Vegetation and Soil Types Affect Microbial Carbon Metabolism in the Black Soil Region of Northeast China

Abstract

This study explored the ecological functions and environmental effects of soil microorganisms in the black soil region by examining microbial carbon source metabolic capacity across four vegetation types (reed wetland, maize field, paddy field, aspen woodland) and three soil types (Histosols, Planosols, Gleysols) in the Naoli River Reserve, with soil samples collected at a depth of 0–20 cm. Using Biolog-ECO microplate technology, we assessed soil microbial carbon source metabolic activity, utilization patterns, and functional diversity. Structural equation modeling and random forest analysis were applied to explore the influence of environmental factors on microbial carbon metabolism. Results showed that microbial carbon metabolic activity was highest in maize fields and Histosols, exceeding that in paddy fields and Gleysols. Microorganisms preferred amino acids, polymers, and carboxylic acids over carbohydrates, amines, and phenolic acids. The Simpson index of microbial diversity was positively correlated with microbial biomass carbon and moisture content, while Chao1 and Ace indices were correlated with microbial biomass phosphorus. Key microbial phyla, such as Bacteroides and Monomonas, were closely related to carbon source utilization. The structural equation modeling indicated that microbial biomass carbon, microbial biomass nitrogen, and soil organic carbon were the main drivers of microbial carbon metabolism. The RF model identified i-erythritol as a key predictor of microbial carbon metabolism, and amines as the best predictor of average well color development (AWCD) changes. The McIntosh index was the most influential variable for AWCD variation. These findings provide a scientific basis for evaluating soil health and supporting sustainable black soil management.

INTRODUCTION

The carbon cycle represents a fundamental Earth system process involving dynamic interactions between carbon sinks (primarily photosynthesis) and sources (mainly respiration), with both natural and anthropogenic processes influencing atmospheric greenhouse gas concentrations [19, 35]. When carbon release exceeds sequestration, atmospheric accumulation occurs, altering radiative balance and contributing to climate change. Terrestrial ecosystems mediate this balance through plant uptake of CO₂ via photosynthesis, followed by microbial-mediated decomposition that returns carbon to the atmosphere. These fluxes are regulated by multiple environmental factors including temperature, precipitation regimes, soil characteristics, and nutrient availability [25, 49, 55]. As the largest terrestrial carbon reservoir, soils play a dual role in the global carbon cycle, functioning simultaneously as a significant carbon sink and potential greenhouse gas source [52]. The soil carbon pool, exceeding vegetation in storage capacity, exerts long-term control over terrestrial carbon balance through its influence on accumulation and decomposition processes [8, 26]. This critical function is governed by diverse soil microbial communities that drive carbon transformations through specialized metabolic activities. Soil microbial carbon metabolism represents a key mechanism controlling net carbon fluxes between soils and the atmosphere. The functional diversity of soil microbial communities in carbon source utilization not only mediates global material cycles and energy flows, but also serves as a sensitive bioindicator of early-stage soil quality changes. Through these metabolic activities, microorganisms fundamentally regulate the environmental impacts of terrestrial carbon cycling, making their functional characteristics crucial for understanding and predicting climate feedback.

Soil microorganisms are integral components of the soil ecosystem, driving nutrient cycling and organic matter decomposition. Their community structure serves as a sensitive indicator of soil fertility [41]. Variations in land use and plant growth alter root exudates and plant residues, leading to divergent microbial communities and functions. Clairmont et al. [9] demonstrated that plant species in vertical-flow constructed wetlands significantly influenced soil microbial composition, which subsequently affected carbon metabolism capacity. Moreover, distinct plant species induced variations in microbial structure and function at rhizosphere and root surfaces. Klimek et al. [28] further highlighted a strong positive correlation between vascular plant diversity and soil bacterial diversity in the Western Carpathians, with reduced bacterial functional diversity in low-plant-diversity areas. Similar studies in forest ecosystems confirmed significant linkages between soil organic matter and microbial ecological functions [53]. Beyond biodiversity, soil microbial communities regulate elemental cycling and energy flow, playing a pivotal role in terrestrial carbon sources and sinks. They mediate the degradation of organic matter and govern the biogeochemical cycles of nitrogen, carbon, and phosphorus [60]. Zhang et al. [70] emphasized that mineral-associated microbial communities critically influence soil carbon cycling, where particle size variations alter biogeochemical processes and carbon sequestration by modulating microbial metabolic activity. Climate warming-induced permafrost thaw and increased plant biomass further reshape tundra soil microbial communities, impacting both carbon source utilization and large-scale carbon cycling [15].

