Previous Article in Journal
A Climate-Sensitive Mixed-Effects Individual Tree Mortality Model for Masson Pine in Hunan Province, South–Central China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 9
10.3390/f15091542
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Afforestation Promotes Soil Organic Carbon and Soil Microbial Residual Carbon Accrual in a Seasonally Flooded Marshland

by
Jie Tang
1,†,
En Liu
2,†,
Yongjin Li
1,
Yuxi Tang
1,
Ye Tian
3,
Shuhui Du
4,
Haoyang Li
4,
Long Wan
5 and
Qian Zhang
2,*
1
Hunan Academy of Forestry, Shaoshan South Road, Changsha 410004, China
2
Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
3
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
4
College of Forestry, Shanxi Agricultural University, Taigu 030801, China
5
Jianshui Research Station, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2024, 15(9), 1542; https://doi.org/10.3390/f15091542 (registering DOI)
Submission received: 5 August 2024 / Revised: 16 August 2024 / Accepted: 30 August 2024 / Published: 1 September 2024
(This article belongs to the Section Forest Soil)
Browse Figures
Versions Notes

Abstract

:
This study aimed to delve deeper into the alterations in the microbial residual carbon (MRC) accumulation in the Yangtze River’s wetland ecosystems as a consequence of afforestation and to evaluate their impact on soil organic carbon (SOC). The hypothesis posited that afforestation could foster soil aggregation by augmenting arbuscular mycorrhizal fungi (AMF) hyphae and glomalin-related soil protein (GRSP) in deep soil, thereby suppressing the proliferation of genes pivotal to microbial residue decomposition and enhancing MRC accumulation. We collected soil samples at 0–20, 20–40, 40–60, 60–80 and 80–100 cm respectively. Metagenomic sequencing, the quantification of soil amino sugars and MRC, soil aggregate distribution profiling and the measurement of AMF mycelium length density alongside GRSP levels were analyzed. Our findings showed that afforestation notably elevated the concentration of soil amino sugars and the levels of total and fungal MRC, with increases ranging from 53%–80% and 82%–135%, respectively, across the five soil depths examined, in stark contrast to the eroded, non-afforested control. The role of MRC in the SOC was observed to escalate with increasing soil depth, with afforestation markedly amplifying this contribution within the 40–60 cm, 60–80 cm and 80–100 cm soil layers. The study concludes that the SOC content in the deeper soil horizons post-afforestation witnessed a significant rise, paralleled by a substantial increase in both total and fungal MRC, which exhibited a robust positive correlation with the SOC levels. This underscores the pivotal role that amino sugar accumulation from microbial residues plays in the retention of SOC in the deeper soil layers of afforested regions, challenging the conventional wisdom that plant residues are recalcitrant to decomposition within forested SOC matrices.

1. Introduction

Significant curiosity has arisen in how afforestation affects soil organic carbon (SOC) in the deeper strata of the soil profile [1,2], which is pivotal in the context of global carbon cycling, given that it (at 20–100 cm) harbors a substantial 50%–67% of the SOC present within the first meter of the soil column [3,4,5,6,7]. Recent studies showed that shelterbelts decreased soil respiration by 15%, enhanced soil carbon stability, and increased SOC storage by 11.5% within 1 m soil profiles compared to cropland [1]. Afforestation increased the SOC in deep soil (60–100 cm) through enhancing the relative proportion of microbial residual carbon to SOC compared with that from farmland, suggesting that afforestation enhanced microbial residue accumulation in deep soils [2]. Despite these advancements, the underlying mechanisms that drive the transformation of deep SOC following afforestation remain to be elucidated through empirical research.
Afforestation does have significant effects on soil microbes, which, in turn, are instrumental in the recycling and sequestration of SOC. Afforestation exerts its influence on SOC primarily through two pathways concerning microbes: the first is the buildup of microbial residues, exemplified by amino sugars; the second is the microbes’ role in decomposing organic matter. Microbial residual C (MRC) stands out as a vital constituent of SOC, constituting a significant proportion—potentially up to 50%—of the stable SOC pool across terrestrial ecosystems [8]. Afforestation changes the soil environment, including soil structure, water status, nutrient content, etc., which provide a varied habitat for soil microorganisms. Some microbial species may thus be promoted, while others may be inhibited. This microbial community change directly affects the quantity and composition of microbial residues, which are among the important sources of SOC. Amino sugars, serving as distinctive biochemical indicators of soil microbial residues [9], have been instrumental in gauging the input of carbon from microbial sources to SOC and its reactions to climate change. Afforestation was found to promote MRC accumulation and its relative proportion in SOC in deep soil compared to that in farmland [2].
On the other hand, the reconfiguration of microbial communities following afforestation exerts a significant influence on the decomposition processes that affect SOC. Microorganisms facilitate the breakdown of SOC by releasing enzymes and bioactive compounds, particularly the class of enzymes known as carbohydrate-active enzymes (CAZymes) [10]. It was shown that land-use alterations could affect SOC dynamics through influencing the activities of specific CAZyme genes [11]. A pertinent example is the reduction in the ligninase-to-cellulase ratio observed in afforestation efforts on degraded lands, which is associated with an upsurge in the input of labile carbon [12]. Afforestation on the Loess Plateau in China increased the abundance of microbial CAZyme gene families with plant dead biomass decomposition, thereby accelerating the decomposition process [13]. Therefore, the effect of SOC dynamics following afforestation is capable of being controlled by the balance between the accumulation of microbial residues and their losses through decomposition by microorganisms. The role of microbial factors and regulatory mechanisms underpinning afforestation’s effects on soil organic carbon are still unclear.
Arbuscular mycorrhizal fungi (AMF), a predominant group within the soil fungal community, establish symbiotic associations with the root systems of an overwhelming majority—over 80%—of terrestrial plant species, facilitating nutrient exchange through structures termed ‘arbuscular mycorrhizae’ [14]. This symbiotic system performs multiple functions in terrestrial ecosystems, including the transformation and stabilization of soil organic carbon [14]. On one hand, arbuscular mycorrhizal fungi have important effects on soil structure by secreting a special protein, glomalin-related soil protein (GRSP) [15]. GRSP exhibits a characteristic insolubility in water, it is not easily hydrolyzed by proteases and it is extremely stable in soil. When GRSP enters the soil, it binds the soil particles together by its own physical winding, and combines with sand, clay particles and organic matter to form a stable agglomeration structure. This structure not only improves the physical properties of the soil, but also protects the relatively unstable organic carbon in the aggregates, thus slowing down their decomposition rate and contributing to the long-term fixation of soil organic carbon [15]. On the other hand, AMF was also considered to stimulate organic carbon degradation and result in considerable SOC losses [16], through the priming effect of secreting sugars from their host into the soil. There is a debate as to whether AM fungi have a significant influence on the degradation of SOC [17,18]. Observations under X-ray fluorescence microscopy showed that AM fungi enhance soil aggregation and protect SOC from degradation [19]. So far, little is known about how AMF affect SOC via accrual or loss of microbial residue, especially following afforestation.
Freshwater marshes and their associated wetlands, serving as ecologically sensitive transition zones adjacent to rivers and islands, are subject to recurrent seasonal inundation and erosion. A vast and ecologically significant marshland, encompassing an area greater than 900,000 hectares, is located in the Yangtze River’s middle and lower basins in China. Characterized by a subtropical monsoon climate, this region is marked by substantial seasonal precipitation, with a pronounced rainy period from April to July and a contrasting dry spell from August to March. The area is particularly vulnerable to ecological stress, with significant land degradation and loss attributed to escalating population pressures, aggressive land reclamation and environmental contamination [20]. Commencing in the early 1980s, efforts to rehabilitate the degraded marshlands along the Yangtze have involved the strategic planting of poplar trees [21]. Our prior research has demonstrated that such afforestation initiatives have led to significant soil structural improvements, increased SOC content, reduced CH4 emission and a dampening of microbial functional genes implicated in the decomposition of soil organic matter in surface soil [21,22,23]. After the establishment of afforested plantations, AMF communities have flourished, even, notably, at considerable soil depths of 80 cm [24]. However, the comprehensive impact and underlying mechanisms of afforestation on SOC within the deeper soil layers remain enigmatic. Specifically, the influence of afforestation on MRC, its role in SOC sequestration within marshlands and the response of genes associated with microbial residual degradation to poplar afforestation, are not well understood. Moreover, the intricate interplay among AMF, soil characteristics, microbial functional genes and soil amino sugars—such as in how the proliferation of AMF hyphae and the buildup of GRSP can enhance soil aggregation, thereby inhibiting degradation-related genes and fostering the accrual of microbial residues—remains largely unexplored.
Our research endeavor aims to delineate the intricate dynamics of microbial residue accrual and its integral role in the soil organic carbon (SOC) pool of the Yangtze River’s marshlands, following afforestation practices. We hypothesized that (1) the stunning hyphal of AMF, together with the GRSP in deep soil, would enhance soil aggregations following afforestation; (2) the presence of more aggregated soil would suppress the abundance of functional genes involved in microbial residual degradation and, thus, promote MRC accrual under afforestation compared to the non-afforested eroded marshland; and (3) the afforestation would promote SOC sequestration and the contribution of MRC to SOC in deep soil.

