Next Article in Journal
Grazing Impacts on Soil Enzyme Activities Vary with Vegetation Types in the Forest-Steppe Ecotone of Northeastern China
Previous Article in Journal
Genetic Diversity in Peripheral Pedunculate Oak (Quercus robur L.) Provenances—Potential Climate Change Mitigators in the Center of Distribution despite Challenges in Natural Populations
Previous Article in Special Issue
Effects of Truffle Inoculation on Root Physiology and Mycorrhizosphere Microbial Communities of Carya illinoinensis Seedlings
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 12
10.3390/f14122291
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 question_answer Discuss in SciProfiles 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

Soil Microbial Community in Relation to Soil Organic Carbon and Labile Soil Organic Carbon Fractions under Detritus Treatments in a Subtropical Karst Region during the Rainy and Dry Seasons

by
Peiwen Liu
1,2,
Suya Ding
1,2,
Ning Liu
1,2,
Yanhua Mo
2,3,
Yueming Liang
4 and
Jiangming Ma
1,2,3,*
1
Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection, Guangxi Normal University, Ministry of Education, Guilin 541006, China
2
Guangxi Key Laboratory of Landscape Resources Conservation and Sustainable Utilization in Lijiang River Basin, Guangxi Normal University, Guilin 541006, China
3
Institute for Sustainable Development and Innovation, Guangxi Normal University, Guilin 541006, China
4
Key Laboratory of Karst Dynamics, Ministry of Natural and Resources & Guangxi Zhuangzu Autonomy Region, Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(12), 2291; https://doi.org/10.3390/f14122291
Submission received: 20 September 2023 / Revised: 17 November 2023 / Accepted: 19 November 2023 / Published: 23 November 2023
(This article belongs to the Special Issue The Compositions, Dynamics and Associated Functioning of Soil Microorganism Communities in Forest Ecosystem)
Browse Figures
Versions Notes

Abstract

:
Climate and detritus influence soil organic carbon (SOC) and labile SOC fractions by affecting soil microbial communities. However, it is not clear how, or to what extent, different detritus treatments affect soil microbial communities and SOC content in karst landscapes during different seasons. Plots in a karst landscape were treated with different detritus input regimes (control, no litter, no roots, no litter or roots, and double litter), and samples were collected during the dry and rainy seasons. We used Illumina sequencing of 16S rRNA to examine shifts in the diversity and composition of the associated soil microbial communities. Additionally, labile SOC fractions, including dissolved organic carbon (DOC) and microbial biomass carbon (MBC), along with soil physicochemical properties and C-degrading enzyme activities, were analyzed. The results revealed that the responses of soil properties and labile SOC fractions to detritus treatments were more pronounced during the rainy season than during the dry season, which mainly reflected that the levels of available potassium (AK), DOC, and MBC were significantly increased during the rainy season. Moreover, SOC and total nitrogen (TN) demonstrated significant changes with the double litter (DL) treatment during the rainy season. The responses of soil microbial communities to detritus treatments varied with the season, as reflected primarily in changes in the relative abundance of Ascomycota, unclassified_K_fungi, Proteobacteria, and Actinobacteriota. Climate, detritus treatments, and their interactions had significant effects on the species richness of soil bacterial communities, but did not influence fungal community diversity. Furthermore, structural equation modeling (SEM) revealed that the soil bacterial composition had the largest total effects on SOC, DOC, and MBC. In addition to directly influencing SOC, DOC, and MBC, soil properties (TN, AK, and pH) indirectly affected SOC, DOC, and MBC by altering C-degrading enzyme activity and the microbial community. We conclude that detritus treatments affect the soil microbial community and labile carbon fractions during both the rainy and dry seasons. Relationships among SOC, labile SOC fractions, enzyme activities, microbial communities, and function differed between seasons and among treatment types. This research advances our knowledge of how variation in detritus treatments affects biogeochemical cycling in karst soils during the rainy and dry seasons.

1. Introduction

Soil organic carbon (SOC) is essential for maintaining productivity and sustainability in terrestrial ecosystems [1]. Labile SOC, which accounts for a small proportion of the total organic carbon, has low stability and is associated with high biological activity. This component, which is sensitive to climate change [2], mainly includes SOC sub-pools, such as organic carbon (DOC) and microbial biomass carbon (MBC). Soil carbon pool dynamics are driven by shifts in microbial community composition and enzyme activities [3]. Labile SOC fractions also represent a critical energy source for soil microorganisms [4]. However, despite the importance of soil microorganisms to SOC dynamics, how bacterial and fungal communities influence changes in SOC or labile SOC fractions is not always clear. The relationships between soil microbial communities and changes in SOC or labile SOC fractions are especially murky in areas dominated by karst. Because water and heat are the main drivers of ecosystem processes, most scientists expect that climate change will alter SOC dynamics. In addition, climate change is predicted to mediate changes in soil microbial community structure and diversity, as well as in soil carbon, nitrogen, and phosphorus contents. Together, these factors would have a significant impact on soil microorganisms [5]. Research has shown that simulated warming can increase MBC and DOC contents, but decrease SOC content [6]. Previous work suggests that SOC content increases under both higher and lower precipitation; in contrast, MBC decreases or increases with decreasing or increasing precipitation, respectively [7]. At the regional level, the effect of climate change on the size of SOC sub-pools is variable. SOC dynamics are regulated through complex mechanisms driven by myriad factors, which themselves exhibit differential responses to climate change. As such, it is difficult to predict how SOC and its constituents will react to changing climatic conditions. Diminished soil carbon release, brought about by drought, appears to be the main cause of increased SOC under decreased precipitation. Under wetter conditions, however, the effects of photosynthetic carbon input on SOC components become more important [8]. Adding further complexity, there are substantial differences in the sensitivity of soil active carbon components to temperature changes. Increased temperatures can alter primary productivity in an ecosystem, which, in turn, alters carbon inputs to soil from vegetation, rapidly depleting easily degradable DOC and increasing MBC content [9].
Forest detritus, which accumulates as forest plants develop and die, forms the primary source of soil nutrients, required for microbial and plant growth [10]. SOC is derived primarily from plant litter, including both above-ground (from leaves, branches, and seeds) and underground (from roots) litter. As these materials enter the soil, they are physically and chemically transformed by the metabolic activities of earthworms, soil microorganisms, and other animals. Previous work has found that the accumulation of plant detritus stimulates litter decomposition and increases soil nutrient content [11]. Thus, changes in the amount and quality of plant detritus may alter SOC storage and stability [12]. Many researchers have hypothesized that SOC formation is strongly linked to plant detritus inputs, but not enough information is available to describe this relationship quantitatively; the conflicting findings surrounding how SOC reservoirs are impacted by alterations to above- and below-ground inputs present a particular challenge. The disparate outcomes of the related research may result from the complicated regulatory relationship between net primary productivity and the quantity and quality (i.e., differences in decomposition rates) of plant detritus [13]. For instance, to assess the significance of detritus on organic carbon content [14], Cao et al. conducted detritus input and removal tests in two artificial subtropical forests. They found that, while root removal had minimal impact on SOC content, litter removal (with or without concomitant root removal) resulted in a considerable decrease in the amount of topsoil SOC. Other studies have also identified strong positive feedback between soil C content and plant detritus addition [15]. The mechanisms that underlie the processing and storage of carbon inputs in different forest systems are extremely variable, and they are governed by climatic conditions and environmental factors. As a result, the impacts of detritus on soil processes vary with the forest and soil environment. In temperate forests, detritus breaks down slowly and accumulates over long periods of time, extending the time it takes for plant litter to enter the SOC pool. In contrast, plant detritus residues decompose rapidly in the hot and humid environments associated with tropical and subtropical areas [16]. Plant detritus represents an essential carbon source for soil microorganisms, and the structural and functional diversity of soil microbial communities may respond differently to changes in carbon inputs [17]. For example, specific fungal communities tend to break down cellulase and ligninase [18]. As a result, many studies have focused on how soil microbial communities respond to detritus treatments or climatic changes. Although research exploring how different two-factor or single-factor treatments affect the soil microbial community is comparably adequate, it is still largely unknown whether a variation in the amount of detritus removed or added impacts soil microbial communities in various climates differently. Previous work suggests that the sizes of detritus inputs will shift with climate change [19]. Via their alteration of soil carbon and nutrient content, climate change and detritus disturbance have the potential to influence the compositions of microbial communities [20].
Southern China’s 540,000 km2 karst area is the third biggest in the world and accounts for approximately 36% of the country’s total area [21]. This zone has experienced significant soil erosion, rocky desertification, and ecological deterioration as a result of intense land usage, deforestation, and a growing human population [22]. Climate change, and subsequent changes in detritus treatments, may drive unavoidable alterations to soil carbon levels. Currently, there is limited information available about how soil fungal and bacterial communities respond to altered detritus treatments under different climatic conditions, and how their responses alter soil carbon dynamics, especially in areas dominated by karst. For this reason, we conducted a detritus input and removal experiment (DIRT) in a karst region to determine the effects of detritus manipulations on soil microbial communities and their functions in a variety of climates. To elucidate the influence of detritus treatments on labile carbon (i.e., DOC and MBC) and total SOC content, and to link changes in carbon content to soil microbial community composition and function (i.e., C-degrading enzyme activity) in a subtropical karst region during both rainy and dry seasons, we performed 16S and ITS rRNA gene sequencing to characterize the soil microbiome. We hypothesized the following: (1) the extents to which soil properties and labile SOC fractions change vary in response to variable detritus treatments during the rainy and dry seasons; (2) soil microbial diversity increases significantly in response to the rainy season and detritus inputs; and (3) following the rainy season, SOC and labile SOC fractions are mainly controlled by the bacterial community under the increased detritus treatment. Few studies have quantified how labile SOC content changes under different detritus treatments across the rainy and dry seasons. The labile SOC component is an essential energy source for soil microorganisms. At the same time, microbial community composition and function are important regulators of SOC dynamics. The purpose of our study was to observe the influences of detritus treatments on soil microbial communities and soil carbon fractions during the rainy and dry seasons in order to identify how altered detritus treatments and climate interact to influence the chemical and biological properties of a karst soil. Thus, our research contributes data and theoretical support to the relationships between soil carbon and microbial and chemical properties in areas dominated by karst.

