Assessment method for different grassland type soil microbial metabolism resource limitation and application

Abstract

The invention discloses an evaluation method for different grassland type soil microbial metabolism resource limitation and application, and belongs to the technical field of ecology. The evaluation method comprises the following steps: collecting soil samples of different grassland types, measuring the soil extracellular enzyme activity of the soil samples, and calculating the stoichiometric ratio of the soil extracellular enzyme; analyzing the correlation between the soil extracellular enzyme activity, the soil extracellular enzyme stoichiometric ratio and the soil physicochemical property; a soil enzyme stoichiometric vector analysis method is adopted to obtain a soil microbial nutrient utilization strategy; performing regression analysis on the stoichiometric ratio of the soil extracellular enzyme; constructing a random forest model; performing unary linear regression analysis on the first five core variables of the contribution degree; and constructing a partial least square path model. The evaluation method provided by the invention can accurately reveal the restriction difference rule of soil enzyme activity and microbial nutrients of different grassland types in Qilian Mountain, and provides a scientific basis for understanding a regional nutrient circulation mechanism and optimizing a grassland protection and management mode.

Description

Technical Field

[0001] This invention belongs to the field of ecological technology, and in particular relates to an assessment method and application for limiting soil microbial metabolic resources in different grassland types. Background Technology

[0002] Grasslands, as one of the most important ecosystems on Earth, provide abundant biological resources for humankind and are crucial for maintaining the Earth's ecological balance. The Qilian Mountains, with their diverse grassland types due to altitude and regional variations, are the largest vegetation type in the region, and their ecosystem stability is of paramount importance. However, Qilian Mountain grasslands are highly vulnerable to human activities and global climate change. Therefore, studying the soil microbial metabolic characteristics of Qilian Mountain grasslands helps to elucidate the mechanisms of soil microbial nutrient cycling in this region and the impact of different factors on microbial metabolic limitations under the background of global climate change.

[0003] Currently, there are differing viewpoints regarding microbial nutrient limitation in grassland ecosystems. Hou et al. proposed that soil microbial metabolism in grassland ecosystems is primarily limited by phosphorus. However, other studies indicate that nitrogen is a major limiting factor. Furthermore, Yu et al. believe that nutrient content and its proportion are the main factors influencing microbial limitation in alpine meadows. Li Qiang et al., on the other hand, pointed out that soil moisture and organic carbon are the main limiting factors affecting enzyme activity and its stoichiometric characteristics. Therefore, the differences in extracellular enzyme activity and microbial metabolic characteristics among different grassland types in the Qilian Mountains, and the influencing factors, urgently require further in-depth research. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an assessment method for soil microbial metabolic resource limitation in different grassland types, which can systematically and accurately reveal the differences in soil enzyme activity and microbial nutrient limitation in different grassland types in the Qilian Mountains, and provide a scientific basis for understanding regional nutrient cycling mechanisms and optimizing grassland protection and management models.

[0005] Another objective of this invention is to provide an application of the aforementioned assessment method in the ecological restoration of degraded grasslands, grassland protection and management, or the establishment of artificial grasslands.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a method for assessing the limitation of soil microbial metabolic resources in different grassland types, the assessment method comprising: Soil samples were collected from different grassland types, the activity of extracellular enzymes in the soil samples was measured, and the stoichiometry of extracellular enzymes in the soil was calculated. The correlation between soil extracellular enzyme activity, soil extracellular enzyme stoichiometry and soil physicochemical properties was analyzed to screen key physicochemical factors affecting microbial metabolism and soil nutrient balance. Vector analysis of soil enzyme stoichiometry was used to obtain soil microbial nutrient utilization strategies; Regression analysis was performed on the stoichiometry of soil extracellular enzymes to assess soil microbial nutrient limitation characteristics. A random forest model was constructed to analyze the relative contributions of soil physicochemical indicators and carbon composition to microbial nutrient limitation; a univariate linear regression analysis was performed on the top five core variables to determine the magnitude of their influence on the potential limitation intensity. A partial least squares path model was constructed to identify pathways that influence the limitation of soil microbial metabolic resources.

[0007] Preferably, the different grassland types include different grassland types in the Qilian Mountains.

[0008] Preferably, the grassland type includes marshy meadow, temperate desert steppe, or alpine meadow.

[0009] Preferably, the soil microorganisms of the marshy meadows, temperate desert steppes, and alpine meadows are limited by carbon and phosphorus; the degree of carbon limitation is significantly positively correlated with the content of total phosphorus and available potassium, and significantly negatively correlated with the nitrogen-phosphorus ratio and the content of easily oxidizable organic carbon; the degree of phosphorus limitation is significantly positively correlated with the content of soil organic carbon, particulate organic carbon, and carbon-nitrogen ratio, and significantly negatively correlated with total potassium and bulk density; the main factors limiting carbon and phosphorus metabolism in soil microorganisms include bulk density, soil moisture content, and pH.

