Effects of Warming on Microbial Community Characteristics in the Soil Surface Layer of Niaodao Wetland in the Qinghai Lake Basin

Abstract

Lakeshore wetlands are important terrestrial ecosystems worldwide, and the lakeshore wetlands of the Tibetan Plateau are sensitive to climate change. Therefore, in the context of global warming, studying the effects of temperature rise on surface soil microbial communities is essential for wetland biodiversity conservation. In this study, we used metagenomic sequencing to examine changes in the structure of surface soil microbial communities and their metabolic pathways in the Niaodao lakeshore wetland (NLW) in Qinghai Lake at 1.2 °C warming. Under natural control and warming conditions, Proteobacteria and Actinobacteria were the most dominant bacterial phyla, and Ascomycota and Basidiomycota were the predominant fungal phyla. Soil pH, electrical conductivity, and temperature affected the relative abundances of the dominant soil microbes. Effect size estimation in a linear discriminant analysis revealed 11 differential pathways between warming and natural conditions. Warming considerably enhanced the peptidoglycan biosynthetic pathways but inhibited the ATP-binding cassette transporter pathway. Warming treatment affected α-diversity indices, with an increase in the Shannon, Chao1, and richness indices and a decrease in the Simpson index compared with the index changes for the natural control conditions. Analysis of similarities showed significant differences between warming and control samples. Overall, temperature rise altered surface soil microbial community structure and increased surface soil microbial diversity and abundance in NLW.

1. Introduction

Global climate change has become a serious concern for governments and researchers, and one of the most prominent features of climate change is warming. According to the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change [1], global greenhouse gas emissions are increasing every year and in 2021 reached the highest level since the Industrial Revolution; thus, global warming has become an undeniable fact.

Soil surface microorganisms are an essential soil component, and their biodiversity is a critical indicator of soil quality [2,3]. Liu et al. [4] demonstrated that the phospholipid fatty acid content of bacteria, particularly of gram-negative bacteria, in the surface soil (0–10 cm) increased after warming. The temperature is a critical factor affecting the ecological processes of the soil. In particular, increased temperature likely affects the community composition and metabolic pathways of soil microorganisms, such as thermogenic metabolism and biofilm anabolism, which further affects soil ecological processes. The effects of warming on soil microbial community structure and soil enzyme activity have been confirmed in many studies. For instance, Yin et al. [5] demonstrated that global warming will make the root exudates of aboveground plants respond to temperature changes, and given interactions among soil microbes, plants, and soil, these changes significantly affect the soil microbial community structure [6]. In addition, warming may affect microbial communities in the short and long term [7,8]. Frey et al. [9] showed that long-term warming decreased soil fungal abundance. Ma et al. [10] found that rising temperatures accelerated soil N transformation processes, whereas Hu et al. [11] reported that warming decreased the abundance of genes involved in soil organic C metabolism. Xu et al. [12] indicated that warming inhibited the activity of N-acetyl-β-D-glucosidase, leucine aminopeptidase, and urease enzymes.

The Tibetan Plateau is known as the third pole of the world [13] and is extremely sensitive to temperature changes. The Tibetan Plateau has become warm and humid due to climate change [14,15]. Niaodao is an internationally important protected (lakeshore) wetland located in the northeastern part of the Tibetan Plateau in the Qinghai Lake basin (QLB). Previous studies on soil microorganisms of the lakeshore wetlands in the Tibetan Plateau primarily included the determination of soil enzyme activity and the community composition of soil microorganisms by 16srRNA high-throughput sequencing [16,17,18,19,20]. However, the effects of warming on microbial metabolic pathways in the soil surface layer remain largely unknown. Therefore, the present study addressed the effects of temperature rise on soil microbial composition, potential changes in soil microbial metabolic pathways and community structure due to warming, and the association between soil physicochemical properties and the relative abundances of the dominant microbial groups with respect to the Niaodao lakeshore wetland (NLW). Our findings are crucial in understanding the effects of warming on the biodiversity of lakeshore wetlands and conserving and restoring these critical habitats.