2. Materials and Methods

2.1. Study Site and Experimental Design

The study area was located in a typical karst region in the southern part of Guilin City (25°12′ N, 110°15′ E), Guangxi Zhuang Autonomous Region, China. This region has a mid-subtropical humid monsoon climate, with longer summers and shorter winters. The annual temperature averages between 17 and 25 °C, and annual precipitation averages between 1900 and 2000 mm.
The detritus input and removal treatment (DIRT) experiment was initiated in March 2022. We selected three 10 m × 10 m experimental sites at random. Each of the three sites were separated from one another by approximately 20 m. Five 1 m × 1m plots were established for the detritus treatments in each study site. Treatment groups included the control (CK—normal litter input), double litter (DL—double normal litter input), no litter (NL—above-ground litter removal), no roots (NR—root removal) and no input (NI—root and above-ground litter removal) (Table S1). Litter was removed monthly from the NL treatment plots and added to the DL plots. Ditches (1 m) were dug along the perimeter of each NR plot, after which they were lined with high density PVC board (0.5 mm thick and 1 m wide) to exclude roots. The dominant tree species in the study area were Loropetalum chinense, Bauhinia championii, Alchornea trewioides, Pistacia chinensis, and Mallotus philippensis.
Soil samples were collected in triplicate during the rainy (July) and dry (December) seasons in 2022. Each replicate was a composite formed from five surface (0–10 cm) point samples. All soil was packed in sealed bags and transported to the laboratory within two hours of collection. In the laboratory, each sample was passed through a 2 mm sieve after plant debris, roots, and gravel were removed. Each sample was divided into three for the following purposes: (1) one was air-dried at room temperature to assess the soil physicochemical properties; (2) one was stored at 4 °C for MBC and C-degrading enzymes; and (3) one was stored at −80 °C for soil DNA extraction.

2.2. Soil Properties and Enzyme Activities

The soil moisture content (SMC) was measured by oven-drying the soil samples to a constant weight at 105 °C. Soil pH was measured in 1:2.5 (w:v) soil and distilled water suspensions using a pH meter. SOC concentration was measured via the oxidation method using K2Cr2O7-H2SO4. Soil total nitrogen (TN) was analyzed via the dry combustion method using a macro elemental analyzer (Vario MAX CN; Frankfurt, Germany). Soil total phosphorus (TP) was analyzed using the molybdenum antimony colorimetric method, following digestion with H2SO4-HClO4 [23]. Soil NH4-N and NO3-N were measured in a 1 M KCl solution using an auto analyzer (Bran Luebbe, Norderstedt, Germany). Soil available potassium (AK) was measured using a flame photometer. DOC was extracted using high-purity deionized water. The supernatant was filtered through a 0.45 μm membrane, after which DOC was measured using a total organic carbon analyzer (TOC-VCPH, SHIMADZU, Kyoto, Japan). The chloroform fumigation–extraction method was used to determine MBC [24].
The potential activities of soil cellulase (β-1,4-glucosidase—BG; β-1,4-xylosidase—BX; cellobiohydrolase—CBH) and ligninase (peroxidase—PER; phenoloxidase—POX) were measured using 96-well microplate fluorometric techniques [25]. The substrate was allowed to react with a soil suspension made with 100 mL sodium acetate solution (pH 5.5) in the dark for two hours at 20 °C. At the end of the incubation period, 10 μL of 1 M NaOH was added to the mixture to terminate the reaction. A fluorimeter was used to quantify fluorescence at 365 nm excitation and 460 nm emission. Enzyme activities were calibrated using a 4-methylumbelliferone curve. In addition, L-3,4-dihydroxyphenylalanine (20 mM) was added to the soil suspension to test the PER and POX activities. Subsequently, 250 μL of each sample was transferred to a transparent 96-well microplate. PER wells received an additional 10 μL of 0.3% H2O2. The microplates were incubated at 20 °C for 20 h. Using a spectrophotometer set at 450 nm, absorbance was measured before and after the incubation. All enzyme activities were reported in nmol h−1 g−1. These data were also used to calculate the carbon quality index (CQI) [26].

2.3. Soil Microbial Community Analysis

Microbial genomic DNA was extracted from 0.3 g of soil using FastDNA Spin Kits (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer’s instructions. DNA quality and concentration were assessed using agarose gel (1%, w/v) electrophoresis. The fungal ITS region was amplified using the ITS1F and ITS4 primers, and the bacterial 16S rRNA V3-V4 region was amplified using the 338F and 806R primers. Three parallels were set for each sample. PE libraries were constructed and used for Illumina sequencing. After the sequencing quantity of each sample was close to saturation, the obtained sequences were assembled into complete fragments, and operational taxonomic units (OTUs) were divided at the 97% sequence similarity level to calculate the relative abundances of different classification levels. PCR products were purified through Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). They were pooled in equimolar concentration and paired-end sequenced using the Illumina MiSeq PE300/NovaSeq PE250 platform (Illumina, San Diego, CA, USA). Data were evaluated using the Majorbio Cloud Platform (www.majorbio.com) (accessed on 9 March 2023).