2. Materials and Methods

2.1. Site Description

Situated in Junshan District, Hunan Province, near the Yangtze River and Dongting Lake, our freshwater marshland sampling site (28°59′–29°38′ N, 112°43′–113°15′ E) features a subtropical humid climate (with mean annual precipitation of 1417 mm and mean annual temperature of 16.4–17 °C) and a unique tidal soil profile (pH = 7.9, organic matter = 2.43 g/kg, total nitrogen = 1.26 g/kg and available nitrogen = 112 mg/kg). The area undergoes seasonal tidal flooding and supports a sparse vegetation cover dominated by Cynodon dactylon (L.) Persoon, Viola verecunda A. Gray, Polygonum flaccidum Meissn. and Clinopodium gracile (Benth.) Matsum.. The vegetation cover is less than 1% in eroded areas of the marshland. In response to erosion and land degradation, a poplar plantation has been sustainably cultivated for four decades without fertilization.
About every 10 years, the trees were cut and harvested, and artificial plantation was re-generated again. Our sampling sites had poplar plantations on the third occasion of artificial plantation establishment, and they had experienced two rounds of planting–cutting.

2.2. Soil Sampling

Spanning an extensive area exceeding 30 hectares, the plantation boasts a robust density of 318 trees per hectare, each with an average stature ranging from 17 to 20 m at the time of sampling. It undergoes the same seasonal flooding pattern from May to August as the non-afforested, eroded areas (Figure S1).
Three 10 m × 10 m plots were established (10 m apart from each other and at least 100 m from the edge of this forest) within the poplar plantation and the adjacent eroded non-afforested marshland, respectively. All sampling sites in the eroded marshland were within a distance of 1 km of the sampled afforestation plots. Soil sampling was executed at the conclusion of August, subsequent to a three-month period of inundation. We excavated soil to a 1 m depth, and five soil cores with inner diameters of 4 cm were collected from the 0–20, 20–40, 40–60, 60–80 and 80–100 cm depths (Figure S1). Composite soil samples were meticulously prepared by amalgamating five soil cores at each depth level from identical profiles, culminating in a comprehensive collection of 30 samples (2 vegetation types × 5 depth × 3 replicates). This sampling regimen was meticulously executed in August 2020, at the cessation of a three-month flooding period. A quantity of 1 kg from each composite sample was promptly sealed within plastic enclosures and refrigerated, and then expeditiously shipped to our laboratory for further processing, which included sieving with a 2 mm mesh to meticulously exclude root litter and stone fragments.

2.3. DNA Extraction and Metagenomic Analysis

Genomic DNA was extracted, utilizing the FastDNA SPIN Kit (MP Biomedicals, Santa Ana, CA, USA), from 500 mg aliquots of freshly collected soil samples, following the manufacturer’s protocol. Next, the DNA concentration was adjusted to 10 ng μL−1, employing a spectrophotometer (NanoDrop ND-1000, NanoDrop Technologies, Wilmington, DE, USA). The DNA was then subjected to sonication with a Covaris M220 (Gene Company Limited, Guangzhou, China) to acquire a fragment size of approximately 350 bp. These fragments were subsequently processed to create paired-end sequencing libraries with the TruSeq DNA Sample Prep Kit (Illumina, San Diego, CA, USA). The blunt-ended DNA fragments were ligated to adapters that are fully compatible with the hybridization sites of sequencing primers. Sequencing was executed on the Illumina HiSeq 4000 platform with a PE150 setup, strictly following Illumina Inc.’s protocols.
Trimmomatic 0.36 was deployed as a robust read-trimming utility to refine the raw sequencing data [25], ensuring the extraction of high-fidelity sequences by meticulously excluding adapter sequences and reads that did not meet the quality threshold. Specifically, sequences containing ambiguous N bases or adapter contamination, or with a quality score (Q value) below 20, were rigorously filtered out. The resultant high-quality reads were subsequently assembled using Megahit (version 1.0.6), a De Bruijn-graph-based assembler [26], which achieved a mapping ratio that varied from 19.7% to 40.1% across the entire sample set. The open reading frames (ORFs) within each metagenomic sample were identified using the Prodigal algorithm, version 2.60 [27]. ORFs exceeding 100 base pairs in length were then translated into their corresponding amino acid sequences, adhering to the codon translation standards outlined by the NCBI [28].
To construct a nonredundant gene catalog, the CD-HIT tool (version 4.8.1) [29] was applied to cluster gene sequences from the assembled data, requiring a 95% sequence identity with at least 90% coverage. These clusters were subsequently aligned using Bowtie 1.1.2. The alignment output was then processed by sam2counts 0.91 to convert the mapped reads into count data relative to the reference sequences, culminating in the generation of a comprehensive gene table that facilitates both taxonomic and functional annotations.
For the purpose of functional and taxonomic characterization, the inferred protein sequences were subjected to a thorough search within the NCBI non-redundant protein database, employing the DIAMOND alignment tool (Version 0.9.30) [30]. An e-value threshold of 10–10 was established, and the LCA algorithm was applied to system classification by MEGAN software (Version 6.22.1) for accurate species-level annotation of the sequences.
The assembled metagenome contigs were identified and annotated using the dbCAN2 pipeline, alongside a curated database of nonredundant proteins [31], in accordance with the manually refined alignments from the CAZy database as of July 2020. The taxonomical classification of these nonredundant proteins was performed through the easy-taxonomy mode and the NR database of the MMseqs2 tool (version 13.45111) [32]. We utilized Salmon to map the high-quality sequences of predicted genes, allowing for the assessment of gene abundance [33].
To avoid the sequencing depth bias, abundance values in the metagenomes were normalized with the transcripts per kilobase per million (TPM) mapped reads, and the total sequencing number was normalized to ten millions of reads per sample.