[0010] Preferably, the metabolic resources include any one or more of carbon, nitrogen, and phosphorus.

[0011] Preferably, the soil sample collection depth includes 0~30cm.

[0012] Preferably, the soil extracellular enzymes include β-1,4-glucosidase, N-acetyl-β-D-glucosidase, and phosphatase.

[0013] Preferably, the soil physicochemical properties include nitrate nitrogen, ammonium nitrogen, total nitrogen, total phosphorus, total potassium, available potassium, bulk density, soil moisture content, pH, soil organic carbon, soluble organic carbon, easily oxidized organic carbon, inert carbon, mineral-bound organic carbon, and particulate organic carbon.

[0014] Preferably, the method for constructing the random forest model includes using the "randomForest" package in R4.3.2 to construct the random forest model to determine the key factors of vector length and vector angle; and using the "rfUtilities" and "rfPermute" packages to evaluate the significance of the model and each factor.

[0015] Preferably, the method for constructing the partial least squares path model includes: constructing the partial least squares path model using the "plspm" package in R4.3.2.

[0016] The present invention also provides an application of the aforementioned assessment method in the ecological restoration of degraded grasslands, grassland protection and management, or the establishment of artificial grasslands.

[0017] Preferably, the grassland protection and management includes precision fertilization of grassland, dynamic monitoring of grassland ecological functions, or early warning of grassland degradation.

[0018] The beneficial effects of this invention are: This invention, by measuring soil extracellular enzyme activity and calculating stoichiometry, combined with vector and regression analysis, can accurately identify the types, intensities, and nutrient utilization strategies of carbon, nitrogen, and phosphorus nutrient limitation in soil microorganisms. Correlation analysis is used to screen key physicochemical factors affecting microbial metabolism and nutrient balance. A random forest model is then used to clarify the relative contributions of soil physicochemical indicators and carbon components to microbial nutrient limitation. Univariate linear regression is used to quantify the impact of core variables on limitation intensity, avoiding the blind screening of influencing factors, accurately identifying regulatory targets, and improving the pertinence of measures. Partial least squares path models are used to analyze the direct and indirect pathways affecting soil microbial metabolic resource limitation, providing support for developing control strategies from the root causes. Using the assessment method of this invention to evaluate metabolic resource limitation in different grassland types in the Qilian Mountains can accurately reveal the differences in soil enzyme activity and microbial nutrient limitation among different grassland types in the Qilian Mountains, providing a scientific basis for understanding regional nutrient cycling mechanisms and optimizing grassland protection and management models. Attached Figure Description

[0019] Figure 1 The figure shows the correlation analysis between soil physicochemical properties and carbon composition and extracellular enzyme activity and their stoichiometric characteristics in Example 1. This indicates a significant difference (P<0.05); Figure 2 The vector length and angle under different grassland type treatments in Example 1 are shown in the error bars, which represent the mean ± standard error. (a) represents the vector length under different grassland type treatments, which represents soil microbial carbon limitation, and (b) represents the vector angle under different grassland type treatments, which represents soil microbial nitrogen and phosphorus limitation. Figure 3 The following are examples of the relative nutrient limitation of soil microbial metabolism in different grassland types in Example 1. (a) is the regression analysis of BG:(BG+NAG) and BG:(BG+AP). The area above the 1:1 line indicates that the microorganisms are limited by P, and the area below the 1:1 line indicates that the microorganisms are limited by N. (b) is the regression analysis of BG:NAG and NAG:AP. The upper left quadrant indicates that C and P are both limited, the upper right quadrant indicates that C and N are both limited, the lower left quadrant indicates that P is limited, and the lower right quadrant indicates that N is limited. Figure 4The figure shows the importance ranking of the variable factors affecting vector length (a) and vector angle (b) in the random forest analysis of Example 1. The difference is significant (P<0.05). ns indicates a highly significant difference (P<0.01), and ns indicates no significant difference. Figure 5 For the linear regression analysis of vector length (a) and vector angle (b) with variable factors in Example 1, the straight line and the shaded area represent the fitted regression and the 95% confidence interval, respectively; Figure 6 In Example 1, partial least squares path modeling was used to analyze possible pathways affecting microbial carbon (a) and phosphorus (c) metabolism limitations. Red and blue arrows represent positive and negative causal relationships, respectively, and the numbers on the arrows represent normalized path coefficients, R0. 2 (b) represents the variance of the dependent variable explained by the model, and (d) represents the total effects of microbial C and phosphorus limitation, respectively. Detailed Implementation