2. Materials and Methods

2.1. Overview of the Study Area

The study area was located in the northwestern part of the QLB at 36°57′–37°04′ N, 99°44′–99°54′ E, belonging to the National Positioning Observation and Research Station of the Qinghai Lake wetland ecosystem. The site elevation ranges from 3194 to 3226 m, and the topography is low and high in the southeast and northwest, respectively [21]. The climate of the study area is the typical semi-arid alpine climate of the plateau [22]. The average annual average temperature of the observation site is −0.7 °C, the mean annual precipitation is 322.7 mm, and the soil is sandy loam [23]. The surface vegetation is dominated by Leymus secalinus, Poa annua, Allium przewalskianum, and Thermopsis lanceolata. Climate data and wind speed data see in Supplementary Materials.

2.2. Research Methodology

2.2.1. Sample Plot Setup and Warming Treatment

We selected the sampling sites from the NLW area of QLB (Figure 1a). An open-top temperature increasing device (OTC) constructed of polyacrylate with light transmittance ≥92% was used to increase the temperature of the soil in the temperature increasing circle (Figure 1b). The OTC had a bottom area, top area, and height of 3.74 m², 1.97 m², and 87 cm, respectively. The angle between the OTC and ground was 60°. The temperature increase was approximately 1.2 °C. The spacing between treatments in the same group was 3 m, and the spacing between warming and control samples in the group was 2 m. We used a net fence to surround the experimental site to prevent grazing. The experimental sample plots were constructed and made operational in June 2018. Soil samples were collected in September 2020, with 5 replicates each for NW and NCK, totaling 10 soil samples. Samples were collected from within the OTC using a soil auger (diameter = 4.5 cm) with a 5-point sampling method. In other words, we collected 5 auger soils from each sample square. Soil from the 0–10 cm layer was pooled as 1 sample and passed through a 2 mm sieve. The treated soil was divided into 2 parts. One part was placed in a 20-mL Eppendorf tube and preserved using liquid nitrogen for metagenomic sequencing of soil microorganisms. The other part was retained in plastic bags for air drying and used for the measurement of total N (TN), total C (TC), pH, and electrical conductivity.

2.2.2. Soil DNA Extraction and Library Construction

DNA was extracted by first grinding the soil samples in liquid nitrogen, followed by a modified extraction method based on sodium dodecyl sulphate extraction [24]. DNA concentration in the samples was measured using a Qubit fluorometer, and the integrity of the DNA samples was determined using 1% agarose gel electrophoresis. The extracted DNA samples were subjected to ultrasonic disruption using a Covaris instrument to obtain short DNA fragments of the required length. DNA fragments were selected using the Agencourt AMPure XP-Medium kit, and the amount of purified DNA was measured using the Qubit dsDNA High Sensitivity Assay Kit 500 assays. The reaction system was prepared and reacted for the indicated time at a suitable temperature to repair the double-stranded DNA ends, to add the A base to the 3′-end, and to make up the joint connection reaction system. It reacted for the indicated time at a suitable temperature. The ligated product was amplified using a polymerase chain reaction (PCR) system. After denaturing the PCR products to single-stranded DNA, the DNA was subjected to cyclisation reactions for the indicated time at a suitable temperature to generate a single-stranded cyclic DNA products, followed by the digestion of the uncyclized linear DNA molecules to obtain the final library. The fragment size and concentration of the library were determined using the Agilent 2100 Bioanalyzer (Agilent DNA 1000 Reagents). Single-stranded circular DNA molecules were replicated by rolling the loop to form a DNA nanoball (DNB) containing over 300 copies. The obtained DNBs were added to minute mesh pores on the chip using high-density DNA nanochip technology. DNA was sequenced by combined probe-anchored polymerization (cPAS) [25].