Black soils, renowned as the “breadbasket of the world”, exhibit exceptionally high fertility and play a vital role in global food security [31]. However, they currently face severe degradation due to over-farming and rural population exodus, leading to declining soil quality [18]. Soil microbial carbon source metabolism serves as a quantitative indicator of microbial community dynamics and ecological functionality, providing critical insights into soil quality changes [57]. Studies employing Biolog ECO microplates have demonstrated the sensitivity of microbial communities to environmental variations. Bannister [2] reported that riparian wetland plant species significantly alter microbial structural and functional diversity. Similarly, Michiel et al. [45] observed higher functional diversity in grassland versus arable soils across the European Netherlands, identifying soil pH as the primary influencing factor. Zhang et al. [67] further validated this approach in the Hulunbeier Sandy Land, showing that specific vegetation restoration (e.g., Leymus chinensis) enhanced microbial metabolic capacity and diversity indices. To address knowledge gaps in black soil ecosystems, this study utilizes Biolog ECO microplates to: (1) Investigate how vegetation and soil type variations drive microbial carbon source metabolic differences; (2) Compare cross-ecosystem patterns in microbial carbon utilization and environmental impacts on functionality; (3) Elucidate linkages between vegetation-soil carbon metabolism and ecosystem-scale carbon cycling.

OBJECTS AND METHODS

Study area. The Naoli River Nature Reserve in Heilongjiang Province, China, is located in the hinterland of the Three River Plain, spanning Baoqing, Fujin, Raohe, and Fuyuan counties, with geographic coordinates ranging from 132°22′29″ to 134°13′45″ E and 46°30′22″ to 47°24′32″ E. Covering a total area of 160 601 hm², the reserve consists of a core area (37 047 hm²), buffer zone (53 128 hm²), and experimental area (70 426 hm²). The region experiences a temperate humid to semi-humid continental monsoon climate, with an annual mean temperature of 3.5°C and an average precipitation of 518 mm, primarily concentrated between June and September. Dominant soil types, including Gleysols, Histosols, and Planosols, support rich wetland vegetation. The reserve is dedicated to ecological restoration and wetland conservation, featuring landscapes such as marshy wetlands, tussock moss communities, and forests.

Sample collection and processing. In the autumn of 2024, soil samples were collected from 0–20 cm depth based on vegetation types (maize field, paddy field, reed wetland, and aspen woodland) and soil types (Histosols, Planosols, and Gleysols) using the five-point sampling method. Samples were transported to the laboratory in portable refrigerators, homogenized after removing impurities, and sieved (2 mm) to create three composite sub-samples. The first sub-sample was air-dried for analysis of SOC, total nitrogen (TN), total phosphorus (TP), and pH. The second was stored at 4°C for determining moisture content (MC), bulk density (BD), microbial metabolic activity, functional diversity, microbial biomass carbon, nitrogen, and phosphorus (MBC, MBN, and MBP). The third sub-sample was preserved at –20°C for microbial DNA extraction and community analysis.

Experimental methods. Microbial functional diversity was assessed using Biolog-ECO microplates. Fresh soil (10.0 g) was suspended in 90 mL of sterile 0.85% NaCl solution, shaken for 30 min, and allowed to settle for 15 min. A 10 mL aliquot of supernatant was diluted with 90 mL saline, then serially diluted to 10^–3 g/mL. In laminar flow hood conditions, 150 μL of suspension was inoculated into each well. Plates were incubated at 25°C for 14 days, with daily absorbance measurements at 590 nm using a microplate reader (Thermo Multiskan Go).

The Biolog-ECO microplate was used to characterize the overall carbon source utilization capacity of the soil microbial community by measuring the Average Well Color Development (AWCD) per well. The optical density values of 216 h were selected to analyze the utilization of different carbon sources and the diversity of metabolic functions of soil microorganisms. Following the methodology outlined in the literature [16], the Shannon index (H), Shannon-evenness index (E), Simpson dominance index (D), and McIntosh diversity index (U) were calculated to represent the functional diversity of the soil microbial community. The AWCD reflects the microbial utilization of specific substrates. The absorbance value at 590 nm, denoted as C590, was measured, and after subtracting the background values from the other 31 wells containing carbon sources, the optical density was obtained:

1

2

3

4

5

where is the optical density value of the ith well at 590 nm. R is the optical density value of the control well. If –R ≤ 0, it is recorded as 0 (n = 31). is the ratio of the relative optical density value (C–R) of the ith well to the total relative optical density of the entire plate. is the relative absorbance value of the ith well.

The SOC was analyzed using the potassium dichromate oxidation method. The TN and TP were determined by Kjeldahl digestion and molybdenum-antimony colorimetry, respectively [39]. Soil MC and BD were measured gravimetrically. Soil pH was determined potentiometrically in a 1 : 2.5 soil-water suspension. The MBC, MBN, and MBP were quantified using chloroform fumigation-K₂SO₄ extraction [54].

