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Article

Effects of Soil Bacterial Taxa under Different Precipitation Gradients on the Multi-Functionality of the Rhizosphere Soils under Caragana intermedia Forests

by
Liguo Dong
1,2,
Xiaoxiong Bai
1,
Sile Hu
1,
Min Zhang
1,
Ying Wang
1 and
Xuan Yu
1,*
1
College of Forestry, Northwest A&F University, Yangling 712100, China
2
Institute of Forestry and Grassland Ecology, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750002, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 6032; https://doi.org/10.3390/su16146032
Submission received: 7 June 2024 / Revised: 5 July 2024 / Accepted: 11 July 2024 / Published: 15 July 2024
(This article belongs to the Special Issue Managing Forest and Plant Resources for Sustainable Development)
Browse Figures
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Abstract

:
Elucidating the impact of rhizosphere microbial communities in the Caragana intermedia forest on soil polyfunctionality can significantly enhance our understanding of the microbial mechanisms underpinning soil multi-functionality, which is crucial for sustainable land management. The rhizosphere soils under Caragana intermedia forests in different regions with variable precipitation gradients (MAP500 (precipitation ≥ 500 mm), MAP450 (400~500 mm), MAP300 (300~400 mm), MAP250 (200~300 mm)) were investigated in the research object. The interrelationships among soil properties, particularly the soil bacterial taxa and soil multi-functionality, were examined using metagenomic analysis, sequencing techniques, redundancy analysis, and partial least squares path modeling. The results show that (1) concurrent with escalating precipitation, Rhizosphere Soil Multi-functionality Index (SMI) exhibited a significant increase (p < 0.05); (2) the community structures of the Always Abundant Taxa (AAT), Always Rare Taxa (ART), Moderate Taxa (MT), Conditionally Abundant Taxa (CAT), Conditionally Rare group (CRT), and Conditionally Abundant Taxa (CRAT) varied significantly across precipitation gradients. Mean annual precipitation (MAP), soil moisture content (SMC), and pH were identified as the most critical environmental factors influencing the overall bacterial community and various taxa; (3) precipitation predominantly exerts indirect effects on AAT, MT, CAT, and CRAT by modulating soil moisture. Bacterial taxa that are conditionally rare or abundant in arid and semi-arid regions are the principal drivers of soil multi-functionality alterations within the rhizosphere of the Caragana intermedia forest. In the stewardship of Caragana intermedia plantations, microbial community composition can be optimized through the regulation of soil moisture and pH, as well as the strategic introduction of conditional microbial taxa, thereby enhancing the plantation’s resilience to climate change. This research contributes to sustainable land use practices by providing insights into microbial management strategies that enhance soil health and ecosystem resilience.

1. Introduction

Human activity and climate change have resulted in a continuous expansion of arid regions [1,2,3], posing a significant threat to lives and livelihoods globally [4]. The intensification of drought has altered global precipitation patterns and induced significant changes in the structures and functions of soil ecosystems [5]. Soil ecosystem functions are inherently multifunctional, reflecting the ecosystem’s ability to provide multiple services simultaneously, such as water and nutrient availability, elemental cycling, and organic matter decomposition [6]. Soil micro-organisms, as the engines driving the recycling processes of key earth elements, mediate the essential ecosystem functions, including primary production, decomposition, nutrient cycling, climate regulation, carbon storage, disease transmission, and pollutant transformation [7], and are crucial for soil multi-functionality. Therefore, understanding the distribution patterns of soil microbial communities across different regions with variable precipitation gradients is vital for comprehending soil multi-functionality and contributing to sustainable land management practices.
Precipitation significantly impacts soil micro-organisms, directly influencing soil multi-functionality. Bahram et al. [8] demonstrated that annual precipitation and soil pH influence soil bacterial and fungal diversity, leading to their niche differentiation on a global scale. Wang et al. [9] identified that drought severity is a critical determinant of bacterial diversity, community composition, and the abundance of key taxa in northern grasslands. Soil microbial diversity differentially affects soil multi-functionality under varying drought indices [10]. Microbial communities comprise a few dominant taxa with high abundance and many rare taxa with low abundance [11,12]. Abundant taxa exhibit high growth rates and broad adaptability, serving as major contributors to carbon flow, energy cycling, and ecosystem biomass turnover [13,14]. Rare microbial taxa, characterized by slow growth and a narrow resource spectrum, have more restricted distributions [15]. Bacteria, being the most abundant micro-organisms, play a significant role in regulating soil multi-functionality. However, most current studies focus on the effects of microbial communities on arid soil ecosystems with a limited understanding of how bacterial taxa and soil multi-functionality change across different regions with variable precipitation gradients and how bacterial taxa drive regional differences in the soil multi-functionality.
Caragana intermedia, commonly known as middle pheasant, is a crucial tree species for soil and water conservation and sand fixation afforestation in northwest China [16]. It plays a vital role in enhancing soil organic matter, reducing soil pH, and promoting soil particle formation [17]. Ningxia is part of China’s national Three-North Shelterbelt Program, with Caragana intermedia widely distributed from south to north across different ecological zones and covering approximately 25.7% of Ningxia’s forested area. It is a key species for desertification control and shelterbelt constructions, which is crucial to Ningxia’s ecological success, particularly in combating desertification. The rhizosphere is the domain in soil directly influenced by root exudates affecting plant growth [18]. Micro-organisms colonizing this microdomain are known as rhizosphere microbes. Rhizosphere micro-organisms alleviate plant drought stress, promote plant growth, and regulate ecosystem functions, playing a crucial role in helping plants cope with droughts [19]. Currently, most studies lack focus on rhizosphere micro-organisms, particularly bacterial taxa, which are closely related to soil multi-functionality and plant growth. It is, therefore, essential to investigate the effects of soil gradients on the response of soil bacterial communities and the synergistic relationships between precipitation, soil bacterial communities, and soil multi-functionality. This knowledge and understanding can provide a theoretical background and technical guidelines for the ecological regulation of plantations, contributing to sustainable forestry and land management practices.

