo, G.J.; Bai, X.Y.; Wang, Y.Y.; Wang, S.J.; Xie, J.; Yang, G.B. Discussing the genesis of karst rocky desertification research based on the correlations between cropland and settlements in typical peak-cluster depressions. Solid Earth 2016, 7, 741–750. [Google Scholar] [CrossRef]
  28. Guo, Y.; Wang, B.; Mallik, A.U.; Huang, F.; Xiang, W.; Ding, T.; Wen, S.; Lu, S.; Li, D.; He, Y.; et al. Topographic species–habitat associations of tree species in a heterogeneous tropical karst seasonal rain forest, China. J. Plant Ecol. 2017, 10, 450–460. [Google Scholar] [CrossRef]
  29. Guo, Y.; Chen, H.Y.; Wang, B.; Xiang, W.; Li, D.; Li, X.; Mallik, A.U.; Ding, T.; Huang, F.; Lu, S.; et al. Conspecific and heterospecific crowding facilitate tree survival in a tropical karst seasonal rainforest. For. Ecol. Manag. 2021, 481, 118700. [Google Scholar] [CrossRef]
  30. Li, Y.; Guo, Y.; Li, X.; Li, D.; Wang, B.; Chen, T.; Lu, F.; Xiang, W.; Huang, F.; Liu, S.; et al. Analysis of the leaf litter decomposition rate and nutrient content in a karst seasonal rainforest in southwest Guangxi. Acta Geosci. Sin. 2022, 43, 483–490. [Google Scholar]
  31. Guo, Y.; Li, D.; Wang, B.; Bai, K.; Xiang, W.; Li, X. C, N and P stoichiometric characteristics of soil and litterfall for six common tree species in a northern tropical karst seasonal rainforest in Nonggang, Guangxi, southern China. Biodivers. Sci. 2017, 25, 1085–1094. [Google Scholar] [CrossRef]
  32. Bai, K.; Lv, S.; Ning, S.; Zeng, D.; Guo, Y.; Wang, B. Leaf nutrient concentrations associated with phylogeny, leaf habit and soil chemistry in tropical karst seasonal rainforest tree species. Plant Soil 2019, 434, 305–326. [Google Scholar] [CrossRef]
  33. Zhang, X.; Wang, B.; Chen, T.; Guo, Y.; Li, X. Revealing the relative importance among plant species, slope positions, and soil types on rhizosphere microbial communities in northern tropical karst and non-karst seasonal rainforests of China. Front. Microbiol. 2023, 14, 1135197. [Google Scholar] [CrossRef]
  34. Allison, S.D.; Vitousek, P.M. Responses of extracellular enzymes to simple and complex nutrient inputs. Soil Biol. Biochem. 2005, 37, 937–944. [Google Scholar] [CrossRef]
  35. Sinsabaugh, R.L.; Hill, B.H.; Shah, J.J.F. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 2009, 462, 795–798. [Google Scholar] [CrossRef] [PubMed]
  36. Guo, Y.; Xiang, W.; Wang, B.; Li, D.; Mallik, A.U.; Chen, H.Y.H.; Huang, F.; Ding, T.; Wen, S.; Lu, S.; et al. Partitioning beta diversity in a tropical karst seasonal rainforest in Southern China. Sci. Rep. 2018, 8, 2409. [Google Scholar] [CrossRef] [PubMed]
  37. Bao, S.D. Soil Agricultural Chemistry Analysis; Agriculture Press: Beijing, China, 2013. [Google Scholar]
  38. Vance, E.D.; Brookes, P.C.; Jenkinson, D.S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 1987, 19, 703–707. [Google Scholar] [CrossRef]
  39. Saiya-Cork, K.R.; Sinsabaugh, R.L.; Zak, D.R. The effects of long-term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biol. Biochem. 2002, 34, 1309–1315. [Google Scholar] [CrossRef]