[0020] This invention provides a method for assessing the limitation of soil microbial metabolic resources in different grassland types, the assessment method comprising: Soil samples were collected from different grassland types, the activity of extracellular enzymes in the soil samples was measured, and the stoichiometry of extracellular enzymes in the soil was calculated. The correlation between soil extracellular enzyme activity, soil extracellular enzyme stoichiometry and soil physicochemical properties was analyzed to screen key physicochemical factors affecting microbial metabolism and soil nutrient balance. Vector analysis of soil enzyme stoichiometry was used to obtain soil microbial nutrient utilization strategies; Regression analysis was performed on the stoichiometry of soil extracellular enzymes to assess soil microbial nutrient limitation characteristics. A random forest model was constructed to analyze the relative contributions of soil physicochemical indicators and carbon composition to microbial nutrient limitation; a univariate linear regression analysis was performed on the top five core variables to determine the magnitude of their influence on the potential limitation intensity. A partial least squares path model was constructed to identify pathways that influence the limitation of soil microbial metabolic resources.

[0021] In this invention, the different grassland types preferably include different grassland types in the Qilian Mountains.

[0022] In this invention, the type of grassland is not specifically limited. In some embodiments, the grassland type preferably includes marshy meadow, temperate desert steppe, or alpine meadow. In some embodiments, the marshy meadow is preferably located in Menyuan Hui Autonomous County, Haibei Tibetan Autonomous Prefecture, Qinghai Province; the temperate desert steppe is preferably located in Delingha City, Haixi Mongolian and Tibetan Autonomous Prefecture, Qinghai Province; and the alpine meadow is preferably located in Tianjun County, Haixi Mongolian and Tibetan Autonomous Prefecture, Qinghai Province.

[0023] In this invention, the metabolic resources preferably include any one or more of carbon, nitrogen, and phosphorus. Carbon, nitrogen, and phosphorus limitation are core regulatory factors of soil microbial metabolism and grassland ecosystem function. They ultimately determine the structure and function of grassland ecosystems by influencing microbial activity, altering enzyme secretion patterns, and regulating nutrient cycling. Assessing these limitation states helps to reveal the ecosystem's operational mechanisms and provides an important theoretical foundation and technical support for the scientific management and sustainable utilization of grassland resources.

[0024] In this invention, in some embodiments, soil microorganisms in different grassland types in the Qilian Mountains—marsh meadow, temperate desert steppe, and alpine meadow—are limited by carbon and phosphorus. Temperate desert steppe exhibits the highest carbon limitation, followed by alpine meadow, while marsh meadow shows the lowest. Marsh meadow is more phosphorus-limited, followed by temperate desert steppe and alpine meadow. The degree of carbon limitation is significantly positively correlated with total phosphorus and available potassium content, and significantly negatively correlated with the nitrogen-to-phosphorus ratio and readily oxidizable organic carbon content. The degree of phosphorus limitation is significantly positively correlated with soil organic carbon, particulate organic carbon content, and carbon-to-nitrogen ratio, and significantly negatively correlated with total potassium and bulk density. Soil physicochemical properties (bulk density, soil moisture content, and pH) are the main factors influencing microbial carbon and phosphorus metabolism limitation, and soil nutrients and their stoichiometric ratios have a certain impact on soil microbial carbon limitation.

[0025] In this invention, the soil sample collection depth is preferably 0-30 cm. In some embodiments, the soil sample collection depth is preferably 0-10 cm, 10-20 cm, and 20-30 cm; the amount of soil sample collected from each layer is not particularly limited, but in some embodiments, it is preferably 0.5-1.5 kg, more preferably 1 kg; the five-point sampling method is preferably used for soil sample collection, and each sampling point is preferably sampled three times.

[0026] Microorganisms are a core component of soil habitats, dominating several key processes such as biogeochemical cycles and carbon storage. Microorganisms are the primary producers of soil enzymes, and environmental factors drive changes in soil enzyme activity by altering the structure and diversity of the microbial community and the expression of related genes. Therefore, soil enzyme activity, as an indicator of microbial metabolic processes, can be used to describe changes in microbial energy status, nutrient requirements, and the absorption and utilization of soil nutrients.

[0027] In this invention, the soil extracellular enzymes preferably include β-1,4-glucosidase (BG), N-acetyl-β-D-glucosidase (NAG), and phosphatase (AP). BG acts on cellulose degradation, and the final product is an important carbon source for soil microbial growth; NAG is responsible for the decomposition of peptidoglycans and leucine; AP participates in the hydrolysis of phosphoproteos and phosphate esters, converting them into inorganic phosphorus that can be absorbed and utilized by plants. During this process, soil microorganisms will change the secretion of extracellular enzymes related to the C, N, and P cycles according to changes in the external environment, optimizing their resource acquisition strategies. Therefore, studying the quantitative characteristics of extracellular enzymes under different soil conditions is crucial for assessing the carbon, nitrogen, and phosphorus turnover of ecosystems.