Microbial community structure was analyzed through 16S rRNA gene sequencing. Total DNA was extracted from environmental samples, followed by PCR amplification of target regions (e.g., V3–V4) and library preparation. High-throughput sequencing was performed using an Illumina MiSeq platform. Raw sequences were processed with QIIME and Mothur for quality filtering, OTU clustering, and diversity analysis [5].

Statistical analysis. Data analysis and visualization were conducted using SPSS (IBM SPSS Statistics 26), Origin 2024, R 4.4.3. One-way ANOVA was employed to examine the effects of vegetation and soil types on metabolic parameters including AWCD, six major carbon source utilizations, and diversity indices (Shannon, Shannon-evenness, Simpson, and McIntosh indices). Microbial community metabolic profiles were analyzed through principal coordinate analysis (PCoA) based on Bray-Curtis distance matrices. Cluster heatmaps were generated to compare carbon source utilization patterns across treatments. Pearson correlation analysis assessed relationships between the top ten bacterial phyla and carbon source types. The linkET package in R was used for Mantel tests evaluating environment-microbe relationships, while Structural equation modeling (SEM) was performed using the lavaan package in R to quantify direct and indirect environmental effects on microbial functions. The randomForest package determined the relative importance of environmental factors (% increase in MSE) for microbial carbon metabolism.

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Fig. 1.

Map of the Naoli river reserve and sampling sites.

RESULTS

Soil microbial carbon metabolism dynamics. The AWCD reflects the overall capacity of soil microbial communities to metabolize diverse carbon sources on Biolog-ECO microplates, serving as a key indicator of microbial activity and functional diversity. Initially, The AWCD values approached zero at 24 h, demonstrating minimal carbon source utilization. Between 24 and 120 h, the AWCD exhibited exponential growth, indicating microbial adaptation to the microplate environment with concomitant enhancement of metabolic activity through rapid carbon source consumption. Beyond 216 h, microbial growth rates declined, marking the onset of the stabilization phase. This trend was consistently observed across varying vegetation and soil types.

Comparative analysis of AWCD values over 336 h revealed distinct metabolic patterns among soil microbial communities from four vegetation types. The maize field exhibited the highest metabolic activity (AWCD 1.24), followed by the reed wetland (AWCD 1.18) and aspen woodland (AWCD 1.17), while the paddy field consistently showed the lowest activity (AWCD 1.05). Temporal dynamics demonstrated maize field dominance during the initial 144 h, followed by reed wetland superiority between 144–192 h, before establishing a stable activity hierarchy (maize field > reed wetland > aspen woodland > paddy field) from 192–336 h (Fig. 2a).

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Fig. 2.

Changes of soil microbial Average well color development (AWCD) under four vegetation (a) and three soil (b) types.

Microbial metabolic activity varied significantly among the three soil types, with Histosols demonstrating the highest AWCD (1.23), followed by Planosols (1.17) and Gleysols (1.06). During the initial 96 h, Histosols exhibited the strongest metabolic activity while Planosols and Gleysols showed comparable values. Between 96–216 h, Planosols surpassed Histosols in microbial activity, though Gleysols consistently remained the least active. In the final stage (216–336 h), Histosols regained its advantage with the highest AWCD, followed by Planosols, while Gleysols maintained the lowest metabolic activity throughout the experiment (Fig. 2b).

Characteristics of soil microbial utilization of different carbon sources. The metabolic profiling revealed significant variations in carbon source utilization patterns across different vegetation and soil types (Fig. 3a). Microbial communities preferentially utilized amino acids, followed by polymers and carboxylic acids, while showing lower metabolic activity for carbohydrates, amines, and phenolic acids (amino acids > polymers > carboxylic acids > carbohydrates > amines > phenolic acids). In terms of vegetation types, the soil carbon utilization capacity of maize field was the highest, followed by aspen woodland, paddy field and reed wetland. Among soil types, Histosols exhibited the strongest metabolic versatility, outperforming Gleysols and Planosols. Phenolic acid and carbohydrate utilization was limited in maize field and reed wetland soils, whereas paddy field and aspen woodland soils showed reduced capacity for amines and phenolic acids. Microbial communities in Histosols and Gleysols displayed particularly low phenolic acid utilization, while Planosols showed minimal amine metabolism (Fig. 3b).

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Fig. 3.

The utilization of six major types of carbon sources by soil microorganisms under different vegetation and soil types. Significance levels are denoted as: *p < 0.05, **p < 0.01, ***p < 0.001. (a) Overall utilization of six major carbon sources by soil microorganisms in the experimental study, (b) Utilization of six major carbon sources by soil microorganisms under four vegetation types and three soil types.

Carboxylic acids and amines showed consistent AWCD trends in different vegetation types. The soil utilization rate was the highest in maize fields and the lowest in aspen woodland. Phenolic acids and polymers displayed parallel utilization patterns, peaking in aspen woodland soils and reaching minimum levels in paddy field soils. Statistical analysis identified significant differences in amine utilization between maize field and paddy field soils (p < 0.001), maize field and aspen woodland soils (p < 0.05), and reed wetland and paddy field soils (p < 0.05). A significant difference in polymer utilization was also observed between aspen woodland and paddy field soils (p < 0.05). In contrast, no significant differences emerged in the utilization of the six major carbon source categories among different soil types in this study (Fig. S1).

The bi-directional clustering heatmap revealed distinct patterns of carbon source utilization among vegetation types (Fig. 4a). Vertical clustering separated the four vegetation types into two main groups. The paddy field formed one distinct cluster. The other cluster contained maize field, aspen woodland and reed wetland, which later separated into maize field by itself and aspen woodland together with reed wetland. This second cluster demonstrated significantly greater carbon source utilization capacity than the paddy field cluster. In the horizontal clustering analysis, microbial communities in paddy fields demonstrated higher utilization of carbohydrates, particularly D,L-α-glycerol phosphate, α-D-lactose, D-galactonic acid γ-lactone, and D-xylose. In maize fields, soil microorganisms predominantly metabolized phenylethylamine, D-glucose-1-phosphate, and L-serine. Microbial communities in aspen woodlands showed increased activity with 2-hydroxy benzoic acid and α-cyclodextrin, while reed wetland microbial communities exhibited enhanced utilization of polymeric compounds, specifically Tween 40 and Tween 80.

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Fig. 4.

Cluster analysis of 31 carbon sources across different vegetation (a) and soil (b) types.

Cluster analysis of soil types revealed distinct carbon utilization patterns, with Gleysols forming a separate cluster demonstrating significantly weaker overall metabolic activity compared to the combined cluster of Planosols and Histosols (Fig. 4b). Gleysols exhibited preferential utilization of pyruvic acid methyl ester, while Planosols showed enhanced metabolism of L‑phenylalanine, D-mannitol, and α-D-lactose. Histosols displayed greater capacity for glycyl-L-glutamic acid and glycogen utilization.

Soil microbial community structure and functional diversity. Analysis of bacterial community composition revealed significant variations in phylum-level relative abundance across vegetation and soil types (Fig. 5a). The predominant phyla included Pseudomonadota (23.85%), Acidobacteriota (21.90%), Verrucomicrobiota (11.25%), Chloroflexota (7.68%), and Actinomycetota (6.75%), collectively representing the core microbial taxa. Principal coordinates analysis based on Bray-Curtis distances revealed significant divergence in microbial functional diversity among vegetation types (Fig. 5b), with cultivated vegetation (paddy field and maize field) distinctly separating from natural vegetation (reed wetland and aspen woodland). In different soil types, microbial community structure showed predominance of MBNT15 and Chloroflexota in Histosols, while Actinomycetota and Pseudomonadota dominated in Planosols (Fig. 5c).

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Fig. 5.

Effects of vegetation and soil types on soil bacterial community diversity and composition. (a) Phylum-level relative abundance of soil bacteria, (b) PCoA analysis of carbon source utilization patterns under different vegetation types, (c) Ternary plot of bacterial phyla distribution across soil types.

Analysis of 336-h soil microbial cultures using multiple diversity indices (Shannon richness, Shannon-evenness, Simpson, McIntosh, Chao1, and Ace) revealed distinct functional diversity patterns across vegetation types (Table 1). The paddy field exhibited the highest values for Shannon index, Shannon-evenness, Chao1, and Ace indices, while the aspen woodland showed the lowest. The maize field demonstrated the highest Simpson and McIntosh indices, with the paddy field displaying the lowest. Statistical comparisons show that there are significant differences in the Shannon index between paddy field and aspen woodland (p < 0.001), and between paddy field and maize field (p < 0.05), but there is no significant difference between paddy field and reed wetland (p > 0.05). Similar significance patterns were observed for Shannon-evenness index. The Simpson index of maize field significantly differed from the paddy field (p < 0.05), while McIntosh index showed no significant variations among vegetation types (p > 0.05). The Chao1 and Ace indices of paddy field were significantly higher than other vegetation types (p < 0.05), particularly higher than versus aspen woodland (p < 0.001).

