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Article

Plant–Soil–Microbial Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry in Mongolian Pine-Planted Forests Under Different Environmental Conditions in Liaoning Province, China

by
Hui Li
1,2,
Yi Yang
1,2,
Xiaohang Weng
1,2,
Yongbin Zhou
3,
Songzhu Zhang
1,2,
Liying Liu
1,2 and
Jiubo Pei
4,*
1
College of Forestry, Shenyang Agricultural University, Shenyang 110866, China
2
Research Station of Liaohe-River Plain Farmland Shelterbelt Ecosystem CEN, Shenyang Agricultural University, Shenyang 110866, China
3
Institute of Modern Agricultural Research, Dalian University, Dalian 110161, China
4
College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(5), 720; https://doi.org/10.3390/f16050720
Submission received: 14 March 2025 / Revised: 10 April 2025 / Accepted: 22 April 2025 / Published: 23 April 2025
(This article belongs to the Section Forest Soil)
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Abstract

:
Mongolian pine (Pinus sylvestris var. Mongolia) has been widely utilized as a key species for afforestation projects within the Three-North Shelterbelt of Liaoning Province in China. Its impressive ecological resilience has made it a favorite choice for this endeavor. However, as the stands mature and climate conditions shift, some areas are experiencing premature decline or even mortality. Ecological stoichiometry is capable of uncovering the supply and equilibrium of plant and soil nutrients within ecosystems and is extensively utilized in the identification of limiting elements. Therefore, studying its ecological stoichiometry and internal stability dynamics is of crucial significance for clarifying the nutrient cycling process in the Mongolian pine region and alleviating the decline situation. The eastern and northwestern regions of Liaoning differ significantly in precipitation and soil nutrient availability. This study examines Mongolian pine plantations in both regions, analyzing the carbon (C), nitrogen (N), and phosphorus (P) content in plant tissues, soil, microbial biomass, and stoichiometric ratio under distinct environmental conditions. In order to provide a theoretical basis for alleviating the decline of artificial poplar forests and healthy management. Results indicate that (1) leaf C, N, and P contents in the eastern Liaoning region averaged 496.67, 15.19, and 1.66 g·kg−1, respectively, whereas those in northwestern Liaoning were 514.16, 14.82, and 1.23 g·kg−1, respectively. Soil C, N, and P concentrations exhibited notable regional differences, with eastern Liaoning recording 34.54, 2.62, and 0.48 g·kg−1, compared to significantly lower values in northwestern Liaoning (7.74, 0.77, and 0.21 g·kg−1). Similarly, microbial biomass C, N, and P were higher in eastern Liaoning (18.63, 5.09, and 7.72 mg·kg−1) than in northwestern Liaoning (10.18, 3.46, and 4.38 mg·kg−1). (2) The stoichiometric ratio of soil in the Mongolian pine plantations is higher than that in northwestern Liaoning, but the stoichiometric ratio of plants shows the opposite trend. Specifically, microbial carbon-to-nitrogen (MBC/MBN) ratios are higher in eastern Liaoning, whereas microbial carbon-to-phosphorus (MBC/MBP) and nitrogen-to-phosphorus (MBN/MBP) ratios are greater in northwestern Liaoning. Correlation analysis of plant–soil–microbe stoichiometry indicates that plant growth in both regions is co-limited by nitrogen, with Mongolian pine exhibiting strong internal stability.

1. Introduction

Ecological stoichiometry has become a pivotal research focus in ecology, synthesizing principles from multiple scientific disciplines. It examines the balance of energy and essential chemical elements—primarily carbon (C), nitrogen (N), and phosphorus (P)—within ecosystems [1,2]. These elements and their stoichiometric ratios are fundamental indicators of soil quality and nutrient status [3]. Variations in the concentrations of soil C, N, and P can influence the stoichiometric ratios across different ecosystem components [4], offering valuable insights into soil-plant interactions [5,6]. The analysis of stoichiometry is widely used to investigate feedback mechanisms among ecosystem components and to elucidate elemental interactions within biological processes [7]. Previous research indicates that soil stoichiometry ratios varied significantly between vegetation types and substrate conditions [8,9]. Furthermore, investigations into C/N/P stoichiometry have broadened to include diverse ecosystem types [10], with leaf stoichiometry closely linked to transpiration and photosynthesis, acting as a key indicator of plant physiological status [11]. Leaf N/P is often used as a yardstick to predict nutrient deficiencies that can stunt plant growth. Specifically, an N/P exceeding 16 indicates phosphorus limitation at the community level, whereas a ratio below 14 suggests nitrogen limitation. When N/P falls between 14 and 16, plant growth may be constrained by either nitrogen, phosphorus, or both [12]. Nitrogen and phosphorus are the primary limiting nutrients in ecosystems, with their bioavailability directly influencing plant growth and metabolic functions. Their bioavailability significantly influences the regulation of biomass yields and carbon storage [13]. The C, N, and P content in microbial biomass, as well as their respective ratios, are essential in determining ecosystem productivity and influencing the intricate dance of nutrient cycling [14,15]. As noted by Chen et al., MBC, MBN, and MBP are essential components of soil microbes. Although extensive research on ecological stoichiometry has explored diverse taxa and ecosystem types [16], investigations into soil microbial stoichiometry have primarily focused on how stand type, stand age, community structure, environmental variables, and anthropogenic disturbances influence microbial biomass [17]. Homeostasis, a critical long-term evolutionary adaptation in plants, enables them to maintain stable internal nutrient levels despite fluctuating environmental conditions, particularly under variable N and P availability. The existing results show that the strength of plant-microbial stoichiometric stability is influenced by many factors, including nutrient supply status, water conditions, etc. [18]. However, a significant research gap remains in the comprehensive investigation of C/N/P stoichiometric linkages within the soil–plant–microbe continuum. Therefore, constructing the stability index model to deeply study the relationship of plant-microbial element coupling in the soil ecosystem from the perspective of ecological stoichiometry is of great significance for evaluating the growth restriction factors of Mongolian pine plantation and improving soil fertility and ecosystem stability for subsequent afforestation projects.
Mongolian pine (Pinus sylvestris var. Mongolia), a highly adaptable evergreen species with notable drought resistance and cold tolerance, has been extensively used in the development of semi-arid plantation forest ecosystems across northern China [19]. As a cornerstone species, it plays a pivotal role in establishing the Three-North Shelter Forest System and implementing extensive desertification management projects. The construction of its planted forests is not only conducive to the development of the forestry planting industry and economic growth but also contributes to the advancement of regional ecological environment construction and ecological protection [20]. However, since the early 1990s, Mongolian pine plantations in northwest Liaoning have exhibited widespread decline, with many stands experiencing severe mortality. If this trend persists, it may result in substantial setbacks to the advancement of the Three-North Shelter Forest System. Therefore, urgent measures are needed to mitigate the decline of Mongolian pine plantations and ensure the sustainability of afforestation efforts [21]. Eastern Liaoning has a temperate monsoon climate, characterized by an average annual precipitation of approximately 750–850 mm and nutrient-rich soils, which collectively create optimal conditions for plant growth [22]. In contrast, northwest Liaoning lies within the dry sub-humid climate zone, receiving an average annual precipitation of approximately 450 mm, which imposes significant constraints on plant growth and development [23]. Previous studies have shown that variations in precipitation in northwest Liaoning can profoundly influence the ecological stoichiometry of soil and plants in Mongolian pine plantations within the Horqin Sandy Land. Increased precipitation exacerbates P limitation in Mongolian pine plantations within this region [20]. In eastern Liaoning, precipitation positively influences soil organic carbon content, though its concentration declines with increasing soil depth [24]. However, comparative studies between eastern and northwestern Liaoning remain limited. Therefore, a comparative analysis of stoichiometric characteristics between these two regions is essential for identifying the key limiting factors influencing Mongolian pine plantation growth. Based on these observations, we propose the following hypotheses: (1) The concentrations of C, N, and P in leaves, soil, and microbes differ markedly across climate zones. (2) The stoichiometric ratios of leaves, soil, and microorganisms showed significant regional differences across climatic zones. (3) What are the main factors restricting the growth of artificial forests of Mongolian pine in different temperature zones in Liaoning Province?