2.2.3. Data Pre-Processing

We sorted the raw data using the Trimmomatic software (v3.3) to remove joint sequences and obtain high-quality, valid DNA sequences [26]. Based on default parameters, the clean connector sequence was as follows: PrefixPE/1 = AAGTCGGAGGCCAAGCGGTCTTAGGAAGACAA and PrefixPE/2 = AAGTCGGATCGTAGCCATGTCGTTCTGTGAGCCAAGGAGTTG.

2.2.4. Metagenomic Assembly and Quality Assessment

The metagenome was assembled using Megahit with default parameters, and all sample sequences were combined for gene assembly.

The results of the assembly were evaluated using MetaQUAST to compare the assembly results with the reference sequence and obtain information on the number of high-quality contigs, longest contig, and N50 of the assembled sequence [27].

2.2.5. Data Analysis

Microbial composition and diversity were analyzed using the known constructed Kraken 2 species and sequence composition database (via Kraken 2 software v2.1.2) for comparative analysis. The taxonomic composition of species was processed using the vegan function in R software (v4.2.1), and species abundance was calculated using the Bracken software [28]. The microbial abundance in soil was refined to the taxonomic level of phylum, order, family, genus, and species. Principal component analysis (PCA) and Wilcoxon rank-sum test were performed in STAMP v2.1.3 to analyze significant changes in the composition of soil microbial communities and the relative abundances of soil microorganisms after warming [29]. First, differential microbial and pathway analyses were performed on samples using cluster analysis with LEfSe to identify significantly different species between the groups. Thereafter, linear regression analysis was performed to calculate the magnitude of the effect of each component’s abundance on the difference effect and identify the key microorganisms that influenced the differences between the groups [30]. The analysis of similarities (ANOSIM) was performed using vegan and ggplot2 functions in the R software [31]. Analysis of variance was used to test for significant differences in the soil physicochemical properties, and the association between soil physicochemical properties and soil microbial communities was statistically analyzed using canonical correspondence analysis on the Biozeron Cloud Platform (http://www.cloud.biomicroclass.com accessed on 6 September 2022) [32].

2.2.6. Determination of Soil Physical and Chemical Properties

Soil temperature and soil water content were determined using a soil thermometer (TZS-2X) with an accuracy of 0.01 °C and a soil moisture meter (JK-100F) with an accuracy of 0.1%, respectively. Soil samples were collected and brought back to the laboratory to determine TC using an elemental analyzer (model CE-440; Milan, Italy), TN using the Kjeldahl method, soil pH (in water to soil suspension ratio of 2.5:1) using pHS-25 (accuracy 0.05), and soil conductivity (in soil to water leachate ratio of 5:1, μS/m) using DDS-307 (accuracy 0.01) [23].

3. Results

3.1. Effects of Warming on Soil Microbial Community Composition in NLW

Comparison of microbial genomic databases with microbial taxonomic databases using microbial classification software Kraken 2 and NCBI revealed that over 65% of sequences were unknown, and 22.90–24.13% were microorganisms (

Table 1. Results of soil microbial sequencing.

Name	Natural Conditions (NCK)	Warming Conditions (NW)
Number of clean reads (pairs)	37,141,761.40 ± 1,483,577.17	37,798,109.20 ± 12,939.17
Classified reads (%)	30.10 ± 0.6	30.4 ± 0.3
Chordate reads (%)	6.50 ± 0.1	6.50 ± 0.1
Unclassified reads (%)	69.90 ± 0.6	69.60 ± 0.3
Microbial reads (%)	23.49 ± 0.5	23.81 ± 0.25
Bacterial reads (%)	15.63 ± 0.42	16.40 ± 0.54
Viral reads (%)	0.10 ± 0.00	0.01 ± 0.00
Fungal reads (%)	1.20 ± 0.10	1.1 ± 0.00
Protozoan reads (%)	0.2 ± 0.00	0.2 ± 0.00

Notes: number of clean reads indicate valid sequences after the removal of spliced and low-quality bases; classified reads indicate valid data; chordate reads indicate chordate sequences; unclassified reads indicate unknown species sequences; microbial reads indicate sequences of microorganisms; bacterial reads indicate bacterial sequences; viral reads indicate viral sequences; fungal reads indicate fungal sequences; and protozoan reads indicate protozoan sequences.

). Sequence analysis revealed the presence of bacteria, fungi, viruses, and protozoa in the soil samples from NLW, with bacteria accounting for 15.14–16.91% of all or more than 50% of microbial sequences, indicating predominance of bacteria in the soil microbial communities, followed by fungi at ~1%. There were fewer viruses and protozoa at 0.1–0.2% of all sequences than bacteria and fungi (Table 1). The highest level of microbial composition domain was occupied by bacteria with over 5.5 million bacterial read pairs under both NW and NCK, with the average number of sequenced bacterial read pairs reaching 6.2 and 5.804 million for NW and NCK, exceeding those of 2.454 and 2.59 million read pairs for fungi, respectively.

A total of 65 phyla of soil microorganisms were detected, and the top 15 phyla with the most relative abundance were analyzed for microbial species composition and species abundance (Figure 2). The results showed a high level of microbial repetition of the same treatment at different taxonomic levels. In particular, Proteobacteria (30.74–32.84%), Actinobacteria (10.48–10.88%), Firmicutes (2.46–2.61%), Bacteroidetes (2.32–2.65%), Planctomycetes (1.92–2.00%), and Cyanobacteria (1.25–2.29%) were the dominant bacteria at the phylum level, with relative abundances >1%. Ascomycota (2.84–2.85%) and Basidiomycota (0.74–0.75%) were the dominant fungi at the phylum level, accounting for >50% of the total relative abundances of fungi. Furthermore, α-proteobacteria phyla were the most abundant under NW, whereas δ-proteobacteria were the most abundant under NCK. A Wilcoxon rank-sum test revealed that among the dominant bacterial groups with relative abundances >1%, only the relative abundance of Proteobacteria increased significantly under warming conditions, and those of fungi and archaea did not change significantly at the phylum level.

To further investigate the effect of temperature rise on the composition of soil microbial communities in NLW, we analyzed the relative abundances of the principal soil microbial communities at the genus level under NW and NCK (Figure 3). A total of 1629 genera of microorganisms were detected in the soil. Microbial species composition and species abundance of the top 30 genera in terms of relative abundance were analyzed. At the genus level, three of the dominant genera of soil bacteria showed relative abundances >1%, namely Streptomyces, Pseudomonas, and Bradyrhizobium, with relative abundances of 2.69–2.78, 1.96–2.23, and 1.43–1.69%, respectively. In contrast, the other six dominant genera of soil bacteria showed relative abundances >0.4%, namely Nostoc, Sphingomonas, Burkholderia, Mesorhizobium, Rhizobium, and Methylobacterium with 0.45–1.67, 0.94–0.96, 0.83–0.84, 0.80–0.84, 0.55–0.56, and 0.43–0.44%, respectively. The dominant genera of the soil fungi were Aspergillus (0.40–0.41%) and Rhizophagus (0.30–0.49%). Streptomyces showed the highest relative abundance under NW. The Wilcoxon rank-sum test showed that the relative abundance of bradyrhizobia in the above nine genus-level bacterial flora was significantly reduced compared with the natural control, while the relative abundance was increased but not significantly different. This indicates that warming treatment has some influence on soil microbial community structure at genus level, and Bradyrhizobium may be more suitable to survive in lower-temperature soil environments.