2.4. Statistical Analysis

Prior to statistical analysis, data were checked for normality and homogeneity of variance and log-transformed when appropriate. Statistical analysis was carried out using IBM Corp.’s SPSS version 25.0 software (Armonk, New York, NY, USA). Indicators of microbial alpha diversity (Chao1, ACE, and the Shannon and Simpson indices) were calculated using the phyloseq package in R 4.2.3 statistical software. To identify variance in SMC, pH, SOC, TN, TP, NO3-N, NH4-N, AK, DOC, MBC, cellulase, ligninase, and microbial communities across treatments and seasons, one-way analysis of variance (ANOVA) was conducted. Two-way ANOVAs were used to test the interactions between and effects of season and detritus treatment on soil properties and microbial community diversity. Pearson correlation analysis was conducted to investigate the links between soil physicochemical properties and microbial community characteristics. Bray–Curtis dissimilarity matrices were used to analyze differences in soil fungal and bacterial communities across treatments and seasons at the OUT level. Structural equation modeling (SEM) was conducted using the lavaan and semPlot R packages to analyze the direct and indirect consequences of soil properties, C-degrading enzymes, and microbial community characteristics on labile SOC fractions. The SEM fit was evaluated using p-values > 0.05, chi-square values/degree values (χ2/df), the goodness-of-fit index (GFI), and the root mean square error of approximation (RMSEA) [27].

3. Results

3.1. SOC and Soil Physicochemical Properties

Different seasons and detritus treatments had significant effects on SOC content and soil physicochemical properties (p < 0.05). SMC ranged from 7% to 30%, with significant increases observed in samples collected during the rainy season. No significant differences in soil pH were observed, except under the NR treatment. SOC concentration ranged from 15.03 g kg−1 to 52.47 g kg−1 and differed significantly among detritus treatments and between different seasons. Compared with the rainy season, TN was significantly lower during the dry season. Measurements of NH4-N and NO3-N were consistent with changes in TN. TP was significantly lower during the rainy season, relative to the dry season. Significant increases in AK were measured during the rainy season, but there were no significant differences among the five detritus treatments (Table 1).

3.2. Soil DOC, MBC, C-Degrading Enzymes, and CQI

We observed significant differences among detritus treatments and between seasons for DOC, MBC, and C-degrading enzymes (Figure 1). No significant differences were observed for the CQI (Figure 1d). SOC and ligninase increased under the DL treatment compared to CK during the rainy season (Figure 1a,f), whereas DOC, MBC, and cellulase did not change significantly (Figure 1b,c,e). In contrast, cellulase increased significantly only under the detritus addition treatments compared to CK during the dry season (Figure 1e). Moreover, SOC, DOC, MBC, cellulase, and ligninase varied significantly between the dry and rainy seasons. Soil MBC and ligninase contents were significantly influenced by the interactions between season and detritus treatment (Figure 1c,f). Neither seasons nor detritus treatments had a significant effect on the CQI (Figure 1d).

3.3. Soil Bacterial and Fungal Community Diversities and Compositions

Variations in microbial alpha diversity are presented in Figure 2. Seasons, detritus treatments, and their interactions had significant effects on bacterial species richness, but did not influence soil fungal community diversity. Compared to CK, NI caused a significant decrease in bacterial Chao1 index values during the dry season (Figure 2b). Moreover, ACE index values were significantly different between CK and the detritus removal treatment (Figure 2d). Consistent with trends observed for the Shannon index values, the greatest fungal diversity was observed in A_NR, whereas A_NL had the lowest fungal diversity. The greatest bacterial diversity was measured in R_NR, and the lowest was in A_NI (Figure 2e,f). Although fungal diversity increased substantially in A_NI, the bacterial Simpson index values did not differ significantly among detritus treatments or between seasons (Figure 2g,h).
The distribution of microorganisms differed at the phylum level across treatments and seasons (Figure 3a,b). The dominant fungal phyla in this study were Ascomycota, unclassified_k__Fungi, and Basidiomycota. The dominant bacterial phyla were Proteobacteria, Actinobacteriota, and Acidobacteriota. The microbial abundance data are presented in Table S2. The top three total proportions of the dominant fungal taxa were A_CK (96.70%), A_NL (96.69%), and A_NI (96.28%). The top three for bacterial taxa were A_DL (64.5%), R_DL (64.25%), and R_NL (64.02%). During the rainy season, Ascomycota showed a notable increase in relative abundance after the DL treatment, while unclassified_k_Fungi decreased in relative abundance compared to with the NL treatment (Figure 3a). Furthermore, the distribution of microorganisms also differed at the genus level (Figure 3c,d). The dominant fungal genera were unclassified_k__Fungi, Mortierella and unclassified_p__Basidiomycota. The dominant bacterial genera were norank_f__Xanthobacteraceae, Candidatus_Udaeobacter, and norank_f__norank_o__Gaiellales. We further divided OTUs into smaller bacterial and fungal groups (Figure 4). The results showed that different seasons and detritus treatments affected fungal groups, but barely influenced bacterial groups. We also found that SOC and SMC were influenced by the shares of arbuscular mycorrhizal fungi (AMFs) and ectomycorrhiza (ECM). Compared with detritus removal, the detritus input treatment significantly increased SOC and AMFs, but there was no specific change between SMC and AMFs (Figure 5). We also found that the proportion of ECM during the rainy season was higher than that during the dry season, except in the NI treatment. This suggests that ECM and SMC are related to one another.
Venn diagrams illustrate how seasons and detritus treatments influenced bacterial and fungal OTUs (Figure S2). During the rainy season, 311 site-shared fungal OTUs were identified, representing 19.48% of total fungal OTUs. In addition, 14.90% of fungal OTUs were specific to CK, 10.66% to NL, 13.15% to NR, 11.27% to NI, and 11.55% to DL. During the dry season, 348 site-shared fungal OTUs were identified, representing 4.64% of total fungal OTUs. Of those, 16.51% of identified fungal OTUs were specific to CK, 11.81% to NL, 14.57% to NR, 12.49% to NI, and 12.80% to DL. The number of site-shared bacterial OTUs was 1122 during the rainy season, accounting for 2.23% of total bacterial OTUs. Additionally, 19.44% of bacterial OTUs were specific to CK, 17.78% to NL, 18.51% to NR, 16.76% to NI, and 16.61% to DL. During the dry season, there were 786 site-shared bacterial OTUs, which made up 1.90% of all bacterial OTUs. Of those, 25.54% of bacterial OTUs were specific to CK, 13.99% to NL, 14.74% to NR, 14.74% to NI, and 16.26% to DL. The proportions of bacterial to fungal OTUs during the rainy season were CK 1.30, NL 1.67, NR 1.41, NI 1.49, and DL 1.44. The ratios during the dry season were CK 1.55, NL 1.18, NR 1.01, NI 1.30, and DL 1.49, respectively.
To demonstrate the influences of seasons and detritus treatments on soil fungal and bacterial communities, PCoA based on Bray–Curtis dissimilarity was used to analyze microbial beta diversity. The first PCoA axis explained 24.49% and 15.59% of bacterial and fungal variations, respectively, at the OUT level. The second axis explained 12.73% and 7.21% of variations in the bacterial and fungal communities, respectively (Figure 6a,b). Furthermore, ANOSIM analysis identified significant variations in soil bacterial (R = 0.075, P = 0.750) and fungal (R = 0.213, P = 0.025) communities across detritus treatments and seasons.

3.4. Soil Properties, Microbial Communities, and Soil Enzyme Activities

To investigate the key drivers of soil microbial community characteristics, RDA analysis was conducted on the most abundant OTUs (>0.5%) (Figure 7). According to RDA and CCA analyses, the first axis explains 13.53% and 12.96% of the total bacterial and fungal variances, respectively, during the rainy season. During the dry season, the first axis explains 19.81% and 12.48% of bacterial and fungal variations, respectively. Pearson correlation analysis demonstrated that microbial diversity was strongly affected by edaphic variables (Table 2). Specifically, soil TP was significantly negatively correlated with bacterial Chao1 diversity during the rainy season. No soil variables were correlated with fungal Chao1, fungal Shannon, or bacterial Shannon diversities during the rainy season. With respect to the dry season, we found that SMC, TN, NH4-N, and ligninase contents were significantly correlated with bacterial Chao1 diversity. SMC, NH4-N, SOC, and ligninase contents were also significantly correlated with bacterial Shannon diversity.
Structural equation modeling (SEM) was used to identify the key factors influencing SOC and labile carbon fractions (Figure 8). Soil properties (TN, AK, and pH), C-degrading enzyme activity, the fungal community, and the bacterial community were the essential contributing elements regulating SOC, DOC, and MBC. Specifically, soil properties (p < 0.01), the soil fungal community, and the soil bacterial community were directly and positively correlated with changes in SOC and labile carbon fractions, while C-degrading enzyme activity was directly and negatively correlated with changes in SOC and labile carbon fractions. C-degrading enzymes have minimal direct, negative impacts on SOC, DOC, and MBC. The soil bacteria had the largest total effect on SOC, DOC, and MBC. In addition to directly influencing SOC, DOC, and MBC, soil properties (TN, AK, and pH) indirectly affected labile carbon fractions (p < 0.001) by altering C-degrading enzyme activity and microbial community characteristics.