2. Materials and Methods

2.1. Study Area

The study area is situated in the Ningxia Hui Autonomous Region, spanning latitudes 35°14′ to 39°14′ N and longitudes 104°17′ to 109°39′ E. It serves as a transition zone between the middle and upper reaches of the Yellow River, and the Loess Plateau and desert regions. The annual average precipitation ranges from 166.9 mm to 647.3 mm, decreasing from the south to the north, creating a climatic gradient from cold and wet climates in the south to warm and dry climates in the north. This gradient makes it an ideal area for assessing the impact of climate change on ecosystem functions. The soil types exhibit distinct horizontal zonation from south to north, namely, from the black Lou soil to gray calcium soil and gray desert soil. The predominant natural vegetation is grassland, varying from meadow grassland in the south to dry grassland and desert grassland towards the north, with the desert grassland covering the largest area. The main artificial vegetation consists of the following species: Larix gmelinii var. principis-rupprechtii (Mayr) Pilg, Pinus sylvestris var. mongolica Litv, Picea crassifolia Kom, Prunus davidiana (Carrière) Franch, Prunus sibirica L., Hippophae rhamnoides L., and Caragana intermedia. Of these species, Caragana intermedia is the most significant afforestation one.

2.2. Test Design and Collection of Soil Samples

Based on the precipitation data in Ningxia for nearly 60 years in the past, the regions selected for the study cover the areas with mean annual precipitation MAP 500 (500 mm), MAP 450 (400~500 mm), MAP 300 (300~400 mm), and MAP 250 (200~300 mm) (Figure 1). Five plots planted with Caragana intermedia were selected for each region (Table 1). The site selection criteria were (1) the same forest age planted from 2000 to 2003; (2) the middle golden pheasant forest area must be over 1 ha in area, and (3) the distance between sample sites must be more than 100 m with a slope below 15°. In early August 2020, during the peak plant growth, three representative intermediate plant species were selected from each sample, with each plant over 10 m apart. Soil samples were taken from the center of the root zone with a size of 30 × 30 × 30 cm3, which were collected from absorptive root around the roots. These samples were mixed to analyze soil chemical properties and enzyme activities. Sterile scissors were used to remove absorptive root within the 0 to 30 cm depth. Three clusters of shrubs were combined into one sample and placed in a sterile bag as the rhizosphere soil. A total of 40 rhizosphere soil and root samples were collected. All samples were immediately placed in an ice-filled incubator and transported back to the laboratory. The roots were thoroughly rinsed in deionized sterile water, and rhizosphere soils were collected (isolated or otherwise) by centrifugation. The soil samples were labeled and stored in the refrigerator at a temperature of −80 °C for metagenomic sequencing.

2.3. Test Method

2.3.1. Determination of Soil Properties

[1] Determination of soil chemical properties: The soil water (or moisture as you used SMC) content (SMC) was measured using the drying method; soil texture (gravel, silt, clay) was analyzed using a laser particle analyzer Mastersizer 3000 (Malvern Instruments Ltd., Malvern, UK); soil pH was measured by the pH potential meter made by Sartorius PB-10 (Tiingen, Germany) with a soil-to-water ratio of 1:2.5 (m); soil organic carbon (SOC) was determined using an Elementar Liqui TOC II (Analytik Jena, Jena, Germany) analyzer; soil total nitrogen (TN) was measured using an automatic nitrogen tester (Foss Electric, Hillard, Denmark); soil total phosphorus (TP) was determined by the H2SO4-HClO4 method using molybdenum antimony; available phosphorus (AP) was measured by sodium bicarbonate extraction-molybdenum antimony [20]; and soil nitrate nitrogen (NO3−-N) and ammonium nitrogen (NH4+-N) were measured using a 1 mol·L−1 KCl oscillation extraction, analyzed on an AA3 flow analyzer made by Seal Analytical, Mequon, WI, USA [21].
[2] Determination of soil enzyme activity: β-glucosidase (BG), cellobiose hydrolase (CBH), acetylglucosidase (NAG), leucine aminopeptidase (LAP), and alkaline phosphatase (ALP) activities were measured using the fluorescent microplate method [22].
[3] Determination of soil microbial biomass: Microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and microbial biomass phosphorus (MBP) were measured using the chloroform fumigation extraction method [23,24] with the conversion coefficients of 0.45, 0.54, and 0.40, respectively [25].