Table 1. . Effects of vegetation types on the Alpha diversity (values are means ± SE)

Vegetation types	Shannon	Shannon-even	Simpson	McIntosh	Chao1	Ace
Reed wetland	6.52 ± 0.16ab	0.77 ± 0.02ab	0.01 ± 0.001ab	7.03 ± 0.24a	5999.52 ± 554.26b	6245.78 ± 609.49b
Aspen woodland	6.17 ± 0.09b	0.74 ± 0.01b	0.01 ± 0.002ab	7.17 ± 0.21a	5352.67 ± 400.25b	5534.75 ± 430.2b
Maize field	6.35 ± 0.16b	0.74 ± 0.02b	0.02 ± 0.01a	7.27 ± 0.41a	6311.57 ± 256.85b	6544.64 ± 260.47b
Paddy field	6.8 ± 0.09a	0.78 ± 0.01a	0 ± 0.0004b	6.5 ± 0.26a	7592.42 ± 478.73a	7812.6 ± 503.66a

Histosols showed the highest Shannon and Shannon-evenness indices but the lowest Simpson index, while Planosols exhibited the opposite trend with lowest Shannon/Shannon-evenness and highest Simpson values. Gleysols demonstrated the highest Chao1 and Ace indices, contrasting with Histosols which showed the lowest values for these indices. Among all comparisons, only the McIntosh index differed significantly between Histosols and Gleysols (p < 0.05), with no other significant differences detected across soil types (Table 2).

Table 2. . Effects of soil types on the Alpha diversity (values are means ± SE)

Soil types	Shannon	Shannon-even	Simpson	McIntosh	Chao1	Ace
Histosols	6.52 ± 0.07a	0.77 ± 0.01a	0.01 ± 0.001a	7.37 ± 0.28a	5985.18 ± 435.84a	6181.55 ± 469.17a
Gleysols	6.48 ± 0.14a	0.75 ± 0.01a	0.01 ± 0.002a	6.61 ± 0.26b	6842.32 ± 371.81a	7101.52 ± 376.81a
Planosols	6.34 ± 0.18a	0.74 ± 0.02a	0.02 ± 0.01a	7 ± 0.19ab	6152.99 ± 491.87a	6345.08 ± 504.74a

Linkages between soil microbial carbon use efficiency and driving factors. Mantel test analysis revealed distinct environmental drivers of microbial carbon utilization across ecosystems (Fig. S2). Reed wetland communities showed strong positive correlation with MBN (r = 0.374, p < 0.01), while aspen woodland exhibited significant MBP influence (r = 0.241, p = 0.003) and BD association (r = 0.241, p = 0.073). Paddy field AWCD correlated significantly with MBC (r = 0.331, p = 0.001) and McIntosh index (r = 0.359, p = 0.013), whereas maize field showed no environmental correlations. Among soil types, Histosols demonstrated strong MBN dependence (r = 0.263, p < 0.01), Gleysols showed MBC correlation (r = 0.271, p < 0.01) with BD (r = 0.225, p = 0.070) and McIntosh index (r = 0.233, p = 0.045) associations, and Planosols displayed BD (r = 0.338, p = 0.023) and MBP (r = 0.193, p = 0.010) influences. The analysis demonstrated that MBC, MBN, MBP, and BD directly or indirectly influenced microbial carbon utilization capacity. Significant correlations were observed among soil parameters.

The MBP showed negative relationships with both MBC and MBN (p < 0.05), while SOC positively correlated with TN, TP, and MC (p < 0.05). Positive associations were also found between TN and MC, and between MBC and MBN (p < 0.05). Regarding α-diversity, the Simpson index correlated positively with MBC and MC (p < 0.05), while both Chao1 and Ace indices showed positive relationships with MBP (p < 0.05).

The SEM integrating physicochemical properties (MC, pH, SOC, TN, TP, MBC, MBN) with microbial metabolic activity (AWCD) revealed distinct drivers of carbon utilization. The MBC exerted the strongest direct negative effect (–0.401, p < 0.05), and SOC and MBN showed positive direct effects (0.255 and 0.309, respectively), while pH, TP and MC contributing through indirect pathways (Fig. 6a). The TN demonstrated the greatest total effect (–0.43), followed by MC (0.36) and MBN (0.31) (Fig. 6b). These results identify MBC, MBN and SOC as primary direct determinants of microbial carbon metabolism.

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Fig. 6.

The Structural equation modeling (SEM) of environmental factors regulating microbial AWCD.

In the path analysis diagram, red and blue arrows denote positive and negative causal relationships respectively, with standardized coefficients displayed along each arrow. Solid lines represent significant pathways (p < 0.05), while dashed lines indicate non-significant relationships. Arrow width corresponds to effect magnitude, and R² values quantify explained variance for each dependent variable. Significance levels are denoted as: *p < 0.05, **p < 0.01, ***p < 0.001. (a) Path diagram of the structural SEM), (b) Bar plot of standardized total effects.