2.4. Soil Amino Sugars and MRC

The quantification of soil amino sugars—muramic acid (MurN), galactosamine (GalN), glucosamine (GluN) and mannosamine (ManN)—was conducted in accordance with the methodology outlined by Indorf et al. [34]. The analytical process, which encompasses hydrolysis, purification, derivatization and gas chromatography, was meticulously executed. Specifically, soil samples containing 0.3 mg of nitrogen were subjected to hydrolysis with 6 M HCl at a temperature of 105 °C for a duration of 8 h. Post-hydrolysis, an internal standard, 1-inositol at a concentration of 1 mg/mL, was incorporated into the hydrolysate. The sample then underwent filtration, vacuum drying and subsequent redissolution. The pH of the sample was meticulously adjusted to fall within the narrow range of 6.6 to 6.8. Centrifugation facilitated the separation of amino sugars from residual matter following the introduction of methanol. The derivatization step involved heating the amino sugars to temperatures between 75 and 80 °C for 30 min in the presence of 300 μL of derivatization reagent, while continuously stirring them using a vortex mixer. Excess anhydride was neutralized using deionized water and 1 mol/L HCl. The resultant organic phase was then reconstituted in a mixture of ethyl acetate and hexane (200 μL) and evaporated under a stream of nitrogen gas at 45 °C. The derivatives of amino sugars were subsequently identified and quantified using high-performance liquid chromatography (HPLC) with a Dionex Ultimate 3000 system (Thermo Fisher Scientific, Waltham, MA, USA), as detailed by Yuan et al. [35].
The calculation of MRC was performed using the following equations:
F-GluN(μg g − 1) = totalGluN(μg g − 1) − 2 × MurN(μg g − 1) × (179.2/251.2)
where GluN represents glucosamine, MurN represents muramic acid and F-GluN denotes the fungal-derived glucosamine, assuming a molar ratio of MurN and GluN of 1 to 2 in bacterial cell walls [36], where 179.2 and 251.2 are the molecular weights of GluN and MurN, respectively [37].
Fungal MRC (μg g − 1) = F − GluN × 9
Bacterial MRC (μg g − 1) = MurN × 45
Total MRC (μg g − 1) = Fungal MRC + Bacterial MRC
where 9 and 45 are conversion factors that were used for calculating GluN-to-fungi-derived carbon (fungal MRC) and MurN-to-bacteria-derived carbon (bacterial MRC), respectively [38]. Fungal MRC, bacterial MRC and total MRC are fungi-derived, bacteria-derived and total MRC, respectively.

2.5. Soil Aggregates

The distribution of soil water-stable aggregates (WSAs) within the size fractions of 2−4 mm, 1−2 mm, 0.5−1 mm and 0.25−0.5 mm was determined employing the wet-sieving technique. This process was facilitated by a Soil Aggregate Analyzer (model DM200-IV, manufactured in Shanghai, China). The mean weight diameter (MWD), a pivotal parameter for assessing the stability of soil WSAs, was computed utilizing the empirical formula developed by Kemper and Rosenau [39], as follows: WSA (%) = (dry matter − coarse matter)/(total weight − coarse matter), where dry matter stands for water-stable fraction and coarse matter stands for particles >0.25 mm.

2.6. Mycorrhizal Hyphal Length Density

Hyphal length density (HLD), a critical measure of the presence of arbuscular mycorrhizal fungi (AMF) in soil, was ascertained through the gridline intersect method [40,41]. Briefly, soil samples (dried 2 g) were immersed in a 0.25 mol L−1 sodium oxalate solution (50 mL) and agitated for 5 min to facilitate the extraction of hyphae. The suspension was then filtered through a 25 µm micro-pore membrane to capture the hyphae. The hyphae-laden membrane was subsequently immersed in 200 mL of deionized water and transferred to a 250 mL Erlenmeyer flask. After a brief agitation for 5 s and a 1-min settling period, a 2 mL sample was carefully pipetted onto 0.45 µm Millipore filters. The hyphae on the filter were stained using a 0.02% (w/v) trypan blue solution in lactoglycerol for 5 min, followed by rinsing with deionized water. The stained material was then transferred onto microscope slides for microscopic examination at a 200× magnification, allowing for the clear visualization of AMF hyphae. Hyphal length density was calculated with the following formula: HLD = 11/14 × number of intercepts × grid unit × membrane area ÷ grid area ÷ soil sample weight [41].

2.7. Glomalin-Related Soil Protein

Glomalin-related soil protein (GRSP), a glycoprotein secreted by arbuscular mycorrhizal fungi (AMF) mycelium, plays a pivotal role in enhancing soil aggregation and sequestering soil carbon. Moreover, GRSP’s concentration serves as an indirect bioindicator of AMF hyphal growth dynamics. This study evaluated two distinct fractions of GRSP: total GRSP (T-GRSP) and easily extractable GRSP (EE-GRSP).
For the quantification of T-GRSP, a methodological approach described by Emran et al. [42] was employed. One gram of air-dried soil was thoroughly mixed with 8 milliliters of a 50 mM trisodium citrate dihydrate solution adjusted to a pH of 8.0, followed by sterilization at 121 °C for 60 min. The resulting mixture was centrifuged at 5000× g for 15 min to separate the supernatant. The precipitate underwent sequential extraction with the trisodium citrate dihydrate solution, repeated a minimum of four times, until the supernatant exhibited a pale-yellow hue. The collected supernatants were pooled, and the GRSP concentration was quantified using the Bradford assay [42].
In contrast, the extraction of EE-GRSP was conducted using a modified protocol, where the soil was treated with a 20 mM trisodium citrate dihydrate solution at a slightly lower pH of 7.0 and subjected to a shorter sterilization period of 30 min at 121 °C. The subsequent steps in the extraction and quantification process mirrored those applied to T-GRSP.

2.8. Statistical Analysis

Two-way analyses of variance (ANOVAs) were performed for the effects of afforestation and soil depth on concentrations of amino sugar, MRC, rate of MRC to SOC, fungal MRC-to-bacterial MRC ratio, abundance of CAZy gene families on fungal and bacterial biomass, MWD, HDL and glomalin-related soil protein. When the ANOVA results indicated significant effects, means were compared among different treatments using Tukey’s test (p < 0.05). Pearson correlation analysis was performed to assess the relationships of measured soil microbial residues (amino sugars and MRC) with soil physicochemical properties (SOC and MWD), abundance of CAZy gene families on fungal and bacterial biomass, as well as AMF-related parameters (HLD and glomalin-related soil protein).

3. Results

3.1. Soil MRC

The two-way ANOVA showed that soil depth affects the concentration of amino sugar, total MRC, fungal MRC and bacterial MRC significantly (p < 0.05; Figure 1a–d). Generally, the soil amino sugar and MRCs decreased with the increasing soil depth (Figure 1a–d). Afforestation, independent of soil depth, significantly increased the soil amino sugar concentration, total MRC and fungal MRC compared to the eroded non-afforested control (Figure 1a–c). The total MRC and fungal MRC increased by 53%–80% and 82%–135% under afforestation across all the five soil depths, respectively (Figure 1a–c). Afforestation also affected the bacterial MRC significantly (p < 0.05). However, a significant difference only existed in the 0–20 cm soil and the responses of the bacterial MRC to the afforestation were not significant at the other sampling depths (Figure 1d).

3.2. Relative Contributions of MRC to SOC

The relative contributions of the soil MRC to the SOC, calculated as the rate of total MRC to SOC, was significantly affected by both afforestation and soil depth (p < 0.05; Figure 2), with increased contributions of MRC to SOC along with increasing soil depths. The afforestation significantly increased the relative contributions of the soil MRC to the SOC at the soil depths of 40–60, 60–80 and 80–100 cm (Figure 2).

3.3. Ratio of Fungal to Bacterial MRC

The ratio of fungal to bacterial MRC was affected significantly by afforestation and soil depth (p < 0.05; Figure 3). The afforestation increased the ratio of fungal to bacterial MRC at all the sampling soil depths (Figure 3).

3.4. CAZy Gene Families

A total of 854,603,529 reads (24,218,735–32,946,854 reads per sample) was achieved after quality filtering in the metagenomic analysis. The annotation with the CAZy database identified 6056 reads as being involved in the degradation of the microbial residual biomass (5241 for fungal and 815 for bacterial biomass), covering a total of 25 CAZy genes (eighteen for fungal and seven for bacterial biomass).
The two-way ANOVA showed that afforestation significantly affected the abundance of CAZy gene families involved in the degradation of the residual fungal biomass (p < 0.05; Figure 4a) but not the residual bacterial biomass (p > 0.05; Figure 4b). The abundance of CAZy gene families involved in the degradation of the residual fungal biomass significantly decreased under the afforestation compared to the eroded control at the 0–20, 20–40, 40–60 and 60–80 cm soil depths. The afforestation had no significant effect on the abundance of CAZy gene families involved in the degradation of residual bacterial biomass, regardless of soil depth.