  40. Rosseel, Y. Lavaan: An R package for structural equation modeling. J. Stat. Softw. 2012, 48, 1–36. [Google Scholar] [CrossRef]
  41. Jiang, L.; He, Y.; Liu, J.; Xing, H. Elevation gradient altered soil C, N, and P stoichiometry of Pinus taiwanensis forest on Daiyun Mountain. Forests 2019, 10, 199. [Google Scholar] [CrossRef]
  42. Li, D.; Wen, L.; Zhang, W.; Yang, L.; Xiao, K.; Chen, H.; Wang, K. Afforestation effects on soil organic carbon and nitrogen pools modulated by lithology. For. Ecol. Manag. 2017, 400, 85–92. [Google Scholar] [CrossRef]
  43. He, X.; Hou, E.; Liu, Y.; Wen, D. Altitudinal patterns and controls of plant and soil nutrient concentrations and stoichiometry in subtropical China. Sci. Rep. 2016, 6, 24261. [Google Scholar] [CrossRef] [PubMed]
  44. Riggs, C.E.; Hobbie, S.E. Mechanisms driving the soil organic matter decomposition response to nitrogen enrichment in grassland soils. Soil Biol. Biochem. 2016, 99, 54–65. [Google Scholar] [CrossRef]
  45. Batjes, N.H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 1996, 47, 151–163. [Google Scholar] [CrossRef]
  46. Cleveland, C.C.; Liptzin, D. C:N:P stoichiometry in soil: Is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 2007, 85, 235–252. [Google Scholar] [CrossRef]
  47. Tian, H.; Chen, G.; Zhang, C.; Melillo, J.M.; Hall, C.A.S. Pattern and variation of C:N:P ratios in China’s soils: A synthesis of observational data. Biogeochemistry 2010, 98, 139–151. [Google Scholar] [CrossRef]
  48. Wang, M.; Chen, H.; Zhang, W.; Wang, K. Soil nutrients and stoichiometric ratios as affected by land use and lithology at county scale in a karst area, southwest China. Sci. Total Environ. 2018, 619–620, 1299–1307. [Google Scholar] [CrossRef]
  49. Lu, M.; Liu, K.; Zhang, L.; Zeng, F.; Song, T.; Peng, W.; Du, H. Stoichiometric variation in soil carbon, nitrogen, and phosphorus following cropland conversion to forest in Southwest China. Forests 2022, 13, 218. [Google Scholar] [CrossRef]
  50. Chen, H.; Li, D.; Xiao, K.; Wang, K. Soil microbial processes and resource limitation in karst and non-karst forests. Funct. Ecol. 2018, 32, 1400–1409. [Google Scholar] [CrossRef]
  51. Müller, M.; Oelmann, Y.; Schickhoff, U.; Böhner, J.; Scholten, T. Himalayan treeline soil and foliar C:N:P stoichiometry indicate nutrient shortage with elevation. Geoderma 2017, 291, 21–32. [Google Scholar] [CrossRef]
  52. Wang, A.; Wang, X.; Tognetti, R.; Lei, J.-P.; Pan, H.-L.; Liu, X.-L.; Jiang, Y.; Wang, X.-Y.; He, P.; Yu, F.-H.; et al. Elevation alters carbon and nutrient concentrations and stoichiometry in Quercus aquifolioides in southwestern China. Sci. Total Environ. 2018, 622–623, 1463–1475. [Google Scholar] [CrossRef]
  53. Qin, H.; Jiao, L.; Zhou, Y.; Wu, J.; Che, X. Elevation affects the ecological stoichiometry of Qinghai spruce in the Qilian Mountains of northwest China. Front. Plant Sci. 2022, 13, 1024626. [Google Scholar] [CrossRef]
  54. Cui, Y.; Fang, L.; Guo, X.; Wang, X.; Zhang, Y.; Li, P.; Zhang, X. Ecoenzymatic stoichiometry and microbial nutrient limitation in rhizosphere soil in the arid area of the northern Loess Plateau, China. Soil Biol. Biochem. 2018, 116, 11–21. [Google Scholar] [CrossRef]