4. Discussion

4.1. Effects of Detritus Treatments on Soil Properties during Different Seasons

Compared to detritus removal, double litter input increased SOC and TN contents, a result which is consistent with previous research (Table 1) [28]. Plant inputs from roots and litter represent the dominant sources of soil nutrients [29]. In addition, NR had a stronger influence on SOC and TN contents, suggesting that roots are more important than litter for SOC formation [30]. Several mechanisms explain this discrepancy. Firstly, roots can provide more carbon than litter [31]. Secondly, roots can provide root exudates to soil bacteria and fungi, which promote microbial growth and the conversion of plant residues to SOC [32]. Finally, root biomass can be degraded and metabolized by soil microorganisms immediately after death, whereas litter must be transported to mineral soil through bioturbation and leaching [33]. Moreover, soil organic carbon was more strongly influenced by seasons than by detritus treatments (Figure 1a). Increased rainfall frequency is associated with increased MBC and DOC, further increasing the amount of SOC [34]. In addition, we found that TN and TP were higher during the rainy season, but NH4-N and NO3-N contents were lower, suggesting that the rainy season can lower soil nitrate and inorganic nitrogen contents via loss through runoff, which has short-term effects. Additionally, precipitation can accelerate the breakdown of detritus and contribute to nutrient accumulation in the soil [35].

4.2. Effects of Detritus Treatments on Labile SOC Fractions, Soil C-Degrading Enzymes, and Functional Genes during Different Seasons

The effects of seasons and detritus on soil carbon have been explored to some degree, but little is known about how the interactions between seasons and detritus influence SOC. Previous work has found that, relative to the dry season, DOC and MBC vary with rainfall volume (Figure 1b,c). Additionally, MBC, which measures the size of the microbial population, was lower during the dry season than during the wet season, indicating that bacteria that use labile C sources are more vulnerable to variation in precipitation [34]. Here, we found significant differences in C-degrading enzyme (cellulase and ligninase) activities across season and treatment type (Figure 1e,f). Despite the addition of plant C, soil bacteria consumed the labile SOC fractions in the early stages of the experiment, which resulted in the depletion of easily decomposable substrate, as reflected in the lower cellulase activity measured for the NI treatment. This may have caused microbial populations to use previously inaccessible refractory SOC fractions as sources of energy [36]. Detritus leaching is the principal source of SOC in surface soil [37]. Moreover, above-ground and below-ground biomass both increased with detritus inputs, and rapidly impacting substrates that facilitate organic development have increased, which increased the soil’s carbon storage capacity and active SOC content [38].

4.3. Effects of Detritus Treatments on Soil Microbial Community Diversities and Structures during Different Seasons

We calculated α- and β-diversity values for each detritus treatment during the rainy and dry seasons. Our findings revealed that detritus removal often enhanced bacterial ACE and Chao1 indices during the rainy season compared to the dry season, but that detritus inputs had no significant impact (Figure 2). This is inconsistent with our second hypothesis, perhaps because soil microbial communities are less sensitive to short-term changes in litter inputs. The detritus-mediated soil nutrients increase the decomposition rate to much higher than the rates observed for the detritus removal treatment. In contrast to previous findings, bacterial ACE and Chao1 diversities experienced greater changes than fungal measures of diversity [28]. This result may be related to the special karst geology. In addition, compared to litter removal, root removal substantially increased fungal Shannon diversity (Figure 2e). This may be due to the fact that root removal generates niches for the establishment of new fungal species and makes soil fungal growth more vulnerable to changes in substrates and soil properties [39]. Our finding that there were more bacterial taxa in soil collected during the rainy season supports the idea that soil bacteria flourish in a moist habitat. Changing climatic conditions did not significantly affect fungal Chao1, ACE, or Simpson diversities in the present study (Figure 2a,c,g). This indicates that soil fungal communities may be stable during the rainy and dry seasons.
At the phylum level, Ascomycota, unclassified_k_Fungi and Basidiomycota were widely distributed in soils with detritus treatments. Previous studies have also found that the relative abundance of Ascomycota increased with the addition of straw or labile carbon [40]. Some have speculated that Ascomycota may regulate detritus decay in this environment [41]. Moreover, previous findings indicate that decomposition processes are led by microbes that were stimulated by the detritus from shrubs or herbs [42]. In the litter addition treatments, we found that dominant microbial species were involved in the breakdown of litter and eventually influenced the labile SOC fractions. In the current study, there was little variation in microbial community characteristics across treatments, suggesting that the dominant fungal and bacterial phyla were stable under detritus treatments and climate changes in the karst environment. Ascomycota is the largest group in the fungal kingdom today, mainly composed of saprophytic fungi that can decompose various difficult-to-degrade substances. Fungi belonging to Basidiomycota can degrade difficult-to-decompose substances, such as lignin, conferring an advantage to members of this taxa that inhabit soil with input from vegetation with high lignin content [43]. This suggests that, although the overall composition of microbial communities may differ greatly among different habitats, the dominant microbial communities are generally similar. In our study, the dominant bacterial phylum was Proteobacteria, which can utilize the active components of soil organic carbon for growth and metabolism, thus enabling it to grow faster in nutrient-rich soil environments [44].
It is important to pay attention to changes in specific functional groups within microbial communities, in addition to patterns of taxonomic diversity within soil microbial communities. We divided OTUs into different functional groups. The bacterial functional group was mainly characterized by Forms_Biofilms and Stress_Tolerant. The fungal functional group was mainly characterized by Undefined Saprotroph and Animal Pathogen, except for the unknown groups. Using Bug Base phenotype prediction and FUNGuild functional prediction, we discovered that detritus treatment influences fungal and bacterial functional communities. Compared to NR, AMFs substantially increased SOC content in the double litter addition treatment (Figure 5a), demonstrating that soil carbon accumulation was promoted by AMFs, a finding consistent with those of previous studies [45]. AMFs can promote the degradation of detritus indirectly via the saprophytic fungal decomposition of organic matter [46]. Thus, detritus treatment impacts soil fungi. Root removal decreases SOC, nitrogen, and enzyme activities, which also affects soil microbial activities [47].

4.4. Driving Factors for Soil Microbial Communities

There is still uncertainty about the effects of interactions between soil microbial communities and labile SOC fractions. Our third hypothesis, that the total effects of SOC and the labile SOC fractions are mainly controlled by soil bacteria, rather than fungi, under detritus addition, was supported. According to SEM analysis, soil properties (TN, AK, and pH) and C-degrading enzyme activities modulated variations in fungal and bacterial communities, and these differences could lead to differences in SOC, DOC, and MBC. Differential reactions to microbial diversity may manifest themselves as alterations in the makeup of bacterial and fungal communities. Soil properties (Figure 6), especially soil pH, TN, and AK, are critical to the growth and reproduction of fungal and bacterial communities. This conclusion is consistent with work by Dou et al., which found that soil pH had a great influence on the phylogenetic structure and composition of the bacterial community [48]. In the karst region, there are numerous small watersheds, and because of spatial variability across multiple sampling sites, it is possible that the impacts of soil quality on microbial communities vary in different areas. The current study was restricted to a single small watershed, which is not representative of the overall karst landscape. To demonstrate the significance of SOC and labile SOC fractions in regulating microbial community characteristics in karst environments under detritus treatments and during different seasons, we recommend that researchers select different typical small watersheds. Therefore, we hypothesized that, although detritus is responsible for controlling microbial decomposition and driving soil nutrient cycling [49], the roles of climate–detritus interactions are unknown in karst environments. Moreover, we concluded that both climate and detritus should have comparable influences over microbial decomposition rates and in their influences on the secretion of enzymes involved in karst litter breakdown.