3.5. Variations of Soil MWD

The afforestation significantly increased the MWD, irrespective of the soil depth (p < 0.05; Figure 5). Compared to the eroded control, the MWD was increased by 106%–139% under afforestation across the five soil depths (p < 0.05; Figure 5).

3.6. AMF-Related Characteristics

The annotation with the NR database in the metagenomic analysis detected a total 1,174,579 reads as fungal species. Ascomycota, Basidiomycota, Mucoromycota and Chytridiomycota were identified as the dominant fungal phyla, with an average relative abundance of >1% across all the samples (Figure S2). The afforestation decreased the relative abundance of Ascomycota, Basidiomycota and Chytridiomycota but increased the relative abundance of Mucoromycota (Figure S2). A total of 12 bacterial phyla were identified as dominant, including Proteobacteria, Actinobacteria, Acidobacteria, etc. (Figure S3). The afforestation also significantly changed the bacterial community structures (Figure S3).
On the species level, the most dominant species was identified as AMF, Rhizophagus irregularis (Blaszk., Wubet, Renker, & Buscot) C. Walker & A. Schler, which had a total of 166,912 reads (14.21% of the total reads). Across all of the five sampling soil depths, the abundance of R. irregularis increased under afforestation compared to the eroded control (p < 0.05; Figure 6a), with 5107–16,248 reads per sample under afforestation and 1146–4978 reads per sample under the eroded control.
The HDL, T-GRSP and EE-GRSP all increased under afforestation compared to the eroded control, regardless of the soil depth (p < 0.05; Figure 6b–d). The afforestation increased the HDL, T-GRSP and EE-GRSP by 92%–208%, 28%–95% and 42%–121% compared to the control, respectively. In the eroded control, the HDL, T-GRSP and EE-GRSP all showed a trend of decreasing with increasing soil depth, but they showed a hump shape under the afforestation, with the highest value at the soil depth of 40–60 cm (Figure 6b–d).

3.7. Pearson Correlations

The correlation analysis showed that the abundance of R. irregularis was positively related to the AMF-related characteristics, i.e., HLD, T-GRSP and EE-GRSP (Figure 7). The HLD, T-GRSP and EE-GRSP were all positively correlated with the soil MWD (Figure 7). The soil MWD was positively correlated with soil amino sugars, fungal MRC, bacterial MRC and total MRC (Figure 7). All these soil=microbe-residue-related parameters (i.e., soil amino sugars, fungal MRC, bacterial MRC and total MRC) were positively correlated with the SOC content (Figure 7). The abundance of the fungal- and bacterial-residual-biomass-degrading genes was negatively related to the ratio of MRC to SOC (Figure 7). There were no significant correlations between the abundance of the residual fungal- and bacterial-biomass-degrading genes and the MWD across any of the five soil depths (Figure 7). However, when the correlation was manipulated for each soil depth, the abundance of the fungal- and bacterial-residual biomass-degrading genes showed significant correlations with the MWD at the 0–20, 20–40, 40–60 and 60–80 cm depths (Figure S2).

4. Discussion

4.1. Afforestation Promotes MRC Accrual in Deep Soil

Afforestation markedly boosts the diversity of soil microbes and their functions in the topsoil [43,44,45], and it increases microbial functionality (increased decomposition activity), which is attributable to the modification of soil environments (e.g., pH, porosity, soil moisture and temperature) and the alleviation of carbon limitations [45,46,47]. Previous studies indicated that afforestation was prone to elevating the prevalence of CAZyme gene families, e.g., the microbial CAZyme gene families significantly increased along a 45-year chronosequence of Robinia pseudoacacia afforestation [13]. Furthermore, recent studies have highlighted that afforestation boosts the activities of soil enzymes, which was attributed to the elevated input of SOM resulting from the accumulation of plant litter and the release of root exudates in forest soils [48]. Afforestation affected microbial C-degrading genes and metabolic activities, with a shift in the dominant microbial taxa from oligotrophic to eutrophic species. For deep soil, a similar pattern to that observed in the topsoil occurs, considering that the organic carbon in deep soil mainly comes from dissolved organic carbon (DOC) and root exudates [3], which are conducive to stimulating microbial proliferation. On the other hand, the proliferation of forest trees with extensive root systems can also narrow the ecological niche available to microbial communities (e.g., moisture, temperature and pH) and inhibit microbial activity, as well as related functional genes [49]. The presence of plant roots can lead to a noticeable reduction in soil water content, subjecting root-associated microbial communities to significant water stress, which may, in turn, impede their metabolic functions [50].
Our results indicated that in deep soil, the concentrations of amino sugars, total MRC and fungal MRC were significantly elevated under afforestation compared with eroded marshland, together with the contribution of the MRC to the SOC (40–100 cm). The heightened accrual of MRC directly stems from a reduction in the abundance of functional genes that are pivotal in microbial residual degradation. The potential explanatory mechanism is multifaceted and can be initially attributed to the increased plant residue input after afforestation, which could boost the development of particular soil-particle aggregates, supplying carbon sources that are necessary for microbial metabolism and chemical bounding [51]. Moreover, enhanced aggregation post-afforestation could concurrently restrict the physical accessibility of soil organic matter (SOM) to microorganisms and the synthesis of enzymes [52,53]. Additionally, the depletion of oxygen in the subsoil due to root respiration is a pivotal factor that could suppress the microbial activity involved in the decomposition of SOC [54].
The bacterial MRC revealed no significant variations between the afforestation and the eroded control in deep soil (deeper than 20 cm). The fungal MRC-to- bacterial MRC ratio also rose under afforestation. This underscores the pivotal role of fungal MRC in SOC accumulation, which was supported by previous studies showing that fungal MRC accumulates more rapidly than bacterial MRC with vegetation succession [55]. Fungi are known to metabolize more complex and less readily available organic substrates compared to bacteria, thereby affording them a competitive edge in these environments [56,57]. The distinct modes of substrate utilization by bacteria and fungi are noteworthy; bacterial assimilation is particularly augmented in nutrient-depleted soils [58], suggesting a dynamic interplay between soil nutrient status and microbial community composition. Further, the abundance of functional genes involved in the degradation of residual fungi but not residual bacteria decreased after afforestation at most of the soil depths (0–80 cm), which will have contributed an enhanced accumulation of fungal MRC.
Forest SOC is predominantly derived from the inputs of leaves and roots [59]. Unlike in forests, where plant residue sequestration is dominant, farmland and grassland exhibit a greater reliance on microbial necro-mass entombment for SOC formation [60]. Our findings, however, present a contrasting perspective. We discovered that the rate of MRC in the SOC was higher than 50% in deep soil (40–100 cm), meaning the microbial pathways dominated in the SOC formation. This observation suggests that a substantial portion of plant-derived carbon compounds are integrated into microbial biomass through the assimilative metabolic processes of microorganisms in afforested areas. Furthermore, the reduced activity of degrading enzymes, as indicated by the downregulation of functional genes, results in a greater conversion of MRC into SOC [61].