  55. Ma, Z.; Zhang, X.; Zheng, B.; Yue, S.; Zhang, X.; Zhai, B.; Wang, Z.; Zheng, W.; Li, Z.; Zamanian, K.; et al. Effects of plastic and straw mulching on soil microbial P limitations in maize fields: Dependency on soil organic carbon demonstrated by ecoenzymatic stoichiometry. Geoderma 2021, 388, 114961. [Google Scholar] [CrossRef]
  56. Su, L.; Du, H.; Zeng, F.; Peng, W.; Rizwan, M.; Núñez-Delgado, A.; Zhou, Y.; Song, T.; Wang, H. Soil and fine roots ecological stoichiometry in different vegetation restoration stages in a karst area, southwest China. J. Environ. Manag. 2019, 252, 109658. [Google Scholar] [CrossRef] [PubMed]
  57. Qin, H.L.; Fu, X.X.; Lu, Y.; Wei, X.H.; Li, B.; Jia, J.C.; Jiang, K. Soil C:N:P stoichiometry at different altitudes in Mao’er Mountain, Guangxi, China. J. Appl. Ecol. 2019, 30, 711–717. [Google Scholar]
  58. Chen, X.; Feng, J.; Ding, Z.; Tang, M.; Zhu, B. Changes in soil total, microbial and enzymatic C-N-P contents and stoichiometry with depth and latitude in forest ecosystems. Sci. Total Environ. 2022, 816, 151617. [Google Scholar] [CrossRef]
  59. Rahman, M.; Wang, Y.; Zhang, K.; Ahmad, B.; Ali, A.; Ahamd, A.; Muhammad, D.; Afzaal, M.; Zhang, Z.; Bohnett, E. Variations in soil C, N, P stocks and stoichiometry with soil depth and forest types in Qilian Mountains of Northwest China. Front. Environ. Sci. 2022, 10, 893981. [Google Scholar] [CrossRef]
  60. Zhang, J.; Ai, Z.; Liang, C.; Wang, G.; Liu, G.; Xue, S. How microbes cope with short-term N addition in a Pinus tabuliformis forest—Ecological stoichiometry. Geoderma 2019, 337, 630–640. [Google Scholar] [CrossRef]
  61. Wu, M.; Liu, Z.; Chen, L.; Pang, D.; Xu, X.; Zhang, Y.; Ni, X.; Hu, Y.; Li, X. Elevation gradient shapes microbial carbon and phosphorous limitations in the Helan Mountains, Northwest China. Front. Environ. Sci. 2022, 10, 944081. [Google Scholar] [CrossRef]
  62. Qi, D.; Wieneke, X.; Tao, J.; Zhou, X.; Desilva, U. Soil pH is the primary factor correlating with soil microbiome in karst rocky desertification regions in the Wushan County, Chongqing, China. Front. Microbiol. 2018, 9, 1027. [Google Scholar] [CrossRef]
  63. Li, Y.; Shen, Q.; An, X.; Xie, Y.; Liu, X.; Lian, B. Organomineral fertilizer application enhances Perilla frutescens nutritional quality and rhizosphere microbial community stability in karst mountain soils. Front. Microbiol. 2022, 13, 966035. [Google Scholar] [CrossRef]
  64. Pan, F.J.; Zhang, W.; Liang, Y.M.; Liu, S.J.; Wang, K.L. Increased associated effects of topography and litter and soil nutrients on soil enzyme activities and microbial biomass along vegetation successions in karst ecosystem, southwestern China. Environ. Sci. Pollut. Res. 2018, 25, 16979–16990. [Google Scholar] [CrossRef] [PubMed]
  65. Li, J.; Liu, Y.; Hai, X.; Shangguan, Z.; Deng, L. Dynamics of soil microbial C:N:P stoichiometry and its driving mechanisms following natural vegetation restoration after farmland abandonment. Sci. Total Environ. 2019, 693, 133620. [Google Scholar] [CrossRef] [PubMed]