[0028] In this invention, the stoichiometry of soil extracellular enzyme activities preferably includes the stoichiometry of carbon and nitrogen enzyme activities, the stoichiometry of carbon and phosphorus enzyme activities, and the stoichiometry of nitrogen and phosphorus enzyme activities. In some embodiments, the stoichiometry of carbon and nitrogen enzyme activities, the stoichiometry of carbon and phosphorus enzyme activities, and the stoichiometry of nitrogen and phosphorus enzyme activities are preferably calculated using the following formulas: E C:N (Stoichiometry of carbon-nitrogenase activity) = ln(BG) / ln(NAG) E C:P (Stoichiometry of carbon phosphokinase activity) = ln(BG) / ln(AP) E N:P (Stoichiometry of nitrogen-phosphorus enzyme activity) = ln(NAG) / ln(AP) In this invention, the types of soil physicochemical properties are not specifically limited. In some embodiments, the soil physicochemical properties preferably include nitrate nitrogen (NO3). - -N), ammonium nitrogen (NH4) + The study analyzed soil extracellular enzyme activity, soil extracellular enzyme stoichiometry, and soil physicochemical properties. This analysis clarified the relationships between different nutrient forms, carbon components, soil structure, and environmental conditions on microbial metabolic function and soil nutrient balance. It also identified key physicochemical factors influencing microbial metabolism and soil nutrient balance, laying the foundation for subsequent assessments of microbial nutrient limitations. These factors included: total nitrogen (TN), total phosphorus (TP), total potassium (TK), available potassium (AK), bulk density (BD), soil moisture content (SWC), pH, soil organic carbon (SOC), soluble organic carbon (DOC), readily oxidizable organic carbon (ROC), inert carbon (RC), mineral-bound organic carbon (MAOC), and particulate organic carbon (POC).

[0029] In this invention, the method for vector analysis of soil enzyme stoichiometry is not particularly limited; the method preferably includes calculating two indicators: vector length and vector angle; wherein the vector length is used to quantify relative carbon limitation, the longer the vector length, the greater the degree of carbon limitation in soil microbial metabolism; the vector angle is used to determine relative phosphorus and nitrogen limitation, a vector angle >45° indicates that microbial metabolism is phosphorus-limited, and a vector angle <45° indicates that microbial metabolism is nitrogen-limited. The calculation methods for the vector length and vector angle are not particularly limited; in some embodiments, the vector length and vector angle are calculated according to the following formula: Vector length = sqrt(x) 2 +y 2 ) Vector angle (°) = degrees(atan2(x, y)) Where x represents the relative activity of C- and N- and P-acquisition enzymes (BG / (BG+AP)), and y represents the related activity of C- and N-acquisition enzymes (BG / (BG+NAG)).

[0030] Vector analysis provides a measure of potential and relative resource use limitations for soil microorganisms, rather than actual resource use limitations.

[0031] In some embodiments of the present invention, regression analysis is preferably performed on BG:(BG+NAG) and BG:(BG+AP), BG:NAG and NAG:AP. By analyzing the positional relationship between the data points and the 1:1 line, the metabolic resource limitation type of soil microorganisms in different grassland types is determined.

[0032] In this invention, the method for constructing the random forest model preferably includes using the "randomForest" package in R4.3.2 to construct the random forest model to determine key factors such as vector length and vector angle; and using the "rfUtilities" and "rfPermute" packages to evaluate the significance of the model and each factor. The factors preferably include total nitrogen, total phosphorus, total potassium, available potassium, nitrate nitrogen, ammonium nitrogen, bulk density, soil moisture content, pH, soil organic carbon, soluble organic carbon, easily oxidizable organic carbon, inert carbon, mineral-bound organic carbon, particulate organic carbon, nitrogen-phosphorus ratio, carbon-phosphorus ratio, and carbon-nitrogen ratio; the nitrogen-phosphorus ratio is preferably the ratio of total nitrogen to total phosphorus, the carbon-phosphorus ratio is preferably the ratio of soil organic carbon to total phosphorus, and the carbon-nitrogen ratio is preferably the ratio of soil organic carbon to total nitrogen.

[0033] In this invention, the preferred method for constructing the partial least squares path model includes: constructing the partial least squares path model using the "plspm" package in R4.3.2. Analysis using the partial least squares path model reveals the influence of soil physicochemical properties, carbon composition, total nutrient content, and nutrient stoichiometry on microbial carbon and phosphorus limitation.

[0034] The present invention also provides an application of the aforementioned assessment method in the ecological restoration of degraded grasslands, grassland protection and management, or the establishment of artificial grasslands.

[0035] The assessment method of this invention can evaluate the differences in nutrient limitation experienced by microorganisms in different grassland types, identify the main environmental factors driving differences in extracellular enzyme activity among different grassland types, and determine the key factors regulating nutrient limitation in microorganisms across different grassland types. This assessment method can reveal the patterns of differences in soil enzyme activity and microbial nutrient limitation among different grassland types in the Qilian Mountains, providing a scientific basis for understanding regional nutrient cycling mechanisms and optimizing grassland protection and management models. Based on the assessment results of soil microbial metabolic resource limitations in different grassland types in the Qilian Mountains, it can provide a basis for the ecological restoration of degraded grasslands, grassland protection and management, or the establishment of artificial grasslands. The grassland protection and management preferably includes precision fertilization, dynamic monitoring of grassland ecological functions, or early warning of grassland degradation.

[0036] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0037] Unless otherwise specified, the following embodiments are all conventional methods.

[0038] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0039] Example 1 1. Research Location Three different grassland types were selected for the study: marshy meadow (HB), temperate desert steppe (DLH), and alpine meadow (TJ). The average annual precipitation in the regions where these three grassland types were located was 582.1, 164.6, and 367.6 mm, respectively, and the average annual temperature was -1.7, 3.8, and -1.5℃, respectively. The marshy meadow area was located in Menyuan Hui Autonomous County, Haibei Tibetan Autonomous Prefecture, Qinghai Province (hereinafter referred to as HB) (37°59′N, 101°34′E, altitude 3207m), and its dominant species was *Liriope muscari* (water lilyturf). Triglochin palustris L.), Qinghai-Tibetan sedge ( Carex moorcroftii Falc. ex Boott), Ranunculus trilobata ( Halerpestes tricuspis (Maxim.) Hand.-Mazz.) etc.; The selected area of ​​temperate desert steppe is located in Delingha City, Haixi Mongolian and Tibetan Autonomous Prefecture, Qinghai Province (hereinafter referred to as DLH) (37°32′N, 98°30′E, altitude 3512m), and its dominant species is Suaeda salsa ( Suaeda glauca (Bunge) Bunge), Achyranthes bidentata ( Achnatherum splendens (Trin.) Nevski), Leymus chinensis ( Leymus secalinus (Georgi) Tzvelev, etc.; the alpine meadow area selected is located in Tianjun County, Haixi Mongolian and Tibetan Autonomous Prefecture, Qinghai Province (hereinafter referred to as TJ) (37°43′N, 99°02′E, altitude 3608m), and its dominant species is *Leymus chinensis* (Gynostemma pentaphyllum). Elymus nutans Griseb., Stipa heterophylla ( Stipa aliena Keng), lanceolate yellow flower ( Thermopsis lanceolata R. Br., etc.

[0040] 2. Sample collection Soil samples were collected in Menyuan County, Delingha City, and Tianjun County in August 2023. At each sampling point, a five-point sampling method was used, with soil samples collected at depths of 0-10, 10-20, and 20-30 cm using a 5 cm diameter soil auger. Three replicate samplings were performed at each sampling point, collecting approximately 1 kg of soil sample from each layer. The samples were sealed in resealable bags, clearly labeled, and stored in a refrigerated box at low temperatures before being brought back to the laboratory. After removing plant roots, stones, and other debris, the soil samples were sieved through a 2 mm sieve. One portion was refrigerated at 4°C for determining soil enzyme activity, while the other portion was air-dried, ground, and used for determining basic soil properties.

[0041] 3. Sample Analysis Soil organic carbon (SOC) and soluble organic carbon (DOC) were determined using the potassium dichromate titration method. Soil microbial biomass carbon (MBC) was determined using the chloroform fumigation method. Oxidizable organic carbon (ROC) was determined using a 333 mmol / L potassium permanganate solution. Inert carbon (RC) was determined by oxidation with 333 mmol / L potassium permanganate followed by spectrophotometry at 565 nm. Mineral-bound organic carbon (MAOC) and particulate organic carbon (POC) were determined using a wet sieving method combined with the potassium dichromate titration method. The activities of β-1,4-glucosidase (BG), phosphatase (AP), and N-acetyl-β-D-glucosidase (NAG) were determined strictly according to the instructions using a commercial enzyme kit (Beijing Solarbio Science & Technology Co., Ltd.). In addition, soil physicochemical properties, including soil moisture content (SWC), bulk density (BD), pH, and the content of various nutrients, were determined.

[0042] 4. Vector Analysis of Measurement Resource Constraints To measure the degree of soil microbial carbon and other nutrient limitation, a vector analysis of soil enzyme stoichiometry was used, following the method proposed by Moorhead et al. Two indices were calculated: vector length to quantify relative carbon limitation and vector angle to determine relative phosphorus and nitrogen limitation. A longer vector length indicates a greater degree of carbon limitation by soil microbial metabolism. A vector angle >45° indicates phosphorus limitation by microbial metabolism, while a vector angle <45° indicates nitrogen limitation. The vector length and angle were calculated as follows: Vector length = sqrt(x) 2 +y 2 ) Vector angle (°) = degrees(atan2(x, y)) Where x represents the relative activity of C- and N- and P-acquisition enzymes (BG / (BG+AP)), and y represents the related activity of C- and N-acquisition enzymes (BG / (BG+NAG)).

[0043] The C:N, C:P, and N:P ratios of enzymes (E) C:N E C:P and E N:P ) is calculated using the following formula: E C:N =ln(BG) / ln(NAG) E C:P =ln(BG) / ln(AP) E N:P =ln(NAG) / ln(AP) 5. Statistical Analysis All data were processed and calculated using Microsoft Excel 2016 and R4.3.2 software. One-way ANOVA was used to analyze soil elements and enzyme activities under different grassland types. The least significant difference (LSD, α=0.05) test was used to examine the variability of different treatments. Spearman correlation heatmaps were plotted using R4.3.2, and data preprocessing was performed using the "dplyr" package. Bar charts, vector analysis plots, and linear regression plots were generated using the "ggplot2" package. A random forest model was constructed using the "randomForest" package in R4.3.2 to identify key factors influencing enzyme vector angles; the significance of the model and individual factors was evaluated using the "rfUtilities" and "rfPermute" packages. Furthermore, a partial least squares path model (PLS-PM) was constructed using the "plspm" package in R4.3.2 to explore potential pathways influencing soil microbial nutrient utilization strategies.

[0044] 6. Experimental Results (1) Soil extracellular enzymes and stoichiometry characteristics of different grassland types Significant differences in soil extracellular enzyme activity and stoichiometry were observed under different grassland types (Table 1). Regarding extracellular enzymes, the activities of BG, NAG, and AP were highest in all soil layers under the TJ treatment. Specifically, BG and AP activities were significantly higher than those under the HB and DLH treatments (P<0.05). Between HB and DLH, only the BG activity in the 10-20 cm soil layer showed a difference; there were no significant differences in enzyme activity in other soil layers (P>0.05). Overall, the activities of all three enzymes decreased with soil depth, showing the highest activity in the surface layer (0-10 cm), followed by 10-20 cm, and the lowest in 20-30 cm. Regarding stoichiometry, E... C:N E C:P and E N:P Significant differences were observed in all cases. Specifically, E C:N In the 20-30cm soil layer, DLH and TJ were significantly higher than HB (P<0.05); E C:P In all soil layers, the DLH and TJ treatments were significantly higher than HB (P<0.05); E N:P In the 0-10cm soil layer, the DLH and TJ treatments were significantly higher than HB (P<0.05). However, there was no significant difference in the stoichiometry between DLH and TJ in any soil layer (P>0.05).

[0045] Table 1. Extracellular enzyme activity and stoichiometry in soil layers under different grassland types.

[0046] Note: Different letters in the table indicate significant differences (P<0.05), and the same applies to the following tables.

[0047] (2) Correlation analysis of soil physicochemical properties and carbon composition with extracellular enzyme activity and its stoichiometric characteristics The results of species diversity under the three grassland types are shown in Table 2, the results of soil physicochemical index detection under the three grassland types are shown in Table 3, and the results of soil carbon composition detection under the three grassland types are shown in Table 4.

[0048] Table 2 Species diversity under three grassland types

[0049] Table 3 Comparison of soil physicochemical properties among three grassland types

[0050] Table 4 Comparison of soil carbon composition among three grassland types

[0051] Spearman correlation heatmap analysis showed that BG, NAG, and AP are all related to nitrate nitrogen (NO3). -E was significantly positively correlated with total phosphorus (TP), total potassium (TK), available potassium (AK), and soluble organic carbon (DOC) (P<0.05), and significantly negatively correlated with soil moisture content (SWC) (P<0.05); C:N It showed a significant negative correlation with total nitrogen (TN), soil organic carbon (SOC), readily oxidizable organic carbon (ROC), and inert carbon (RC) (P<0.05); E C:P With NO3 - -N, bulk density (BD), TP, TK, AK, pH, and DOC showed significant positive correlations (P<0.05), while E showed significant negative correlations (P<0.05) with TN, SOC, ROC, RC, particulate organic carbon (POC), mineral-bound organic carbon (MAOC), and SWC. N:P Then with NO3 - -N, TP, TK, AK and DOC were significantly positively correlated (P<0.05), and significantly negatively correlated with SWC (P<0.05); Figure 1 ).

[0052] (3) Soil microbial nutrient utilization strategies under different grassland types The extracellular enzymes and their dosages in the soils of the three grassland types are shown in Table 5.

[0053] Table 5 Comparison of soil extracellular enzymes and their stoichiometry among three grassland types

[0054] On a global scale, the logarithmically transformed stoichiometric ratio of C, N, and P in soil enzymes is 1:1:1, and E... C：N E C：P E N：P The values ​​were 1.41, 0.62, and 0.44. In this study, the values ​​were 1.90:1.00:2.64 and 7.61, 0.79, and 0.42. Compared to the global scale, the soil enzyme E in this study... C:P Ratio and E C:N The significantly higher level indicates that the availability of nitrogen in the soil of this region is relatively sufficient.

[0055] Soil enzyme stoichiometry characteristics of different grassland types, such as Figure 2 As shown in the figure. The vector length represents the degree of carbon limitation of soil microorganisms; the longer the vector length, the higher the degree of carbon limitation of soil microorganisms. Figure 2(a) DLH has the longest vector length, followed by TJ, and HB has the shortest, indicating that DLH microorganisms are most carbon-limited. With increasing soil depth, the vector lengths of HB and DLH decrease, indicating a gradual weakening of their microbial carbon limitation intensity. The vector length of TJ shows no significant change across different soil layers, indicating that its carbon limitation state is relatively stable. The vector angle represents the degree of nitrogen and phosphorus limitation of soil microorganisms. A vector angle <45° indicates nitrogen limitation, and a vector angle >45° indicates phosphorus limitation. Figure 2 (b) In the figure, the vector angles of all treatments are greater than 45°, indicating that the microorganisms in all three grassland types are generally phosphorus-limited. Among them, the vector angle of HB is greater than that of TJ and DLH treatments, indicating that its microorganisms are more phosphorus-limited.

[0056] Soil extracellular enzyme stoichiometry can be used to assess soil microbial nutrient limitation characteristics. Figure 3 Regression analysis based on BG:(BG+NAG) and BG:(BG+AP) showed that ( Figure 3 (a) All data points are located above the 1:1 line, indicating that soil microorganisms in all three grassland types are phosphorus-limited. Furthermore, analysis of the BG:NAG and NAG:AP ratios revealed that the microorganisms in all three grassland types are primarily limited by both carbon and phosphorus, with only a few HB samples showing sole phosphorus limitation. Figure 3 (b)

[0057] (4) The influence of soil properties and carbon composition on soil microbial nutrient utilization strategies The relative contributions of soil physicochemical indicators and carbon composition to microbial nutrient limitation were analyzed using a random forest model. Figure 4 The results showed that: TP, AK, nitrogen-to-phosphorus ratio (N:P), NO3 - -N, ROC, carbon-to-phosphorus ratio (C:P), and MAOC significantly contributed to the vector length (P<0.05); Figure 4 (a) TK, SOC, POC, BD, carbon-nitrogen ratio (C:N), nitrogen-phosphorus ratio (N:P), and AK significantly contributed to the vector angle (P<0.05). Figure 4 (b)

[0058] Select the top five most important variables for univariate linear regression analysis. Figure 5 The results showed that the vector length was significantly positively correlated with TP and AK content (p<0.05), and significantly negatively correlated with the nitrogen-to-phosphorus ratio (TN:TP) and ROC content (p<0.05). NO3 - Although the -N content showed a positive correlation with the vector length, it did not reach a significant level (p>0.05); Figure 5(a) Furthermore, the vector angle was significantly positively correlated with SOC, POC content, and carbon-nitrogen ratio (SOC:TN) (p<0.05); and significantly negatively correlated with TK content and BD (p<0.05). Figure 5 (b)

[0059] Partial Least Squares Path Model (PLS-PM) analysis revealed the effects of soil physicochemical properties, carbon composition, total nutrient content, and nutrient stoichiometry on microbial carbon and phosphorus limitation. Figure 6 The results showed that soil physicochemical properties have a significant positive effect on vector length. Figure 6 (a); while the nutrient stoichiometry and total nutrient content showed a significant negative overall effect ( Figure 6 (b) For the vector angle, carbon composition, total nutrient content, and nutrient stoichiometry all show a positive effect ( Figure 6 (c) Physicochemical properties have a negative direct effect and a total effect on it ( Figure 6 (d).

[0060] The total, direct, and indirect effects of soil elements on vector length (VL) and vector angle (VA) are shown in Table 6.

[0061] Table 6. Total, direct, and indirect effects of soil elements on VL and VA

[0062] It can be seen that soil nutrients significantly affect enzyme activity and its stoichiometry by altering the concentration of available substrates and the C, N, and P stoichiometry. The C, N, and P stoichiometry, in turn, influences the abundance and activity of specific microbial groups involved in element cycling. These specific microorganisms, in order to maintain stoichiometric homeostasis and address soil nutrient imbalances, will synergistically regulate soil enzymes, thereby improving the limitation of key nutrients. Overall, while microbial metabolic limitation is influenced by specific soil factors, the combined effect of soil physicochemical properties is the dominant reason for regulating microbial carbon and phosphorus limitation.

[0063] After logarithmic transformation, the stoichiometric ratio of carbon (C), nitrogen (N), and phosphorus (P) for grassland extracellular enzymes was 1.90:1:2.64, deviating from the global ecological stoichiometric ratio of 1:1:1. This indicates that microorganisms tend to allocate more metabolic resources to the synthesis of carbon- and phosphorus-acquired enzymes (especially phosphorus-acquired enzymes). Among the three grassland types, DLH had the longest vector length, indicating the highest degree of carbon limitation among its microorganisms; while HB had the largest vector angle, indicating the highest degree of phosphorus limitation among its microorganisms. Although microbial metabolic limitation is mainly driven by specific soil factors (TP, AK, N:P, ROC, TK, SOC, POC, BD, C:N), structural equation modeling reveals that the combined effect of soil physicochemical properties (BD, SWC, and pH) is the dominant reason influencing microbial carbon and phosphorus limitation. The results of this study deepen our understanding of soil enzyme activity characteristics, microbial nutrient limitation status, and key driving factors under different grassland types.

[0064] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the limitation of soil microbial metabolic resources in different grassland types, characterized in that, The evaluation method includes: Soil samples were collected from different grassland types, the activity of extracellular enzymes in the soil samples was measured, and the stoichiometry of extracellular enzymes in the soil was calculated. The correlation between soil extracellular enzyme activity, soil extracellular enzyme stoichiometry and soil physicochemical properties was analyzed to screen key physicochemical factors affecting microbial metabolism and soil nutrient balance. Vector analysis of soil enzyme stoichiometry was used to obtain soil microbial nutrient utilization strategies; Regression analysis was performed on the stoichiometry of soil extracellular enzymes to assess soil microbial nutrient limitation characteristics. A random forest model was constructed to analyze the relative contributions of soil physicochemical indicators and carbon composition to microbial nutrient limitation; a univariate linear regression analysis was performed on the top five core variables to determine the magnitude of their influence on the potential limitation intensity. A partial least squares path model was constructed to identify pathways that influence the limitation of soil microbial metabolic resources.

2. The evaluation method according to claim 1, characterized in that, The different grassland types include different grassland types in the Qilian Mountains; the grassland types include marshy meadows, temperate desert steppes, or alpine meadows.

3. The evaluation method according to claim 2, characterized in that, Soil microorganisms in the marshy meadows, temperate desert steppes, and alpine meadows were limited by carbon and phosphorus. The degree of carbon limitation was significantly positively correlated with total phosphorus and available potassium content, and significantly negatively correlated with nitrogen-phosphorus ratio and easily oxidizable organic carbon content. The degree of phosphorus limitation was significantly positively correlated with soil organic carbon, particulate organic carbon content, and carbon-nitrogen ratio, and significantly negatively correlated with total potassium and bulk density. The main factors limiting carbon and phosphorus metabolism in soil microorganisms included bulk density, soil moisture content, and pH.

4. The evaluation method according to claim 1, characterized in that, The metabolic resources include any one or more of carbon, nitrogen, and phosphorus.

5. The evaluation method according to claim 1, characterized in that, The soil samples were collected at depths ranging from 0 to 30 cm.

6. The evaluation method according to claim 1, characterized in that, The soil extracellular enzymes include β-1,4-glucosidase, N-acetyl-β-D-glucosidase, and phosphatase.

7. The evaluation method according to claim 1, characterized in that, The soil physicochemical properties include nitrate nitrogen, ammonium nitrogen, total nitrogen, total phosphorus, total potassium, available potassium, bulk density, soil moisture content, pH, soil organic carbon, soluble organic carbon, easily oxidized organic carbon, inert carbon, mineral-bound organic carbon, and particulate organic carbon.

8. The evaluation method according to claim 1, characterized in that, The method for constructing the random forest model includes using the "randomForest" package in R4.3.2 to construct the random forest model to determine the key factors of vector length and vector angle; and using the "rfUtilities" and "rfPermute" packages to evaluate the significance of the model and each factor.

9. The evaluation method according to claim 1, characterized in that, The method for constructing the partial least squares path model includes: using the "plspm" package in R4.3.2 to construct the partial least squares path model.

10. The application of the assessment method according to any one of claims 1 to 9 in the ecological restoration of degraded grassland, grassland protection and management, or artificial grassland establishment.

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