5. Conclusions

Our study showed that both the rainy season and detritus inputs significantly increased SOC, indirectly affected C-degrading enzymes, and improved the soil microbial community. In addition, the bacterial community was more sensitive to SOC and labile SOC fractions than the fungal community was. Therefore, microorganisms, especially bacteria, were actively involved in detritus decomposition and soil nutrient enhancement. Fungal and bacterial abundances were impacted by both seasons and by detritus removal, which also resulted in altered community compositions. Microbial community diversity was primarily affected by SMC, TN, NH4-N, NO3-N, SOC, and ligninase. The knowledge gaps regarding SOC and labile SOC fractions, and the mechanisms by which they are regulated by microbes in karst soils in response to detritus inputs and seasons, were addressed in this work. Herein, we shed light on the relationships between the soil microbial communities and labile carbon fractions under different detritus treatment regimes during the rainy and dry seasons, as well as provide theoretical support for further soil SOC and labile SOC fraction research in karst areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14122291/s1, Figure S1: Aerial photo of the at the study site; Figure S2: Venn diagram of soil fungal (a,b), and bacterial (c,d) OTUs in the rainy and dry season under different detritus treatments; Table S1: DIRT (Detritus Input and Removal Treatments) experiment description; Table S2: Relative abundance of dominant phyla in different seasons and detritus treatments.

Author Contributions

Conceptualization, P.L., Y.M. and J.M.; methodology, P.L., S.D. and J.M.; software and formal analysis, P.L.; writing—original draft preparation, P.L.; writing—review and editing, P.L., J.M. and Y.L.; funding acquisition, J.M.; project administration, J.M.; validation, J.M.; experiments, P.L., N.L. and S.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (U21A2007; 32260387); the Guangxi Innovation-Driven Development Project (Guike AA20161002-1); and Guangxi Key Research and Development Projects (Guike AB21220057; Guike AB21196065).

Data Availability Statement

Data are available from corresponding authors.