2. Overview of the Study Area and Research Methodology

2.1. Overview of the Study Area

Study area 1, Liaoning Forestry School Haiyang Experimental Forestry Farm (East Liaoning), located in the east Liaoning mountainous area of Liaoning Province, is located in Haiyang Village, Nankouqian Township, Qingyuan Manchu Autonomous County, Fushun City (124°20′06″ E–125°28′58″ E, 41°47′52″ N–42°28′25″ N), with a total land area of 3932.96 km2, with an average elevation of 241.4 m. The area belongs to the mesothermal East Asia continental The climate zone is characterized by hot summers with little rain, cold winters with long duration, large temperature differences, and four seasons like spring, with an average annual temperature of 7.1 °C, 750–850 mm of precipitation, 2091 h of sunshine, and a frost-free period that typically lasts 130–150 days. The soils are predominantly brown loamy, with acidic soils accounting for 65.72% of the total.
Study area 2, Liaoning Liaohe Plain Forest Ecosystem Positioning Research Station (Northwest Liaoning), is located in the northwest corner of Changtu County, Tieling City, Liaoning Province (123°32′ E–124°26′ E, 42°33′ N–43°29′ N), which is located in the southeast Liaoning edge of the huqin sandy land, with an average elevation of 167 m. A moderate continental monsoon climate zone encompasses the research region, with four distinct seasons, and the precipitation is mainly in the Kerchin sandland, which overlaps with the high-temperature period, creating a rainy and hot period. The average annual rainfall is approximately 450 mm, and the sunlight duration is 2775.5 h [25]. The average annual temperature of the site is at the level of 7 °C, with a significant seasonal temperature difference, rising to 36 °C in summer and plummeting to minus 33 °C in winter. The soils are deep, and in order from east to west, they are dark brown loam, black or meadow soil, and windy sandy soil.

2.2. Research Method

2.2.1. Study the Sample Area Setting

Sampling sites were identified in Qingyuan Manchu Autonomous County, east Liaoning, and Changtu County, northwest Liaoning, in 2018. Fifteen plots were selected for each region, three squares of 20 m × 30 m were set in each site, and “S”-shaped sites were used in each square, for a total of 90 samples. Detailed measurements were taken for each tree, and the records included the diameter at the geographic coordinates (longitude and latitude), diameter at breast height (DBH), elevation, slope, and slope aspect of each sample plot, which were investigated and recorded (Table 1).

2.2.2. Methods of Collecting Plant Samples

Collection of needle leaf samples: A representative sample was collected from each square following the selection of three Mongolian pines at random. The needle leaves were meticulously harvested from the top, middle, and bottom sections of each tree’s canopy in the east, south, west, and north quadrants, utilizing sharp pruning shears. The leaves were then carefully stored in separate, clearly marked, self-sealing bags. The bags were then hermetically sealed to prevent cross-contamination, ensuring the integrity of foliar nutrient analysis. The samples were heat-treated at 105 °C to terminate the enzymatic activity, then dried at 65 °C until a constant weight was achieved. They were ground into 0.15 mm powder using a ball mill for the determination of the contents of C, N, and P in the plant needles.

2.2.3. Methods of Soil Sample Collection

Collection of soil samples: Five to twenty soil samples were taken from 0 to 20 cm depths within each sample plot using the ring knife technique. The samples were combined into a single sample and rejected for roots, apoplast, and gravel. Each sample was mixed and then separated into two sections: one section was refrigerated at 4 °C to measure the amount of soil microbial C, N, and P, and the other section was left in a cool, well-ventilated area to dry naturally before being sealed in self-sealing bags and filtered through a 100-mesh sieve (0.15 mm) to determine the physicochemical characteristics of the soil.

2.2.4. Methods for Determination of Physical and Chemical Properties of Plants and Soils

A blank control group was set for instrument calibration, and three replicates were performed for each sample to ensure the reliability of the data.
Determination of plant C, N, and P contents: leaf TC and TN contents: A TOC analyzer (Vario MAXCN, Elementar, Langenselbold, Germany) was used to measure 6.00–7.00 mg of sample from the ground plant samples using a one-millionth balance, which was placed into a tin boat and subsequently placed in the sample cauterization tube of the elemental analyzer. After setting the relevant parameters, the machine automatically measured the leaf’s TC and TN content. Leaf TP content: after digestion with H2SO4-H2O2, the measurement was conducted using a spectrophotometer (MAPADA P4, Mapada Instruments Co. Ltd., Shanghai, China) by the molybdenum blue colorimetric method [26].
Soil TC and TN contents: Soil TC and TN contents were determined by grinding and sieving the collected soil using a TOC analyzer (Vario MAXCN, Elementar, Langenselbold, Germany), putting it into a bag, taking 30–35 mg of soil samples with a small spoon, and analyzing the samples after wrapping them in a tin boat. Soil total phosphorus (TP) content: The sample is slightly moistened, and 8 mL of concentrated sulfuric acid are added. Shake well and let it stand overnight. The next day, add an appropriate amount of perchloric acid with a concentration of 70%–72%. At this point, a small funnel is placed at the mouth of the bottle. Then, heat the sample in an electric furnace until boiling. After boiling, cool it down. Once cooled, filter through filter paper, and finally adjust to 100 mL. Finally, the resulting filtrate will be analyzed by a flame atomic spectrophotometer (Hitachi Z2000, Hitachi, Tokyo, Japan) [27].

2.2.5. Methods for Determination of Soil MBC, MBN, and MBP Content

MBC and MBN will be determined by chloroform fumigation–K2SO4 leaching with a TOC analyzer (Vario MAXCN, Langenselbold, Germany), and MBP will be determined by chloroform fumigation–K2SO4 leaching with a molybdenum antimony colorimetric assay [28].

2.2.6. Homeostasis Index Calculation Method for Nutrient Content and Stoichiometry of Plants and Soil Microorganisms

To assess the nutrient composition, microbial biomass, and various properties such as plant, soil, and elemental ratio in Mongolian pine plantations across eastern and northwest Liaoning, a Pearson correlation analysis was conducted. Additionally, the homeostasis model was applied to delve deeper into the internal stability of C, N, and P contents, as well as their proportions, in both regions, in light of environmental fluctuations. This was performed in order to shed light on Mongolian pine’s ability to maintain the stability of its element characteristics when the test results showed significance (p < 0.05).
Calculation   formula :   log 10 ( y ) = log 10 ( c ) + log 10 ( x ) / H
In the given equation, the variable ’y’ denotes the nutrient profile and stoichiometric balance of both leaf and soil microorganisms, while ’x’ signifies the nutrient content and stoichiometric balance of the soil, and ’c’ stands as a constant. The slope of the equation is calculated as the reciprocal of ’H’ (1/H). According to the classification of Su Bingqian et al. [29], strict homeostasis is deemed present when the equation’s fit lacks statistical significance (p ≥ 0.1). Should the fit be statistically significant, the classification unfolds as follows: If 0 < 1/H ≤ 0.25, it indicates a stable condition; for 0.25 < 1/H ≤ 0.5, it is categorized as a weakly stable state; 0.5 < 1/H ≤ 0.75 denotes a weakly sensitive state; and when 1/H exceeds 0.75, it represents a sensitive state.

2.3. Statistical Analysis of Data

In Excel 2016 software, all the statistical data were expressed as mean ± standard error; SPSS 26.0 was used to carry out data analysis; GraphPad Prism8 and Canoco5 were used to draw the experimental data visualization graphs; and the correlation heat map was plotted using Origin 2025. A critical component of the analytical process was to conduct an independent sample t-test, which allowed for the examination of significant variations in the nutrient levels between plant–soil–microbe interactions and their chemical balance. The correlation between plant–soil–microbe stoichiometric characteristics was explored using the Pearson correlation coefficient method. To further reveal the complex relationship between plant, soil, and microbial nutrient contents, statistical tools such as internal stability analysis as well as redundancy analysis were also applied in this study. When peering at the statistical figures, it becomes important to scrutinize when the coefficient of variation tips over the 15% mark, as such high variance could indicate irregularities in the data. The coefficient of variation, abbreviated as CV, is calculated by taking the standard deviation, dividing it by the mean value, and then multiplying by 100.