  66. Chi, Y.; Song, S.; Xiong, K.; Albasher, G.; Fang, J. Responses of soil microbial biomass, microbial entropy and soil-microorganism stoichiometry imbalance to different utilization patterns in the artificial grassland of karst desertification area. Front. Microbiol. 2023, 14, 1195082. [Google Scholar] [CrossRef] [PubMed]
  67. Pan, Y.; Fang, F.; Tang, H. Patterns and internal stability of carbon, nitrogen, and phosphorus in soils and soil microbial biomass in terrestrial ecosystems in China: A data synthesis. Forests 2021, 12, 292. [Google Scholar] [CrossRef]
  68. Zhao, R.; He, M.; Jiang, C.; Liu, F. Soil microbial stoichiometry and community structure responses to long-term natural forest conversion to plantations in a subtropical region. Environ. Sci. Pollut. Res. 2022, 29, 27560–27570. [Google Scholar] [CrossRef]
  69. Deng, L.; Peng, C.; Huang, C.; Wang, K.; Liu, Q.; Liu, Y.; Hai, X.; Shangguan, Z. Drivers of soil microbial metabolic limitation changes along a vegetation restoration gradient on the Loess Plateau, China. Geoderma 2019, 353, 188–200. [Google Scholar] [CrossRef]
  70. Jiang, W.; Gong, L.; Yang, L.; He, S.; Liu, X. Dynamics in C, N, and P stoichiometry and microbial biomass following soil depth and vegetation types in low mountain and hill region of China. Sci. Rep. 2021, 11, 8717. [Google Scholar] [CrossRef]
  71. Xu, T.; Chen, X.; Hou, Y.; Zhu, B. Changes in microbial biomass, community composition and diversity, and functioning with soil depth in two alpine ecosystems on the Tibetan Plateau. Plant Soil 2021, 459, 137–153. [Google Scholar] [CrossRef]
  72. Chang, E.-H.; Chen, T.-H.; Tian, G.; Chiu, C.-Y. The effect of altitudinal gradient on soil microbial community activity and structure in moso bamboo plantations. Appl. Soil Ecol. 2016, 98, 213–220. [Google Scholar] [CrossRef]
  73. Siles, J.A.; Cajthaml, T.; Minerbi, S.; Margesin, R. Effect of altitude and season on microbial activity, abundance and community structure in Alpine forest soils. FEMS Microbiol. Ecol. 2016, 92, fiw084. [Google Scholar] [CrossRef] [PubMed]
  74. Zuo, Y.; Li, J.; Zeng, H.; Wang, W. Vertical pattern and its driving factors in soil extracellular enzyme activity and stoichiometry along mountain grassland belts. Biogeochemistry 2018, 141, 23–39. [Google Scholar] [CrossRef]
  75. Guan, P.; Yang, J.; Yang, Y.; Wang, W.; Zhang, P.; Wu, D. Land conversion from cropland to grassland alleviates climate warming effects on nutrient limitation: Evidence from soil enzymatic activity and stoichiometry. Glob. Ecol. Conserv. 2020, 24, e01302. [Google Scholar] [CrossRef]
  76. Guo, Z.; Zhang, X.; Green, S.M.; Dungait, J.A.; Wen, X.; Quine, T.A. Soil enzyme activity and stoichiometry along a gradient of vegetation restoration at the Karst Critical Zone Observatory in Southwest China. Land Degrad. Dev. 2019, 30, 1916–1927. [Google Scholar] [CrossRef]
  77. Yang, L.; Jia, Y.; Li, Q.; Cui, H.; Lu, J.; Ma, J.; Xu, Z. Ecoenzymatic stoichiometry in the rhizosphere and bulk soil of a Larix principis-rupprechtii plantation in North China. Forests 2023, 14, 812. [Google Scholar] [CrossRef]