Acknowledgments

We acknowledge Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) for their help with the 16S and ITS rRNA gene sequences. We thank Daniel Petticord at the University of Cornell for his assistance with English language and grammatical editing of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bradford, M.A.; Carey, C.J.; Atwood, L.; Bossio, D.; Fenichel, E.P.; Gennet, S.; Fargione, J.; Fisher, J.R.B.; Fuller, E.; Kane, D.A.; et al. Soil carbon science for policy and practice. Nat. Sustain. 2019, 2, 1070–1072. [Google Scholar] [CrossRef]
  2. Sowerby, A.; Emmett, B.A.; Williams, D.; Beier, C.; Evans, C.D. The response of dissolved organic carbon (DOC) and the ecosystem carbon balance to experimental drought in a temperate shrubland. Eur. J. Soil Sci. 2010, 61, 697–709. [Google Scholar] [CrossRef]
  3. Liang, C.; Schimel, J.P.; Jastrow, J.D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2017, 2, 17105. [Google Scholar] [CrossRef] [PubMed]
  4. Ramírez, P.B.; Fuentes-Alburquenque, S.; Díez, B.; Vargas, I.; Bonilla, C.A. Soil microbial community responses to labile organic carbon fractions in relation to soil type and land use along a climate gradient. Soil Biol. Biochem. 2020, 141, 107692. [Google Scholar] [CrossRef]
  5. MMenge, D.N.L.; Wolf, A.A.; Funk, J.L. Diversity of nitrogen fixation strategies in Mediterranean legumes. Nat. Plants 2015, 1, 15064. [Google Scholar] [CrossRef]
  6. Lu, M.; Zhou, X.; Yang, Q.; Li, H.; Luo, Y.; Fang, C.; Chen, J.; Yang, X.; Li, B. Responses of ecosystem carbon cycle to experimental warming: A meta-analysis. Ecology 2013, 94, 726–738. [Google Scholar] [CrossRef]
  7. Zhou, X.; Zhou, L.; Nie, Y.; Fu, Y.; Du, Z.; Shao, J.; Zheng, Z.; Wang, X. Similar responses of soil carbon storage to drought and irrigation in terrestrial ecosystems but with contrasting mechanisms: A meta-analysis. Agric. Ecosyst. Environ. 2016, 228, 70–81. [Google Scholar] [CrossRef]
  8. Aanderud, Z.T.; Richards, J.H.; Svejcar, T.; James, J.J. A Shift in Seasonal Rainfall Reduces Soil Organic Carbon Storage in a Cold Desert. Ecosystems 2010, 13, 673–682. [Google Scholar] [CrossRef]
  9. Hopkins, F.M.; Filley, T.R.; Gleixner, G.; Lange, M.; Top, S.M.; Trumbore, S.E. Increased belowground carbon inputs and warming promote loss of soil organic carbon through complementary microbial responses. Soil Biol. Biochem. 2014, 76, 57–69. [Google Scholar] [CrossRef]
  10. Zeng, Q.; Liu, Y.; Zhang, H.; An, S. Fast bacterial succession associated with the decomposition of Quercus wutaishanica litter on the Loess Plateau. Biogeochemistry 2019, 144, 119–131. [Google Scholar] [CrossRef]
  11. Burns, R.G.; DeForest, J.L.; Marxsen, J.; Sinsabaugh, R.L.; Stromberger, M.E.; Wallenstein, M.D.; Weintraub, M.N.; Zoppini, A. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biol. Biochem. 2013, 58, 216–234. [Google Scholar] [CrossRef]
  12. Wang, X.; Tang, C.; Severi, J.; Butterly, C.R.; Baldock, J.A. Rhizosphere priming effect on soil organic carbon decomposition under plant species differing in soil acidification and root exudation. New Phytol. 2016, 211, 864–873. [Google Scholar] [CrossRef]
  13. Crow, S.E.; Lajtha, K.; Filley, T.R.; Swanston, C.W.; Bowden, R.D.; Caldwell, B.A. Sources of plant-derived carbon and stability of organic matter in soil: Implications for global change. Glob. Chang. Biol. 2009, 15, 2003–2019. [Google Scholar] [CrossRef]
  14. Cao, J.; He, X.; Chen, Y.; Chen, Y.; Zhang, Y.; Yu, S.; Zhou, L.; Liu, Z.; Zhang, C.; Fu, S. Leaf litter contributes more to soil organic carbon than fine roots in two 10-year-old subtropical plantations. Sci. Total Environ. 2020, 704, 135341. [Google Scholar] [CrossRef] [PubMed]
  15. Mayzelle, M.M.; Krusor, M.L.; Lajtha, K.; Bowden, R.D.; Six, J. Effects of Detrital Inputs and Roots on Carbon Saturation Deficit of a Temperate Forest Soil. Soil Sci. Soc. Am. J. 2014, 78, S76–S83. [Google Scholar] [CrossRef]
  16. Nemergut, D.R.; Cleveland, C.C.; Wieder, W.R.; Washenberger, C.L.; Townsend, A.R. Plot-scale manipulations of organic matter inputs to soils correlate with shifts in microbial community composition in a lowland tropical rain forest. Soil Biol. Biochem. 2010, 42, 2153–2160. [Google Scholar] [CrossRef]
  17. Zhao, M.; Xue, K.; Wang, F.; Liu, S.; Bai, S.; Sun, B.; Zhou, J.; Yang, Y. Microbial mediation of biogeochemical cycles revealed by simulation of global changes with soil transplant and cropping. Isme J. 2014, 8, 2045–2055. [Google Scholar] [CrossRef] [PubMed]
  18. Orwin, K.H.; Dickie, I.A.; Holdaway, R.; Wood, J.R. A comparison of the ability of PLFA and 16S rRNA gene metabarcoding to resolve soil community change and predict ecosystem functions. Soil Biol. Biochem. 2018, 117, 27–35. [Google Scholar] [CrossRef]
  19. Wei, H.; Man, X. Increased Litter Greatly Enhancing Soil Respiration in Betula platyphylla Forests of Permafrost Region, Northeast China. Forests 2021, 12, 89. [Google Scholar] [CrossRef]
  20. Liu, R.; Zhang, Y.; Hu, X.-F.; Wan, S.; Wang, H.; Liang, C.; Chen, F.-S. Litter manipulation effects on microbial communities and enzymatic activities vary with soil depth in a subtropical Chinese fir plantation. For. Ecol. Manag. 2021, 480, 118641. [Google Scholar] [CrossRef]
  21. Jiang, Z.; Lian, Y.; Qin, X. Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth-Sci. Rev. 2014, 132, 1–12. [Google Scholar] [CrossRef]
  22. Xue, Y.; Tian, J.; Quine, T.A.; Powlson, D.; Xing, K.; Yang, L.; Kuzyakov, Y.; Dungait, J.A.J. The persistence of bacterial diversity and ecosystem multifunctionality along a disturbance intensity gradient in karst soil. Sci. Total Environ. 2020, 748, 142381. [Google Scholar] [CrossRef]
  23. Liu, R.; Wang, D. C:N:P stoichiometric characteristics and seasonal dynamics of leaf-root-litter-soil in plantations on the loess plateau. Ecol. Indic. 2021, 127, 107772. [Google Scholar] [CrossRef]
  24. Ren, C.; Chen, J.; Lu, X.; Doughty, R.; Zhao, F.; Zhong, Z.; Han, X.; Yang, G.; Feng, Y.; Ren, G. Responses of soil total microbial biomass and community compositions to rainfall reductions. Soil Biol. Biochem. 2018, 116, 4–10. [Google Scholar] [CrossRef]
  25. Zhang, H.; Fang, Y.; Zhang, B.; Luo, Y.; Yi, X.; Wu, J.; Chen, Y.; Sarker, T.C.; Cai, Y.; Chang, S.X. Land-use-driven change in soil labile carbon affects microbial community composition and function. Geoderma 2022, 426, 116056. [Google Scholar] [CrossRef]
  26. Yang, B.; Qi, K.; Bhusal, D.R.; Huang, J.; Chen, W.; Wu, Q.; Hussain, A.; Pang, X. Soil microbial community and enzymatic activity in soil particle-size fractions of spruce plantation and secondary birch forest. Eur. J. Soil Biol. 2020, 99, 103196. [Google Scholar] [CrossRef]
  27. Grace, J.B.; Keeley, J.E. A structural equation model analysis of postfire plant diversity in California shrublands. Ecol. Appl. A Publ. Ecol. Soc. Am. 2006, 16, 503–514. [Google Scholar] [CrossRef]
  28. Wang, L.X.; Deng, D.Z.; Feng, Q.H.; Xu, Z.J.R.; Pan, H.L.; Li, H.C. Changes in litter input exert divergent effects on the soil microbial community and function in stands of different densities. Sci. Total Environ. 2022, 845, 157297. [Google Scholar] [CrossRef]
  29. Feng, J.; He, K.; Zhang, Q.; Han, M.; Zhu, B. Changes in plant inputs alter soil carbon and microbial communities in forest ecosystems. Glob. Chang. Biol. 2022, 28, 3426–3440. [Google Scholar] [CrossRef]
  30. Sokol, N.W.; Kuebbing, S.E.; Karlsen-Ayala, E.; Bradford, M.A. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. New Phytol. 2019, 221, 233–246. [Google Scholar] [CrossRef]
  31. Jackson, R.B.; Lajtha, K.; Crow, S.E.; Hugelius, G.; Kramer, M.G.; Pineiro, G. The Ecology of Soil Carbon: Pools, Vulnerabilities, and Biotic and Abiotic Controls. Annu. Rev. Ecol. Evol. Syst. 2017, 48, 419–445. [Google Scholar] [CrossRef]
  32. Villarino, S.H.; Pinto, P.; Jackson, R.B.; Pineiro, G. Plant rhizodeposition: A key factor for soil organic matter formation in stable fractions. Sci. Adv. 2021, 7, eabd3176. [Google Scholar] [CrossRef] [PubMed]
  33. Sokol, N.W.; Sanderman, J.; Bradford, M.A. Pathways of mineral-associated soil organic matter formation: Integrating the role of plant carbon source, chemistry, and point of entry. Glob. Chang. Biol. 2019, 25, 12–24. [Google Scholar] [CrossRef]
  34. Chen, X.; Deng, Q.; Lin, G.; Lin, M.; Wei, H. Changing rainfall frequency affects soil organic carbon concentrations by altering non-labile soil organic carbon concentrations in a tropical monsoon forest. Sci. Total Environ. 2018, 644, 762–769. [Google Scholar] [CrossRef]