3.2. Effect of Warming on Microbial Diversity in NLW

3.2.1. Effect of Warming on Microbial α-Diversity

We measured Simpson, Shannon, and Chao1 indices to examine the inter- and intra-species diversity and richness of microorganisms in soil samples from NLW. The Chao1 index represents microbial species richness. The higher the Chao1 index, the higher the microbial richness. The Chao1 index of soil microbes was 0.26% higher under NW than under NCK, although the difference was not significant (p = 0.296) (Figure 4b). The Shannon and Simpson indices represent microbial species diversity. The higher the Shannon index, the higher the species diversity. The Shannon index significantly increased after warming (p < 0.05) (Figure 4a). Meanwhile, the higher the Simpson index, the lower the species diversity. The Simpson index was 30.22% lower under NW than under NCK, although the difference was not significant (p = 0.07) (Figure 4c). Soil microbial richness was 0.26% higher under NW than under NCK, although the difference was not significant (p = 0.296). These results indicate that warming increased microbial richness and diversity in the soil surface layer.

3.2.2. Effect of Warming on Microbial β-Diversity

In Figure 5, each point on the graph represents a sample, and the two colors indicate the two treatment groups; the closer the soil samples in the same group, the better the biological replication within the group (Figure 5). The overall explanatory power of the first, second, and third principal components was 98.5%. The differences in microbial community structure between the two treatment groups on the PCA biplot were mainly in PC1, with the NW treatment located at the positive end and the NCK treatment at the opposing end of PC1. ANOSIM revealed significant differences between NW and NCK treatments, indicating significant biometric differences (p < 0.05) (Figure 6). Therefore, warming altered the microbial community structure.

3.3. Differential Analysis of Warming on Soil Microorganisms in NLW

Differences in microbial composition between the warming and control natural conditions were analyzed with LEfSe. In Figure 7, red and green represent differential microbes in NCK and NW, respectively. The LEfSe analysis of soil microorganisms at different levels revealed 19 differential bacterial groups (Figure 7), with eight differential groups in NW and 11 in NCK. In addition, the number of differential bacterial groups detected in the order, family, genus, phylum, and class level were five, five, five, two, and two, respectively. The relative abundance of Bradyrhizobium was more than 5% among the five genus-level bacterial groups, but its relative abundance significantly decreased after the warming treatment.

3.4. Effect of Warming on Soil Microbial Pathways

The raw number information of each gene obtained using Salmon software was employed to count the number of genes related to each pathway, and information on critical metabolic pathways under warming and natural control treatments was obtained using LEfSe software. A total of 11 differential pathways were identified. The warming treatment included four metabolic pathways related to peptidoglycan biosynthesis, biofilm formation (Vibrio), nucleotide excision repair, and lipopolysaccharide biosynthesis. The natural control treatment included seven metabolic pathways related to retrograde endogenous cannabinoid signaling, metabolism of xenobiotics by cytochrome P450, protein processing in endoplasmic reticulum, longevity regulating pathway multiple species, meiosis-yeast, thermogenesis, and ATP-binding cassette (ABC) transporters (Figure 8). These 11 pathways belonged to five classes of primary pathways, namely metabolic (n = 3), organic systems (n = 3), cellular processes (n = 2), genetic information processing (n = 2), and environmental information processing (n = 1). With the increase in temperature, peptidoglycan and lipopolysaccharide biosyntheses were enhanced, whereas metabolism of xenobiotics by cytochrome P450 was not, indicating that warming promoted the metabolism of soil microbes but inhibited the bio-oxidation and degradation of exogenous substances. ABC transporter and thermogenesis pathways, reduced under warming treatment conditions, are related to genetic and environmental information processing, respectively.

3.5. Analysis of the Relationship between Soil Environmental Factors and Microbial Community Composition under Warming and Natural Control Conditions

To further investigate the mechanisms of the effect of warming and major physicochemical factors on the composition of soil microbial communities, canonical correspondence analyses were conducted between soil physicochemical properties, as explanatory variables, and dominant microbial phyla, as response variables.

Table 2. Effects of warming on soil physicochemical properties.