2.3.2. Metagenomic Sequencing and Bioinformatics Analysis

DNA from the rhizosphere soil samples was extracted using the FastDNA SPIN Kit for Soils (MP Biomedicals, Santa Ana, CA, USA). The integrity of the genomic DNA was assessed by agarose gel electrophoresis, and the DNA concentration and quality were measured using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The DNA was fragmented to an average size of 400 bp using a Covaris ME220 (Covaris, Woburn, MA, USA) following the manufacturer’s protocol. Illumina sequencing adaptors were ligated to both ends of the library DNA using T4 DNA ligase. High-fidelity polymerase was used to amplify the original library to ensure a sufficient quantity of sequencing libraries. The number of PCR amplification cycles was limited to eight. The library concentration and fragment length distribution were examined using a Qubit fluorometer (Life Technologies, Carlsbad, CA, USA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany), respectively. Concentrations greater than 5 ng/μL were required, with fragment lengths concentrated between 300 and 500 bp. Sequencing was performed on the Illumina NovaSeq 6000 platform at Shanghai Tianhao Biotechnology Co., Ltd. (Illumina Inc., San Diego, CA, USA). Readings with low-quality base sequences (length < 100 bp, high-quality bases < 20, or containing N bases) were removed, resulting in 1,646,267,216 clean readings with an average of 8,231,360.8 clean readings per sample. With the Genome Taxonomy Database (GTDB) and the National Center for Biotechnology Information (NCBI) database, nucleic acid sequences of dereplicated contigs/genes were aligned using MMseqs2 (version 13.45111). The dereplicated genes were then aligned to the species protein sequence database using DIAMOND (version 2.0.14) to obtain information for species classification.

2.4. Data Analysis

Indicators related to various soil functions selected for the analysis include soil organic carbon (SOC), dissolved organic carbon (DOC), total nitrogen (TN), total phosphorus (TP), nitrate nitrogen (NO3−-N), ammonium nitrogen (NH4+-N), available phosphorus (AP), microbial biomass phosphorus (MBP), microbial biomass nitrogen (MBN), β-glucosidase (BG), cellobiose hydrolase (CBH), N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and alkaline phosphatase (ALP). These indicators were normalized using the formula (Xraw − Xmin)/(Xmax − Xmin), and the mean value was calculated to obtain the soil multifunctional index (SMI) [19].
The calculation formula of soil multifunctional index is
S M I = 1 n ∑ j = 1 n Z i j
where Z i j is the value of a single soil function after data standardization, and n is the number of indices used to calculate the soil multi-functionality. The SMI is calculated by averaging standardized values of individual soil functions Z i j .
According to the classification criteria suggested by Dai et al. [26] (Figure 2), the operational taxonomic units (OTUs) were classified into six categories: Always Abundant Taxa (AAT), which are OTUs with ≥1% abundance in all samples; Always Rare Taxa (ART), which are OTUs with ≤0.1% abundance in all samples; Moderate Taxa (MT), which are OTUs with >0.1% and <1% abundance in all samples; Conditionally Rare Taxa (CRT), which are OTUs with <1% abundance in all samples but <0.1% abundance in some samples; Conditionally Abundant Taxa (CAT), which are OTUs with >0.1% abundance in all samples but >1% abundance in some samples, and Conditionally Rare or Abundant Taxa (CRAT), which are OTUs with abundance spanning from rare (lowest abundance ≤ 0.1%) to abundant (with the highest abundance ≥ 1%).
According to Peng [27], average annual precipitation data were extracted. R language was used to perform permutational Multivariate Analysis of Variance (PERMANOVA) with 999 permutations to analyze the significance of differences. Distance-based Redundancy Analysis (dbRDA) was conducted on soil environmental factors and soil bacterial communities based on Bray–Curtis distance. Variance Partitioning Analysis (VPA) was performed to determine the contributions of precipitation and soil factors to bacterial taxa. The explanatory power of each environmental variable in dbRDA and the contributions of precipitation and soil factors were calculated using hierarchical partitioning methods. Linear Discriminant Analysis Effect Size (LEfSe) was used to identify species with significantly different abundances across various precipitation gradients. The Sparse Correlations for Compositional data (SparCC) algorithm was employed to construct microbial networks and identify key species within these networks. To explore the relationships among precipitation, soil moisture, pH, soil bacterial communities, and the SMI, the Generalized Least Squares (GLS) were determined using Amos (version 24.0, IBM, Armonk, NY, USA). The model was evaluated using the Comparative Fit Index (CFI) and the Root Mean Square Error of Approximation (RMSEA). The analysis primarily utilized the R 4.2.1 (The R Foundation for Statistical, Vienna, Austria, https://www.r-project.org/) packages “RColorBrewer”, “tidyverse”, “multcompView”, “ggsci”, “EasyStat”, “psych”, “ggcor”, “vegan”, “corrplot”, and “randomForest.”

3. Results and Analysis

3.1. Effect of the Precipitation Gradient on Soil Multi-Functionality and Soil Factors in the Rhizosphere

The precipitation gradient significantly influenced the SMI and various soil traits (Table 2). The SMI for MAP 500, MAP 450, and MAP 300 was 96.55%, 100%, and 3.45% higher than that for MAP 250, respectively. A linear relationship was observed between soil multi-functionality and precipitation (p < 0.001) (Figure 3). The precipitation gradient significantly impacted soil nutrients, microbial biomass, and extracellular enzyme activity (Table S1).
The soil water content increased significantly as the precipitation increased (p < 0.05). The results showed that the highest particle content, ranging from 48.72% to 76.74%, is responsible for the increase in the soil mechanical composition, with the particle content increasing significantly with higher precipitation (p < 0.05).