  35. Schuster, M.J. Increased rainfall variability and N addition accelerate litter decomposition in a restored prairie. Oecologia 2016, 180, 645–655. [Google Scholar] [CrossRef]
  36. Guo, X.; Meng, M.; Zhang, J.; Chen, H.Y.H. Vegetation change impacts on soil organic carbon chemical composition in subtropical forests. Sci. Rep. 2016, 6, 29607. [Google Scholar] [CrossRef] [PubMed]
  37. Hatton, P.-J.; Castanha, C.; Torn, M.S.; Bird, J.A. Litter type control on soil C and N stabilization dynamics in a temperate forest. Glob. Chang. Biol. 2015, 21, 1358–1367. [Google Scholar] [CrossRef]
  38. Ruiz-Colmenero, M.; Bienes, R.; Eldridge, D.J.; Marques, M.J. Vegetation cover reduces erosion and enhances soil organic carbon in a vineyard in the central Spain. Catena 2013, 104, 153–160. [Google Scholar] [CrossRef]
  39. Shi, L.; Feng, W.; Jing, X.; Zang, H.; Mortimer, P.; Zou, X. Contrasting responses of soil fungal communities and soil respiration to the above- and below-ground plant C inputs in a subtropical forest. Eur. J. Soil Sci. 2019, 70, 751–764. [Google Scholar] [CrossRef]
  40. Zhou, J.; Wen, Y.; Shi, L.; Marshall, M.R.; Kuzyakov, Y.; Blagodatskaya, E.; Zang, H. Strong priming of soil organic matter induced by frequent input of labile carbon. Soil Biol. Biochem. 2021, 152, 108069. [Google Scholar] [CrossRef]
  41. Hollister, E.B.; Schadt, C.W.; Palumbo, A.V.; Ansley, R.J.; Boutton, T.W. Structural and functional diversity of soil bacterial and fungal communities following woody plant encroachment in the southern Great Plains. Soil Biol. Biochem. 2010, 42, 1816–1824. [Google Scholar] [CrossRef]
  42. Liu, J.; Wang, J.; Morreale, S.J.; Schneider, R.L.; Li, Z.; Wu, G.-L. Contributions of plant litter to soil microbial activity improvement and soil nutrient enhancement along with herb and shrub colonization expansions in an arid sandy land. Catena 2023, 227, 107098. [Google Scholar] [CrossRef]
  43. Beimforde, C.; Feldberg, K.; Nylinder, S.; Rikkinen, J.; Tuovila, H.; Doerfelt, H.; Gube, M.; Jackson, D.J.; Reitner, J.; Seyfullah, L.J.; et al. Estimating the Phanerozoic history of the Ascomycota lineages: Combining fossil and molecular data. Mol. Phylogenetics Evol. 2014, 78, 386–398. [Google Scholar] [CrossRef] [PubMed]
  44. Fierer, N.; Strickland, M.S.; Liptzin, D.; Bradford, M.A.; Cleveland, C.C. Global patterns in belowground communities. Ecol. Lett. 2009, 12, 1238–1249. [Google Scholar] [CrossRef]
  45. Wu, B.; Umer, M.; Guo, Y.; He, M.; Han, X.; Shen, K.; Xia, T.; He, Y.; He, X. Positive responses of soil nutrients and enzyme activities to AM fungus under interspecific and intraspecific competitions when associated with litter addition. Rhizosphere 2023, 27, 100728. [Google Scholar] [CrossRef]
  46. Hodge, A. Interactions between Arbuscular Mycorrhizal Fungi and Organic Material Substrates. In Advances in Applied Microbiology; Sariaslani, S., Gadd, G.M., Eds.; Academic Press: Cambridge, MA, USA, 2014; Volume 89, pp. 47–99. [Google Scholar]
  47. Zhu, H.; Gong, L.; Luo, Y.; Tang, J.; Ding, Z.; Li, X. Effects of Litter and Root Manipulations on Soil Bacterial and Fungal Community Structure and Function in a Schrenk’s Spruce (Picea schrenkiana) Forest. Front. Plant Sci. 2022, 13, 849483. [Google Scholar] [CrossRef]
  48. Dou, Y.; Liao, J.; An, S. Importance of soil labile organic carbon fractions in shaping microbial community after vegetation restoration. Catena 2023, 220, 106707. [Google Scholar] [CrossRef]
  49. Ridgeway, J.R.; Morrissey, E.M.; Brzostek, E.R. Plant litter traits control microbial decomposition and drive soil carbon stabilization. Soil Biol. Biochem. 2022, 175, 108857. [Google Scholar] [CrossRef]
Forests 14 02291 g001
Figure 1. Effects of seasons and detritus treatments on soil carbon. (a) Soil organic carbon (SOC); (b) soil dissolved organic carbon (DOC); (c) microbial biomass carbon (MBC); (d) carbon quality index (CQI); (e) soil cellulase; and (f) soil ligninase. Different letters indicate significant differences (p < 0.05). The main factors (season and detritus treatment) and their interactions (S × D) were assessed using two-way ANOVA, and the results are presented in each sub-figure. LSD was employed to compare simple effects. Bars represent standard errors of the means (n = 3). CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Figure 1. Effects of seasons and detritus treatments on soil carbon. (a) Soil organic carbon (SOC); (b) soil dissolved organic carbon (DOC); (c) microbial biomass carbon (MBC); (d) carbon quality index (CQI); (e) soil cellulase; and (f) soil ligninase. Different letters indicate significant differences (p < 0.05). The main factors (season and detritus treatment) and their interactions (S × D) were assessed using two-way ANOVA, and the results are presented in each sub-figure. LSD was employed to compare simple effects. Bars represent standard errors of the means (n = 3). CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Forests 14 02291 g001
Forests 14 02291 g002
Figure 2. Effects of seasons and detritus treatments on soil fungal and bacterial alpha diversity indices. (a) soil fungal Chao1 diversity; (b) bacterial Chao1 diversity; (c) fungal ACE diversity; (d) bacterial ACE diversity; (e) fungal Shannon index diversity; (f) bacterial Shannon index diversity; (g) fungal Simpson index diversity; and (h) bacterial Simpson index diversity. Different letters indicant statistically significant differences (p < 0.05). The main factors (season and detritus treatment) and their interactions (S × D) were examined using two-way ANOVA, and the results are presented in each sub-figure. LSD was employed to compare simple effects. Bars represent standard errors of the means (n = 3). CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Figure 2. Effects of seasons and detritus treatments on soil fungal and bacterial alpha diversity indices. (a) soil fungal Chao1 diversity; (b) bacterial Chao1 diversity; (c) fungal ACE diversity; (d) bacterial ACE diversity; (e) fungal Shannon index diversity; (f) bacterial Shannon index diversity; (g) fungal Simpson index diversity; and (h) bacterial Simpson index diversity. Different letters indicant statistically significant differences (p < 0.05). The main factors (season and detritus treatment) and their interactions (S × D) were examined using two-way ANOVA, and the results are presented in each sub-figure. LSD was employed to compare simple effects. Bars represent standard errors of the means (n = 3). CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Forests 14 02291 g002
Forests 14 02291 g003aForests 14 02291 g003b
Figure 3. Relative abundances of fungal (a,c) and bacterial (b,d) taxa at the phylum and genus levels under different detritus treatments during the rainy and dry seasons. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Figure 3. Relative abundances of fungal (a,c) and bacterial (b,d) taxa at the phylum and genus levels under different detritus treatments during the rainy and dry seasons. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Forests 14 02291 g003aForests 14 02291 g003b
Forests 14 02291 g004
Figure 4. Fungal trophic groups (a) and bacterial phenotype groups (b) under different detritus treatments during the rainy and dry seasons. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Figure 4. Fungal trophic groups (a) and bacterial phenotype groups (b) under different detritus treatments during the rainy and dry seasons. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Forests 14 02291 g004
Forests 14 02291 g005
Figure 5. Soil organic carbon (SOC) and arbuscular mycorrhizal fungi (AMFs) (a) soil moisture content (SMC) and AMFs (b), SOC and ectomycorrhiza (ECM) (c), SMC and ECM (d) contents under different detritus treatments during the rainy and dry seasons. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Figure 5. Soil organic carbon (SOC) and arbuscular mycorrhizal fungi (AMFs) (a) soil moisture content (SMC) and AMFs (b), SOC and ectomycorrhiza (ECM) (c), SMC and ECM (d) contents under different detritus treatments during the rainy and dry seasons. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Forests 14 02291 g005
Forests 14 02291 g006
Figure 6. Principal coordinate analysis of soil microbial communities based on Bray–Curtis dissimilarity matrices for different detritus treatments and seasons at the OUT level. (a) Soil fungal communities; (b) soil bacterial communities. Explained variabilities are presented as percentages along the axes. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter (n = 3).
Figure 6. Principal coordinate analysis of soil microbial communities based on Bray–Curtis dissimilarity matrices for different detritus treatments and seasons at the OUT level. (a) Soil fungal communities; (b) soil bacterial communities. Explained variabilities are presented as percentages along the axes. R—rainy season; A—dry season; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter (n = 3).
Forests 14 02291 g006
Forests 14 02291 g007
Figure 7. RDA plots illustrating the associations among soil physicochemical characteristics during the dry and rainy seasons and the most abundant bacterial (c,d) and fungal (a,b) OTUs with abundance > 0.5%. Blue and red shading represent the rainy and dry season, respectively. CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Figure 7. RDA plots illustrating the associations among soil physicochemical characteristics during the dry and rainy seasons and the most abundant bacterial (c,d) and fungal (a,b) OTUs with abundance > 0.5%. Blue and red shading represent the rainy and dry season, respectively. CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter.
Forests 14 02291 g007
Forests 14 02291 g008
Figure 8. Structural equation modeling (SEM) was used to evaluate how soil characteristics, C-degrading enzyme activities, and the soil microbial population affected SOC, DOC, and MBC. (a) Values closest to the line represent the standardized path coefficients and indicate the significance of the relationships they illustrate. The magnitude of the path coefficient is proportional to the line’s thickness. R2 values represent the proportion of variance explained for each variable. Red and blue arrows indicate significant positive and negative correlations, respectively. (b) The standardized total effects of each predictor on SOC, DOC, and MBC. ** Significant at p < 0.01 (two-tailed), *** Significant at p < 0.001 (two tailed).
Figure 8. Structural equation modeling (SEM) was used to evaluate how soil characteristics, C-degrading enzyme activities, and the soil microbial population affected SOC, DOC, and MBC. (a) Values closest to the line represent the standardized path coefficients and indicate the significance of the relationships they illustrate. The magnitude of the path coefficient is proportional to the line’s thickness. R2 values represent the proportion of variance explained for each variable. Red and blue arrows indicate significant positive and negative correlations, respectively. (b) The standardized total effects of each predictor on SOC, DOC, and MBC. ** Significant at p < 0.01 (two-tailed), *** Significant at p < 0.001 (two tailed).
Forests 14 02291 g008
Table 1. Rainy and dry season measurements of soil physicochemical properties for each of five experimental treatments. Abbreviations: SMC—soil moisture content; SOC—soil organic carbon; TN—total nitrogen; TP—total Phosphorus; NH4-N—ammonium nitrogen; NO3-N—nitrate nitrogen; AK—available potassium; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter. Different letters indicate significant differences in detritus treatment (p < 0.05). Bold typeface indicates statistical significance (p < 0.05). Data are presented as mean ± standard error (SE) (n = 3).
Table 1. Rainy and dry season measurements of soil physicochemical properties for each of five experimental treatments. Abbreviations: SMC—soil moisture content; SOC—soil organic carbon; TN—total nitrogen; TP—total Phosphorus; NH4-N—ammonium nitrogen; NO3-N—nitrate nitrogen; AK—available potassium; CK—control; NL—no litter; NR—no roots; NI—no inputs; DL—double litter. Different letters indicate significant differences in detritus treatment (p < 0.05). Bold typeface indicates statistical significance (p < 0.05). Data are presented as mean ± standard error (SE) (n = 3).
Detritus TreatmentSMC (%)pHSOC
(g kg−1)		TN
(g kg−1)		TP
(g kg−1)		NH4-N
(mg kg−1)		NO3-N
(mg kg−1)		AK
(mg kg−1)
CKRainy0.28 ± 0.02 ab6.78 ± 0.10 a44.43 ± 3.28 b7.93 ± 1.48 ab0.21 ± 0.06 ab104.37 ± 8.58 d129.67 ± 53.85 c5.33 ± 0.11 a
Dry0.09 ± 0.01 c6.45 ± 0.14 ab20.28 ± 1.19 c2.23 ± 0.24 d0.29 ± 0.01 b305.57 ± 62.83 b255.73 ± 95.96 abc2.82 ± 0.20 b
NLRainy0.26 ± 0.002 b6.68 ± 0.12 a44.27 ± 2.37 b7.40 ± 0.29 bc0.19 ± 0.07 ab65.07 ± 16.36 d145.60 ± 12.63 bc5.23 ± 0.15 a
Dry0.08 ± 0.004 c6.38 ± 0.16 ab17.46 ± 1.30 cd2.10 ± 0.16 d0.28 ± 0.01 b285.27 ± 15.50 bc590.83 ± 6.91 a2.59 ± 0.15 b
NRRainy0.29 ± 0.02 a6.65 ± 0.16 a41.40 ± 0.94 b6.40 ± 0.22 c0.17 ± 0.06 ab153.57 ± 37.75 cd194.87 ± 27.97 bc5.24 ± 0.13 a
Dry0.07 ± 0.01 c6.05 ± 0.61 b15.03 ± 0.37 d1.73 ± 0.05 d0.23 ± 0.02 ab493.93 ± 149.61 a204.47 ± 34.21 abc2.57 ± 0.31 b
NIRainy0.29 ± 0.02 a6.49 ± 0.18 ab42.67 ± 3.18 b7.40 ± 0.51 bc0.20 ± 0.02 ab113.77 ± 25.42 d201.27 ± 6.41 abc5.31 ± 0.10 a
Dry0.07 ± 0.01 c6.50 ± 0.28 ab17.34 ± 1.25 cd1.83 ± 0.21 d0.25 ± 0.05 b599.57 ± 60.73 a307.80 ± 63.29 abc2.74 ± 0.33 b
DLRainy0.30 ± 0.02 a6.39 ± 0.14 ab52.47 ± 1.76 a9.37 ± 0.84 a0.19 ± 0.06 b77.70 ± 17.60 d168.30 ± 21.40 bc5.34 ± 0.05 a
Dry0.09 ± 0.01 c6.42 ± 0.14 ab20.73 ± 1.18 c2.53 ± 0.17 d0.32 ± 0.04 a294.00 ± 136.33 bc357.23 ± 89.35 ab3.33 ± 0.24 a
p-value<0.0010.285<0.001<0.0010.069<0.0010.048<0.001
Table 2. Pearson correlation analysis between soil microbial diversity and soil properties during the rainy and dry seasons. Abbreviations: SMC—soil moisture content; TN—total nitrogen; TP—total phosphorus; NH4-N—ammonium nitrogen; NO3-N—nitrate nitrogen; AK—available potassium; SOC—soil organic carbon; DOC—dissolved organic carbon; MBC—microbial biomass carbon; CQI—carbon quality index. Bold typeface indicates statistical significance (p < 0.05). Data are displayed as means ± standard error (SE) for n = 3. * Correlation is significant at p < 0.05 (two-tailed); ** Correlation is significant at p < 0.01 (two-tailed).
Table 2. Pearson correlation analysis between soil microbial diversity and soil properties during the rainy and dry seasons. Abbreviations: SMC—soil moisture content; TN—total nitrogen; TP—total phosphorus; NH4-N—ammonium nitrogen; NO3-N—nitrate nitrogen; AK—available potassium; SOC—soil organic carbon; DOC—dissolved organic carbon; MBC—microbial biomass carbon; CQI—carbon quality index. Bold typeface indicates statistical significance (p < 0.05). Data are displayed as means ± standard error (SE) for n = 3. * Correlation is significant at p < 0.05 (two-tailed); ** Correlation is significant at p < 0.01 (two-tailed).
Rainy SeasonDry Season
Fungal Chao1Bacterial Chao1Fungal ShannonBacterial ShannonFungal Chao1Bacterial Chao1Fungal ShannonBacterial Shannon
SMC (%)0.42−0.140.20−0.170.400.68 **0.110.70 **
pH−0.240.20−0.090.050.240.110.130.23
TN (g kg)−0.08−0.490.14−0.24−0.050.54 *−0.330.49
TP (g kg)−0.05−0.74 **−0.28−0.460.190.28−0.230.26
NH4-N (mg kg)0.37−0.120.01−0.210.15−0.67 **0.39−0.64 *
NO3-N (mg kg)−0.080.410.100.42−0.59 *0.20−0.64 *0.10
AK (mg kg)−0.230.03−0.19−0.180.380.100.010.19
SOC (g kg)−0.20−0.38−0.10−0.060.160.48−0.250.55 *
DOC0.05−0.330.08−0.070.250.10−0.160.14
MBC (mg kg)0.13−0.28−0.07−0.33−0.150.41−0.360.30
Cellulase−0.130.08−0.170.270.200.180.100.15
Ligninase0.310.180.370.080.210.68 **−0.040.67 **
CQI0.15−0.110.10−0.31−0.04−0.280.10−0.20
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

Liu, P.; Ding, S.; Liu, N.; Mo, Y.; Liang, Y.; Ma, J. Soil Microbial Community in Relation to Soil Organic Carbon and Labile Soil Organic Carbon Fractions under Detritus Treatments in a Subtropical Karst Region during the Rainy and Dry Seasons. Forests 2023, 14, 2291. https://doi.org/10.3390/f14122291

AMA Style

Liu P, Ding S, Liu N, Mo Y, Liang Y, Ma J. Soil Microbial Community in Relation to Soil Organic Carbon and Labile Soil Organic Carbon Fractions under Detritus Treatments in a Subtropical Karst Region during the Rainy and Dry Seasons. Forests. 2023; 14(12):2291. https://doi.org/10.3390/f14122291

Chicago/Turabian Style

Liu, Peiwen, Suya Ding, Ning Liu, Yanhua Mo, Yueming Liang, and Jiangming Ma. 2023. "Soil Microbial Community in Relation to Soil Organic Carbon and Labile Soil Organic Carbon Fractions under Detritus Treatments in a Subtropical Karst Region during the Rainy and Dry Seasons" Forests 14, no. 12: 2291. https://doi.org/10.3390/f14122291

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

Article Metrics

Back to TopTop