Factors	NW	NCK
pH	9.19 ± 0.07 *	8.99 ± 0.06
EC	191.82 ± 4.5 **	164.92 ± 7.99
ST	16.74 ± 0.43 *	15.56 ± 0.36
TC	27.33 ± 1.70	26.26 ± 3.07
TN	1.06 ± 1.10	2.22 ± 0.49
SMC (%)	24 ± 0.5	24.6 ± 0.1

Note: ** indicates a significant correlation at the level of 0.01; * indicates a significant correlation at the level of 0.05.

shows that the soil pH, conductivity, and temperature under NW treatment are significantly higher than those of NCK, whereas TN content under NW is lower than that of NCK. As shown in Figure 9, the first and second axes explain 84.34 and 14.56% of the total variation, respectively. The positions of sample points in the figure indicate large differences in soil microbial community structure between the NW and NCK. Based on the projection length of different soil physicochemical factors on the first axis, soil temperature, conductivity, and pH were the key factors affecting the composition of soil microbial communities. Proteobacteria, Planctomycetes, and Firmicutes positively correlated with soil water content, pH, and conductivity. Specifically, a significant positive correlation of Planctomycetes, Proteobacteria, and Firmicutes was observed with electrical conductivity (p = 0.048), soil water content (p = 0.012), and pH (p = 0.049), respectively, whereas the rest did not reach the significant level (p > 0.05). In contrast, Basidiomycota and Ascomycota negatively correlated with soil water content, pH, and conductivity but did not meet the significance test level (p > 0.05).

4. Discussion

Soil microorganisms are critical indicators of soil quality [33], and the species diversity of soil microorganisms is quite abundant [34]. In the context of global climate change, the response of soil microbial community structure and diversity, carbon and nitrogen, and other physicochemical factors have gradually become global ecological research hotspots [35,36,37]. In the present study, we examined the effects of warming on the composition of soil microbial communities in NLW. Our results indicated that Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, Planctomycetes, Cyanobacteria, and Verrucomicrobia among bacteria and Ascomycota and Basidiomycota among fungi were the dominant microbial groups in the soil from the region. These results are similar to the findings of Zhang et al. [38], who reported that Proteobacteria, Acidobacteria, Actinobacteria, and Gemmatimonadetes were the most abundant soil bacteria in lakeshore wetlands. At the genus level, Streptomyces, Pseudomonas, and Bradyrhizobium were the dominant bacterial groups, which differs from that in the previous reports [38], likely because of the variability in sampling time and locations. In addition, temperature rises significantly altered the diversity of soil microbial communities. The Shannon index increased significantly, whereas the Simpson index decreased after warming, indicating that warming was conducive to improved soil microbial diversity. The diversity of soil microorganisms was affected by several soil physicochemical factors, such as soil temperature, pH, and water content. In particular, warming affected the environment in which the soil microorganisms lived, and temperature rise favored the survival, growth, and development of soil microorganisms. In a previous study, Zeng [39] showed that temperature significantly affected soil microbial diversity. In the present study, PCA and ANOSIM revealed significant differences in soil microbial communities between warming and natural conditions, indicating that soil microorganisms are specifically sensitive to temperature changes. In addition, the LEfSe analysis revealed 19 differential bacterial groups between the two treatments. At the genus level, the relative abundances of Bradyrhizobium and Nostoc were relatively high. Among bacterial genera, the relative abundance of only Bradyrhizobium and Nostoc decreased, and the decrease was significant for the former. Bradyrhizobium and Nostoc are involved in nitrogen fixation [40]. Rui et al. [41] found that temperature significantly affected the relative abundances and activity of nitrogen-fixing bacterial groups, both of which increased with rising temperature. These reports contrast our findings, due perhaps to differences in sampled soil types, and the exact reason warrants further investigation.

Soil microorganisms are specifically sensitive to environmental changes. The metabolic pathways of soil microorganisms inhabiting different environments are diverse [42], as they constantly adapt to environmental fluctuations [43]. The present study indicates that soil microbial pathways differ considerably between warming and natural conditions. As such, among the 11 differential pathways identified, those related to metabolic function and organic systems were the most common, indicating that these are critical pathways in soil microbial communities in NLW. In particular, the differential metabolic pathways under warming conditions included peptidoglycan and lipopolysaccharide biosyntheses, whereas those under natural conditions included xenobiotic metabolism via cytochrome P450, among others. Both peptidoglycans and lipopolysaccharides are components of the bacterial cell wall. The presence of peptidoglycan and lipopolysaccharide biosynthetic pathways under warming conditions indicated that elevated temperature was conducive to the biosynthesis of these cell wall components, occupying part of the ecological niche in microbial life processes. This may be because warming favored the growth and development of bacteria in lakeshore wetlands and increased their relative abundances, necessitating the synthesis of peptidoglycans and lipopolysaccharides for survival. Metabolism of xenobiotics via cytochrome P450 appeared under natural conditions. Cytochrome P450 is a monooxygenase, one of the most abundant in the family of enzymatically active proteins. It is involved in the metabolism of endogenous and exogenous substances, including pesticides, through reactions such as epoxidation, deamination, and isomerisation [44]. The suppression of metabolic pathways related to xenobiotic metabolism via cytochrome P450 under warming implies that global warming may reduce the ability of soil microorganisms to degrade exogenous substances, such as pesticides.

Furthermore, warming significantly affects soil physicochemical properties, which further alters soil habitats and, ultimately, the composition and stability of soil microbial communities [45]. Our canonical correspondence analysis revealed that pH, soil temperature, and electrical conductivity were influential in affecting soil microbial community diversity, and the angles between these soil physicochemical factors were acute, indicating a common mechanism of action among them. Many studies have found soil pH significantly affected soil microbial community structure [46,47,48]. In addition, Qu et al. found that soil electrical conductivity significantly affected soil microbial diversity by altering the forms of available substrates in soil [49]. These reports are consistent with our results. All dominant soil bacterial groups, except Cyanobacteria and Actinobacteria, were positively correlated with pH and electrical conductivity, whereas the dominant soil fungal groups were negatively correlated with pH and electrical conductivity. These trends can be attributed to the fact that warming significantly altered specific soil physicochemical properties, and the microbial community responded to these environmental changes.

In summary, warming significantly impacts soil microbial community structure and soil physicochemical properties in NLW. In the present study, we used metagenomic sequencing to examine the effect of warming on soil microbial communities. Relevant results on the composition of microbial communities and their metabolic pathways can be obtained through DNA deep sequencing and functional genetic screening. However, the present study only explored the response of soil microbial communities to warming but not the specific mechanism of effect, which warrants future scientific research.

5. Conclusions

The present study on the response of the soil microbial communities in NLW in QLB to warming revealed the following: (1) upon an increase in temperature by approximately 1.2 °C, the structure of soil microbial communities in NLW was altered significantly, overall relative abundances of dominant soil microbial groups was increased, and diversity of soil microbial communities was improved; (2) specifically, the dominant bacterial and fungal groups were Proteobacteria, Actinobacteria, and Firmicutes and Ascomycota and Basidiomycota, respectively, in NLW; and (3) different environmental factors produced varying effects on soil microbial community structure, with soil pH, electrical conductivity, and temperature showing a notable impact. Soil is the core component of the earth’s critical zone. Moreover, it is the basis for various ecosystems on the planet. Soil microorganisms are involved in numerous soil processes and can directly or indirectly affect ecosystem services for human beings. Therefore, changes in soil microbial communities reflect the soil ecosystem. In the face of current global warming, rising temperatures may alter soil microbial community structure, which in turn may affect the function of the entire ecosystem. Our findings can provide a scientific basis for formulating ecosystem management measures to adapt to climate change and realize the creation of a mutually beneficial, multi-level, agricultural–environmental–ecological system.