3. Results

3.1. Leaf C, N, and P Contents and Stoichiometric Characteristics of Mongolian Pine Plantation Forests in Different Regions

As shown in Figure 1, in Mongolian pine plantation forests in the east Liaoning region, the mean values of TC, TN, and TP contents of leaves were 496.67, 15.19, and 1.66 g·kg−1, respectively. In the northwest Liaoning region, the mean values of TC, TN, and TP contents of leaves were 514.16, 14.82, and 1.23 g·kg−1, respectively. The TC contents of the leaves in the east Liaoning region were lower than those in the northwest Liaoning. The differences in TC content were larger and lower in east Liaoning than in northwest Liaoning, while the differences in TN and TP content were smaller, and the trend was the same: higher in east Liaoning than in western Liaoning. An independent sample t-test showed that the TC and TP contents of the needles in the Mongolian pine plantation forests in the two regions differed significantly (p < 0.01); however, there was no significant difference in the TN contents (p > 0.05). In the east Liaoning area, the average values of C/N, C/P, and N/P for the leaves of the Mongolian pine plantation forest were 32.73, 299.05, and 9.14, respectively; in the northwest Liaoning area, the corresponding mean values were 34.69, 416.86, and 12.08. The east of the Liaoning area had lower levels of C/N, C/P, and N/P than the northwest, and the pattern of these changes was constant. There were substantial differences in C/N (p < 0.05) and very significant differences in C/P and N/P (p < 0.01) between the two regions. Previous studies have demonstrated that N and P distributions show pronounced spatial heterogeneity even at fine regional scales, primarily driven by variations in species composition, stand age, and tissue-specific allocation patterns [30]. Our findings reveal distinct biogeochemical patterns between regions: foliar samples from eastern Liaoning exhibited significantly higher concentrations of C and P, but that of N was relatively low (p < 0.05), suggesting N limitation may be the primary factor constraining productivity in Mongolian pine plantations. In contrast, northwest Liaoning stands showed elevated C content but reduced N and P concentrations, indicating potential co-limitation by both N and P availability.

3.2. Soil C, N, and P Contents and Stoichiometric Characteristics of Mongolian Pine Plantation Forests in Different Regions

The mean values of soil TC, TN, and TP contents in the east Liaoning area were 34.54, 2.62, and 0.48 g·kg−1, respectively, as illustrated in Figure 2. In the northwest Liaoning area, the corresponding mean values were 7.74, 0.77, and 0.21 g·kg−1. TC, TN, and TP all showed the same trend of change. The trend for TC, TN, and TP was similar, with east Liaoning showing a stronger trend than northwest Liaoning. The two regions’ soil TC, TN, and TP contents differed significantly (p < 0.01). The average soil C/N, C/P, and N/P values in east Liaoning were 13.20, 71.32, and 5.41, respectively, while in northwest Liaoning, the corresponding mean values were 10.07, 37.18, and 3.70. The trends of C/N, C/P, and N/P were consistent and were higher in east Liaoning than in northwest Liaoning. Soil nutrient stoichiometric ratios of the two regions were all very significantly different (p < 0.01). These patterns suggest that eastern soils have greater organic matter accumulation and nutrient retention efficiency, likely due to climatic and vegetation differences. East Liaoning receives higher precipitation, supporting more robust plant growth and subsequent organic matter input into soils. In contrast, the arid and semi-arid conditions of the northwest limit vegetation productivity, leading to lower carbon and nitrogen accumulation. Additionally, differences in soil parent material, land use, and microbial activity may further contribute to these regional variations.
Forests 16 00720 g002
Figure 2. Soil nutrient content and stoichiometric ratio of Mongolian pine plantations in different regions. (A) Soil TC; (B) Soil TN; (C) Soil TP; (D) Soil TC/TN; (E) Soil TC/TP; (F) Soil TN/TP.
Figure 2. Soil nutrient content and stoichiometric ratio of Mongolian pine plantations in different regions. (A) Soil TC; (B) Soil TN; (C) Soil TP; (D) Soil TC/TN; (E) Soil TC/TP; (F) Soil TN/TP.
Forests 16 00720 g002

3.3. Microbial C, N, and P Contents and Stoichiometric Characteristics of Mongolian Pine Plantation Forests in Different Regions

As shown in Figure 3, the average values of soil MBC, MBN, and MBP contents in the east Liaoning region were 18.63 mg·kg−1, 5.09 mg·kg−1, and 7.72 mg·kg−1, respectively, and the average values of soil MBC, MBN, and MBP contents in the northwest Liaoning region were 10.18 mg·kg−1, 3.46 mg·kg−1, and 4.38 mg·kg−1, respectively. The differences in soil microbial biomass between the two regions were larger, and both showed that the east Liaoning region was higher than the western Liaoning region (p < 0.01).
Forests 16 00720 g003
Figure 3. Microbial nutrient content and stoichiometric ratio of Mongolian pine plantations in different regions. (A) MBC; (B) MBN; (C) MBP; (D) MBC/MBN; (E) MBC/MBP; (F) MBN/MBP.
Figure 3. Microbial nutrient content and stoichiometric ratio of Mongolian pine plantations in different regions. (A) MBC; (B) MBN; (C) MBP; (D) MBC/MBN; (E) MBC/MBP; (F) MBN/MBP.
Forests 16 00720 g003

3.4. Correlation Analysis of Plant–Soil–Microorganism C, N, P Content and Stoichiometric Ratio of Mongolian Pine Plantation Forests in Different Regions

Pearson correlation analysis indicated a relationship between the nutrient levels of plants, soil, and microbes and the stoichiometric properties of Mongolian pine plantation forests located in both eastern and northwestern Liaoning.
As illustrated in Figure 4A, a noteworthy positive correlation exists in the eastern region of Liaoning between the leaf C/P ratio and the concentrations of both leaf TC and soil TC, with a statistically significant p-value of less than 0.05. In contrast, there is a strong negative correlation (p < 0.01) between the concentration of leaf TN and the leaf C/N ratio, while a positive correlation (p < 0.01) is observed with leaf N/P. Furthermore, the content of soil TP demonstrates a negative correlation (p < 0.05) with both soil C/P and soil N/P ratios, indicating a complex relationship among these variables. Additionally, a significant difference was noted concerning MBP with a p-value under 0.05, and a positive correlation was identified between the content of MBC and both MBN and MBP. The concentration of MBN displayed a significant negative correlation with the MBC/MBN ratio, supported by a p-value of less than 0.01, which signifies a robust relationship. Conversely, the MBP content was positively correlated with MBC, again with statistical significance (p < 0.05). Notably, MBP content also exhibited a negative correlation with both the MBC/MBP and MBN/MBP ratios, while it maintained a significant positive correlation with the MBC/MBN ratio (p < 0.01). Additionally, the relationship between MBP content and the MBC/MBN ratio further underscored a negative correlation that was statistically significant, with a p-value of less than 0.05.
Figure 4B illustrates a notable correlation between leaf total nitrogen (TN) in northwest Liaoning and the associated leaf carbon-to-nitrogen (C/N) and nitrogen-to-phosphorus (N/P) ratios. In detail, TN reveals a strong positive relationship with the C/N ratio (p < 0.01), while displaying a significant negative relationship with the N/P ratio (p < 0.01). A similar pattern is noted for leaf carbon-to-phosphorus (C/P) and N/P, with both ratios showing significant increases as the leaf total phosphorus (TP) content decreases (p < 0.01). Regarding soil characteristics, higher C/N and C/P ratios significantly boost total carbon (TC) levels. Soil C/P shows an exceptionally strong positive correlation with TC (p < 0.01), whereas soil C/N indicates a moderate yet still significant correlation (p < 0.05). Additionally, the concentration of soil TN is intimately connected to both C/N and N/P ratios (p < 0.01). Interestingly, as the C/N ratio increases, the levels of TN decline, whereas they rise in response to escalating N/P ratios, suggesting a dynamic balance among these factors. Additionally, TP levels in the soil were found to decrease in relation to both the C/P and N/P ratios; a significant correlation with the C/P ratio was observed (p < 0.05), and this correlation became even more pronounced with the N/P ratio (p < 0.01). A significant inverse relationship was discovered between MBC and MBP levels, with significance at p < 0.05. Conversely, a notable positive relationship was detected between MBC and the MBC/MBN and MBC/MBP ratios. Furthermore, MBN levels demonstrated a negative correlation with the MBC/MBN ratio, also reaching significance at p < 0.05. In addition, MBP levels exhibited significant negative correlations with both MBC/MBP and MBN/MBP levels (p < 0.01). Moreover, the MBC/MBN ratio showed a positive correlation with the MBC/MBP ratio, which in turn was positively correlated with the MBN/MBP ratio (p < 0.05). The MBP levels were negatively correlated with both MBC/MBP and MBN/MBP levels, with this relationship being highly significant (p < 0.01). The MBP concentrations also displayed a significant negative correlation with MBC/MBP and MBN/MBP (p < 0.01). Lastly, the MBC/MBN ratio showed a positive association with the MBC/MBP concentration, while the concentration of MBC/MBP was positively linked with the MBN/MBP concentration (p < 0.05).
The findings indicated a positive relationship between the stand characteristics observed in the two regions. As the leaf C/N ratio increased, there was a notable decrease in leaf TN content (p < 0.01). Similarly, a significant reduction in leaf TP content was found with a higher leaf C/P ratio (p < 0.01). The soil TC levels in both regions markedly rose as soil C/P increased (p < 0.05). While TN content saw a significant rise with increasing N/P ratios (p < 0.01), it showed a significant decrease alongside rising C/N ratios (p < 0.05). Additionally, TP content significantly declined as C/P and N/P ratios increased (p < 0.05). Significant negative relationships were identified between MBN content and the ratios of MBC/MBN, MBP content with MBC/MBP, and MBN/MBP across the two regions (p < 0.05). A negative correlation was also observed between MBP content and MBC/MBN/MBP, while MBC/MBN displayed a negative correlation with MBN/MBP (p < 0.01). Conversely, MBC/MBN had a positive correlation with MBC/MBP, which was significantly correlated with MBN/MBP (p < 0.05).

3.5. Homeostasis and RDA Analysis of Plant–Soil–Microorganism C, N, and P Contents and Stoichiometric Ratios in Mongolian Pine Plantation Forests in Different Regions

As shown in Table 2 and Table 3, according to the coefficient of internal stability balance, the values of soil–plant–microbe nutrient contents and stoichiometric ratios of east Liaoning and northwest Liaoning regions showed “strict internal stability”. Plant–soil–microbial systems in both study areas demonstrated consistent homeostasis in elemental composition under varying soil conditions, indicating robust biogeochemical stabilization mechanisms.
The findings from the RDA analysis concerning the eastern Liaoning region (see Figure 5A) indicated that Axis 1 and Axis 2 accounted for 62.48% and 11.54% of the variations in the content and stoichiometric ratios of C, N, and P within plants, soil, and soil microbial biomass, respectively. The overall cumulative explanation reached 74.01%, with MBC/MBP contributing the most at a rate of 18.5%. The stoichiometric characteristics and contents of C, N, and P in plants influenced the corresponding characteristics and contents in soil and microorganisms. In the northwestern part of Liaoning Province (refer to Figure 5B), the contributions of C, N, and P content in plant, soil, and microbial biomass, as well as their stoichiometric properties, were accounted for 76.91% and 7.73% by Axis 1 and Axis 2 of the RDA, respectively, achieving a total cumulative explanation of 84.65%. The soil TP contribution was recorded as the highest at 46.2%. It was determined that soil TP content significantly (p < 0.01) influences leaf stoichiometry, impacting both the concentrations of elements (C, N, P) and their ecological stoichiometric ratios.

4. Discussion

4.1. Leaf-Soil-Microbial C, N, and P Contents of the Mongolian Pine Plantation

Forest ecosystems rely fundamentally on the interdependent relationships among vegetation, edaphic factors, and microbial communities, which collectively govern structural integrity, functional processes, and biogeochemical cycling dynamics [31]. The contents of C, N, and P varied significantly among leaves, soil, and microorganisms, as well as between different regions. These findings align with our first hypothesis and are consistent with previous studies [32,33]. In eastern Liaoning, the mean TC concentration in leaves was 496.67 g·kg−1, while northwestern Liaoning recorded a slightly higher value of 514.16 g·kg−1. Both figures surpassed the global average of 464 g·kg−1 found in 492 terrestrial plant species [34]. Furthermore, these values were also higher than the average carbon content of 480.1 g·kg−1 reported for 102 forest species across northern and southern China [35], and the average carbon content of 60 major plant species in the Horqin Sandy Land, 424.20 g·kg−1 [36]. TN content in leaves was lower than the global average of 20.1 g·kg−1 [37], in Chinese plants 20.24 g·kg−1 [38], and in plant nitrogen content in arid areas 18.1 g·kg−1 [39]. In eastern Liaoning, the TN levels in leaves were greater than those found in northwestern Liaoning when comparing the two regions. The nitrogen nutrients in the soil mostly exist in the form of a water-soluble state, so when the plant roots absorb water from the soil, the nitrogen nutrients are taken up simultaneously [40], and the soil moisture content in the region of Northwest Liaoning province is relatively low, which leads to the low proportion of nutrients that plants can absorb, which finally leads to the low leaf nitrogen content in this region, which also becomes the dominant factor showing obvious differences in plant nitrogen content in different geographical areas. The TP content in leaves was lower than the global level of 1.99 g·kg−1, and the phosphorus content in plants in eastern Liaoning was higher than that in China at 1.30 g·kg−1 [41], while it was relatively lower in northwestern Liaoning. In humid climates, regular rainfall facilitates the movement of phosphorus derived from decomposed leaves and topsoil to deeper layers of soil via leaching. This process leads to a deficiency of phosphorus nutrients in the surface soil, contributing to the low levels of phosphorus found in plant leaves. In this study, the leaves of eastern Liaoning had higher carbon and phosphorus content and lower nitrogen content. The larch leaves in the northwestern part of Liaoning Province are characterized by high C content but low N and P contents. This is consistent with the findings of Han Zongyu, who measured the nutrient composition of Mongolian larch (Larix gmelinii) in the Zhanggutai region of Liaoning Province. The results suggest that Mongolian larch forests in eastern Liaoning may be N-limited, whereas those in the northwestern part of the province may be co-limited by both N and P [42]. Nitrogen and phosphorus exhibit substantial spatial heterogeneity even at small regional scales, likely due to variations in species composition, plant age, and differences in tissues and organs [35].
Soil, as a critical nutrient reservoir in ecosystems, provides essential support for plant growth and development. In this study, the TC and TN contents in soils from eastern Liaoning were found to be higher than the global averages of 25.71 and 2.10 g·kg−1, respectively [43], and the national average of 29.51 g·kg−1 [44]. In comparison, the concentrations of TC and TN in soils from the northwest region of Liaoning were found to be below the national averages, yet they exceeded the figures recorded by Zhang et al. for the pine forests in the same area, which were 2.88 and 0.21 g·kg−1, respectively [45]. The carbon content in northwest Liaoning was lower than the 9.80 g·kg−1 reported by Cheng Haotian et al. for the rhizosphere soil of Mongolian pine, while the nitrogen content was found to be higher [46]. These differences are closely related to the environmental contrasts between the two regions. Rainfall is a key ecological factor influencing the geographical distribution and nutrient composition of soil. Globally, differing patterns of precipitation play a role in creating various types of terrestrial soil [47]. In contrast to northwestern Liaoning, the eastern part of the Liaoning province experiences greater rainfall, promoting plant growth and resulting in increased carbon and nitrogen levels in the soil. Additionally, the soil’s TP content was observed to be less than the global average of 0.80 g·kg−1 and the national average of 0.77 g·kg−1 [48]. This lower phosphorus content may be attributed to the fact that soil phosphorus primarily derives from the weathering of parent material, a process that occurs over long periods and results in low spatial variability. Ongoing observation can be conducted to evaluate the effects of climate change on soil nutrient levels, combined with isotopic tracers (δ13C, δ1⁵N) to distinguish between plant sources and microbial contributions to soil carbon and nitrogen pools.
Microbial life in the soil is not just key to breaking down organic material and cycling nutrients; it is also a crucial storage system for the nutrients that plants need. This vital role it plays is essential for maintaining soil health and fostering robust plant growth. In this study, the contents of MBC, MBN, and MBP in soils in the two regions were lower than those in global soils (680.4, 105.0, and 40.3 mg·kg−1) and forest soils (629, 98, and 32 mg·kg−1) on average [33]. Research indicates that regular soil disruption during brief rotation cycles hinders the proliferation and development of soil microorganisms; however, the precise mechanism underlying this effect requires additional investigation [49]. Wardle pointed out that soil microorganisms are mainly composed of heterotrophic bacteria and fungi, and the available nutrient resources in soil are the driving factors regulating the growth of soil microbial biomass; the variation in soil nutrients typically aligns with the levels of microbial biomass present in the soil [50]. In the eastern and northwestern areas of Liaoning Province, however, there was no notable correlation found between soil microbial biomass and soil nutrient content, which may be because the plantation of Mongolian pine in the study area was in a rapid growth period, the above-ground biomass increased significantly with the age of the forest, and there was a competitive relationship between above-ground vegetation growth and the fixed soil microbial biomass [51,52]. Following control experiments, which may include the introduction of foreign materials and the removal of root systems, can be conducted to measure the competition for nitrogen (N) and phosphorus (P) between plants and microorganisms. Additionally, transcriptomic analyses can be employed to investigate the nutrient regulation mechanisms in artificial pine forests and to assess the influence of microbial diversity.

4.2. Leaf–Soil—The Microbial Stoichiometric Ratio of the Mongolian Pine Plantation

The C/N/P ratios serve as a useful tool for examining the relationships and fluctuations among these elements within the plant–soil–microbial system [53]. The stoichiometric ratios of carbon (C), nitrogen (N), and phosphorus (P) exhibit considerable variability and differing patterns depending on various environmental factors, supporting the second hypothesis of this research. In the Mongolian pine plantations located in eastern and northwestern Liaoning, the C/N and C/P ratios surpassed the global averages, which are 22.5 and 222, respectively. In contrast, the N/P ratio was found to be below the global average of 12.7 and the Chinese average of 16.3 for plants [4,38]. Elser et al. indicated that C/N and N/P ratios can be used to characterize plant growth rates, with lower ratios typically indicating faster plant growth [54]. The C/N and N/P ratios in eastern Liaoning were lower than those in northwestern Liaoning, suggesting that plant growth in eastern Liaoning is faster than in the northwest. Additionally, N/P is a useful indicator of nutrient limitations in plants, reflecting nitrogen deficiencies (N) and phosphorus (P) availability [55,56]. When the nitrogen-to-phosphorus ratio (N/P) is less than 14, nitrogen primarily restricts plant growth; if N/P exceeds 16, phosphorus becomes the key limiting element; and in the range of 14 to 16, both nitrogen and phosphorus together constrain plant growth [12]. In general, nitrogen is the primary limiting nutrient for global plants, while phosphorus tends to be the limiting factor for Chinese plants. In this study, in eastern and northwestern Liaoning, where the N/P ratio dips below 14, it is clear that nitrogen is the primary bottleneck for plant development. This aligns neatly with the figures Wang cited (10.62 and 10.54) for dark coniferous forests in Xianrendong, Liaoning [57], and Lei in the Mongolian pine shelterbelt forests of the Horqin Sandland, respectively [58]. The findings further validate the conclusion that nitrogen limitation predominantly hampers plant growth in Liaoning, aligning with the study’s observations.
Soil N/P serves as an effective tool to assess the nitrogen enrichment status in soil and to gauge its approach to saturation [59]. Some scholars argue that, in addition to absorbing nutrients from the soil, plants can also reabsorb nutrients before senescence and leaf fall [60]. Therefore, the soil N/P ratio alone may not fully capture the nutrient constraints faced by plants during their growth stages [61]. In this study, the C/N ratios in the soils of eastern and northwestern Liaoning were within the typical range for Chinese soils, 10–12 [2,43] The C/P in eastern Liaoning was found to be higher than the national average of 61.0 [62]; the low phosphorus levels found in the soil of this region indicate that phosphorus serves as the main limiting nutrient for restoring vegetation. Conversely, the C/P in northwestern Liaoning is below the national mean. Studies have shown that when the soil C/P ratio falls below 200, there is a temporary increase in MBC, whereas MBP improves the availability of phosphorus in the soil via net mineralization [44]. In this research, both regions had soil C/P below 200, suggesting relatively higher phosphorus content in the soils, but a higher C/P also indirectly reflects weaker phosphorus availability. This conclusion aligns with the outcomes of the conversion of total phosphorus to available phosphorus in karst regions [63]. The soil N/P in both eastern and northwestern Liaoning was lower than the global forest soil average of 6.6 [64] but higher than the national average for land soils of 5.2 in eastern Liaoning [48], with northwestern Liaoning showing a relatively lower N/P. The higher N/P in eastern Liaoning further supports the notion that nitrogen is relatively abundant in this region. Additionally, in terrestrial ecosystems, nitrogen fixation by organisms tends to decrease as soil N/P increases [32]. These results indicate that available nitrogen in eastern Liaoning is relatively scarce and suggest that nitrogen availability limits plant growth in the region.
Organic matter is a crucial nutrient source for soil microorganisms, supporting their growth and reproduction, and it is also the material basis for plant development. Soil microbial activity is closely associated with the concentrations of plant-derived organic matter and total organic carbon in the soil. Gao demonstrated that the ratios of MBC/MBN and MBC/MBP are indicative of soil organic matter quality [62]. Specifically, higher levels of available nitrogen and phosphorus in soil are associated with lower MBC/MBN and MBC/MBP ratios. Comparative analysis shows that the global stoichiometric ratio of soil microorganisms is higher than that of the two regions of Liaoning province [33]. The TN content in eastern Liaoning exceeds that in the northwestern region, and the lower nitrogen availability in the latter limits microbial utilization. Consequently, the microbial biomass in eastern Liaoning is higher. The composition and diversity of soil microbial communities are often represented by the MBC/MBN. Typically, bacterial communities exhibit an MBC/MBN ranging from 3 to 5, while fungal communities range from 4 to 15. A higher MBC/MBN generally reflects a greater proportion of fungal biomass [19,65]. The elevated MBC/MBN in eastern Liaoning suggests a higher fungal biomass in the plantation soils of the region.

5. Conclusions

Our analysis revealed pronounced regional heterogeneity in plant–soil–microbial nutrient pools (p < 0.01). Stoichiometric analyses demonstrated nitrogen limitation as the primary constraint for Mongolian pine growth across both eastern and northwestern Liaoning, with the species maintaining strong homeostatic regulation. Notably, while eastern Liaoning plantations exhibited higher soil nutrient stocks and greater fungal biomass (p < 0.05), plant-available nutrients remained comparatively low. The system-wide nutrient composition and stoichiometric relationships consistently exhibited absolute homeostasis, suggesting tight biogeochemical coupling across all compartments.

Author Contributions

Methodology, H.L.; Data curation, Y.Y.; Writing—original draft, Y.Y.; Writing—review & editing, H.L. and X.W.; Supervision, Y.Z., S.Z., L.L. and J.P.; Project administration, H.L.; Funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (31700552, 41450007, 31800364, and 31400611) and the Doctoral Research Start-up Fund (880416020).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Lu, T.; Shi, Z.; Niu, J.; Zhang, W. Research Progresses and Prospects of Terrestrial Ecological Stoichiometry in China. Soil 2016, 48, 29–35. [Google Scholar] [CrossRef]
  2. Wang, S.; Yu, G. Ecological stoichiometry characteristics of ecosystem carbon, nitrogen and phosphorus elements. Acta Ecol. Sin. 2008, 28, 3937–3947. [Google Scholar]
  3. Yang, J.-W.; Zhang, Q.-L.; Song, W.-Q.; Zhang, X.; Li, Z.-S.; Zhang, Y.-D.; Wang, X.-C. Response differences of radial growth of Larix gmelinii and Pinus sylvestris var. mongolica to climate change in Daxing’an Mountains, Northeast China. Ying Yong Sheng Tai Xue Bao 2021, 32, 3415–3427. [Google Scholar] [CrossRef]
  4. 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]
  5. Zhou, Y.; Boutton, T.W.; Wu, X.B. Soil C:N:P Stoichiometry Responds to Vegetation Change from Grassland to Woodland. Biogeochemistry 2018, 140, 341–357. [Google Scholar] [CrossRef]
  6. 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, 151583. [Google Scholar] [CrossRef]
  7. de Souza Oliveira Filho, J.; Vieira, J.N.; Ribeiro da Silva, E.M.; Beserra de Oliveira, J.G.; Pereira, M.G.; Brasileiro, F.G. Assessing the Effects of 17 Years of Grazing Exclusion in Degraded Semi-Arid Soils: Evaluation of Soil Fertility, Nutrients Pools and Stoichiometry. J. Arid Environ. 2019, 166, 1–10. [Google Scholar] [CrossRef]
  8. Fanin, N.; Fromin, N.; Buatois, B.; Hättenschwiler, S. An Experimental Test of the Hypothesis of Non-Homeostatic Consumer Stoichiometry in a Plant Litter–Microbe System. Ecol. Lett. 2013, 16, 764–772. [Google Scholar] [CrossRef]
  9. Zechmeister-Boltenstern, S.; Keiblinger, K.M.; Mooshammer, M.; Peñuelas, J.; Richter, A.; Sardans, J.; Wanek, W. The Application of Ecological Stoichiometry to Plant–Microbial–Soil Organic Matter Transformations. Ecol. Monogr. 2015, 85, 133–155. [Google Scholar] [CrossRef]
  10. Hessen, D.O.; Elser, J.J.; Sterner, R.W.; Urabe, J. Ecological Stoichiometry: An Elementary Approach Using Basic Principles. Limnol. Oceanogr. 2013, 58, 2219–2236. [Google Scholar] [CrossRef]
  11. Wang, C.; Ji, P.; Liu, X.; Xu, W.; Zhang, Z.; Huang, X. Ecological C,N and P stoichiometry of the needles, twigs and fine roots in pure and mixed stands of Larix principis-rupprechtii. J. Arid. Land Resour. Environ. 2020, 34, 176–181. [Google Scholar] [CrossRef]
  12. Koerselman, W.; Meuleman, A.F.M. The Vegetation N:P Ratio: A New Tool to Detect the Nature of Nutrient Limitation. J. Appl. Ecol. 1996, 33, 1441–1450. [Google Scholar] [CrossRef]
  13. Menezes, R.S.C.; Sampaio, E.; Giongo, V.; Pérez-Marin, A.M. Biogeochemical Cycling in Terrestrial Ecosystems of the Caatinga Biome. Braz. J. Biol. 2012, 72, 643–653. [Google Scholar] [CrossRef] [PubMed]
  14. Philippot, L.; Spor, A.; Hénault, C.; Bru, D.; Bizouard, F.; Jones, C.M.; Sarr, A.; Maron, P.-A. Loss in Microbial Diversity Affects Nitrogen Cycling in Soil. ISME J. 2013, 7, 1609–1619. [Google Scholar] [CrossRef]
  15. Wu, W.; Wang, F.; Xia, A.; Zhang, Z.; Wang, Z.; Wang, K.; Dong, J.; Li, T.; Wu, Y.; Che, R.; et al. Meta-Analysis of the Impacts of Phosphorus Addition on Soil Microbes. Agric. Ecosyst. Environ. 2022, 340, 108180. [Google Scholar] [CrossRef]
  16. Liu, X.; Zhou, G.; Zhang, D.; Liu, S.; Chu, G.; Yan, J. N and P stoichiometry of plant and soil in lower subtropical forest successional series in southern China. Chin. J. Plant Ecol. 2010, 34, 64–71. [Google Scholar] [CrossRef]
  17. Sun, H.; Koal, P.; Gerl, G.; Schroll, R.; Gattinger, A.; Joergensen, R.G.; Munch, J.C. Microbial Communities and Residues in Robinia- and Poplar-Based Alley-Cropping Systems under Organic and Integrated Management. Agroforest. Syst. 2018, 92, 35–46. [Google Scholar] [CrossRef]
  18. Lin, S.; Zeng, Y.; Yang, W.; Chen, B.; Ruan, M.; Yin, X.; Yang, X.; Wang, W. Effects of straw and biochar addition on carbon, nitrogen and phosphorus ecological stoichiometry in Jasminum sambac plant and soil. Chin. J. Plant Ecol. 2023, 47, 530–545. [Google Scholar] [CrossRef]
  19. Li, H.; Huo, Y.; Weng, X.; Zhou, Y.; Sun, Y.; Zhang, G.; Songzhu, Z.; Liu, L.; Pei, J. Regulation of the Growth of Mongolian Pine (Pinus sylvestris var. mongolica) by Calcium-Water Coupling in a Semiarid Region. Ecol. Indic. 2022, 137, 108736. [Google Scholar] [CrossRef]
  20. Zhao, S.; Li, J.; Sun, X.; Zeng, D.; Hu, Y. Responses of soil and plant stoichiometric characteristics along rainfall gradients in Mongolian pine plantations in native and introduced regions. Acta Ecol. Sin. 2018, 38, 7189–7197. [Google Scholar] [CrossRef]
  21. Wu, X.; Jiang, F.; Li, X.; Xue, Y.; Qiu, S. Decline regularity and causes of Pinus sylvestris var. mongolica plantation on sandy land. Ying Yong Sheng Tai Xue Bao 2004, 15, 2225–2228. [Google Scholar] [PubMed]
  22. Zhu, J.; Tan, H.; Kang, H.; Xu, M. Comparison of Foliar Nutrient Concentrations between Natural and Artificial Forests of Pinus sylvestris var. mongolica on Sandy Land, China. J. For. Res. 2006, 17, 177–184. [Google Scholar] [CrossRef]
  23. Chen, F.; Zeng, D.; Chen, G.; Yu, Z.; Zhao, Q. Available nitrogen in forest soil of Pinus sylvestris var. mongolica plantations in Zhanggutai sandy lands. J. Beijing For. Univ. 2005, 27, 6–11. [Google Scholar]
  24. Jiang, J.; Li, Y.; Lei, Z. Spatial Characteristics of Soil Organic Carbon in Matured Pinus Sylvestris var. Mongolica Forest under Different Climatic Conditions. Chin. J. Soil Sci. 2021, 52, 42–46. [Google Scholar] [CrossRef]
  25. Bian, Z.; Li, X.; Yu, M. The plant diversity of agro-landscapes in typical maize planting areas in the Northeast Plain, China-A case study of Changtu County. Chin. J. Eco-Agric. 2018, 26, 480–492. [Google Scholar] [CrossRef]
  26. Miyazawa, M.; Pavan, M.A.; Block, M.F.M. Determination of Ca, Mg, K, Mn, Cu, Zn, Fe, and P in Coffee, Soybean, Corn, Sunflower, and Pasture Grass Leaf Tissues by a HCl Extraction Method. Commun. Soil Sci. Plant Anal. 1984, 15, 141–147. [Google Scholar] [CrossRef]
  27. Bao, S.D. Soil and Agricultural Chemistry Analysis; China Agriculture Press: Beijing, China, 2000. [Google Scholar]
  28. Lu, R.-K. Soil and Agro-Chemistry Analytical Methods; Chinese Agricultural Science and Technology Press: Beijing, China, 1999; Volume 15. [Google Scholar]
  29. Su, B. Adaptation and Trade-Off of Artificial Robinia Pseudoacacia Plantation to Soil Water and Nutrient on the Loess Plateau. Ph.D. Thesis, University of Chinese Academy of Sciences and Ministry of Education, Beijing, China, 2022. [Google Scholar]
  30. Wang, S.; Du, Y.; Liu, S.; Pan, J.; Wu, F.; Wang, Y.; Wang, Y.; Li, H.; Dong, Y.; Wang, Z.; et al. Response of C:N:P Stoichiometry to Long-Term Drainage of Peatlands: Evidence from Plant, Soil, and Enzyme. Sci. Total Environ. 2024, 919, 170688. [Google Scholar] [CrossRef]
  31. Kaisermann, A.; de Vries, F.T.; Griffiths, R.I.; Bardgett, R.D. Legacy Effects of Drought on Plant–Soil Feedbacks and Plant–Plant Interactions. New Phytol. 2017, 215, 1413–1424. [Google Scholar] [CrossRef]
  32. Wu, W.; He, X.; Zhou, Q. Review on N:P Stoichiometry in Eco-system. J. Desert Res. 2010, 30, 296–302. [Google Scholar]
  33. Zhu, W.; Xu, Y.; Wang, Z.; Du, A. Soil-microbial stoichiometry of Eucalyptus urophylla×E. grandis plantation at different growth stages. J. Zhejiang A F Univ. 2021, 38, 692–702. [Google Scholar] [CrossRef]
  34. Post, W.M.; Pastor, J.; Zinke, P.J.; Stangenberger, A.G. Global Patterns of Soil Nitrogen Storage. Nature 1985, 317, 613–616. [Google Scholar] [CrossRef]
  35. Ren, S.; Yu, G.; Jiang, C.; Fang, H.; Sun, X. Stoichiometric characteristics of leaf carbon, nitrogen, and phosphorus of 102 dominant species in forest ecosystems along the North-South Transect of East China. Chin. J. Appl. Ecol. 2012, 23, 581–586. [Google Scholar] [CrossRef]
  36. Ning, Z.-Y.; Li, Y.-L.; Yang, H.-L.; Sun, D.-C.; Bi, J.-D. Carbon, Nitrogen and Phosphorus Stoichiometry in Leaves and Fine Roots of Dominant Plants in Horqin Sandy Land. Chin. J. Plant Ecol. 2017, 41, 1069. [Google Scholar] [CrossRef]
  37. Oleksyn, J. Global Patterns of Plant Leaf N and P in Relation to Temperature and Latitude. Proc. Natl. Acad. Sci. USA 2004, 101, 11001–11006. [Google Scholar] [CrossRef]
  38. Han, W.; Fang, J.; Guo, D.; Zhang, Y. Leaf Nitrogen and Phosphorus Stoichiometry across 753 Terrestrial Plant Species in China. New Phytol. 2005, 168, 377–385. [Google Scholar] [CrossRef]
  39. Li, Y.; Mao, W.; Zhao, X.; Zhang, T. Leaf nitrogen and phosphorus stoichiometry in typical desert and desertified regions, north China. Huan Jing Ke Xue 2010, 31, 1716–1725. [Google Scholar]
  40. Dise, N.B.; Matzner, E.; Forsius, M. Evaluation of Organic Horizon C:N Ratio as an Indicator of Nitrate Leaching in Conifer Forests across Europe. Environ. Pollut. 1998, 102, 453–456. [Google Scholar] [CrossRef]
  41. Wang, J.; Wang, S.; Li, R.; Yan, J.; Sha, L.; Han, S. C: N: P Stoichiometric Characteristics of Four Forest Types’ Dominant Tree Species in China. Chin. J. Plant Ecol. 2011, 35, 587–595. [Google Scholar]
  42. Han, Z.; Lei, Z.; Wang, G.; Gao, Z.; Liu, Y. Effects of annual growth on soil carbon, nitrogen and phosphorus in mature pinus sylvestris var. mongolica plantations. J. Anhui Agric. Sci. 2024, 52, 105–109. [Google Scholar] [CrossRef]
  43. Sun, Z.; Li, Y.; Zhao, Y.; Huang, Y.; Guo, X.; Cao, M. Analysis on Spatial Distribution Characteristics and Driving Forcesof Soil Organic Carbon Density in Dry Farming Region. Trans. Chin. Soc. Agric. Mach. 2019, 50, 255–262. [Google Scholar]
  44. Persson, J.; Fink, P.; Goto, A.; Hood, J.M.; Jonas, J.; Kato, S. To Be or Not to Be What You Eat: Regulation of Stoichiometric Homeostasis among Autotrophs and Heterotrophs. Oikos 2010, 119, 741–751. [Google Scholar] [CrossRef]
  45. Zhang, M.; Kong, T.; Huang, L.; Zhang, K.; Zhang, J. Root Distribution and Soil and Microbial Ecological Stoichiometry Characteristics of Pinus sylvestris in Sandy Land. J. Shanxi Agric. Sci. 2023, 51, 912–920. [Google Scholar] [CrossRef]
  46. Chen, X.; Zhang, H.; Yao, X.; Zeng, W.; Wang, W. Latitudinal and Depth Patterns of Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus in Grasslands of an Agro-Pastoral Ecotone. Land Degrad. Dev. 2021, 32, 3833–3846. [Google Scholar] [CrossRef]
  47. Liu, D. Relationship between soil distribution and climatic condition in China. Acta Pedol. Sin. 1983, 20, 60–68. [Google Scholar]
  48. 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]
  49. Hu, Y.; Wang, S.; Yan, S. Research Advances on the Factors Influencing the Activity and Community Structure of Soil Microorganism. Chin. J. Soil Sci. 2006, 37, 170–176. [Google Scholar] [CrossRef]
  50. Wardle, D.A. A Comparative Assessment of Factors Which Influence Microbial Biomass Carbon and Nitrogen Levels in Soil. Biol. Rev. 1992, 67, 321–358. [Google Scholar] [CrossRef]
  51. Chen, Y.; Liu, Z.; Rao, X.; Wang, X.; Liang, C.; Lin, Y.; Zhou, L.; Cai, X.; Fu, S. Carbon Storage and Allocation Pattern in Plant Biomass among Different Forest Plantation Stands in Guangdong, China. Forests 2015, 6, 794–808. [Google Scholar] [CrossRef]
  52. Chen, Y.; Yu, S.; Liu, S.; Wang, X.; Zhang, Y.; Liu, T.; Zhou, L.; Zhang, W.; Fu, S. Reforestation Makes a Minor Contribution to Soil Carbon Accumulation in the Short Term: Evidence from Four Subtropical Plantations. For. Ecol. Manag. 2017, 384, 400–405. [Google Scholar] [CrossRef]
  53. Dai, X.; Fu, X.; Kou, L.; Wang, H.; Clinton, C. C:N:P Stoichiometry of Rhizosphere Soils Differed Significantly among Overstory Trees and Understory Shrubs in Plantations in Subtropical China. Can. J. For. Res. 2018, 48, 1398–1405. [Google Scholar] [CrossRef]
  54. Elser, J.J.; Acharya, K.; Kyle, M.; Cotner, J.; Makino, W.; Markow, T.; Watts, T.; Hobbie, S.; Fagan, W.; Schade, J.; et al. Growth Rate–Stoichiometry Couplings in Diverse Biota. Ecol. Lett. 2003, 6, 936–943. [Google Scholar] [CrossRef]
  55. Aerts, R. Nitrogen Partitioning between Resorption and Decomposition Pathways: A Trade-Off between Nitrogen Use Efficiency and Litter Decomposibility? Oikos 1997, 80, 603–606. [Google Scholar] [CrossRef]
  56. Güsewell, S. N: P Ratios in Terrestrial Plants: Variation and Functional Significance. New Phytol. 2004, 164, 243–266. [Google Scholar] [CrossRef] [PubMed]
  57. Wang, X. Leaf-Litter-Soil Eco-stoichiometric Relationship of Different Forest Types in Xianren Cave. Master’s Thesis, Liaoning Normal Univesity, Liaoning, China, 2020. [Google Scholar]
  58. Lei, H. Carbon, Nitrogen and Phosphorus Stoichiometry of Main Protective Forests in Horqin Sandy Land. Master’s Thesis, Liaoning Technical University, Liaoning, China, 2019. [Google Scholar]
  59. Yang, H.; Tu, C.; Li, Q.; Yang, L.; Cao, J. Analysis of C, N and P stoichiometry of secondary forest in different landforms in karst area. J. South. Agric. 2015, 46, 777–781. [Google Scholar]
  60. Zhu, Q.; Xing, X.; Zhang, H.; An, S. Soil ecological stoichiometry under different vegetation area on loess hilly-gully region. Acta Ecol. Sin. 2013, 33, 4674–4682. [Google Scholar] [CrossRef]
  61. Vergutz, L.; Manzoni, S.; Porporato, A.; Novais, R.F.; Jackson, R.B. Global Resorption Efficiencies and Concentrations of Carbon and Nutrients in Leaves of Terrestrial Plants. Ecol. Monogr. 2012, 82, 205–220. [Google Scholar] [CrossRef]
  62. Gao, S.; Li, J.; Xu, M.; Chen, X.; Dai, J. Leaf N and P Stoichiometry of common species in successional stages of the evergreen broad-leaved forest in Tiantong National Forest Park, Zhejiang Province, China. Acta Ecol. Sin. 2007, 27, 947–952. [Google Scholar]
  63. Qin, J.; Kong, H.; Liu, H. Stoichiometric characteristics of soil C,N,P and K in different Pinus massoniana forests. J. Northwest A F Univ. (Nat. Sci. Ed.) 2016, 44, 68–76, 82. [Google Scholar] [CrossRef]
  64. 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]
  65. Xu, X.; Thornton, P.E.; Post, W.M. A Global Analysis of Soil Microbial Biomass Carbon, Nitrogen and Phosphorus in Terrestrial Ecosystems. Glob. Ecol. Biogeogr. 2013, 22, 737–749. [Google Scholar] [CrossRef]
Forests 16 00720 g001
Figure 1. Plant nutrient content and stoichiometric ratio of Mongolian pine plantations in different regions. Note: A significant difference is shown by a p-value at the top of the table; p < 0.001 indicates an extremely significant difference; and p < 0.05 indicates a significant difference. (Figure 2 and Figure 3 is the same). (A) Leaf TC; (B) Leaf TN; (C) Leaf TP; (D) Leaf TC/TN; (E) Leaf TC/TP; (F) Leaf TN/TP.
Figure 1. Plant nutrient content and stoichiometric ratio of Mongolian pine plantations in different regions. Note: A significant difference is shown by a p-value at the top of the table; p < 0.001 indicates an extremely significant difference; and p < 0.05 indicates a significant difference. (Figure 2 and Figure 3 is the same). (A) Leaf TC; (B) Leaf TN; (C) Leaf TP; (D) Leaf TC/TN; (E) Leaf TC/TP; (F) Leaf TN/TP.
Forests 16 00720 g001
Forests 16 00720 g004
Figure 4. The relationship between the content of carbon, nitrogen, and phosphorus in plant, soil, and microbial biomass, as well as their stoichiometric ratios, in the eastern (A) and northwestern (B) regions of Liaoning Province. Note: * represents significance at p < 0.05, ** represents high significance at p < 0.01, and *** represents extremely high significance at p < 0.001.
Figure 4. The relationship between the content of carbon, nitrogen, and phosphorus in plant, soil, and microbial biomass, as well as their stoichiometric ratios, in the eastern (A) and northwestern (B) regions of Liaoning Province. Note: * represents significance at p < 0.05, ** represents high significance at p < 0.01, and *** represents extremely high significance at p < 0.001.
Forests 16 00720 g004
Forests 16 00720 g005
Figure 5. RDA analysis of plants, soils, and microbial biomass under different environmental conditions. Note: Redundancy analysis of plant–soil–microorganisms in eastern Liaoning (A) and northwestern Liaoning (B).
Figure 5. RDA analysis of plants, soils, and microbial biomass under different environmental conditions. Note: Redundancy analysis of plant–soil–microorganisms in eastern Liaoning (A) and northwestern Liaoning (B).
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Table 1. Sampling point position.
Table 1. Sampling point position.
SampleLongitudeLatitudeAltitude/mSlope/°Slope AspectDBH/cm
East of Liaoning-1124°39′7469″41°58′9934″23014East18.153
East of Liaoning-2124°39′6889″41°58′9871″23120South18.153
East of Liaoning-3124°39′6815″41°58′9759″23220South19.745
East of Liaoning-4124°39′6711″41°58′9825″24120South22.611
East of Liaoning-5124°39′6655″41°58′9598″23614North19.786
East of Liaoning-6124°39′676″41°58′9656″23812North20.382
East of Liaoning-7124°39′697″41°58′9872″2315Southeast20.701
East of Liaoning-8124°39′7032″41°58′9861″2372North18.287
East of Liaoning-9124°41′7120″41°59′0022″24611North22.771
East of Liaoning-10124°41′7120″41°59′0022″24611North19.790
East of Liaoning-11124°41′7091″41°58′9976″25711North20.064
East of Liaoning-12124°41′7052″41°59′0058″24813Northwest19.108
East of Liaoning-13124°41′6994″41°59′0065″24413Northwest19.745
East of Liaoning-14124°41′7022″41°59′0171″25715Northwest21.159
East of Liaoning-15124°41′7034″41°59′0177″24717Northwest19.427
Northwest of Liaoning-1123°50′4520″43°14′8631″1208South23.567
Northwest of Liaoning-2123°50′4865″43°14′8425″1213South25.032
Northwest of Liaoning-3123°50′5254″43°14′8380″1163Northeast26.847
Northwest of Liaoning-4123°50′5728″43°14′8387″1173Northeast29.490
Northwest of Liaoning-5123°50′5764″43°14′8011″1243South33.758
Northwest of Liaoning-6123°49′6017″43°17′6013″15210East21.656
Northwest of Liaoning-7123°48′5738″43°17′6182″14610South23.635
Northwest of Liaoning-8123°49′5748″43°17′6297″14710South21.976
Northwest of Liaoning-9123°49′5722″43°17′6499″1503East22.930
Northwest of Liaoning-10123°49′5609″43°17′6630″1483East25.259
Northwest of Liaoning-11123°49′5430″43°17′7001″1463East23.436
Northwest of Liaoning-12123°49′9602″43°18′1005″1567Northwest21.745
Northwest of Liaoning-13123°49′9514″43°18′1078″1585South22.930
Northwest of Liaoning-14123°49′9575″43°18′1525″1555North19.745
Northwest of Liaoning-15123°49′9575″43°18′1658″1525North20.867
Note: The table records the longitude, latitude, diameter at breast height (DBH), elevation, slope, and slope aspect of the quadrangle in eastern Liaoning and northwestern Liaoning.
Table 2. The internal stability index of microbial biomass nutrient content and stoichiometry of plants and soil in east Liaoning.
Table 2. The internal stability index of microbial biomass nutrient content and stoichiometry of plants and soil in east Liaoning.
log10(y)log10(x)1/HR2pGrade
LeafLeaf TCSoil TC0.1470.0830.299strict internal stability
Leaf TNSoil TN0.2750.0730.33strict internal stability
Leaf TPSoil TP−0.0760.0180.635strict internal stability
Leaf TC/TNSoil TC/TN0.0017.6270.975strict internal stability
Leaf TC/TPSoil TC/TP−0.0180.0010.925strict internal stability
Leaf TN/TPSoil TN/TP0.270.0770.316strict internal stability
Soil microbial biomassMBCSoil TC−0.0010.0010.931strict internal stability
MBNSoil TN0.0580.0030.849strict internal stability
MBPSoil TP−0.4350.4110.469strict internal stability
MBC/MBNSoil TC/TN−0.3250.0850.291strict internal stability
MBC/MBPSoil TC/TP−0.2190.0170.642strict internal stability
MBN/MBPSoil TN/TP−0.0210.0010.963strict internal stability
Note: When the equation fitting is not significant (p ≥ 0.1), it is considered to be absolute homeostasis. When the equation fit is significant, the classification is as follows: 0 < 1/H ≤ 0.25 is a stable state, 0.25 < 1/H ≤ 0.5 is a weak stable state, 0.5 < 1/H ≤ 0.75 is the weak sensitive state, and 1/H > 0.75 is the sensitive state. (Table 3 is the same).
Table 3. The internal stability index of microbial biomass nutrient content and stoichiometry of plants and soil in northwest Liaoning.
Table 3. The internal stability index of microbial biomass nutrient content and stoichiometry of plants and soil in northwest Liaoning.
log10(y)log10(x)1/HR2pGrade
LeafLeaf TCSoil TC0.1510.0520.416strict internal stability
Leaf TNSoil TN−0.5630.1260.195strict internal stability
Leaf TPSoil TP−1.2670.3580.018strict internal stability
Leaf TC/TNSoil TC/TN0.2630.0390.481strict internal stability
Leaf TC/TPSoil TC/TP−0.1830.0380.485strict internal stability
Leaf TN/TPSoil TN/TP−0.6820.2080.087strict internal stability
Soil microbial biomassMBCSoil TC−0.0310.0010.893strict internal stability
MBNSoil TN0.1290.0620.37strict internal stability
MBPSoil TP0.1610.0140.894strict internal stability
MBC/MBNSoil TC/TN0.0660.0080.749strict internal stability
MBC/MBPSoil TC/TP−0.6180.0940.266strict internal stability
MBN/MBPSoil TN/TP0.3380.0390.480strict internal stability
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Li, H.; Yang, Y.; Weng, X.; Zhou, Y.; Zhang, S.; Liu, L.; Pei, J. Plant–Soil–Microbial Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry in Mongolian Pine-Planted Forests Under Different Environmental Conditions in Liaoning Province, China. Forests 2025, 16, 720. https://doi.org/10.3390/f16050720

AMA Style

Li H, Yang Y, Weng X, Zhou Y, Zhang S, Liu L, Pei J. Plant–Soil–Microbial Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry in Mongolian Pine-Planted Forests Under Different Environmental Conditions in Liaoning Province, China. Forests. 2025; 16(5):720. https://doi.org/10.3390/f16050720

Chicago/Turabian Style

Li, Hui, Yi Yang, Xiaohang Weng, Yongbin Zhou, Songzhu Zhang, Liying Liu, and Jiubo Pei. 2025. "Plant–Soil–Microbial Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry in Mongolian Pine-Planted Forests Under Different Environmental Conditions in Liaoning Province, China" Forests 16, no. 5: 720. https://doi.org/10.3390/f16050720

APA Style

Li, H., Yang, Y., Weng, X., Zhou, Y., Zhang, S., Liu, L., & Pei, J. (2025). Plant–Soil–Microbial Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry in Mongolian Pine-Planted Forests Under Different Environmental Conditions in Liaoning Province, China. Forests, 16(5), 720. https://doi.org/10.3390/f16050720

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