Analysis of the 31 carbon sources (Fig. 7a) identified i-Erythritol as the most significant predictor of microbial metabolic capacity (p < 0.05), followed by Glycyl-L-Glutamic Acid and D-Xylose (p < 0.05). Among carbon source categories, amines showed the strongest predictive power for AWCD variation (p < 0.01), with the following hierarchy: amines > amino acids > carboxylic acids > carbohydrates > polymers > phenolic acids (Fig. 7b). Functional diversity assessment revealed the McIntosh index as the most important predictor of AWCD dynamics (p < 0.01) (Fig. 7c).

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Fig. 7.

Relative importance ranking of 31 individual carbon sources (a), six carbon categories (b), and α-diversity (c) in driving AWCD variations of soil microbial communities.

Pearson correlation analysis of the top ten bacterial phyla (Fig. S3) revealed significant associations with carbon source utilization patterns. Bacteroidota, Gemmatimonadota, and Chloroflexota showed positive correlations, specifically with carbohydrates (D‑glucose-1-phosphate), carboxylic acids (α-ketobutyric acid), and polymers (glycogen) for Bacteroidota, polymers (α-cyclodextrin) for Gemmatimonadota, and carboxylic acids (pyruvic acid methyl ester) for Chloroflexota. The MBNT15 and Armatimonadota were extremely negatively correlated with carbohydrates (D-Xylose). Acidobacteriota was significantly negatively correlated with amino acids (L-Asparagine). Actinomycetota was significantly positively correlated with carbohydrates (D-Xylose) and significantly negatively correlated with 1-Phosphate Glucose.

DISCUSSION

Carbon use efficiency in relation to microbial community structure and functional diversity across vegetation types. Soil microbial metabolic activity and functional diversity, which are closely associated with soil organic matter decomposition and nutrient cycling [62, 64], displayed distinct patterns between cultivated and natural vegetation types. The maize field among cultivated vegetation types showed the highest AWCD values, which was attributable to plastic film mulching practices that improve soil microclimate and substrate availability, thereby enhancing microbial activity [29]. This was similar to the findings of Cui [10, 68] and Zhang [10, 68]. In contrast, the paddy field exhibited the lowest AWCD values due to its stable soil environment and limited cultivation impacts on microbial communities [50]. Among natural vegetation types, the reed wetland demonstrated relatively higher microbial metabolic activity, while the aspen woodland showed more efficient utilization of specific carbon sources, likely resulting from forest biomass-induced enrichment of metabolic functional genes [21]. The anaerobic conditions of wetland from prolonged waterlogging further constrained microbial activity compared to forest soils [71].

Analysis of six major carbon source categories revealed distinct microbial utilization patterns between agricultural systems. The dryland and irrigated cropland systems showed significant differences in microbial amine utilization, with paddy soils demonstrating the strongest amine metabolism due to nitrogen fertilizer inputs providing combined carbon and nitrogen substrates [42]. Reed wetland and paddy systems also differed in amine utilization. Polymer utilization varied between aspen woodland soils with diverse microbial communities adapted to complex organic matter decomposition [46] and paddy soils where methane oxidation intermediates including acetate, propionate and lactate link methane and nitrogen cycles [7]. Root exudates such as organic acids and esters along with plant residues differentially regulated soil enzyme activities and carbon availability across systems [30, 57], with paddy systems providing particularly abundant organic inputs through rice root exudation [62].

Vegetation changes drive ecological niche modifications that significantly alter soil microbial community structure [37, 56, 74]. Microbial communities in cultivated vegetation (paddy and maize fields) differed markedly from those in natural vegetation (reed wetland and aspen woodland). Paddy field soils showed lower microbial diversity (McIntosh index) compared to wetlands, resulting from reduced vegetation biomass, greater human disturbance, and soil aggregate disruption that accelerated organic carbon mineralization [51]. Cluster analysis demonstrated that microbial communities in maize field, aspen woodland and reed wetland shared similar carbon utilization patterns with higher overall metabolic activity than paddy fields. From the perspective of horizontal clustering, there were significant differences in the soil microbial utilization of different carbon sources across various vegetation types. For example, paddy field showed stronger utilization of certain sugars, maize field exhibited stronger utilization of specific amino acids and amines, aspen woodland had higher utilization of 2-Hydroxy Benzoic Acid and α-Cyclodextrin, and reed wetland showed stronger utilization of Tween-type polymers. These functional differences reflected vegetation-mediated selection on microbial community structure and their metabolic adaptation to specific carbon substrates [24, 27].

Carbon use efficiency in relation to microbial community structure and functional diversity across soil types. Soil contains numerous unique microhabitats with specific environmental conditions, forming a highly complex ecosystem [1]. Soil type differences represent a major factor causing variations in microbial community structure and population density [63]. Smith et al. [48] examined microbial community differences between soil types (e.g., calcareous soil and red soil) and their effects on SOC storage and carbon cycling. Long et al. [34] conducted phylogenetic analyses of bacterial communities in Antarctic Dry Valley soils, revealing their diversity patterns. Hemkemeyer et al. [22] demonstrated that soil particle size fractions host distinct microbial groups with different organic pollutant mineralization capacities. Feigl et al. [12] employed Biolog EcoPlates to evaluate red mud amendment effects, recommending 5–20% concentrations to avoid negative microbial impacts. These studies demonstrated that soil type and particle size significantly influenced microbial diversity and metabolic potential through their effects on microbial metabolic processes, consequently affecting soil organic carbon cycling and storage.

The AWCD varied significantly among the three soil types, with Histosols showing the highest values and Gleysols the lowest. This pattern reflected the abundant organic matter and optimal moisture in Histosols that support microbial activity [3, 13], contrasting with the nutrient-poor, moisture-limited conditions in Gleysols that restricted carbon availability [58]. The microbial utilization of phenolic acid carbon sources is lowest in Histosols and Gleysols, while the microbial utilization of amine carbon sources is lowest in Planosols. This may be due to the anaerobic environment in Histosols and the microbial community structure in Gleysols, which are not suitable for the decomposition of phenolic acid carbon sources [14, 73]. The soil properties of Planosols, such as pH, nutrient content, and microbial community preferences for other carbon sources, result in a weaker ability to utilize amine carbon sources [69].

The McIntosh index showed significant differences only between Histosols and Gleysols, with no significant variations in other indices. Histosols maintain waterlogged conditions that support diverse anaerobic microbial communities with broad metabolic capabilities [17]. In contrast, Gleysols have moisture-limited, nutrient-poor environments that sustain sparse microbial populations with reduced diversity [73]. Cluster analysis indicates that the carbon utilization capacity of Gleysols is significantly weaker than that of Planosols and Histosols. Limited moisture and organic matter availability in Gleysols favor dominance by a few microbial species specialized for simple substrates like pyruvic acid methyl ester, leading to reduced community evenness and metabolic activity. Histosols and Planosols, with their favorable moisture and nutrient conditions, maintain more diverse microbial communities that effectively process complex substrates including Glycyl-L-Glutamic Acid, Glycogen, and L-Phenylalanine, resulting in stronger metabolic activity [65, 69].

The Chloroflexota phylum in Histosols plays a crucial role in organic matter decomposition, facilitating nutrient release and transformation that maintains soil fertility and supports plant growth [72]. In Planosols, the phylum Actinomycetota is renowned for its robust ability to synthesize secondary metabolites, enabling the production of various antibiotics and other compound [20]. During the composting process, Actinomycetota can participate in nitrogen metabolism and break down recalcitrant organic matter in the compost into small molecular organic acids, lowering the pH of the compost pile and reducing NH₃ volatilization. Pseudomonadota members participate in fiber degradation and nitrogen fixation, providing nitrogen sources that reduce overall nitrogen loss, while their versatile metabolic capacity enables decomposition of diverse organic substrates [47].

Relationships between microbial carbon metabolism and environmental factors in ecosystem carbon cycling. Variations in microbial carbon source utilization are shaped by multiple factors including vegetation type, soil properties, and habitat characteristics [43]. Plant-derived inputs (litter and root exudates) enhance SOC and TN content while accelerating nutrient cycling, influencing microbial metabolic capacity and carbon source diversity [40]. Ecosystem-specific analyses demonstrated that reed wetland microbial activity correlated strongly with MBN, and aspen woodland communities were influenced by MBP and BD (reflecting root-mediated soil modifications), while Histosols and Gleysols showed MBN and MBC dependent patterns, respectively. The microbial community structure also has a significant impact on carbon source metabolism [4]. Different microbial phyla exhibit varying capabilities in utilizing carbon sources. Bacteroidota shows a significant positive correlation with the utilization of various carbon sources, while Acidobacteriota shows a negative correlation with the utilization of certain carbon sources. The SEM demonstrated that MBC, MBN, and SOC content served as key direct drivers of soil microbial carbon source metabolic activity. The TN, MC, TP, and pH significantly influenced AWCD through indirect pathways. Regarding total effects, TN exerted the strongest control on microbial carbon metabolism, with MC and MBN showing secondary but substantial effects.

Soil microorganisms mediate ecosystem carbon cycling through metabolic transformation of carbon substrates, significantly influencing carbon sequestration potential [11, 59, 61]. Photosynthetically fixed carbon enters the soil system through root exudates and plant litter, serving as essential substrates for microbial communities. Microbial metabolic activities determine the fate of this carbon by directing it toward three primary pathways, including incorporation into microbial biomass, stabilization as soil organic carbon, and mineralization to carbon dioxide. This metabolic partitioning critically regulates carbon allocation within plant-soil systems and ultimately controls ecosystem-scale carbon storage capacity [23, 32, 44]. Carbon sequestration processes reciprocally influence microbial activity through microenvironment modification. The development of soil organic matter and aggregate formation creates distinct habitats characterized by reduced oxygen availability and enhanced moisture retention, preferentially supporting anaerobic microbial populations. Organic matter-induced alterations in fundamental soil properties, particularly pH and redox potential, further shape microbial community composition and metabolic function.

Microorganism preferences and abilities in metabolizing different carbon sources influence soil organic carbon stability [33, 38]. Some microorganisms prefer to use carbon sources that are easy to decompose and less likely to use more resistant forms. Over time, this selective consumption leads to gradual depletion of labile organic carbon and accumulation of recalcitrant forms, enhancing soil organic carbon stability and long-term storage that strengthens the soil carbon sink function [6, 66]. Simple compounds like sugars and amino acids are readily absorbed and metabolized by microorganisms, rapidly increasing their metabolic activity. In contrast, complex materials such as lignin and cellulose require specific microbial communities and enzyme systems for decomposition, significantly influencing the development and selection of microbial metabolic capabilities [36]. In this study, i-Erythritol emerged as the most important predictor of microbial carbon source metabolic capacity, followed by Glycyl-L-Glutamic Acid and D-Xylose. Among carbon source types, amines showed the strongest predictive power for AWCD variations. The quantity and quality of carbon sources in the soil directly affect the microbial carbon source metabolism capacity. Abundant carbon sources can provide microorganisms with ample energy and material support, promoting their growth and reproduction, thus enhancing their carbon source metabolism ability. When carbon source supply is insufficient, microorganisms may adapt to carbon-limited environments by adjusting metabolic pathways, reducing metabolic activity, or producing specific metabolic products, leading to changes in their carbon source metabolism capacity.

Microbial carbon metabolism represents a fundamental component of ecosystem carbon cycling, where vegetation structure, soil properties, and microbial community composition collectively regulate carbon transformation processes including fixation, conversion, and release. Clarifying the mechanism of microbial carbon substrate utilization in black soil provides important insights into the dynamics of the carbon cycle and establishes a theoretical framework for mitigating climate change through strengthening carbon sequestration strategies.

CONCLUSIONS

Significant variations in soil microbial carbon metabolism were found across vegetation and soil types in the Naoli River Reserve. Maize fields and Histosols showed the highest metabolic activity, exceeding paddy fields and Gleysols. Microorganisms preferred amino acids, polymers and carboxylic acids over carbohydrates and amines. The Simpson index correlated positively with MBC and MC, while Chao1 and Ace indices linked to MBP. The McIntosh index best predicted AWCD variations. The AWCD of reed wetland and paddy field is significantly influenced by soil MBN and MBC, while the AWCD of aspen woodland is significantly influenced by MBP and BD. Among soils, Histosols correlated with MBN, whereas Gleysols and Planosols associated with MBC, MBP and BD. The key microbial phyla (Bacteroidota, Gemmatimonadota, Chloroflexota) showed strong carbon utilization links. The SEM identified MBC, MBN and SOC as primary drivers of carbon metabolism. The RF modeling highlighted i-Erythritol as the top predictor of metabolic capacity, with amines best predicting AWCD changes. These findings revealed microbial metabolic responsed to environmental factors, offering valuable insights for sustainable land management and carbon sequestration in black soil ecosystems.

ACKNOWLEDGMENTS

The authors are grateful to the reviewers and editors for their comments and suggestions, as well as the funding from the Heilongjiang Provincial Natural Science Foundation (LH2022D001).

AUTHOR CONTRIBUTION

All authors contributed to the study conception and design. Chen Yang: Soil sampling, Writing—original draft, Software, Methodology, Investigation, Conceptualization. Li Zhang, Siyan Meng, Linlin Fan: Laboratory experimentation, Soil sampling. Guangxin Wang: Supervision, Soil sampling. Bing Yu: Supervision, Conceptualization.

FUNDING

This study was supported by the Natural Science Foundation of Heilongjiang Province, grant no. LH2022D001.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This work does not contain any studies involving human and animal subjects.

CONFLICT OF INTEREST

The authors of this work declare that they have no conflicts of interest.

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

AI tools may have been used in the translation or editing of this article.

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