3.2. Influence of Precipitation Gradients and Rhizosphere Soil Attributes on the Soil Bacterial Community Composition

In the analysis of bacterial operational taxonomic units (OTUs), each precipitation gradient excluded those present in fewer than 80% of the samples, resulting in a total of 6433 OTUs (Figure 4). Specifically, the MAP 250, MAP 300, MAP 450, and MAP 500 gradients contained 5202, 5222, 5447, and 4781 OTUs, respectively. As precipitation increased, the number of OTUs initially rose and then declined. Specifically, the rhizosphere bacterial count in regions with moderate precipitation (between 300 and 500 mm) was lower than in areas with either higher or lower precipitation levels. In areas receiving less than 500 mm of precipitation, the OTU count exhibited an upward trend with increasing precipitation. The four precipitation gradients collectively accounted for 3796 OTUs, which constituted 59.01% of the total. The proximity of the precipitation gradients correlates with the similarity of their shared OTUs. Each precipitation gradient had a specific OTU with 289, 361, 151, and 155 for MAP 500, MAP 450, MAP 300, and MAP 250, respectively.
The bacterial community composition at the phylum level is shown in Figure 5. The most abundant phyla, listed in order of their relative abundance, were Proteobacteria (40.46%), Actinobacteriota (39.44%), Acidobacteriota (7.37%), Chloroflexota (3.56%), Methylomirabilota (1.46%), Gemmatimonadota (1.29%), Bacteroidota (1.24%), Verrucomicrobiota (1.18%), Latescibacterota (0.99%), and SAR324 (0.81%). With the increase in precipitation, the relative abundance of Actinobacteriota initially declined and then rose, whereas the relative abundances of Acidobacteriota, Methylomirabilota, Bacteroidota, Verrucomicrobiota, and SAR324 exhibited an initial increase followed by a decrease.
Based on the abundance, 6433 OTUs were classified into 6 groups of taxa [26] (Figure 6); the number of OTUs and their respective relative abundances among the six taxa exhibit considerable variation. The OTUs were ranked from the greatest to the least values as follows: ART, CRT, MT, CAT, CRAT, and AAT. In terms of relative abundance, the taxa were ordered from the most to the least abundant as CAT, CRT, ART, AAT, MT, and CRAT (Figure 6).
The relative abundance of AAT, ART, CAT and MT at MAP 500 was significantly less than those at MAP 250, MAP 300, and MAP 450 (p < 0.05) (Figure 6). In contrast, the relative abundances of CRT and CRAT at MAP 500 were significantly greater than those at MAP 250, MAP 300, and MAP 450 (p < 0.05). Three abundant taxa, categorized under AAT, are affiliated with Acidobacteria, Actinobacteriota, and Chloroflexi. The ART, numbering between 4640 and 4900, are primarily composed of representatives from Proteobacteria, Actinobacteriota, Bacteroidota, Planctomycetota, Chloroflexi, Verrucomicrobiota, Acidobacteriota, Myxomycetes, Firmicutes, and an additional 50 phyla. Of these figures, 32 intermediate taxa (MT) encompass Proteobacteria, Acidobacteriota, Actinobacteriota, and Latescibacterota. The CRT, totaling 306, consist predominantly of Actinobacteriota, Proteobacteria, Acidobacteriota, Chloroflexi, Bacteroidota, Firmicutes, Gemmatimonadota, Nitrospirae, and 17 other phyla. The CAT, comprising 29 members, includes Proteobacteria, Actinobacteriota, Acidobacteriota, Gemmatimonadota, Methylomirabilota, and SAR324, while 21 CRAT encompass Actinobacteriota, Proteobacteria, Bacteroidota, and Verrucomicrobiota.
Multivariate variance analysis (PERMANOVA) revealed that the precipitation gradient significantly influences the community structure of bacteria and bacterial taxa. The explanation of the community structure for different soil bacterial taxa is detailed in Table 3. The precipitation gradient significantly differentiates the soil bacterial community and the six bacterial taxa (p < 0.01), with a variance contribution (R2) ranging from 0.54 to 0.72, particularly impacting CRT (R2 = 0.71) and CRAT (R2 = 0.72), the rate of explanation for variations in community structure is the highest, surpassing that for differences in the overall bacterial community structure (R2 = 0.68). This implies that both CRT and CRAT have a stronger correlation with the precipitation gradient. The pattern of pairwise differences among the taxa mirrored that of the six key taxa, with the exception of the non-significant differences between MAP 300 and MAP 250; these taxa account for 26.8% to 74.3% of the variation in soil bacterial communities. Furthermore, the precipitation gradient exerts a consistently significant influence on the structural composition of the six soil microbial taxa.
The interrelationship among precipitation and soil environmental factors including the soil bacterial community structure was elucidated using RDA (Figure 7a), demonstrating that these factors significantly influence soil bacteria and their respective community structures, accounting for 44.0%, 16.2%, 42.4%, 23.7%, 32.8%, 39.4%, and 39.1% of the variance in ALL, ART, MT, CAT, CRT, and CRAT, respectively, with the least variance explained for the abundant and intermediate taxa. Variance Partitioning Analysis (VPA) (Figure 7b) indicated that precipitation and soil factors collectively account for 23% to 52% of the variation within soil bacterial communities, with CRAT alone accounting for a maximum of 10% of the explained variance. The hierarchical segmentation analysis (Figure 7c) revealed that environmental factors accounted for 58.4%, 59.8%, 54.5%, 50.2%, 57.8%, 61.5%, and 64.9% of the community structure for ALL, AAT, ART, MT, CAT, and CRAT, respectively. PH, MAP, SMC, and NO3−-N significantly influenced the community structure, with pH being the most critical environmental factor impacting ALL, AAT, MT, CAT, and CRAT, explaining variances of 12.9%, 21.9%, 13.3%, 12.4%, and 14.9%, respectively. MAP was the predominant environmental factor influencing ART and CRT, with variance explanations of 13.0% and 13.4%, respectively. The RDA and hierarchical segmentation analyses indicate that mean annual precipitation, soil moisture content and pH are the most influential factors on the overall community structure and among various taxa.

3.3. Relationship between Multi-Functionality and Bacterial Taxa of Rhizosphere Soil

The Linear Discriminant Analysis (LDA) was employed to identify the bacterial species exhibiting significant differences in abundance across various precipitation gradients. The analysis revealed that of the 13 predominant phyla, which showed significant variation at the phylum level, Firmicutes were significantly enriched in MAP500, whereas Myxococcota, Bacteroidota, Nitrospirota, Desulfobacterota, Planctomycetota, Armatimonadota, Acidobacteriota, Verrucomicrobiota, and Chloroflexota were enriched in MAP450. It also showed that the phylum Gemmatimonadota, including the genus Bacillus, was enriched in MAP300, and Actinobacteriota was enriched in MAP250.
At the species level, it is revealed that the 160 distinct species identified are predominantly within 12 phyla, namely, Actinobacteriota (79 species), Proteobacteria (62 species), Acidobacteriota (9 species), and Bacteroidota (2 species). Species with significant differences were subsequently analyzed using the SparCC algorithm to construct bacterial co-occurrence networks and to calculate Zi-Pi values, thereby identifying keystone species within the network. With this method, the 160 species were categorized into three principal types of network components, which are module centers (1), junctions (98), and peripheral nodes (61), as depicted in Figure 8a,b. The 99 species located at hub centers and junctions are regarded as keystone species. These keystone species are distributed across several phyla, including Actinobacteriota (52 species), Proteobacteria (36 species), Acidobacteriota (4 species), Bacteroidota, Chloroflexota, Desulfobacterota, Firmicutes, Gemmatimonadota, SAR324, and Verrucomicrobiota.
A random forest analysis of the 99 keystone species revealed that 21 species exert a significant influence on the soil multi-functionality (Figure 8c), and the 21 species belong to the phyla Actinobacteria, Proteobacteria, Bacteroidetes, and Firmicutes in bacterial taxonomy with the exception of UBA11600_sp002714165 and Lysobacter_sp, and the remaining 19 species show a significant correlation with the soil multi-functionality (Figure 8c).
Given the interactions among environmental factors, keystone species, and soil bacterial groups, the soil moisture, precipitation, pH, AAT, MT, CAT, CRT, and CRAT were identified as influential factors on the soil multi-functionality (Figure 9). Precipitation, soil moisture, pH, along with bacterial taxa AAT, MT, CAT, CRT, and CRAT, accounted for 90% of the variance in soil multifunctional conditions. Among the five soil microbial taxa, CRAT exhibited the most substantial positive influence on soil multi-functionality, whereas CAT demonstrated a significant negative impact. Precipitation primarily influences AAT, MT, CAT, and CRAT through its effects on soil moisture and pH, thereby exerting an indirect impact on soil multi-functionality.

4. Discussions

4.1. Precipitation Gradient Significantly Affected the Rhizosphere Soil Multi-Functionality of the Caragana intermedia Forests

Following the extensive analyses in this study, it is revealed that the soil multifunction index for MAP 500, MAP 450, and MAP 300 exhibited increments of 96.55%, 100%, and 345%, respectively, compared to MAP 250, accompanied by a significant positive correlation between precipitation and the soil multifunction index, suggesting that elevated precipitation levels in semi-arid regions can enhance soil nutrient availability and functionality. Precipitation influences the soil’s physical structure and nutrient status by modulating its moisture content [28]. Wang et al. [29] conducted a controlled field experiment on the effect of precipitation on semi-arid grasslands and found that reduced precipitation significantly impeded nutrient availability, microbial growth efficiency, and the decomposition of oxidized organic matter, consequently diminishing soil multi-functionality by 42.6%. Hu et al. [10] investigated the impact of a large-scale drought gradient on soil multi-functionality in northern China, concluding that soil multi-functionality exhibited a non-linear decrease as drought conditions intensified, corroborating the findings of this study with its strong negative effects. Precipitation in arid and semi-arid regions significantly enhances soil multi-functionality.

4.2. Precipitation Gradient Significantly Affects Rhizosphere Soil Bacterial Taxa of Caragana intermedia Forest

The region characterized by an annual precipitation ranging from 200 to 500 mm is conventionally classified as a semi-arid zone [30]. This study demonstrates that the rhizosphere bacterial OTU count is constrained by precipitation less than 500 mm, with a concomitant decline as precipitation diminishes (Figure 4), and bacterial abundance exhibits a similar decline, echoing the findings of Ami [31]. This study concludes that the six soil bacterial taxa exhibit substantial variability in OTU numbers and relative abundances, characterized by the rarest OTUs and the least prevalent taxa. Nonetheless, the enriched taxa constituted only 5.4% of the relative abundance despite representing only 3 to 17.6% of the OTUs. Jiao et al. [32] discovered that abundant fungal taxa possess a greater capacity for environmental adaptation than their rare counterparts, characterized by a lower number of OTUs but a higher proportion of sequence counts, akin to the findings of this study.
This study revealed that abundant species exhibit broader environmental tolerances compared to rare species, which are also supported by findings by others [33], potentially elucidating the pervasive occurrence and heightened prevalence of abundant taxa across diverse environmental samples. Within this study, the relative abundance of Actinobacteria increased as precipitation diminished, while that of Acidobacteria, Methylomirabilota, Bacteroidetes, Verrucomicrobiota, and SAR324 declined correspondingly (Figure 5).
In this study, 160 divergent species were mainly distributed in Proteobacteria, Actinobacteriota, and Acidobacteriota and were significantly affected by changes in precipitation. Concurrently, the 21 keystone species encompass representatives from Actinobacteria, Acidobacteria, Bacteroidetes, and Firmicutes, suggesting that diminished precipitation in semi-arid regions results in alterations in the soil bacterial community composition. Gao et al. [34] conducted a study on soil micro-organisms in the rhizosphere and non-rhizosphere of Caragana in desert regions, identifying Firmicutes, Actinobacteria, Proteobacteria, and Acidobacter as the predominant bacterial taxa, and additionally, Acidobacteria, Bacteroidetes, Actinobacteria, Planctomycetes, and Gemmatimonadetes exhibited pronounced sensitivity to environmental conditions. Prior research findings have established that Proteobacteria, Actinobacteria, Firmicutes, and Acidobacteria exhibit considerable drought tolerance [35]. Actinomycetes predominantly engage in the hydrolysis of complex polymeric substances, stabilize clay particles and organic matter, enhance soil water absorption, and foster microbial growth [36,37].
The relative abundance of actinobacteria in warm, arid soils such as desert environments can reach up to 62% [38]. This suggests that actinomycetes are adept at adapting to arid conditions and play a pivotal role in mitigating the effects of drought stress. This study indicates that a reduction in precipitation is associated with an elevated relative abundance of actinomycetes, and the pervasive presence of actinobacteria across diverse taxa underscores their high abundance in arid ecosystems and their significance as key micro-organisms in such environments. Within this study, precipitation had a pronounced impact on the rhizosphere soil bacteria, specifically AAT, ART, MT, CRT, CAT, and CRAT. Luo et al. [39] reported that a reduction in precipitation significantly diminished the relative abundance of Acidobacteria and enhanced that of Actinobacteria (p < 0.05), leading to a substantial alteration in bacterial diversity and community structure, akin to the findings from this study.

4.3. Drivers of Rhizosphere Soil Bacterial Taxa and Their Relationships with Soil Multi-Functionality

In this study, it was discovered that precipitation, soil moisture, and pH significantly influence bacterial taxa. The spatial organization of microbial communities at a regional scale is intricate and subject to a multitude of influencing factors [40]. In an investigation of the bacterial community structure in the rhizosphere of seven species across arid and semi-arid grasslands, Ma et al. [41] determined that the regional distribution of these communities was predominantly shaped by annual precipitation, thereby establishing a biogeographic distribution pattern that aligns with the precipitation gradient. Wang et al. [9] observed that microbial coexistence across expansive regional scales was primarily influenced by average annual precipitation. Zeng et al. [42] found that soil pH and soil organic carbon have significant impacts on the structure of soil bacterial communities in the Loess Plateau, China, with climatic conditions exerting a predominant influence on the diversity of rare bacteria and pH being identified as the principal driver of microbial communities in the rhizosphere.
In this study, annual precipitation emerged as a significant environmental factor influencing the six taxa, suggesting that precipitation in the study area is among the primary environmental determinants shaping the rhizosphere bacterial community. Soil microbial communities exhibit a high degree of sensitivity to environmental fluctuations [43]. Precipitation constitutes the primary source of soil moisture in arid and semi-arid regions. Micro-organisms exhibit a profound reliance on water, which serves as the conduit for environmental interaction, resource acquisition, and dispersal [44]. Consequently, alterations in precipitation directly precipitate changes in the soil moisture, which further exerts a significant influence on soil micro-organisms.
Precipitation, soil moisture, and pH were identified as significant determinants of soil bacterial taxa and soil multi-functionality. The partial least squares path modeling analysis revealed that precipitation primarily influences soil bacterial communities indirectly through its effects on soil moisture and pH, consequently impacting soil multi-functionality; the varying mechanisms by which different microbial taxa contribute to soil multi-functionality may stem from the complex interplay between group-specific and environmental factors. For instance, there is a significant positive correlation between key species within the CRAT and the soil multi-functionality index, whereas in the CAT, there exists a significant negative correlation between all species and the soil multi-functionality index, and in the MT and CRT, key species exhibit both positive and negative correlations with the soil multi-functionality index.
Rare and pivotal species may serve as indicator micro-organisms of multi-functionality [45], which can be justified by findings from this study. The analysis shows that CRAT directly influence soil multi-functionality, providing further evidence that CRAT could be an indicator species for ecosystem versatility. The research indicates that while rare taxa are sensitive to environmental perturbations, they are also more prone to collaborate with other taxa, thereby enhancing the resilience and resistance of microbial communities. In contrast, abundant taxa exhibit reduced susceptibility to abiotic factors [46]; consequently, CRAT may possess the dual capacity to significantly contribute to microbial cooperation and resistance, thereby enhancing the stability of soil microbial communities and augmenting soil multi-functionality.
Soil microbial biogeography is the scientific study of the spatial distribution patterns of microbes in soil and their temporal dynamics, and its core objective is to establish a linkage between microbial community distributions and their ecological functions. Future research should aim to reinforce the understanding of the relationship between microbial taxa and their functions while intensifying the investigation into the interactions among species within these taxa.

5. Conclusions

In this study, metagenomic sequencing was employed to explore the relationships between soil bacterial communities and soil multi-functionality in the rhizosphere soil of Caragana intermedia forests under different precipitation gradients. The study concluded that precipitation has a significant impact on the multi-functionality of the rhizosphere soil of Caragana intermedia forests and that soil multi-functionality shows a significant increasing trend with the increase in precipitation.
Precipitation significantly affects the community structure of bacterial taxa and soil moisture content, while pH is also an important environmental factor affecting the structure of various groups. Precipitation indirectly affects the Always Abundant Taxa, Moderate Taxa, Conditionally Abundant Taxa, and Conditionally Rare or Abundant Taxa by altering soil moisture and pH, thereby changing the soil multi-functionality. The Conditionally Rare or Abundant Taxa have a direct positive effect on soil multi-functionality and are the main microbial groups driving soil multi-functionality. Therefore, soil multi-functionality can be improved by regulating soil moisture and pH and introducing suitable soil microbial groups to enhance the structure of soil microbial communities. The findings from this study enable further understanding of the relationship between bacteria in the rhizosphere and soil multi-functionality under different precipitation gradients in arid and semi-arid regions. Furthermore, it delivers valuable technical insights that contribute to the sustainable stewardship of artificial forests when facing the threat of drought. Future research should focus on intensifying the investigation of conditional species, soil multi-functionality, and the symbiotic relationships between plants. By enhancing our knowledge of microbial community management, we can develop more effective strategies for sustainable land use and ecosystem resilience in the face of climate change.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16146032/s1.

Author Contributions

L.D.: Investigation, formal analysis, data curation, writing—original draft, funding acquisition, project administration, writing—review and editing. X.Y.: conceptualization, funding acquisition, project administration, writing—review and editing. X.B.: formal analysis, investigation, methodology, software, visualization. S.H. and M.Z.: investigation and methodology. Y.W.: investigation and data curation. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by Ningxia Key R & D Project (2023BEG02050; 2023BEG02042), Ningxia High-quality Agricultural Development and Ecological Protection Science and Technology Innovation Demonstration Project (NGSB-2021-14-01; NGSB-2021-14-02),National Natural Science Foundation of China (31660375), Ningxia Top Young Talents Training Project (RQ0025), the 111 Project (No. B20052).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We thank Fei Wang, Shaoshan An, Jiyong Zheng, and Xiaofeng Chang from Northwest A&F University and Xiaohu Dang from Xi’an University of Science and Technology for the help with the sampling. We thank Ninghu Su from James Cook University, Australia, for his invaluable assistance in polishing the English language of this manuscript.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Study area and experimental design.
Figure 1. Study area and experimental design.
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Figure 2. Classification criteria for microbial taxa.
Figure 2. Classification criteria for microbial taxa.
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Figure 3. Linear relationship between soil multi-functionality and precipitation gradient.
Figure 3. Linear relationship between soil multi-functionality and precipitation gradient.
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Figure 4. UpSet plot of bacterial species composition across different precipitation gradients. Note: The y-axis displays the intersection sizes using a bar chart, the x-axis on the lower left corner shows the sizes of each individual set with a bar chart, and the matrix of points on the lower right corner indicates the intersection status between the sets.
Figure 4. UpSet plot of bacterial species composition across different precipitation gradients. Note: The y-axis displays the intersection sizes using a bar chart, the x-axis on the lower left corner shows the sizes of each individual set with a bar chart, and the matrix of points on the lower right corner indicates the intersection status between the sets.
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Figure 5. Phyla in the top 10 relative abundance of bacterial material community composition across different precipitation gradients.
Figure 5. Phyla in the top 10 relative abundance of bacterial material community composition across different precipitation gradients.
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Figure 6. Changes in the relative abundance of different taxa across different precipitation gradients. Note: AAT (Always Abundant Taxa), ART (Always Rare Taxa), MT (Moderate Taxa), CRT (Conditionally Rare Taxa), CAT (Conditionally Abundant Taxa), CRAT (Conditionally Rare or Abundant Taxa). Different lowercase letters indicate significant differences among various MAP levels within the same microbial group (p < 0.05).
Figure 6. Changes in the relative abundance of different taxa across different precipitation gradients. Note: AAT (Always Abundant Taxa), ART (Always Rare Taxa), MT (Moderate Taxa), CRT (Conditionally Rare Taxa), CAT (Conditionally Abundant Taxa), CRAT (Conditionally Rare or Abundant Taxa). Different lowercase letters indicate significant differences among various MAP levels within the same microbial group (p < 0.05).
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Figure 7. Effects of precipitation and soil properties on the structure of soil bacterial taxa. The Redundancy analysis (RDA) results of soil bacterial community structure and soil environmental factors (a), the variation partitioning analysis (VPA) of the contributions of MAP and soil factors to soil bacterial community structure (b), the hierarchical partitioning of soil factors explaining the soil bacterial community structure (c). Note: MAP (mean annual precipitation), SMC (soil moisture content), SOC (soil organic carbon), DOC (dissolved organic carbon), TN (total nitrogen), TP (total phosphorus), NO3−-N (nitrate nitrogen), NH4+-N (ammonium nitrogen), and AP (rapidly available phosphorus). “*” Represents p < 0.05.
Figure 7. Effects of precipitation and soil properties on the structure of soil bacterial taxa. The Redundancy analysis (RDA) results of soil bacterial community structure and soil environmental factors (a), the variation partitioning analysis (VPA) of the contributions of MAP and soil factors to soil bacterial community structure (b), the hierarchical partitioning of soil factors explaining the soil bacterial community structure (c). Note: MAP (mean annual precipitation), SMC (soil moisture content), SOC (soil organic carbon), DOC (dissolved organic carbon), TN (total nitrogen), TP (total phosphorus), NO3−-N (nitrate nitrogen), NH4+-N (ammonium nitrogen), and AP (rapidly available phosphorus). “*” Represents p < 0.05.
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Figure 8. Differential species network and the key species. Bacterial co-occurrence networks (a); Key species identified based on Zi-Pi values (b); Correlation analysis between bacterial species and soil multifunctionality (c). * p < 0.05, ** p < 0.01.
Figure 8. Differential species network and the key species. Bacterial co-occurrence networks (a); Key species identified based on Zi-Pi values (b); Correlation analysis between bacterial species and soil multifunctionality (c). * p < 0.05, ** p < 0.01.
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Figure 9. The generalized least squares path model analysis of soil multi-functionality by environmental factors and bacterial taxa (key species) (a); The standardized indirect and direct effects of environmental factors and bacterial taxa (key species) on SMI (b). * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 9. The generalized least squares path model analysis of soil multi-functionality by environmental factors and bacterial taxa (key species) (a); The standardized indirect and direct effects of environmental factors and bacterial taxa (key species) on SMI (b). * p < 0.05, ** p < 0.01, *** p < 0.001.
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Table 1. Characteristics of the four habitats along the precipitation gradients.
Table 1. Characteristics of the four habitats along the precipitation gradients.
PrecipitationLatitude and Longitude
(°)		Precipitation
(mm)		Altitude
(m)		Forest Age
(Years)		Stand Density
(Bush/hm2)		Ground Diameter
(cm)		Height
(m)		Crown Breadth
(cm)
MAP25037.9–38.1106.4–106.7228.9–256.41236–1441202687.1 ± 187310.1 ± 3.6107.1 ± 39.399.9 ± 43.0
MAP30037.2–37.34106.1–106.8284.2–319.61417–1671202067.7 ± 689.313.3 ± 3.8125.1 ± 28.3125.1 ± 36.7
MAP45036.1–36.2106.3–106.4443.4–451.61716–1929202067.7 ± 734.314.8 ± 3.6174.0 ± 19.9154.6 ± 39.1
MAP50035.5–35.6106.0–106.1484.6–531.42019–2158203121.6 ± 996.512.0 ± 4.2177.0 ± 36.9196.4 ± 78.1
Table 2. Changes of soil multifunction index and soil factors with the precipitation gradient.
Table 2. Changes of soil multifunction index and soil factors with the precipitation gradient.
Precipitation GradientpHSMC (%)Sand (%)Silt (%)Clay (%)SMI
MAP 2508.52 ± 0.05a1.27 ± 0.58c44.98 ± 25.9a48.72 ± 22.82b6.29 ± 3.79b0.29 ± 0.05b
MAP 3008.65 ± 0.07a2.62 ± 0.83b41.53 ± 10.07a50.88 ± 9.04b7.59 ± 1.39ab0.3 ± 0.05b
MAP 4508.63 ± 0.06a8 ± 0.59a20.15 ± 1.96b72.58 ± 1.25a7.27 ± 0.87ab0.58 ± 0.13a
MAP 5008.32 ± 0.21b8.58 ± 0.62a14.16 ± 2.82b76.74 ± 2.25a9.11 ± 0.75a0.57 ± 0.05a
Note: SMI: soil multifunctional index. Different letters indicate significant differences within the same soil factor (p < 0.05).
Table 3. Differential test of soil bacterial community structure by the precipitation gradient.
Table 3. Differential test of soil bacterial community structure by the precipitation gradient.
PrecipitationALLAATARTMTCATCRTCRAT
R2pR2pR2pR2pR2pR2pR2p
ALL0.6760.0010.5400.0020.6690.0010.5830.0010.6640.0010.7060.0010.7240.001
MAP 500/MAP 4500.6260.0130.7020.0100.5810.0080.5850.0110.5560.0040.6540.0110.6970.010
MAP 500/MAP 3000.6600.0180.5160.0200.6430.0060.5720.0110.6280.0110.7000.0100.7430.008
MAP 500/MAP 2500.6670.0030.5340.0200.6660.0190.5090.0090.6590.0080.7050.0100.7030.014
MAP 450/MAP 3000.4970.0080.2680.1010.5180.0050.4320.0060.5310.0100.5430.0080.4750.015
MAP 450/MAP 2500.5680.0080.3190.0530.5880.0070.5070.0080.6370.0060.5890.0040.5010.010
MAP 300/MAP 2500.1430.2670.0040.9700.1790.1110.2190.0520.1180.3400.1570.1920.1940.101
Note: ALL (bacterial community).
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Dong, L.; Bai, X.; Hu, S.; Zhang, M.; Wang, Y.; Yu, X. Effects of Soil Bacterial Taxa under Different Precipitation Gradients on the Multi-Functionality of the Rhizosphere Soils under Caragana intermedia Forests. Sustainability 2024, 16, 6032. https://doi.org/10.3390/su16146032

AMA Style

Dong L, Bai X, Hu S, Zhang M, Wang Y, Yu X. Effects of Soil Bacterial Taxa under Different Precipitation Gradients on the Multi-Functionality of the Rhizosphere Soils under Caragana intermedia Forests. Sustainability. 2024; 16(14):6032. https://doi.org/10.3390/su16146032

Chicago/Turabian Style

Dong, Liguo, Xiaoxiong Bai, Sile Hu, Min Zhang, Ying Wang, and Xuan Yu. 2024. "Effects of Soil Bacterial Taxa under Different Precipitation Gradients on the Multi-Functionality of the Rhizosphere Soils under Caragana intermedia Forests" Sustainability 16, no. 14: 6032. https://doi.org/10.3390/su16146032

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