4.2. Afforestation Promotes AMF and Soil Aggregation

Our results showed that afforestation benefited AMF hyphal growth and GRSP accumulation. The rationale for the observed rise in AMF biomass due to afforestation can be attributed to a dual mechanism. The increase in fine-root density, a consequence of afforestation, has been shown to augment AMF biomass. Approximately 80% of terrestrial flora engage in a symbiotic relationship with AMF, wherein fungi provide essential nutrients while plants, in turn, supply carbon to the fungi [14]. Roots offer suitable habitats for AMF colonization, leading to a general increase in AMF biomass as fine-root density rises [62]. Consequently, an enhanced fine-root density would facilitate the release of a higher amount of glomalin. In our prior research, it was observed that the highest concentration of fine roots occurred within the 40–80 cm soil layer of the afforested marshland [24]. In this context, the highest prevalence of dominant AMF species, including HLD, EE-GRSP and T-GRSP, was noted at the 40–60 cm soil depth, signifying that the abundance of fine roots promotes the generation and aggregation of GRSP by enhancing AMF biomass. This observation contrasts with certain earlier findings that reported a decline in GRSP content with soil depth progression [63,64]. For instance, the GRSP content was notably diminished at greater soil depths due to the reduced root biomass [63], and an analogous pattern was discerned for AMF biomass in conjunction with the decline in fine-root biomass. Thus, it is clear that the vertical stratification of soil GRSP is intricately influenced by the combined effects of root density and the metabolic activities of AMF [64,65].
Afforestation has been found to elevate soil MWD, a measure of soil aggregation stability, irrespective of soil depths. A statistically significant positive relationship was observed between the soil MWD and the hyphal density of AMF (HLD), as well as GRSP, underscoring the pivotal role that mycorrhizal associations play in bolstering the resilience of soil aggregates. In fact, the extraradical hyphae of mycorrhizal fungi have the capability to interweave soil particles, forming larger aggregates, and these fungi also emit GRSP into the rhizosphere, functioning as a colloidal substance that aids in the stabilization of aggregates [66]. The heightened stability of soil aggregates as a result of AMF activity led to an increased proportion of water-stable aggregates in the size ranges of 2–4 mm and 1–2 mm, a result of the intertwining action of mycorrhizal hyphae [67]. Additionally, the most dominant fungal species, R. irregularis, which is an AM fungus, has been approved to have a strong ability to improve the stability of soil macroaggregates, irrespective of soil texture and carbonate contents [68].

4.3. Afforestation Decreased Microbial Functionality on MRC

Afforestation on grasslands is linked to increased gene expression for enzymes degrading chitin, glucans and peptidoglycans, due to the higher levels of microbial biomass-derived carbon in forest soils [69]. In contrast to this, here, we found that afforestation in eroded marshland inhibited functional genes relevant to fungal amino sugar metabolism, but not bacterial amino sugar. The lower abundance of chitin- and glucans-decomposition genes under afforestation also exhibited a lower potential for processing complex carbon compound (e.g., residual fungi) decomposition, which is consistent with our previous discussions emphasizing that fungal MRC is crucial for the accumulation of SOC. In essence, vegetation-type transitions affected both the composition and the function of microbial communities, particularly by suppressing the decomposition of plant- and microbial-derived carbon through CAZyme family regulation. We suggest this was due to the enhanced soil MWD, as such aggregation protects SOC/MRC from microbial decomposition (Figure 8). First, soil aggregates can provide physical protection for organic matter. The porous structure inside the aggregates allows the organic matter to be wrapped in it, reducing direct contact with microbes and oxygen, and thus reducing the rate of microbial decomposition and oxidation. Second, the soil aggregates can also stabilize the organic matter by chemisorption. The clay minerals and organic cemented materials in the aggregates have a strong adsorption capacity, which allows them to adsorb the organic matter on the surface and form stable complexes. This chemisorption not only enhances the binding force between organic matter and soil particles, but also reduces the fluidity of organic matter in the soil, further inhibiting its decomposition activity [14].

4.4. Research Limitations

This study provided novel evidence that afforestation promotes SOC and MRC accrual through the proliferation of AMF and soil aggregation. It is worth noting that the sampling site experienced months of flooding when the soil samples were collected. The transition from aerobic to anaerobic conditions in the environment signifies a shift in the availability of oxygen. Under anaerobic conditions, the activity of aerobic microorganisms is inhibited, while anaerobic microorganisms such as methanogens and sulfate-reducing bacteria increase their activity. The metabolic effects of these microorganisms may lead to a reduced rate of organic carbon decomposition, as they generally decompose organic matter less efficiently than aerobic microorganisms. In addition, reducing substances, such as methane and hydrogen sulfide, which are produced under anaerobic conditions, may form insoluble complexes with metal ions in the soil, thus reducing the mineralization of organic carbon. Thus, prolonged flooding may lead to an increased accumulation of soil organic carbon. This study, together with prior research by our group (e.g., [23]), showed that afforestation reduced SOC mineralization and CH4 emission compared to the eroded non-afforested marshland. Thus, the interactive effect of root zone function and flooding on SOC stabilization should be further investigated. Second, our study collected soil samples after 3 months of flooding, while microbial activities might exhibit different dynamics at varying stages of flooding–non flooding cycling. Long-term monitoring was necessary to understand the effect of afforestation on the SOC in seasonally flooded marshland.

5. Conclusions

In summary, the present study found that SOC in deep soil increases after afforestation in a seasonally flooding marshland in China. The total MRC and fungal MRC also significantly increase after afforestation, showing a significant positive correlation with SOC content. This indicates that the accumulation of microbial-residue-originated amino sugars contributes greatly to SOC sequestration in deep soil in afforested areas, which is contrary to the traditional viewpoint that difficult-to-decompose plant residues predominate in forest SOC. The mechanism of the decrease in amino sugar loss originates directly in the fact that the abundance of the key functional gene related to MRC degradation decreases. Our results suggest that the development of AMF hyphae and the accumulation of soil GRSP enhances soil aggregation, which protects MRC and SOC from microbial degradation (Figure 8). One key underexplored aspect is the impact of afforestation on SOC and MRC accrual, microbial turnover, functional genes related to SOC degradation, and the AMF within multiple level soil aggregates (e.g., macro-aggregate, etc.), which is a crucial aspect to reveal the mechanisms of SOC accumulation from a microbial residual biomass perspective. Consequently, it is necessary to incorporate the microbial activity and microbial residue of various soil aggregates to predict SOC accumulation and dynamics following afforestation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15091542/s1, Figure S1: Sampling site (a), sampling site suffering the seasonally flooding (b) and the soil profile (c); Figure S2: Relative abundance of the dominant fungal phylum with relative abundance >1% across all the samples; Figure S3: Relative abundance of the dominantbacterial phylum with relative abundance >1% across all the samples; Figure S4: Pearson’s correlations among microbial residues (amino sugars and MRCs), AMF-related physiochemical properties (abundance of Rhizophagus irregularis, HLD, t-GRSP, ee-GRSP) and soil organic carbon at each of the soil sampling depth.

Author Contributions

Conceptualization, Q.Z.; Methodology, J.T., E.L., Y.T. (Yuxi Tang) and Y.T. (Ye Tian); Formal analysis, J.T., E.L., Y.T. (Yuxi Tang), S.D., H.L., L.W. and Q.Z.; Investigation, E.L., Y.L., H.L. and L.W.; Data curation, Y.L.; Writing—original draft, J.T. and Q.Z.; Writing—review and editing, Q.Z.; Supervision, Q.Z.; Funding acquisition, J.T. and Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by The National Key Research and Development Program of China (grant number 2021YFD2201202) and Key Research and Innovation Project of Forestry Science and Technology in Hunan Province (grant number XLKY202309).