  78. Zuccarini, P.; Asensio, D.; Ogaya, R.; Sardans, J.; Peñuelas, J. Effects of seasonal and decadal warming on soil enzymatic activity in a P-deficient Mediterranean shrubland. Glob. Change Biol. 2020, 26, 3698–3714. [Google Scholar] [CrossRef]
  79. Peng, X.; Wang, W. Stoichiometry of soil extracellular enzyme activity along a climatic transect in temperate grasslands of northern China. Soil Biol. Biochem. 2016, 98, 74–84. [Google Scholar] [CrossRef]
  80. Darby, B.A.; Goodale, C.L.; Chin, N.A.; Fuss, C.B.; Lang, A.K.; Ollinger, S.V.; Lovett, G.M. Depth patterns and connections between gross nitrogen cycling and soil exoenzyme activities in three northern hardwood forests. Soil Biol. Biochem. 2020, 147, 107842. [Google Scholar] [CrossRef]
  81. Yang, H.; Zhang, P.; Zhu, T.; Li, Q.; Cao, J. The characteristics of soil C, N, and P stoichiometric ratios as affected by geological background in a karst graben area, Southwest China. Forests 2019, 10, 736. [Google Scholar] [CrossRef]
  82. Song, S.; Xiong, K.; Chi, Y. Ecological stoichiometric characteristics of plant–soil–microorganism of grassland ecosystems under different restoration modes in the karst desertification area. Agronomy 2023, 13, 1503. [Google Scholar] [CrossRef]
  83. Sridhar, B.; Wilhelm, R.C.; Debenport, S.J.; Fahey, T.J.; Buckley, D.H.; Goodale, C.L. Microbial community shifts correspond with suppression of decomposition 25 years after liming of acidic forest soils. Glob. Change Biol. 2022, 28, 5399–5415. [Google Scholar] [CrossRef]
  84. Wei, S.; Ding, S.; Lin, H.; Li, Y.; Zhang, E.; Liu, T.; Duan, X. Microbial and enzymatic C:N:P stoichiometry are affected by soil C:N in the forest ecosystems in southwestern China. J. Environ. Manag. 2024, 443, 116819. [Google Scholar] [CrossRef]
  85. Steenbergh, A.K.; Bodelier, P.L.E.; Hoogveld, H.L.; Slomp, C.P.; Laanbroek, H.J. Phosphatases relieve carbon limitation of microbial activity in Baltic Sea sediments along a redox-gradient. Limnol. Oceanogr. 2011, 56, 2018–2026. [Google Scholar] [CrossRef]
  86. Godin, A.M.; Lidher, K.K.; Whiteside, M.D.; Jones, M.D. Control of soil phosphatase activities at millimeter scales in a mixed paper birch–Douglas-fir forest: The importance of carbon and nitrogen. Soil Biol. Biochem. 2015, 80, 62–69. [Google Scholar] [CrossRef]
  87. Guo, P.; Han, T.; Zhang, L.; Li, S.; Ma, D.; Du, Y. Changes of soil bacterial activities and functions after different N additions in a temperate forest. Environ. Sci. Pollut. Res. 2017, 24, 3853–3860. [Google Scholar] [CrossRef] [PubMed]
  88. Zhu, F.; Cakmak, E.K.; D'AMico, F.; Candela, M.; Turroni, S.; Cetecioglu, Z. Phosphorus mining from marine sediments adopting different carbon/nitrogen strategies driven by anaerobic reactors: The exploration of potential mechanism and microbial activities. Sci. Total Environ. 2024, 914, 169936. [Google Scholar] [CrossRef] [PubMed]
Forests 16 01216 g001
Figure 1. Location of the Nonggang National Nature Reserve, illustrating the karst peak-cluster depression landscape and the associated seasonal rainforest.
Figure 1. Location of the Nonggang National Nature Reserve, illustrating the karst peak-cluster depression landscape and the associated seasonal rainforest.
Forests 16 01216 g001
Forests 16 01216 g002
Figure 2. Distributions of soil total C, N, and P contents and their stoichiometric ratios across different elevations and soil depths. (a) SOC; (b) TN; (c) TP; (d) SCN; (e) SCP; (f) SNP. Lowercase letters denote significant differences among elevations within the same soil depth, whereas uppercase letters indicate significant differences between soil depths within the same elevation (p < 0.05). Error bars show mean ± standard error.
Figure 2. Distributions of soil total C, N, and P contents and their stoichiometric ratios across different elevations and soil depths. (a) SOC; (b) TN; (c) TP; (d) SCN; (e) SCP; (f) SNP. Lowercase letters denote significant differences among elevations within the same soil depth, whereas uppercase letters indicate significant differences between soil depths within the same elevation (p < 0.05). Error bars show mean ± standard error.
Forests 16 01216 g002
Forests 16 01216 g003
Figure 3. Distributions of soil microbial C, N, and P contents and their stoichiometric ratios across different elevations and soil depths. (a) MBC; (b) MBN; (c) MBP; (d) MCN; (e) MCP; (f) MNP. Lowercase letters denote significant differences among elevations within the same soil depth, whereas uppercase letters indicate significant differences between soil depths at the same elevation (p < 0.05). Bars represent the mean ± standard error.
Figure 3. Distributions of soil microbial C, N, and P contents and their stoichiometric ratios across different elevations and soil depths. (a) MBC; (b) MBN; (c) MBP; (d) MCN; (e) MCP; (f) MNP. Lowercase letters denote significant differences among elevations within the same soil depth, whereas uppercase letters indicate significant differences between soil depths at the same elevation (p < 0.05). Bars represent the mean ± standard error.
Forests 16 01216 g003
Forests 16 01216 g004
Figure 4. Distributions of soil enzymatic activities and their stoichiometric ratios across different elevations and soil depths. (a) BG; (b) NAG + LAP; (c) AP; (d) ECN; (e) ECP; (f) ENP. Lowercase letters indicate significant differences among elevations within the same soil depth, while uppercase letters indicate significant differences between soil depths at the same elevation (p < 0.05). The bars show mean ± standard error.
Figure 4. Distributions of soil enzymatic activities and their stoichiometric ratios across different elevations and soil depths. (a) BG; (b) NAG + LAP; (c) AP; (d) ECN; (e) ECP; (f) ENP. Lowercase letters indicate significant differences among elevations within the same soil depth, while uppercase letters indicate significant differences between soil depths at the same elevation (p < 0.05). The bars show mean ± standard error.
Forests 16 01216 g004
Forests 16 01216 g005
Figure 5. Correlations (Pearson’s r) of C-N-P contents (a) and stoichiometry (b) between soil, microbes, enzymes, and environmental factors. * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 5. Correlations (Pearson’s r) of C-N-P contents (a) and stoichiometry (b) between soil, microbes, enzymes, and environmental factors. * p < 0.05; ** p < 0.01; *** p < 0.001.
Forests 16 01216 g005
Forests 16 01216 g006
Figure 6. RDA illustrating the relationships between total soil, microbial, and enzymatic C:N:P stoichiometry and environmental factors. Red lines represent response variables, whereas blue lines represent explanatory variables.
Figure 6. RDA illustrating the relationships between total soil, microbial, and enzymatic C:N:P stoichiometry and environmental factors. Red lines represent response variables, whereas blue lines represent explanatory variables.
Forests 16 01216 g006
Forests 16 01216 g007
Figure 7. SEM was used to explore the pathways by which Ele, pH, and ECa affect soil, microbial, and enzymatic C:N:P ratios. Single-headed arrows represent hypothesized causal paths, with blue and red solid arrows indicating positive and negative effects, respectively, and arrow width denoting relationship strength. Standardized path coefficients are shown along the arrows, while R2 values represent the variance explained. Solid and dashed lines indicate direct and indirect effects, respectively. Red “↑” and “↓” symbols denote positive or negative associations with PCA1. * p < 0.05; *** p < 0.001.
Figure 7. SEM was used to explore the pathways by which Ele, pH, and ECa affect soil, microbial, and enzymatic C:N:P ratios. Single-headed arrows represent hypothesized causal paths, with blue and red solid arrows indicating positive and negative effects, respectively, and arrow width denoting relationship strength. Standardized path coefficients are shown along the arrows, while R2 values represent the variance explained. Solid and dashed lines indicate direct and indirect effects, respectively. Red “↑” and “↓” symbols denote positive or negative associations with PCA1. * p < 0.05; *** p < 0.001.
Forests 16 01216 g007
Table 1. Summary of mixed effect models assessing the effects of elevation, soil depth, and their interaction on soil total, microbial, and enzymatic C-N-P contents and their stoichiometric ratios. (The F-values are presented in the table. * p < 0.05; ** p < 0.01).
Table 1. Summary of mixed effect models assessing the effects of elevation, soil depth, and their interaction on soil total, microbial, and enzymatic C-N-P contents and their stoichiometric ratios. (The F-values are presented in the table. * p < 0.05; ** p < 0.01).
CategoryFactorsElevationDepthElevation × Depth
SoilSOC3.99 *46.53 **0.22
TN5.14 *35.72 **0.57
TP36.57 **2.320.07
SCN2.8418.05 **0.96
SCP48.25 **17.26 **1.846
SNP66.44 **4.76 *0.43
MicrobeMBC0.8721.74 **0.25
MBN0.2116.57 **0.90
MBP20.96 **2.740.19
MCN2.813.973.65 *
MCP2.472.460.16
MNP3.49 *6.35 *1.00
EnzymeBG0.9920.49 **1.00
NAG + LAP3.36 *4.51 *0.16
AP4.43 *44.50 **1.02
ECN0.425.39 *0.60
ECP0.230.480.47
ENP0.7211.57 **0.63
Table 2. Contribution of individual environmental factors in the redundancy analysis.
Table 2. Contribution of individual environmental factors in the redundancy analysis.
Environment FactorExplanatory Contribution (%)Explanatory Rate (%)p Value
pH23.0633.860.001
Ele16.724.520.001
ECa8.9513.140.032
SD7.310.720.005
EMg5.067.430.002
SR2.713.980.033
DBH2.513.690.170
SWC1.782.610.006
Total68.07100.00
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, S.; Xu, C.; Hu, C.; Zhong, C.; Zhang, Z.; Hu, G. Elevational Patterns and Drivers of Soil Total, Microbial, and Enzymatic C:N:P Stoichiometry in Karst Peak-Cluster Depressions in Southwestern China. Forests 2025, 16, 1216. https://doi.org/10.3390/f16081216

AMA Style

Chen S, Xu C, Hu C, Zhong C, Zhang Z, Hu G. Elevational Patterns and Drivers of Soil Total, Microbial, and Enzymatic C:N:P Stoichiometry in Karst Peak-Cluster Depressions in Southwestern China. Forests. 2025; 16(8):1216. https://doi.org/10.3390/f16081216

Chicago/Turabian Style

Chen, Siyu, Chaohao Xu, Cong Hu, Chaofang Zhong, Zhonghua Zhang, and Gang Hu. 2025. "Elevational Patterns and Drivers of Soil Total, Microbial, and Enzymatic C:N:P Stoichiometry in Karst Peak-Cluster Depressions in Southwestern China" Forests 16, no. 8: 1216. https://doi.org/10.3390/f16081216

APA Style

Chen, S., Xu, C., Hu, C., Zhong, C., Zhang, Z., & Hu, G. (2025). Elevational Patterns and Drivers of Soil Total, Microbial, and Enzymatic C:N:P Stoichiometry in Karst Peak-Cluster Depressions in Southwestern China. Forests, 16(8), 1216. https://doi.org/10.3390/f16081216

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop