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Article

Reduced Precipitation Frequency Decreases the Stability of the Soil Organic Carbon Pool by Altering Microbial Communities in Degraded Grasslands

by
Junda Chen
1,
Yifan Gao
1,
Yizhu Zeng
1,
Muping Huang
1,
Xuechen Yang
2,
Raúl Ochoa-Hueso
3,4,
Wei Sun
1,5,* and
Tianxue Yang
1,5,*
1
Key Laboratory of Vegetation Ecology, Ministry of Education, Institute of Grassland Science, Northeast Normal University, Changchun 130117, China
2
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3
Department of Biology, Instituto de Viticultura y Agroalimentación (IVAGRO), University of Cádiz, 11510 Cádiz, Spain
4
Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands
5
State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun 130117, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(4), 977; https://doi.org/10.3390/agronomy15040977
Submission received: 10 March 2025 / Revised: 13 April 2025 / Accepted: 16 April 2025 / Published: 17 April 2025
(This article belongs to the Section Soil and Plant Nutrition)
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Abstract

:
Decreasing precipitation frequency (DPF) has the potential to alter soil microbial community structure, enzyme activity, and the stoichiometry of microbial biomass in grassland ecosystems. Grasslands have undergone degradation, often driven by anthropogenic activities such as overgrazing, which further intensifies their sensitivity to environmental changes such as altered precipitation. Changes in soil microbial communities can in turn impact the soil organic carbon pool (SOCP) and its stability, particularly in degraded grasslands shaped by agricultural practices. Here, we evaluated how DPF affects different types of soil carbon pools, soil microbial community structure, the stoichiometry of microbial biomass, and the potential activity of exoenzymes related to microbial nutrient acquisition in three steppe grasslands representing a degradation gradient (from light to moderate to severe degradation). We also developed a systematic model linking microbial stoichiometry, community structure, enzyme activity, and the SOCP and its stability. Our results showed that DPF significantly reduced the soil total carbon pool (STCP), SOCP, and dissolved organic carbon pool (DOCP) in all degraded grasslands, while it increased the DOCP/SOCP ratio in the grasslands with light to moderate degradation, indicating lower stability of the SOCP. Decreased precipitation frequency reduced microbial biomass in grasslands with light to moderate degradation but had the opposite effect on grasslands with severe degradation. Additionally, the promoting effects of DPF on the fungi/bacteria ratio and β-1,4-xylosidase activity diminished with increasing grassland degradation. The fungi/bacteria ratio, microbial biomass carbon/nitrogen ratio, and β-1,4-xylosidase activity were identified as the main predictors for the SOCP and its stability. In lightly and moderately degraded grasslands, decreased soil water content (SWC) and increased soil moisture variation induced by lower precipitation frequency promoted β-1,4-xylosidase activity by decreasing the microbial biomass carbon/nitrogen ratio. The lower stability of the SOCP in degraded grasslands under altered precipitation frequency highlights the challenges posed by climate change regarding soil carbon sequestration in these fragile ecosystems. Our results also stress the importance of targeted water management for soil carbon sequestration in agriculture and livestock management, which could be achieved by altering soil microbial activity and stoichiometry, For example, fertilization increases nutrient availability, enhances microbial growth, and shifts C/N/P ratios, promoting carbon allocation to biomass over respiration and thus enhancing soil carbon retention.

1. Introduction

As one of the largest carbon reservoirs on Earth, the soil carbon pool is integral to the global carbon cycle, with the soil organic carbon pool (SOCP) serving as a pivotal component of this process [1,2,3]. The dissolved organic carbon pool (DOCP) constitutes the most dynamic component of the SOCP and plays a central role in its transformation and fluxes [4]. Beyond pool size, the stability of the SOCP is essential for sustaining soil fertility and carbon sequestration and plays a critical role in the global carbon cycle and, thus, climate regulation [5,6]. The ratio of the dissolved organic carbon pool to the soil organic carbon pool (i.e., DOCP/SOCP) is an intuitive and effective parameter for reflecting the bioavailability vs. storage capacity of soil organic carbon (SOC) [4,7]. A higher DOCP/SOCP typically indicates a greater bioavailability of organic carbon, an unstable carbon pool, and, therefore, an increased risk of carbon loss. The soil organic carbon pool and its stability are predicted to be particularly sensitive to soil water availability, especially in arid and semi-arid areas [8], but this has not been evaluated systematically.
Global climate models predict that growing seasons will generally experience more intense but less frequent rainfall events, a trend that has already been observed in the northeastern region of China [9]. The reduction in precipitation frequency is expected to alter soil water conditions [10], characterized by increased soil moisture variability and extended soil drought, a phenomenon that has been previously documented [11]. Under varying soil moisture conditions, microbial community properties, such as microbial biomass, structure, metabolic activity, and stoichiometric characteristics, regulate soil carbon sequestration and alter the stability of the SOCP [12]. Higher fungi/bacteria ratios under severe soil water conditions may reduce the DOCP and increase organic carbon stability due to the slower degradation capacity of fungi, linked to their specialized enzymes and complex metabolic processes [13,14,15]. However, the DOCP may also increase due to enhanced microbial enzyme activity due to the stress response of microorganisms under extreme drought, where microbial acquisition strategies shift toward carbon compounds [16]. Additionally, environmental stress triggers microbial physiological adaptation strategies, disrupting stoichiometric balance and driving increased enzymatic activity to alleviate resource limitations, ultimately elevating the DOCP while reducing SOCP stability [17,18]. Although previous studies have preliminarily explored microbial responses to drought stress, the mechanisms by which reduced precipitation frequency and associated soil moisture fluctuations regulate microbially mediated soil carbon pools and their stability remain insufficiently understood. It is still unclear whether shifts in microbial community composition (e.g., fungi/bacteria ratios) or stoichiometric imbalances play a more dominant role in controlling SOCP stability under water-limiting conditions. This lack of mechanistic understanding limits our ability to accurately predict soil carbon pools under future climate change scenarios.
The scale and stability of the SOCP in grassland ecosystems are very relevant to ensuring the sustainability of these ecosystems, which are expected to be highly sensitive to altered soil moisture fluctuations [19,20]. Prolonged and intense grazing has resulted in varying degrees of grassland degradation, posing significant challenges to the sustainable development of animal husbandry [21,22]. Thus, the impact of precipitation frequency on SOCP stability may vary in magnitude across grasslands with different levels of degradation due to differences in ecosystem properties. Grasslands with higher degradation levels are predicted to be more sensitive to altered environments due to their lower resistance and stability, which could amplify the negative effects of altered precipitation regimes on SOC [23,24]. However, recent research carried out in a severely degraded grassland showed that the prolonged low input of organic matter may lead to an increased proportion of relatively recalcitrant organic carbon, thereby reducing the sensitivity of the SOCP to environmental disturbances [25]. However, there is still a lack of systematic research on whether the regulatory effects of microbial community structure and stoichiometric characteristics on enzyme activity and SOCP stability differ across grasslands with varying levels of degradation. Clarifying the specific responses of grasslands with varying degradation levels to altered precipitation frequency is critical for understanding how degradation alters the microbial regulation of soil carbon pool stability under changing moisture regimes. Since grasslands are often used as agricultural land for grazing and forage production, such insights are essential for providing a scientific basis for enhancing the sustainability of agricultural production, thereby improving soil health and optimizing agricultural management.
Here, we carried out a mesocosm experiment to evaluate the effects of reduced precipitation frequency (–50% of the long-term mean) on soil carbon pools (soil total carbon pool (STCP), SOCP and DOCP) across grasslands in northern China with varying levels of degradation (lightly, moderately, and severely degraded). Further, we investigated the response of soil microbial communities (microbial biomass, community structure, enzyme activity, and stoichiometry) to reduced precipitation frequency. Finally, we carried out correlation analysis and structural equation modeling to investigate the main indicators affecting the soil carbon pool and its stability. This study aims to address the existing knowledge gap regarding how microbial communities influence SOCP stability in degraded grasslands, particularly under altered precipitation regimes. We also seek to quantitatively assess the potential differences in these responses across grasslands with varying degrees of degradation. Specifically, we aim to investigate how changes in microbial community characteristics determine alterations in SOCP stability while also examining potential variations in these processes across grasslands with different degradation levels. We hypothesized that (i) reduced rainfall frequency would decrease soil carbon pools while increasing SOCP stability [26]; (ii) the negative effects of decreased precipitation frequency on organic carbon pool stability would progressively diminish with increasing grassland degradation due to greater resilience [24]; and (iii) microbial enzyme activity and community structure would be main predictors for soil carbon pools and SOCP stability [27].

2. Materials and Methods

2.1. Description of the Study Site

The study site (43°79′ N, 123°69′ E) is situated in Jilin Province, China, located in the eastern region of the Horqin Sandy Land. This region is classified as experiencing a temperate continental climate, characterized by an average annual precipitation of 395 mm, with approximately 80% occurring during the growing season from May to August, and a mean annual temperature of 6.4 °C. The soil type is classified as Chernozem, characterized by high alkalinity and salinity [28]. Historically, the grasslands in this region were utilized as open grazing pastures. As a consequence, prolonged and intensive grazing has led to varying degrees of degradation [29].
For the mesocosm experiment, we randomly selected three experimental sites with varying degrees of degradation: lightly degraded grassland (LDG), moderately degraded grassland (MDG), and severely degraded grassland (SDG) (Figure S1). The degree of grassland degradation was quantified using the grassland degradation index [30], which is based on a comprehensive system of vegetation and soil variables, with specific values provided in Table S1.

2.2. Experimental Design

In April 2019 (i.e., at the start of the growing season), we collected 60 soil monoliths (each with a depth of 50 cm and a diameter of 40 cm), along with living plants from the three study sites representing different levels of degradation. These monoliths were contained in PVC cylinders (hereafter termed mesocosms) and transported to the experimental station, where the climatic conditions were similar to those at the study site. To simulate real-world conditions and prevent surface runoff, the mesocosm system was embedded in the ground, with the top of the system positioned 3 cm above the surface (Figure S2). A rainfall shelter was constructed using materials with more than 90% light transparency to exclude external rainfall.

2.3. Experimental Setup

The manipulation experiment implemented a full factorial design, including three stages of grassland degradation (LDG, MDG, and SDG) and two levels of precipitation frequency (14 and 7 events across the ~4-month period); each treatment consisted of five replicates. To minimize systematic errors, a randomized block design was used, ensuring the random allocation of treatments within each block. Such a design ensures the statistical reliability of the experimental results and effectively minimizes biases arising from random errors and environmental variation [11]. Moreover, the choice of five replicates represents a practical balance between experimental cost and data precision, providing sufficient statistical power while constituting a manageable experimental setup. The experiment was conducted throughout the entire growing season, from May to August 2019.
The 51-year (1968–2018) average growing season precipitation amount (304 mm) and frequency (14 events) were calculated based on daily precipitation data from the China Meteorological Data Network, with the definition of effective precipitation determined by Heisler-White et al. [31] (more details are given in the Supplementary Material). We designated 14 precipitation events as the control precipitation frequency (CPF). To investigate the effects of low precipitation frequency on microbial influence over soil carbon pools while ensuring that the precipitation frequency was observed within the past 51 years of recorded data for the region, we established a 50% reduction in precipitation frequency (7 events) as the decreased precipitation frequency treatment (DPF) (Figure S3). The average growing season precipitation amount (304 mm) was added to mesocosms and distributed across 14 events or 7 events (Figure S4a). Simulated rainfall was applied using a handheld sprayer with water from a nearby well.

2.4. Soil Sampling and Soil Properties

At the end of August, 300 g of mixed soil was collected from the 0–10 cm soil layer in each mesocosm. To reduce variability and potential bias, soil samples were homogenized after collection. Sampling was conducted simultaneously across treatments, and all mesocosms were exposed to uniform environmental conditions during the experiment. One portion was stored at −20 °C for the measurement of total microbial phospholipid fatty acids (PLFAs), dissolved organic carbon (DOC), and microbial biomass carbon (MBC) and nitrogen (MBN). A second portion was air dried and stored for the measurement of soil total carbon (STC) and SOC. In addition, the dry mass-to-volume ratio of the 200 cm3 soil core was calculated as soil bulk density.
Because the precipitation amount and event size in June were representative of the four-month average (Figure S4a), a typical rainfall cycle in mid-June was selected for soil water content measurement (Figure S4b). At each sampling event, a 3 cm diameter soil core was extracted from the 0–10 cm layer. The mean soil water content (SWC) was calculated by averaging the measurements from three sampling events within a complete rainfall cycle, and the coefficient of variation (standard deviation/mean) for the SWC was subsequently determined.
Microbial biomass carbon and MBN were measured using the chloroform fumigation–extraction method [32]. We used 0.5M K2SO4 to extract the DOC, which was further analyzed using an elemental analyzer (TOC-L, Shimadzu Corporation, Kyoto, Japan). Soil total carbon and SOC were analyzed using an elemental analyzer (vario MACRO cube, Elementar, Langenselbold, Germany).

2.5. Soil Microbial Communities

Microbial communities were analyzed using the PLFA method [33], and identification was carried out using capillary gas chromatography (7890A, Agilent Technologies, Palo Alto, CA, USA) and the MIDI Sherlock Microbial Identification System (MIDI Inc., Newark, DE, USA). The concentrations of all fatty acids were calculated using a FAME 19:0 standard solution. Fatty acids were assigned to fungal groups (18:1 w9c, 18:2x6,9, 16:1 w5c), bacterial groups (10Me16:0, 10Me18:0, i14:0, a15:0, i15:0, i16:0, 16:1x7c, a17:0, cy17:0, i17:0, 17:1x8, 18:1x7c, 18:1x9, and cy19:0) [34], Gram-positive bacteria (i13:0, i14:0, i15:0, a15:0, i16:0, a16:0, i17:0, and a17:0), Gram-negative bacteria (11:0 3OH, i11:0 3OH, 14:1u5c, i15:1G, 16:1u9c, 16:1u5c, 16:1 2OH, i16:1H, cy17:0, 17:1u8c, 18:1u5c, 11Me 18:1u7c, and cy19:0u8c) [35], actinobacteria (16:0 10methyl, 17:0 10methyl, and 18:0 10methyl) [36], and saprophytic fungi (C18:1u9, C18:2u6,9, and C18:3u3,6,9) [37]. Additionally, the ratios of fungi/bacteria, Gram-positive bacteria/Gram-negative bacteria, actinobacteria/total microbial biomass, and saprophytic fungi/total microbial biomass were calculated. An increase in these microbial ratios reflects an augmented decomposition capacity within the soil microbial community, particularly in the breakdown of complex organic carbon, thereby accelerating carbon cycling and promoting the efficient release of nutrients.

2.6. Soil Carbon Pool Calculation

Each soil carbon pool (STCP, SOCP, and DOCP) was calculated using Equation (1):
Soil carbon pool (g m−2) = Xc × XSBD × XT/1000
where XC represents the total carbon content (mg kg−1) of each carbon pool, XSBD indicates the soil bulk density (g cm−3), and XT denotes the soil layer thickness (cm).
Additionally, we calculated the DOCP/SOCP, where a higher value suggests greater instability in the carbon pool, which may lead to increased carbon loss [7]. It also indicates a higher availability of carbon sources, providing more accessible carbon for microbial and plant use [4].

2.7. Statistical Analysis

Mixed-effects models were employed to evaluate the effects of variations in precipitation frequency and degradation levels, as well as their interactions on soil carbon pools, microbial community characteristics. Least significant difference post hoc comparisons were employed to analyze the differences in factors among the various degraded grasslands. An independent samples t-test was used to assess the effect of DPF on each degraded grassland. Effect values were compared to assess the potential differences in the impact of DPF on variables among three degraded grasslands (Equation (2)). The positive and negative effect values represent the positive and negative impacts of the DPF treatment, respectively.
Effect value (%) = (XDPF − XCPF/XCPF) × 100
where X represents the response variables (i.e., MBC and MBN). CPF and DPF indicate the control and decreased precipitation frequency treatments, respectively. All statistical analyses were conducted with the SPSS 20.0 software program (SPSS Inc., Chicago, IL, USA).
The relationships between microbial communities (microbial group PLFAs and microbial structure), microbial stoichiometric characteristics, and enzyme activity in soil carbon pools were evaluated using Pearson’s correlation analysis with R statistical software (https://cran.rproject.org, accessed on 15 December 2024), version 4.0.5. This preliminary step was intended to identify key variables significantly associated with SOCP stability, thereby reducing model complexity and multicollinearity in subsequent analysis. To further explore the direct and indirect pathways through which these selected variables influence SOCP stability under DPF across grasslands with varying degradation levels, we employed structural equation modeling (SEM). Structural equation modeling, based on a priori conceptual models, allows for the system-level simultaneous evaluation of multiple causal relationships and is particularly suitable for disentangling the complex interactions among biotic and abiotic factors in ecological systems. In addition, the standardized total effect of each variable on soil carbon pool stability was calculated. The fit of the SEM models was tested using the following criteria: chi-squares/df (0 ≤ chi-squares/df ≤ 2) and p (0.05 < p ≤ 1). All SEM analyses were conducted using the Amos 25 software (IBM SPSS Inc., Armonk, NY, USA).

3. Results

Soil water content and the variation coefficient of SWC were significantly influenced by both grassland degradation level (DL) (SWC: F = 7.62, p < 0.01, variation coefficient of SWC: F = 21.15, p < 0.001) and precipitation frequency (PF) (SWC: F = 15.54, p < 0.001, variation coefficient of SWC: F = 189.17, p < 0.001; Figure S5). The negative effects of DPF on SWC gradually strengthened with increasing grassland degradation (from −10% to −20%, Figure S5), while the variation coefficient of SWC gradually increased with increasing degradation (from 57% to 132%, Figure S5). Aboveground plant biomass was significantly influenced by both grassland degradation levels and precipitation frequency (DL: F = 32.27, p < 0.001; PF: F = 41.67, p < 0.001; Figure S6). The negative effects of DPF on aboveground plant biomass gradually intensified with increasing grassland degradation (from −10% to −23%, Figure S6).

3.1. Soil Carbon Pools in Response to Grassland Degradation and Reduced Precipitation Frequency

Precipitation frequency and DL had significant effects on different soil carbon pools (STCP: DL: F = 32.27, p < 0.001, PF: F = 41.67, p < 0.001; SOCP: DL: F = 225.99, p < 0.001, PF: F = 6.71, p < 0.05; DOCP: DL: F = 391.09, p < 0.001, PF: F = 7.34, p < 0.05; DOCP/SOCP: DL: F = 17.61, p < 0.001, PF: F = 3.99, p < 0.05; Figure 1). Specifically, STCP, SOCP, and DOCP were the lowest in the SDG (Figure 1a–c). The soil dissolved organic carbon pool-to-soil organic carbon pool ratio in the LDG was significantly higher than that in the other two grasslands (Figure 1d). Compared to the CPF, the DPF markedly reduced the STCP in the LDG and MDG (LDG: F = 0.02, p < 0.01, MDG: F = 0.02, p < 0.05) while notably enhancing the DOCP/SOCP ratio (LDG: F = 0.31, p < 0.05, MDG: F = 0.84, p < 0.05).
The significant responses of different carbon pools to DPF were contingent on the degree of grassland degradation (Figure 1a,c,d). For example, the promotive effects of DPF on DOCP/SOCP gradually weakened and eventually disappeared with increasing grassland degradation (from 21% to −9%, Figure 1d).

3.2. Effects of Decreased Precipitation Frequency and Grassland Degradation on Microbial Communities

Microbial PLFAs and community structures exhibited significant responses to DL (total microbial PLFAs: F = 233.25, p < 0.001; bacterial PLFAs: F = 131.72, p < 0.001; fungal PLFAs: F = 215.79, p < 0.001; Gram-positive bacterial PLFAs: F = 111.09, p < 0.001; Gram-negative bacterial PLFAs: F = 180.47, p < 0.001; saprophytic PLFAs: F = 256.80, p < 0.001; actinomycetes PLFAs: F = 167.67, p < 0.001; fungal PLFA-to-bacterial PLFA ratio: F = 622.87, p < 0.001; Gram-positive bacterial PLFA-to-Gram-negative bacterial PLFA ratio: F = 38.86, p < 0.001; saprophytic PLFA-to-total microbial PLFA ratio: F = 71.00, p < 0.001; actinomycetes PLFA-to-total microbial PLFA ratio: F = 28.50, p < 0.001; Figure 2). With increasing grassland degradation, microbial group PLFAs, the fungal PLFA-to-bacterial PLFA ratio, the actinomycetes PLFA-to-total microbial PLFA ratio, and the saprophytic PLFA-to-total microbial PLFA ratio exhibited a progressive decline (Figure 2a–h,j,k). However, the Gram-positive bacterial PLFA-to-Gram-negative bacterial PLFA ratio showed the opposite trend (Figure 2i). In general, DPF notably decreased microbial group PLFAs (total microbial PLFAs: LDG: F = 7.38, p < 0.05, MDG: F = 0.14, p < 0.01; bacterial PLFAs: LDG: F = 8.65, p < 0.01, MDG: F = 0.68, p < 0.05; fungal PLFAs: LDG: F = 0.02, p < 0.01, MDG: F = 2.77, p < 0.01; Gram-positive bacterial PLFAs: LDG: F = 12.84, p < 0.01; Gram-negative bacterial PLFAs: LDG: F = 4.10, p < 0.01, MDG: F = 0.45, p < 0.05; saprophytic PLFAs: LDG: F = 14.19, p < 0.01, MDG: F = 1.72, p < 0.01) but showed limited effects on microbial community structure, only increasing the fungal PLFA-to-bacterial PLFA ratio (fungal PLFA-to-bacterial PLFA ratio: LDG: F = 0.73, p < 0.05, MDG: F = 3.28, p < 0.05; Figure 2a–h).
The effects of precipitation frequency on most microbial group PLFAs and the fungal PLFA-to-bacterial PLFA ratio were contingent on the degree of grassland degradation (Figure 2a–h). For example, the promotive effects of DPF on the fungal PLFA-to-bacterial PLFA ratio progressively weakened with increasing grassland degradation (from 13% to 3%, Figure 2h).

3.3. Responses of Microbial Stoichiometric Characteristics and Enzyme Activity to Precipitation Manipulation and Grassland Degradation

Precipitation frequency and grassland degradation had significant effects on MBC, MBN, and MBC/MBN (MBC: DL: F = 150.00, p < 0.001, PF: F = 28.74, p < 0.001; MBN: DL: F = 99.13, p < 0.001, PF: F = 210.16, p < 0.001; MBC/MBN: DL: F = 10.87, p < 0.001, PF: F = 20.96, p < 0.001; Figure 3). In the grassland with severe degradation, MBC, MBN, and MBC/MBN were the lowest (Figure 3a–c). Compared to the CPF, DPF significantly increased MBC and MBN in the LDG and MDG (MBC: LDG: F = 2.02, p < 0.05, MDG: F = 3.52, p < 0.05; MBN: LDG: F = 2.86, p < 0.001, MDG: F = 0.67, p < 0.001) but decreased MBC/MBN (MBC/MBN: LDG: F = 5.20, p < 0.05, MDG: F = 7.00, p < 0.05; Figure 3a–c). Additionally, the negative effects of DPF on MBC/MBN gradually weakened and even diminished with increasing grassland degradation (from −78% to 6%, Figure 3c).
The impact of grassland degradation was significant only for ɑ-1,4-glucosidase activity (F = 446.76, p < 0.001), with its lowest value recorded in the SDG (Figure 4a). Lower precipitation frequency showed few effects on ɑ-1,4-glucosidase activity while notably increasing β-1,4-xylosidase activity in the LDG and MDG (LDG: F = 27.28, p < 0.01; MDG: F = 1.25, p < 0.05; Figure 4). Moreover, the effects of DPF on β-1,4-xylosidase activity were progressively attenuated with increasing grassland degradation (Figure 4b).

3.4. Relationships Between Microbial Community Properties, the Soil Organic Carbon Pool, and Its Stability

Pearson correlation analysis revealed that β-1,4-xylosidase activity, MBC/MBN, MBC, and fungi/bacteria were significantly correlated with all soil carbon pools (Figure 5). In the LDG and MDG, β-1,4-xylosidase activity and fungi/bacteria exhibited strong positive correlations with DOCP/SOCP, whereas MBC/MBN showed negative correlations (Figure 5a,b). In the SDG, fungi/bacteria was negatively correlated with DOCP/SOCP (Figure 5c).
According to SEM, in the LDG and MDG, SWC and soil moisture variation directly increased β-1,4-xylosidase activity by decreasing MBC/MBN and finally promoted DOCP/SOCP (Figure 6a,b). Furthermore, the increased variation coefficient of SWC elevated the fungi/bacteria ratio, thereby promoting β-1,4-xylosidase activity, which ultimately contributed to an increase in DOCP/SOCP (Figure 6a,b). In the SDG, the decline in SWC and the increase in soil moisture variation increased MBC/MBN but did not induce any changes in the DOCP/SOCP ratio (Figure 6c).

4. Discussion

Our results showed that DPF decreased SOCP, which was consistent with part of our first hypothesis. On the one hand, according to the “Birch effect”, heavy rainfall following drought conditions induced by lower precipitation frequency may stimulate rapid microbial recovery, as evidenced by the increased enzyme activity in our study, which leads to the release of carbon dioxide, ultimately reducing the size of the SOCP [38]. On the other hand, a decline in aboveground plant biomass (reduced by 17%) likely resulted in reduced litter inputs, thereby further diminishing the exogenous supply of organic carbon to the soil [39,40]. However, one study carried out in the Qinghai–Tibetan Plateau showed that DPF can lead to an increase in SOC [41]. This may be attributed to intense or frequent wetting–drying pulses accelerating litter degradation, thereby increasing litter surface area and enhancing decomposition, ultimately contributing to the accumulation of SOC [42]. Our study further indicated the impact of reduced litter inputs on the SOCP, highlighting the profound influence of plant biomass variation on the supply and transformation processes of the SOCP.
In our study, β-1,4-xylosidase activity increased in response to DPF, which is consistent with previous research evidencing enzyme accumulation under dry conditions [43,44]. Moreover, β-1,4-xylosidase activity was positively associated with soil organic stability, which was consistent with our third hypothesis. First, hydrolases may bind to specific regions on the surface of particulate organic matter, altering its physical structure or exposing more degradation sites [45,46]. Second, β-1,4-xylosidase may catalyze the conversion of polymers into smaller molecules, such as sugars and amino acids, which increases their water solubility and facilitates their entry into the soil as part of the DOCP [47]. High enzyme activity has been widely recognized as a reliable predictor of the DOCP [48,49,50], and our findings further support this established relationship. However, the stimulating effect of β-1,4-xylosidase on soil organic pool stability disappeared in the SDG, partially due to the low decomposition efficiency of β-1,4-xylosidase resulting from more stable organic substances [25].
Our model showed that fungi/bacteria was an important predictor of β-1,4-xylosidase activity. Reduced precipitation frequency increased soil water variability and then enhanced fungi/bacteria, finally promoting enzyme activity. Soil fungi are more efficient at regulating osmotic stress and ensuring efficient water and nutrient transfer through their hyphal networks, demonstrating greater resistance to water stress and moisture fluctuations than bacteria, thus leading to higher fungi/bacteria ratios [45,51]. The fact that an increased fungi/bacteria ratio promoted the decomposition of organic carbon is consistent with the role of fungi as the main contributors to β-1,4-xylanase activity. Although previous studies have shown that a higher fungi-to-bacteria ratio can accelerate litter decomposition [52], the extent to which this relationship holds under varying environmental conditions remains to be further explored. Our study addressed this gap by demonstrating that the relationship between fungi/bacteria and enzyme activity varied significantly depending on the specific environmental context, particularly the degree of grassland degradation. Decreased precipitation frequency showed little effect on fungi/bacteria and related enzyme activity in the SDG. As soil conditions worsened, fungi’s resilience weakened, limiting their competitive advantage over bacteria due to slower recovery after disturbance despite greater drought resistance [53,54]. In addition, under environmental stress, fungi adjust carbon allocation by channeling a significant proportion of carbon into intracellular storage compounds (such as enhanced investment in triglycerides) rather than allocating the limited carbon resources to the energy-intensive process of enzyme synthesis [55]. Our study revealed that the response of fungi/bacteria, a key driver of β-1,4-xylosidase activity, was contingent on grassland degradation levels. This study fills the gap left by earlier research, demonstrating that degradation and altered precipitation patterns reshaped microbial structure and enzyme activity, particularly in degraded grasslands, where the role of fungi in carbon decomposition was diminished.
Besides fungi/bacteria, microbial biomass carbon/nitrogen was another main predictor of enzyme activity. The asynchronous increase in soil microbial carbon and nitrogen due to reduced rainfall frequency led to a decline in the soil microbial carbon-to-nitrogen ratio. The resource allocation theory posits that when microorganisms are limited by a particular element, they respond by synthesizing extracellular enzymes, thereby enhancing the acquisition of that element [56,57]. For example, a study conducted in a semi-arid grassland showed that soil microorganisms increased the secretion of C cycle-related enzymes to alleviate carbon limitation induced by nitrogen addition [58]. However, in the SDG, DPF did not significantly affect microbial biomass carbon/nitrogen or the regulation of enzyme activity, which is partially explained by microbial economic theory [59]. Enzyme synthesis should only be induced when it leads to a substantial increase in resource acquisition; however, if the cost of producing enzymes outweighs the benefits derived from acquiring carbon and nutrients, the production of enzymes would be deemed inefficient [60]. In the SDG, we speculate that microorganisms did not invest in the synthesis of enzymes due to resource scarcity. According to the microbial Y–A–S framework (Y: high yield, A: resource acquisition, S: stress tolerance) [61,62], when resources are scarce, microorganisms focus more on surviving stress and adapting to the environment by accumulating biomass instead acquiring resources [63].
This study advances our understanding of how microbial community traits regulate the stability of the SOCP under reduced precipitation frequency, an increasingly common but understudied facet of climate change. Our results revealed that environmental stress can stimulate microbial activity, enhancing enzyme-driven decomposition and ultimately weakening carbon pool stability. This suggests that when climatic pressures limit plant growth, agricultural management should also consider the unintended ecological impacts of enhanced microbial activity. For instance, strategies that regulate microbial activity, such as adjusting irrigation practices or using microbial inhibitors, could help maintain carbon pool stability. This study also provides novel evidence regarding the stability of carbon pools under reduced precipitation frequency in grasslands across different degradation levels, highlighting the need to consider degradation status in grassland management. Another strength of our study is that it allowed for the precise manipulation of treatments, thus allowing us to disentangle the simultaneous effects of grassland degradation and reduced precipitation frequency, which are not frequently evaluated in tandem. However, we acknowledge some limitations, including that this study does not fully represent the complexity of natural systems. Thus, future research should incorporate field-based experimental studies across diverse degradation gradients to refine our understanding of the temporal and ecosystem-specific thresholds governing microbial and enzymatic responses.

5. Conclusions

Here, we showed that microbial enzyme activity was the main predictor of soil carbon pool stability, which was, in turn, regulated by microbial biomass carbon/nitrogen and fungi/bacteria ratios. This highlights that the maintenance of soil carbon pool stability can be achieved by adjusting the microbial community structure and microbial stoichiometric characteristics. In addition, the differences in the response of the soil carbon pool and its stability to changes in precipitation frequency across grasslands with varying degrees of degradation highlight the critical role of assessing grassland degradation in predicting and managing soil carbon stability. Based on our findings, we suggest two directions for future research. First, extending our experiment for two or three years could have offered more comprehensive insights, but long-term studies on extreme events should be approached cautiously since these events may not persist. Second, future research should investigate the legacy effects of sporadic precipitation events on soil organic carbon stability. As extreme climatic events become more frequent, it is crucial to evaluate their long-term impacts, especially if they lead to irreversible changes once they subside.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15040977/s1, Figure S1: Field images of degraded grasslands and the mesocosm experiment; Figure S2: Photo of the established mesocosm experiment. Treatments and degradation conditions were randomized throughout the polytunnel; Figure S3: Historical record of precipitation frequency during the growing season (May–August) from 1968 to 2018 in the study area; Figure S4: (a) Precipitation experiment schedule and (b) time points for soil sampling to measure soil water content (SWC) during a single rainfall cycle in June. CPF: control precipitation frequency, DPF: decreased precipitation frequency; Figure S5: (a) Soil water content (SWC) and (b) variation coefficient of SWC under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG); Figure S6: Aboveground plant biomass under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG); Table S1: Key characteristics of the experimental sites used to calculate the grassland degradation index (GDI).

Author Contributions

Conceptualization, J.C., X.Y., W.S. and T.Y; methodology, J.C.; software, J.C.; validation, J.C., X.Y., W.S. and T.Y; formal analysis, J.C., X.Y., W.S. and T.Y; investigation, J.C., Y.G., Y.Z., M.H., R.O.-H. and X.Y.; resources, J.C.; data curation, J.C.; writing—original draft preparation, J.C., X.Y., W.S. and T.Y.; writing—review and editing, Y.G., Y.Z., M.H., R.O.-H., X.Y., W.S. and T.Y.; visualization, J.C., supervision, J.C., W.S. and T.Y.; project administration, W.S. and T.Y.; funding acquisition, W.S. and T.Y. 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 (32471670, 32001182, 32101396), by the Natural Science Foundation of Jilin Province (YDZJ202101ZYTS004), and by the Key Projects of Jilin Province Science and Technology Development Plan (20230303008SF).

Data Availability Statement

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

Acknowledgments

We thank Shicheng Jiang, Xiuquan Yue, Yanan Li, and Chengliang Wang for their help during laboratory analyses. We would like to thank the editor and anonymous reviewers for their helpful comments on the manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. (a) Soil total carbon pool (STCP), (b) soil organic carbon pool (SOCP), (c) dissolved organic carbon pool (DOCP), and (d) dissolved organic carbon pool-to-soil organic carbon pool ratio (DOCP/SOCP) under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and their interaction on variables are presented. * p < 0.05, ** p < 0.01, *** p < 0.001, ns: p > 0.05. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
Figure 1. (a) Soil total carbon pool (STCP), (b) soil organic carbon pool (SOCP), (c) dissolved organic carbon pool (DOCP), and (d) dissolved organic carbon pool-to-soil organic carbon pool ratio (DOCP/SOCP) under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and their interaction on variables are presented. * p < 0.05, ** p < 0.01, *** p < 0.001, ns: p > 0.05. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
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Figure 2. (a) Total microbial PLFAs, (b) bacterial PLFAs, (c) fungal PLFAs, (d) Gram-positive bacterial PLFAs, (e) Gram-negative bacterial PLFAs, (f) actinomycetes PLFAs, (g) saprophytic PLFAs, (h) fungal PLFA-to-bacterial PLFA ratio, (i) Gram-positive bacterial PLFA-to-Gram-negative bacterial PLFA ratio, (j) actinomycetes PLFA-to-total microbial PLFA ratio, and (k) saprophytic PLFA-to-total microbial PLFA ratio under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and their interaction on variables are presented. * p < 0.05, ** p < 0.01, *** p < 0.001, ns: p > 0.05. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
Figure 2. (a) Total microbial PLFAs, (b) bacterial PLFAs, (c) fungal PLFAs, (d) Gram-positive bacterial PLFAs, (e) Gram-negative bacterial PLFAs, (f) actinomycetes PLFAs, (g) saprophytic PLFAs, (h) fungal PLFA-to-bacterial PLFA ratio, (i) Gram-positive bacterial PLFA-to-Gram-negative bacterial PLFA ratio, (j) actinomycetes PLFA-to-total microbial PLFA ratio, and (k) saprophytic PLFA-to-total microbial PLFA ratio under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and their interaction on variables are presented. * p < 0.05, ** p < 0.01, *** p < 0.001, ns: p > 0.05. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
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Figure 3. (a) Microbial biomass carbon (MBC), (b) microbial biomass nitrogen (MBN), and (c) microbial biomass carbon-to-microbial biomass nitrogen ratio (MBC/MBN) under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and interaction on variables are presented. * p < 0.05, *** p < 0.001. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
Figure 3. (a) Microbial biomass carbon (MBC), (b) microbial biomass nitrogen (MBN), and (c) microbial biomass carbon-to-microbial biomass nitrogen ratio (MBC/MBN) under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and interaction on variables are presented. * p < 0.05, *** p < 0.001. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
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Figure 4. (a) ɑ-1,4-glucosidase activity and (b) β-1,4-xylosidase activity under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and their interaction on variables are presented. * p < 0.05, *** p < 0.001, ns: p > 0.05. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
Figure 4. (a) ɑ-1,4-glucosidase activity and (b) β-1,4-xylosidase activity under different treatments (control (CPF) and decreased precipitation frequency (DPF)) in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG). Mixed-effects model results of the effects of grassland degradation level (DL), precipitation frequency (PF), and their interaction on variables are presented. * p < 0.05, *** p < 0.001, ns: p > 0.05. * indicates independent samples t-test results between the control (CPF) and decreased precipitation frequency (DPF) treatments (p < 0.05). To further explore significant interactive effects, effect size was calculated to assess the impact of decreased precipitation frequency (DPF) on parameters among degraded grasslands (inset graphs). Different lowercase and uppercase letters indicate differences among the three grasslands, p < 0.05.
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Figure 5. Pearson correlations of microbial PLFAs, microbial community composition, hydrolase activity, and the stoichiometry of microbial biomass with soil carbon pool parameters in the (a) lightly degraded (LDG), (b) moderately degraded (MDG), and (c) severely degraded grasslands (SDG). * p < 0.05, ** p < 0.01.
Figure 5. Pearson correlations of microbial PLFAs, microbial community composition, hydrolase activity, and the stoichiometry of microbial biomass with soil carbon pool parameters in the (a) lightly degraded (LDG), (b) moderately degraded (MDG), and (c) severely degraded grasslands (SDG). * p < 0.05, ** p < 0.01.
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Figure 6. Structural equation modeling (SEM) analysis of the effects of soil water content (SWC), variation coefficient of SWC (CVSWC), microbial biomass carbon-to-microbial biomass nitrogen ratio (MBC/MBN), fungal PLFA-to-bacterial PLFA ratio (F/B), and β-1,4-xylosidase activity (βX activity) on the dissolved organic carbon pool-to-soil organic carbon pool ratio (DOCP/SOCP) in the (a) lightly degraded (LDG), (b) moderately degraded (MDG), and (c) severely degraded grasslands (SDG). Standardized total effects of each factor on DOCP/SOCP in the (d) lightly degraded (LDG), (e) moderately degraded (MDG), and (f) severely degraded grasslands (SDG). Red and black continuous arrows indicate significant positive and negative paths, respectively, and dotted arrows indicate non-significant paths. The thickness of the colored solid arrows reflects the magnitude of the standardized SEM coefficients. R2 denotes the proportion of variance explained. Significant correlations were reported as *, p < 0.05; and ***, p < 0.001.
Figure 6. Structural equation modeling (SEM) analysis of the effects of soil water content (SWC), variation coefficient of SWC (CVSWC), microbial biomass carbon-to-microbial biomass nitrogen ratio (MBC/MBN), fungal PLFA-to-bacterial PLFA ratio (F/B), and β-1,4-xylosidase activity (βX activity) on the dissolved organic carbon pool-to-soil organic carbon pool ratio (DOCP/SOCP) in the (a) lightly degraded (LDG), (b) moderately degraded (MDG), and (c) severely degraded grasslands (SDG). Standardized total effects of each factor on DOCP/SOCP in the (d) lightly degraded (LDG), (e) moderately degraded (MDG), and (f) severely degraded grasslands (SDG). Red and black continuous arrows indicate significant positive and negative paths, respectively, and dotted arrows indicate non-significant paths. The thickness of the colored solid arrows reflects the magnitude of the standardized SEM coefficients. R2 denotes the proportion of variance explained. Significant correlations were reported as *, p < 0.05; and ***, p < 0.001.
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MDPI and ACS Style

Chen, J.; Gao, Y.; Zeng, Y.; Huang, M.; Yang, X.; Ochoa-Hueso, R.; Sun, W.; Yang, T. Reduced Precipitation Frequency Decreases the Stability of the Soil Organic Carbon Pool by Altering Microbial Communities in Degraded Grasslands. Agronomy 2025, 15, 977. https://doi.org/10.3390/agronomy15040977

AMA Style

Chen J, Gao Y, Zeng Y, Huang M, Yang X, Ochoa-Hueso R, Sun W, Yang T. Reduced Precipitation Frequency Decreases the Stability of the Soil Organic Carbon Pool by Altering Microbial Communities in Degraded Grasslands. Agronomy. 2025; 15(4):977. https://doi.org/10.3390/agronomy15040977

Chicago/Turabian Style

Chen, Junda, Yifan Gao, Yizhu Zeng, Muping Huang, Xuechen Yang, Raúl Ochoa-Hueso, Wei Sun, and Tianxue Yang. 2025. "Reduced Precipitation Frequency Decreases the Stability of the Soil Organic Carbon Pool by Altering Microbial Communities in Degraded Grasslands" Agronomy 15, no. 4: 977. https://doi.org/10.3390/agronomy15040977

APA Style

Chen, J., Gao, Y., Zeng, Y., Huang, M., Yang, X., Ochoa-Hueso, R., Sun, W., & Yang, T. (2025). Reduced Precipitation Frequency Decreases the Stability of the Soil Organic Carbon Pool by Altering Microbial Communities in Degraded Grasslands. Agronomy, 15(4), 977. https://doi.org/10.3390/agronomy15040977

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