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zhang, X.; Chen, S.; Yang, Y.; Wang, Q.; Wu, Y.; Zhou, Z.; Wang, H.; Wang, W. Shelterbelt farmland-afforestation induced SOC accrual with higher temperature stability: Cross-sites 1 m soil profiles analysis in NE China. Sci. Total Environ. 2022, 814, 151942. [Google Scholar] [CrossRef] [PubMed]
  2. Li, Y.; Wang, B.; Zhang, Y.; Ao, D.; Feng, C.; Wang, P.; Bai, X.; An, S. Afforestation increased the microbial necromass carbon accumulation in deep soil on the Loess Plateau. J. Environ. Manag. 2024, 349, 119508. [Google Scholar] [CrossRef] [PubMed]
  3. Lorenz, K.; Lal, R. The depth distribution of soil organic carbon in relation to land use and management and the potential of carbon sequestration in subsoil horizons. Adv. Agron. 2005, 88, 35–66. [Google Scholar]
  4. Harrison, R.B.; Footen, P.W.; Strahm, B.D. Deep soil horizons: Contribution and importance to soil carbon pools and in assessing whole-ecosystem response to management and global change. For. Sci. 2011, 57, 67–76. [Google Scholar] [CrossRef]
  5. Johnson, D.W.; Murphy, J.D.; Rau, B.M.; Miller, W.W. Subsurface carbon contents: Some case studies in forest soils. For. Sci. 2011, 57, 3–10. [Google Scholar] [CrossRef]
  6. Rumpel, C.; Kögel-Knabner, I. Deep soil organic matter—A key but poorly understood component of terrestrial C cycle. Plant Soil 2011, 338, 143–158. [Google Scholar] [CrossRef]
  7. Jobbágy, E.G.; Jackson, R.B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 2000, 10, 423–436. [Google Scholar] [CrossRef]
  8. Liang, C.; Amelung, W.; Lehmann, J.; Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Chang. Biol. 2019, 25, 3578–3590. [Google Scholar] [CrossRef]
  9. Joergensen, R.G. Amino sugars as specific indices for fungal and bacterial residues in soil. Biol. Fertil. Soils 2018, 54, 559–568. [Google Scholar] [CrossRef]
  10. López-Mondéjar, R.; Tláskal, V.; Větrovský, T.; Štursová, M.; Toscan, R.; da Rocha, U.N.; Baldrian, P. Metagenomics and stable isotope probing reveal the complementary contribution of fungal and bacterial communities in the recycling of dead biomass in forest soil. Soil Biol. Biochem. 2020, 148, 107875. [Google Scholar] [CrossRef]
  11. Huff, M.; Seaman, J.; Wu, D.; Zhebentyayeva, T.; Kelly, L.J.; Faridi, N.; Nelson, C.D.; Cooper, E.; Best, T.; Steiner, K.; et al. A high-quality reference genome for Fraxinus pennsylvanica for ash species restoration and research. Mol. Ecol. Resour. 2022, 22, 1284–1302. [Google Scholar] [CrossRef] [PubMed]
  12. Wu, J.; Cheng, X.; Luo, Y.; Liu, W.; Liu, G. Identifying carbon-degrading enzyme activities in association with soil organic carbon accumulation under land-use changes. Ecosystems 2022, 25, 1219–1233. [Google Scholar] [CrossRef]
  13. Ren, C.; Zhang, X.; Zhang, S.; Wang, J.; Xu, M.; Guo, Y.; Wang, J.; Han, X.; Zhao, F.; Yang, G.; et al. Altered microbial CAZyme families indicated dead biomass decomposition following afforestation. Soil Biol. Biochem. 2021, 160, 108362. [Google Scholar] [CrossRef]
  14. Rillig, M.C. Arbuscular mycorrhizae and terrestrial ecosystem processes. Ecol. Lett. 2004, 7, 740–754. [Google Scholar] [CrossRef]
  15. Rillig, M.C.; Ramsey, P.W.; Morris, S.; Paul, E.A. Glomalin, an arbuscular-mycorrhizal fungal soil protein, responds to land-use change. Plant Soil 2003, 253, 293–299. [Google Scholar] [CrossRef]
  16. Cheng, L.; Booker, F.L.; Tu, C.; Burkey, K.O.; Zhou, L.; Shew, H.D.; Rufty, T.W.; Hu, S. Arbuscular mycorrhizal fungi increase organic carbon decomposition under elevated CO2. Science 2012, 337, 1084–1087. [Google Scholar] [CrossRef]
  17. Carrillo, Y.; Dijkstra, F.A.; LeCain, D.; Pendall, E. Mediation of soil C decomposition by arbuscular mycorrizhal fungi in grass rhizospheres under elevated CO2. Biogeochemistry 2016, 127, 45–55. [Google Scholar] [CrossRef]
  18. Shahzad, T.; Chenu, C.; Genet, P.; Barot, S.; Perveen, N.; Mougin, C.; Fontaine, S. Contribution of exudates, arbuscular mycorrhizal fungi and litter depositions to the rhizosphere priming effect induced by grassland species. Soil Biol. Biochem. 2015, 80, 146–155. [Google Scholar] [CrossRef]
  19. Morris, E.K.; Morris, D.; Vogt, S.; Gleber, S.; Bigalke, M.; Wilcke, W.; Rillig, M. Visualizing the dynamics of soil aggregation as affected by arbuscular mycorrhizal fungi. ISME J. 2019, 13, 1639–1646. [Google Scholar] [CrossRef]
  20. Jiang, T.-t.; Pan, J.-f.; Pu, X.-M.; Wang, B.; Pan, J.-J. Current status of coastal wetlands in China: Degradation, restoration, and future management. Estuar. Coast. Shelf Sci. 2015, 164, 265–275. [Google Scholar] [CrossRef]
  21. Zhou, J.-X.; Sun, Q.-X.; Yang, Y.-F. Research on sustainable use of the middle and lower beach land of the Yangtze River. Resour. Environ. Yangtze Basin 2010, 19, 878–883. [Google Scholar]
  22. Gao Shenghua, C.; Zhang Xudong, C.; Yuxi, T. Short-term effects of clear-cutting of Populus deltoides plantation on methane flux on the beach land of Yangtze River. Sci. Silvae Sin. 2013, 49, 7–13. [Google Scholar]
  23. Zhang, Q.; Tang, J.; Angel, R.; Wang, D.; Hu, X.; Gao, S.; Zhang, L.; Tang, Y.; Zhang, X.; Koide, R.T.; et al. Soil properties interacting with microbial metagenome in decreasing CH4 emission from seasonally flooded marshland following different stages of afforestation. Front. Microbiol. 2022, 13, 830019. [Google Scholar] [CrossRef]
  24. Yang, H.; Koide, R.T.; Zhang, Q. Short-term waterlogging increases arbuscular mycorrhizal fungal species richness and shifts community composition. Plant Soil 2016, 404, 373–384. [Google Scholar] [CrossRef]
  25. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  26. Li, D.; Liu, C.-M.; Luo, R.; Sadakane, K.; Lam, T.-W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015, 31, 1674–1676. [Google Scholar] [CrossRef] [PubMed]
  27. Hyatt, D.; LoCascio, P.F.; Hauser, L.J.; Uberbacher, E.C. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 2012, 28, 2223–2230. [Google Scholar] [CrossRef]
  28. NCBI. Available online: https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1 (accessed on 1 June 2023).
  29. Tool. Cd-hit. Available online: http://www.bioinformatics.org/cd-hit/ (accessed on 15 August 2023).
  30. Buchfink, B.; Xie, C.; Huson, D.H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 2015, 12, 59–60. [Google Scholar] [CrossRef]
  31. Zhang, H.; Yohe, T.; Huang, L.; Entwistle, S.; Wu, P.; Yang, Z.; Busk, P.K.; Xu, Y.; Yin, Y. dbCAN2: A meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018, 46, W95–W101. [Google Scholar] [CrossRef]
  32. Steinegger, M.; Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 2017, 35, 1026–1028. [Google Scholar] [CrossRef]
  33. Patro, R.; Duggal, G.; Love, M.I.; Irizarry, R.A.; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 2017, 14, 417–419. [Google Scholar] [CrossRef] [PubMed]
  34. Indorf, C.; Dyckmans, J.; Khan, K.S.; Joergensen, R.G. Optimisation of amino sugar quantification by HPLC in soil and plant hydrolysates. Biol. Fertil. Soils 2011, 47, 387–396. [Google Scholar] [CrossRef]
  35. Yuan, Y.; Li, Y.; Mou, Z.; Kuang, L.; Wu, W.; Zhang, J.; Wang, F.; Hui, D.; Peñuelas, J.; Sardans, J.; et al. Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest. Glob. Chang. Biol. 2021, 27, 454–466. [Google Scholar] [CrossRef] [PubMed]
  36. Engelking, B.; Flessa, H.; Joergensen, R.G. Shifts in amino sugar and ergosterol contents after addition of sucrose and cellulose to soil. Soil Biol. Biochem. 2007, 39, 2111–2118. [Google Scholar] [CrossRef]
  37. Shao, S.; Zhao, Y.; Zhang, W.; Hu, G.; Xie, H.; Yan, J.; Han, S.; He, H.; Zhang, X. Linkage of microbial residue dynamics with soil organic carbon accumulation during subtropical forest succession. Soil Biol. Biochem. 2017, 114, 114–120. [Google Scholar] [CrossRef]
  38. Appuhn, A.; Joergensen, R.G. Microbial colonisation of roots as a function of plant species. Soil Biol. Biochem. 2006, 38, 1040–1051. [Google Scholar] [CrossRef]
  39. Kemper, W.D.; Rosenau, R.C. Aggregate stability and size distribution. Methods Soil Anal. Part 1 Phys. Mineral. Methods 1986, 5, 425–442. [Google Scholar]
  40. Jakobsen, I.; Abbott, L.; Robson, A. External hyphae of vesicular-arbuscular mycorrhizal fungi associated with Trifolium subterraneum L. 1. Spread of hyphae and phosphorus inflow into roots. New Phytol. 1992, 120, 371–380. [Google Scholar] [CrossRef]
  41. Rillig, M.C.; Field, C.B.; Allen, M.F. Soil biota responses to long-term atmospheric CO2 enrichment in two California annual grasslands. Oecologia 1999, 119, 572–577. [Google Scholar] [CrossRef]
  42. Emran, M.; Gispert, M.; Pardini, G. Patterns of soil organic carbon, glomalin and structural stability in abandoned Mediterranean terraced lands. Eur. J. Soil Sci. 2012, 63, 637–649. [Google Scholar] [CrossRef]
  43. Han, X.; Ren, C.; Li, B.; Yan, S.; Fu, S.; Gao, D.; Zhao, F.; Deng, J.; Yang, G. Growing seasonal characteristics of soil and plants control the temporal patterns of bacterial communities following afforestation. Catena 2019, 178, 288–297. [Google Scholar] [CrossRef]
  44. Hu, Y.; Zhang, Z.; Huang, L.; Qi, Q.; Liu, L.; Zhao, Y.; Wang, Z.; Zhou, H.; Lv, X.; Mao, Z.; et al. Shifts in soil microbial community functional gene structure across a 61-year desert revegetation chronosequence. Geoderma 2019, 347, 126–134. [Google Scholar] [CrossRef]
  45. Zhong, Z.; Li, W.; Lu, X.; Gu, Y.; Wu, S.; Shen, Z.; Han, X.; Yang, G.; Ren, C. Adaptive pathways of soil microorganisms to stoichiometric imbalances regulate microbial respiration following afforestation in the Loess Plateau, China. Soil Biol. Biochem. 2020, 151, 108048. [Google Scholar] [CrossRef]
  46. Cui, Y.; Fang, L.; Guo, X.; Han, F.; Ju, W.; Ye, L.; Wang, X.; Tan, W.; Zhang, X. Natural grassland as the optimal pattern of vegetation restoration in arid and semi-arid regions: Evidence from nutrient limitation of soil microbes. Sci. Total Environ. 2019, 648, 388–397. [Google Scholar] [CrossRef]
  47. Yin, R.; Deng, H.; Wang, H.-l.; Zhang, B. Vegetation type affects soil enzyme activities and microbial functional diversity following re-vegetation of a severely eroded red soil in sub-tropical China. Catena 2014, 115, 96–103. [Google Scholar] [CrossRef]
  48. Huang, H.; Tian, D.; Zhou, L.; Su, H.; Ma, S.; Feng, Y.; Tang, Z.; Zhu, J.; Ji, C.; Fang, J. Effects of afforestation on soil microbial diversity and enzyme activity: A meta-analysis. Geoderma 2022, 423, 115961. [Google Scholar] [CrossRef]
  49. Kong, W.; Wei, X.; Wu, Y.; Shao, M.; Zhang, Q.; Sadowsky, M.J.; Ishii, S.; Reich, P.B.; Wei, G.; Jiao, S.; et al. Afforestation can lower microbial diversity and functionality in deep soil layers in a semiarid region. Glob. Chang. Biol. 2022, 28, 6086–6101. [Google Scholar] [CrossRef] [PubMed]
  50. Shi, S.; Herman, D.J.; He, Z.; Pett-Ridge, J.; Wu, L.; Zhou, J.; Firestone, M.K. Plant roots alter microbial functional genes supporting root litter decomposition. Soil Biol. Biochem. 2018, 127, 90–99. [Google Scholar] [CrossRef]
  51. Cotrufo, M.F.; Wallenstein, M.D.; Boot, C.M.; Denef, K.; Paul, E. The M icrobial E fficiency-M atrix S tabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: Do labile plant inputs form stable soil organic matter? Glob. Chang. Biol. 2013, 19, 988–995. [Google Scholar] [CrossRef]
  52. Boyno, G.; Yerli, C.; Çakmakci, T.; Sahin, U.; Demir, S. The effect of arbuscular mycorrhizal fungi on carbon dioxide (CO2) emission from turfgrass soil under different irrigation intervals. J. Water Clim. Chang. 2024, 15, 541–553. [Google Scholar] [CrossRef]
  53. Hu, X.; Chen, D.; Yan, F.; Zheng, X.; Fang, X.; Bai, Y.; Zhao, J.; Ma, X.; Ma, C.; Cai, X.; et al. Global research trends on the effects of arbuscular mycorrhizal fungi on the soil carbon cycle: A bibliometric analysis. Ecol. Indic. 2024, 158, 111543. [Google Scholar] [CrossRef]
  54. Ni, X.; Liao, S.; Tan, S.; Peng, Y.; Wang, D.; Yue, K.; Wu, F.; Yang, Y. The vertical distribution and control of microbial necromass carbon in forest soils. Glob. Ecol. Biogeogr. 2020, 29, 1829–1839. [Google Scholar] [CrossRef]
  55. Zhang, Y.-Q.; Liu, J.-B.; Jia, X.; Qin, S.-G. Soil Organic Carbon Accumulation in Arid and Semiarid Areas after Afforestation: A Meta-Analysis. Pol. J. Environ. Stud. 2013, 22, 611–620. [Google Scholar]
  56. Bahram, M.; Hildebrand, F.; Forslund, S.K.; Anderson, J.L.; Soudzilovskaia, N.A.; Bodegom, P.M.; Bengtsson-Palme, J.; Anslan, S.; Coelho, L.P.; Harend, H.; et al. Structure and function of the global topsoil microbiome. Nature 2018, 560, 233–237. [Google Scholar] [CrossRef] [PubMed]
  57. Xia, Y.; Chen, X.; Zheng, X.; Deng, S.; Hu, Y.; Zheng, S.; He, X.; Wu, J.; Kuzyakov, Y.; Su, Y. Preferential uptake of hydrophilic and hydrophobic compounds by bacteria and fungi in upland and paddy soils. Soil Biol. Biochem. 2020, 148, 107879. [Google Scholar] [CrossRef]
  58. Di Lonardo, D.; De Boer, W.; Gunnewiek, P.K.; Hannula, S.; Van der Wal, A. Priming of soil organic matter: Chemical structure of added compounds is more important than the energy content. Soil Biol. Biochem. 2017, 108, 41–54. [Google Scholar] [CrossRef]
  59. Wu, Q.; Wu, F.; Zhu, J.; Ni, X. Leaf and root inputs additively contribute to soil organic carbon formation in various forest types. J. Soils Sediments 2023, 23, 1135–1145. [Google Scholar] [CrossRef]
  60. Wang, B.; An, S.; Liang, C.; Liu, Y.; Kuzyakov, Y. Microbial necromass as the source of soil organic carbon in global ecosystems. Soil Biol. Biochem. 2021, 162, 108422. [Google Scholar] [CrossRef]
  61. He, M.; Fang, K.; Chen, L.; Feng, X.; Qin, S.; Kou, D.; He, H.; Liang, C.; Yang, Y. Depth-dependent drivers of soil microbial necromass carbon across Tibetan alpine grasslands. Glob. Chang Biol. 2022, 28, 936–949. [Google Scholar] [CrossRef]
  62. Zhang, G.; Zhou, G.; Zhou, X.; Zhou, L.; Shao, J.; Liu, R.; Gao, J.; He, Y.; Du, Z.; Tang, J.; et al. Effects of tree mycorrhizal type on soil respiration and carbon stock via fine root biomass and litter dynamic in tropical plantations. J. Plant Ecol. 2023, 16, rtac056. [Google Scholar] [CrossRef]
  63. Cissé, G.; Essi, M.; Kedi, B.; Nicolas, M.; Staunton, S. Accumulation and vertical distribution of glomalin-related soil protein in French temperate forest soils as a function of tree type, climate and soil properties. Catena 2023, 220, 106635. [Google Scholar] [CrossRef]
  64. Yang, Y.; He, C.; Huang, L.; Ban, Y.; Tang, M. The effects of arbuscular mycorrhizal fungi on glomalin-related soil protein distribution, aggregate stability and their relationships with soil properties at different soil depths in lead-zinc contaminated area. PLoS ONE 2017, 12, e0182264. [Google Scholar] [CrossRef]
  65. Huang, B.; Zhang, L.; Cao, Y.; Yang, Y.; Wang, P.; Li, Z.; Lin, Y. Effects of land-use type on soil organic carbon and carbon pool management index through arbuscular mycorrhizal fungi pathways. Glob. Ecol. Conserv. 2023, 43, e02432. [Google Scholar] [CrossRef]
  66. Wright, S.F.; a Nichols, K. Glomalin: Hiding place for a third of the world’s stored soil carbon. Agric. Res. 2002, 50, 4. [Google Scholar]
  67. Gispert, M.; Pardini, G.; Emran, M.; Doni, S.; Masciandaro, G. Seasonal evolution of soil organic matter, glomalin and enzymes and potential for C storage after land abandonment and renaturalization processes in soils of NE Spain. Catena 2018, 162, 402–413. [Google Scholar] [CrossRef]
  68. Kohler, J.; Roldán, A.; Campoy, M.; Caravaca, F. Unraveling the role of hyphal networks from arbuscular mycorrhizal fungi in aggregate stabilization of semiarid soils with different textures and carbonate contents. Plant Soil 2017, 410, 273–281. [Google Scholar] [CrossRef]
  69. Huang, Q.; Wang, B.; Shen, J.; Xu, F.; Li, N.; Jia, P.; Jia, Y.; An, S.; Amoah, I.D.; Huang, Y. Shifts in C-degradation genes and microbial metabolic activity with vegetation types affected the surface soil organic carbon pool. Soil Biol. Biochem. 2024, 192, 109371. [Google Scholar] [CrossRef]
Forests 15 01542 g001
Figure 1. Concentrations of total amino sugars (a), total MRC (b), fungal MRC (c) and bacterial MRC (d) between afforestation and the non−afforested eroded marshland. MRC: microbial residual carbon. ***: p < 0.001; **: p < 0.01; *: p < 0.05; ns: p > 0.05.
Figure 1. Concentrations of total amino sugars (a), total MRC (b), fungal MRC (c) and bacterial MRC (d) between afforestation and the non−afforested eroded marshland. MRC: microbial residual carbon. ***: p < 0.001; **: p < 0.01; *: p < 0.05; ns: p > 0.05.
Forests 15 01542 g001
Forests 15 01542 g002
Figure 2. Rate of contribution of MRC to SOC between afforestation and the non–afforested eroded marshland. MRC: microbial residual carbon; SOC: soil organic carbon. ***: p < 0.001; ns: p > 0.05.
Figure 2. Rate of contribution of MRC to SOC between afforestation and the non–afforested eroded marshland. MRC: microbial residual carbon; SOC: soil organic carbon. ***: p < 0.001; ns: p > 0.05.
Forests 15 01542 g002
Forests 15 01542 g003
Figure 3. Ratio of fungal MRC to bacterial MRC between afforestation and the non–afforested eroded marshland. MRC: microbial residual carbon. ***: p < 0.001.
Figure 3. Ratio of fungal MRC to bacterial MRC between afforestation and the non–afforested eroded marshland. MRC: microbial residual carbon. ***: p < 0.001.
Forests 15 01542 g003
Forests 15 01542 g004
Figure 4. Abundance of functional genes involved in degradation of residual fungi (a) and bacteria (b) between afforestation and the non–afforested eroded marshland. MRC: microbial residual carbon. ***: p < 0.001; ns: p > 0.05.
Figure 4. Abundance of functional genes involved in degradation of residual fungi (a) and bacteria (b) between afforestation and the non–afforested eroded marshland. MRC: microbial residual carbon. ***: p < 0.001; ns: p > 0.05.
Forests 15 01542 g004
Forests 15 01542 g005
Figure 5. Soil MWD between afforestation and the non–afforested eroded marshland. MWD: mean weight diameter. ***: p < 0.001.
Figure 5. Soil MWD between afforestation and the non–afforested eroded marshland. MWD: mean weight diameter. ***: p < 0.001.
Forests 15 01542 g005
Forests 15 01542 g006
Figure 6. AMF–related characteristics between afforestation and the non–afforested eroded marshland. Relative abundance of (a) Rhizophagus irregularis; (b) hyphal length density; (c) total glomalin–related soil protein; (d) easily extractable glomalin–related soil protein. HDL: hyphal length density; T–GRSP: total glomalin–related soil protein; EE–GRSP: easily extractable glomalin–related soil protein. ***: p < 0.001.
Figure 6. AMF–related characteristics between afforestation and the non–afforested eroded marshland. Relative abundance of (a) Rhizophagus irregularis; (b) hyphal length density; (c) total glomalin–related soil protein; (d) easily extractable glomalin–related soil protein. HDL: hyphal length density; T–GRSP: total glomalin–related soil protein; EE–GRSP: easily extractable glomalin–related soil protein. ***: p < 0.001.
Forests 15 01542 g006
Forests 15 01542 g007
Figure 7. Pearson’s correlations among microbial residues (amino sugars and MRCs), AMF-related physiochemical properties (abundance of Rhizophagus irregularis, HLD, t-GRSP, ee-GRSP) and soil organic carbon. ×:p > 0.05, *: p < 0.05; **: p < 0.01; ***: p < 0.001.
Figure 7. Pearson’s correlations among microbial residues (amino sugars and MRCs), AMF-related physiochemical properties (abundance of Rhizophagus irregularis, HLD, t-GRSP, ee-GRSP) and soil organic carbon. ×:p > 0.05, *: p < 0.05; **: p < 0.01; ***: p < 0.001.
Forests 15 01542 g007
Forests 15 01542 g008
Figure 8. Schematic diagram summarizing the effects of afforestation on soil aggregate, soil microbial residues and soil organic carbon. The dotted line indicates negative effect.
Figure 8. Schematic diagram summarizing the effects of afforestation on soil aggregate, soil microbial residues and soil organic carbon. The dotted line indicates negative effect.
Forests 15 01542 g008
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tang, J.; Liu, E.; Li, Y.; Tang, Y.; Tian, Y.; Du, S.; Li, H.; Wan, L.; Zhang, Q. Afforestation Promotes Soil Organic Carbon and Soil Microbial Residual Carbon Accrual in a Seasonally Flooded Marshland. Forests 2024, 15, 1542. https://doi.org/10.3390/f15091542

AMA Style

Tang J, Liu E, Li Y, Tang Y, Tian Y, Du S, Li H, Wan L, Zhang Q. Afforestation Promotes Soil Organic Carbon and Soil Microbial Residual Carbon Accrual in a Seasonally Flooded Marshland. Forests. 2024; 15(9):1542. https://doi.org/10.3390/f15091542

Chicago/Turabian Style

Tang, Jie, En Liu, Yongjin Li, Yuxi Tang, Ye Tian, Shuhui Du, Haoyang Li, Long Wan, and Qian Zhang. 2024. "Afforestation Promotes Soil Organic Carbon and Soil Microbial Residual Carbon Accrual in a Seasonally Flooded Marshland" Forests 15, no. 9: 1542. https://doi.org/10.3390/f15091542

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

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop