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Article

Karst Ecosystem: Moso Bamboo Intercropping Enhances Soil Fertility and Microbial Diversity in the Rhizosphere of Giant Lily (Cardiocrinum giganteum)

by
Jie Zhang
1,2,
Haoyu Wu
3,
Guibin Gao
1,2,
Yuwen Peng
1,2,
Yilin Ning
1,2,
Zhiyuan Huang
1,2,
Zedong Chen
1,2,
Xiangyang Xu
1,2 and
Zhizhuang Wu
1,2,*
1
China National Bamboo Research Center, Key Laboratory of State Forestry and Grassland Administration on Bamboo Forest Ecology and Resource Utilization, Hangzhou 310012, China
2
National Long-Term Observation and Research Station for Forest Ecosystem in Hangzhou-Jiaxing-Huzhou Plain, Hangzhou 310012, China
3
Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(11), 2004; https://doi.org/10.3390/f15112004
Submission received: 29 September 2024 / Revised: 21 October 2024 / Accepted: 9 November 2024 / Published: 13 November 2024
(This article belongs to the Special Issue Ecological Research in Bamboo Forests)
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Abstract

:
Intercropping affects soil microbial community structure significantly; however, the effects on understory medicinal plants in karst areas remain unclear. We investigated the effects of four intercropping systems (Moso bamboo, Chinese fir, bamboo-fir mixed forest, and forest gap) on the rhizosphere microbial communities of giant lily (Cardiocrinum giganteum), an economically important medicinal plant in China. We assessed the intercropping impact on rhizosphere microbial diversity, composition, and co-occurrence networks and identified key soil properties driving the changes. Bacterial and fungal diversity were assessed by 16S rRNA and ITS gene sequencing, respectively; soil physicochemical properties and enzyme activities were measured. Moso bamboo system had the highest fungal diversity, with relatively high bacterial diversity. It promoted a distinct microbial community structure with significant Actinobacteria and saprotrophic fungi enrichment. Soil organic carbon, total nitrogen, and available potassium were the most influential drivers of microbial community structure. Co-occurrence network analysis revealed that the microbial network in the Moso bamboo system was the most complex and highly interconnected, with a higher proportion of positive interactions and a greater number of keystone taxa. Thus, integrating Moso bamboo into intercropping systems can enhance soil fertility, microbial diversity, and ecological interactions in the giant lily rhizosphere in karst forests.

1. Introduction

Owing to the intricate interplay between human activities and natural processes, the resilience of ecosystems and socio-economic advancement in southwestern China are encountering major challenges [1,2]. The restoration of such fragile ecosystems is critical; however, the development of effective management strategies is hindered by a rather poor understanding of the intricate relationships among plants, plant microbiota, and karst environments [1,3]. In karst landforms, the interactions between vegetation and the corresponding microbial communities are of great significance for the restoration of ecosystems, nutrient cycling, and carbon sequestration [4,5]. Intercropping refers to a method of planting multiple crops in the same area, and intercropping can affect rhizosphere microbial community structure and improve soil quality in agricultural environments [6]. In intercropping systems, complex interactions between upper and lower layers of vegetation can affect rhizosphere microbial communities significantly through a range of mechanisms, such as changes in resource allocation, root secretion composition, and local environmental factors [7]. Changes in rhizosphere microbiota would influence the adaptability of understory vegetation and the ecological processes that ecosystem functions rely on significantly [8]. Little is known about how intercropping affects understory plants and their microbiomes in karst forest ecosystems despite extensive research on intercropping. Research has shown that changes in land use and ecological restoration can have significant impacts on soil properties, enzyme activity, and microbial community structure in karst environments [9,10]. Studies have shown that transforming a mono-tree plantation into a mixed forest composed of multiple trees can enhance soil structure resilience and affect microbial ecosystem characteristics [10]. Recent research has shown that the rhizosphere microbial community structure in karst environments is significantly affected by different land use modes, affecting soil characteristics and multiple ecosystem functions [11]. The findings highlight that in assessing ecosystem function and repair strategy, it is necessary to comprehensively explore plant diversity and the complex interaction between soil properties and microbial community. Giant lily (Cardiocrinum giganteum), a medicinal plant native to East Asian karst regions traditionally used as a cough suppressant and recently found to possess antispasmodic and neuroprotective properties [12,13], presents an opportunity to investigate the influence of intercropping on understory plant–microbe interactions. Despite the substantial ecological and economic value ascribed to giant lily [13,14,15], the effect of overstory plants on the microbiome of its rhizosphere soil remains poorly understood.
Co-occurrence network analysis has become an important method for microbial ecology research by providing a rigorous approach to elucidating the complex relationships among microbial taxa and identifying key species that are critical for maintaining ecosystem function [8,16]. This analysis method not only focuses on the diversity and composition of microorganisms but also comprehensively reflects the complex relationships among microbial groups [17,18]. Cooccurrence network analysis has revealed the basic ecological processes that control microbial interactions and the internal mechanisms that affect community structure [19,20]. Recent research has revealed the important role of mutualistic networks in identifying key taxa that are centrally located in the network and have a disproportionate influence on community structure and function [7,17]. They play crucial roles in preserving ecosystem equilibrium, promoting plant development, and regulating nutrient transformation processes [21]. Recent studies have shown that soil productivity and crop growth are closely related to key microbiota [22]. Network analysis could offer valuable insights into the unique microbial interaction patterns in karst ecosystems and the effects of human activities on the ecological processes [23,24]. Communities with higher network connectivity and complexity tend to better adapt to environmental pressures, such as extreme weather events or man-made pollution [25,26]. According to a previous study, network structure and topology structure affect microbial community stability [27]. In addition, by studying the symbiosis network characteristics, researchers could predict microbial community response to global environmental change and formulate strategies for ecosystem management and restoration [28].
There are several aspects of the rhizobial microbiome of large lily populations in karst forests that remain unexplored. These include the variability and composition of microbial assemblages in different stand types, the main factors shaping the community structure, as well as interspecies associations and variations in central microbes in different forests. Plants adapt to clarify these relations, and understanding the karst environment and ecosystem stability mechanism is very important. The aim of this study was to evaluate the effects of different forest types on the microbial ecology, soil properties, and enzyme profiles in the rhizosphere of giant lily roots in a mountainous karst ecosystem. We postulated that (1) overstory vegetation would significantly influence the composition and diversity of the rhizosphere microbial community associated with giant lily, (2) soil properties, particularly nutrient availability, would be key drivers of the rhizosphere microbiome structure, and (3) the rhizosphere microbial community would exhibit distinct co-occurrence patterns and keystone taxa across different intercropping systems.

2. Materials and Methods

2.1. Study Area

The study area is located in Jianxin Village, Jiangdi Township, Longsheng Autonomous County, Guilin, Guangxi Province, China (110°24′ E, 25°80′ N), which has a subtropical monsoon climate with an elevation of 845 m to 850 m above sea level, an annual sunshine duration of 1670 h, an average rainfall of 1500 mm to 2400 mm, an average annual temperature of 18. 1 °C, recorded temperature extremes of 4.8 °C and 39.5 °C, and a frost-free period of approximately 314 days. The landscape is characterized by distinctive karst topography, and the soil is classified as Haplic Lixisols according to the World Reference Base for Soil Resources 4th edition, 2022. To investigate the effects of different intercropping systems on the rhizosphere microbial communities of giant lily, an economically important medicinal plant, four intercropping systems were established: natural Moso bamboo (Phyllostachys edulis) forest stand–giant lily intercropping (BG), natural Chinese fir (Cunninghamia lanceolata) forest stand–giant lily intercropping (FG), natural bamboo–fir mixed forest stand–giant lily intercropping (MG), and a 400 m2 forest gap–giant lily intercropping (GG). The GG system served as a control group to simulate the monoculture system. Each intercropping system was implemented within a 400 m2 experimental plot to ensure standardized sampling and comparison conditions. Giant lily was introduced to all four intercropping systems at a planting density of three plants per square meter, and at the time of sampling, the giant lily plants were three years old. The experimental plots were established on slopes ranging from 23° to 29°. The study area has been under consistent forest management practices for over a decade. Table 1 presents detailed characteristics of the dominant tree species, including diameter at breast height (DBH) and tree height, while Table S1 summarizes the composition of the understory vegetation among the intercropping systems.

2.2. Soil and Litterfall Sampling

Soil sampling was conducted on 29 March 2024 across four intercropping systems (BG, FG, MG, and GG). In each system, five 5 m × 5 m plots were established. Using a 5-point sampling method, rhizosphere soil was collected from 25 giant lilies per plot, sampling from the four corners and center of each plot. The rhizosphere soil was obtained by carefully detaching the loose soil particles from the root system and using sterile brushes to remove the firmly attached soil (n = 5). The collected samples were promptly passed through a 2 mm sieve to eliminate roots, rocks, and other unwanted materials. After sieving, the soil from each plot was thoroughly mixed and separated into three equal portions. The first portion was sealed in a sterile bag, initially kept in a cool box (4–10 °C) on site, and subsequently moved to an ultra-low-temperature freezer (−80 °C) for evaluating soil microbial activity. The second portion was allocated for immediate analysis, while the third was maintained at 4 °C for assessing enzyme activity. The final portion was taken to the laboratory in a sterile container, where it was air-dried at ambient temperature to facilitate further examination of soil physicochemical characteristics.

2.3. Analyses of Soil Properties

The soil samples’ total organic carbon (TOC) content was quantified using a total organic carbon analyzer (Muti N/C 3100, Analytik Jena, Jena, Germany). Various crucial soil attributes and enzymatic activities were assessed to characterize the samples. The Kjeldahl method, employing a K1160 automatic Kjeldahl apparatus (Haineng Instruments, Jinnan, China), was used to determine total soil nitrogen (TN) following concentrated sulfuric acid digestion, with quantification achieved through titration [29]. Colorimetric analysis based on the molybdenum blue method was employed to measure total soil phosphorus (TP) after persulfate oxidation [30]. Ammonium acetate extraction with flame photometry was used to analyze available potassium (AK) [31]. A pH meter was utilized to assess soil pH, maintaining a soil-to-water ratio of 1:2.5 (w/v) after shaking and standing for 30 min. The alkali dispersion method was employed to measure soil alkaline hydrolyzed nitrogen (AN) [32], while 0.03 mol·L−1 NH4F and 0.025 mol·L−1 HCl were used to determine soil available P (AP) [33]. The activities of several soil enzymes involved in C, N, and P cycling were evaluated colorimetrically using p-nitrophenol-linked substrates. The activity of β-D-glucosidase (BDG, Enzyme Commission EC 3.2.1.21) was assessed by measuring the release of p-nitrophenol from p-nitrophenyl-β-D-glucopyranoside [34]. Acid phosphatase (ACP, EC 3.1.3.2) activity was determined based on the liberation of p-nitrophenol from p-nitrophenyl phosphate substrate [35]. The activity of N-acetyl-β-D-glucosaminidase (NAG, EC 3.2.1.52) was assayed following the method described by Parham and Deng (2000), which relies on the release of p-nitrophenol from p-nitrophenyl-N-acetyl-β-D-glucosaminide. Lastly, the activity of leucine aminopeptidase (LAP, EC 3.4.11.1) was measured through the hydrolysis of L-leucine-p-nitroanilide [36].

2.4. Extraction of Soil DNA and Sequencing of Amplicon

The CTAB method was employed to extract DNA from the various soil samples, following the manufacturer’s protocol. This technique is particularly effective for obtaining DNA from minute sample quantities and the majority of bacterial species. To eliminate false-positive PCR results, ultrapure water was utilized as a negative control during the DNA extraction process, replacing the sample solution. The extracted DNA was eluted in 50 μL Elution buffer and preserved at −80 °C until PCR amplification, which was conducted by LC-Bio Technology Co., Ltd. (Hangzhou, China). The 16S rRNA gene’s V3-V4 region was amplified using 341F and 805R primers for bacterial identification [37], while the ITS2 region was amplified using ITS4 primers for fungal analysis [38]. PCR amplification was carried out in 25 μL reaction volumes, each containing 25 ng DNA template, 12.5 μL PCR premix, 2.5 μL of each primer, and PCR-grade water. Sample-specific barcodes and sequencing adapters were attached to the 5′ ends of the primers. The thermal cycling conditions included an initial denaturation step at 98 °C for 30 s, followed by 32 cycles of denaturation at 98 °C for 10 s, annealing at 54 °C for 30 s, and extension at 72 °C for 45 s. A final extension step was performed at 72 °C for 10 min. The PCR products were verified by 2% agarose gel electrophoresis, purified using AMPure XT beads (Beckman Coulter, Brea, CA, USA), and quantified with a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). The amplicon libraries’ size and quantity were assessed using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) and the Kapa Library Quantification Kit for Illumina (Kapa Biosciences, Wilmington, MA, USA) before being sequenced on a NovaSeq PE250 platform (Illumina, San Diego, CA, USA).

2.5. Bioinformatics and Statistics

To evaluate the microbial diversity within each sample, alpha diversity indices such as observed ASVs, Shannon, Simpson, and Chao1 were calculated. The vegan package in R 4.4.1 [39] was used to perform principal coordinate analysis (PCoA), and the statistical significance was determined by permutational multivariate analysis of variance (PERMANOVA) with 999 permutations. Mantel tests were employed to investigate correlations between distance matrices. Based on the gradient lengths (<3) of the first detrended correspondence analysis axis for both bacterial and fungal communities, redundancy analysis (RDA) was chosen. Co-occurrence network analysis was performed by calculating Spearman’s rank correlations, retaining only robust (|r| > 0.8) and significant (p < 0.05) associations [40]. The symbiotic network was constructed by filtering species with >80% occurrence frequency. Networks were visualized using Gephi 3.10.3 [41], and key taxa were identified following Liu et al. [40]. Functional profiles were predicted using PICRUSt2 and FAPROTAX for bacteria and FUNGuild for fungal guilds [42,43,44]. SPSS statistical software 23.0 (IBM, Armonk, NY, USA) was utilized to conduct correlation analysis and ANOVA.

3. Results

3.1. Analysis of Soil Properties

The soil physicochemical properties displayed notable discrepancies among the four systems (Figure 1). The TOC and TN content were found to be consistently higher in the plots that had undergone the BG, FG, and MG systems in comparison to the GG system. The BG and FG systems yielded the most substantial increases in TOC, with values 26.9% and 27.2% higher than those observed in the GG system, respectively. Additionally, the BG system demonstrated the greatest enhancement in TN, exhibiting a 19.8% increase above the GG system. The increases in TN were more modest for FG and MG compared with BG. In contrast, the TP content was found to be lower across all systems in comparison to GG, with MG exhibiting the most substantial decrease (23.1% below GG), followed by FG (14.6% lower) and BG (10.0% lower). The available nutrients exhibited disparate patterns across the various systems. AN was elevated in all treated plots, with BG showing the highest increase (27.1% above GG), followed by MG (24.7% higher) and FG (8.2% higher). The greatest variation was observed in AP, which was substantially higher in BG (441% above GG) but lower in FG and MG (5% and 64% below GG, respectively). AK was notably higher in BG (56.2% above GG) and slightly higher in FG, but it was lower in MG (28.8% below GG). Significant differences were also observed in soil pH, with FG exhibiting the most acidic characteristics (20.2% lower than GG), followed by BG (14.1% lower). Significant variation was also observed in soil enzyme activities (Figure 1). BG demonstrated consistently elevated activities for both BDG and ACP, at 35.4% and 21.4% above GG, respectively. In contrast, FG and MG generally exhibited lower enzyme activities compared with GG for these enzymes. BDG activity was observed to be 27.7% and 16.9% lower in FG and MG, respectively, while ACP was found to be 15.0% and 11.0% lower. No notable discrepancies in NAG activity were found among the different systems. Of note, LAP activity was highest in the GG control, with all other systems showing reduced levels (21.6%, 48.6%, and 30.3% lower in BG, FG, and MG, respectively).

3.2. Microbial Diversity

The UpSet diagrams of fungal and bacterial ASVs revealed a total of 170 and 494 ASVs, respectively, shared among all four systems. Each system also exhibited a substantial number of unique ASVs, ranging from 1269 to 1575 for fungi and 3259 to 4597 for bacteria (Figure 2).
The alpha diversity indices revealed contrasting patterns between the fungal and bacterial communities across the intercropping systems. Significant differences in fungal alpha diversity were observed among the systems (Figure 3, p < 0.05), suggesting that intercropping systems differentially influence fungal communities. However, bacterial alpha diversity showed minimal variation across all systems (Figure 3, p > 0.05). The beta diversity analysis, performed using PCoA based on weighted UniFrac distances, further emphasized the distinct separations among the systems for both bacterial and fungal communities (Figure 4a,b). ANOSIM tests yielded confirmation of the significant impact of intercropping on microbial community structure. These findings illustrate the disparate responses of fungal and bacterial communities to intercropping systems, with fungi exhibiting heightened sensitivity to environmental fluctuations in comparison to the more resilient bacterial communities (Figure 4c,d).

3.3. Composition of Microbial Community

Across the four intercropping systems in the karst region, a total of 47 bacterial and 14 fungal phyla were identified (Figure 5a,b; Table S2). Four phyla dominated the bacterial communities: Proteobacteria, Acidobacteriota, Planctomycetota, and Verrucomicrobiota, which collectively represented over 70% of the total relative abundances in all systems. Proteobacteria was the most abundant bacterial phylum, ranging from 24.30% in MG to 34.65% in FG, followed by Acidobacteriota, which varied from 19.54% in MG to 23.44% in GG. Planctomycetota exhibited notable variations, with the highest relative abundance in MG (25.78%) and the lowest in FG (9.63%). In the fungal communities, Ascomycota and Basidiomycota were the most prevalent, collectively representing over 80% of the total relative abundances across all systems. The peak abundance of Ascomycota was observed in GG (69.47%), followed by BG (64.54%) and FG (55.33%), while a significantly lower relative abundance was noted in MG (31.50%). In contrast, Basidiomycota was the most prevalent fungal group in MG (60.62%), exhibiting a significantly higher abundance than in the other systems, which ranged from 11.51% to 26.70%. A notable proportion of fungal sequences remained unclassified at the phylum level, particularly in FG (11.68%) and GG (10.38%) in comparison with BG (3.21%) and MG (3.63%).
LEfSe analysis (LDA > 4) revealed notable variations in microbial taxa from the phylum to the species level among the four intercropping systems (Figure 5c,d). In the bacterial communities, FG exhibited an enrichment of Proteobacteria, particularly Rhizobium leguminosarum and Burkholderia cepacia, as well as Acidobacteriota, predominantly Candidatus Solibacter usitatus. The BG system demonstrated a heightened prevalence of Actinobacteriota, including Acidothermus cellulolyticus. The proportion of Verrucomicrobiota was higher in GG, predominantly Chthoniobacter flavus, while Methylomirabilota (Methylomirabilis oxyfera) was more abundant in MG. MG exhibited a pronounced enrichment of Planctomycetota, particularly Gemmata obscuriglobus. In the fungal communities, GG exhibited a markedly elevated relative abundance of Ascomycota, particularly unclassified Ascomycota taxa. Specific Ascomycota groups, including Exophiala dermatitidis (Chaetothyriales) and Trichoderma harzianum (Hypocreales), exhibited greater abundances in BG. Additionally, BG exhibited a notable enrichment of Basidiomycota taxa, including Mycena galopus and Auricularia auricula-judae. FG and MG were distinguished by higher proportions of Basidiomycota taxa, with FG having more Rhizoctonia solani (Cantharellales) and Cryptococcus neoformans (Tremellales) and MG having a greater abundance of Entoloma sinuatum.

3.4. Correlation Between Soil Properties and Microbial Community

To investigate the associations between dominant microbial taxa and soil properties, a Pearson correlation analysis was conducted between the top 20 genera (based on relative abundance) and key soil physicochemical parameters. The results revealed significant correlations between several bacterial and fungal genera and soil physicochemical properties (Figure S1; Table S3). Soil pH showed strong positive correlations with bacterial genera such as Candidatus Udaeobacter and unclassified Rokubacteriales (r > 0.920, p < 0.001) and strong negative correlations with Acidibacter and Acidothermus (r < −0.913, p < 0.001), suggesting that pH may be a key factor shaping the bacterial community composition. Among the fungal genera, Mycena and Auricularia exhibited robust positive correlations with AP, AK, and ACP activity (all r > 0.890, p < 0.001), suggesting their potential role in nutrient cycling. A positive correlation was observed between TOC and TN and the microbial genera Hypocrea and Bradyrhizobium, and it was negatively correlated with ADurb.Bin063-1 (|r| > 0.810, p < 0.001), highlighting the complex interactions between soil organic matter and microbial communities. It is noteworthy that Sordariomycetes unclassified exhibited contrasting correlations with AK (r = 0.969, p < 0.001) and soil pH (r = −0.675, p < 0.01), which serves to underscore the multifaceted relationships between soil properties and microbial taxa. RDA further elucidated the relationships between soil properties and microbial community structures (Figure 6a,b). With regard to bacteria, RDA1 and RDA2 explained 42.62% of the total variation, with pH, TN, AN, and TOC identified as the most influential factors (all r2 > 0.830, p < 0.001; Table S4). With regard to fungi, RDA1 and RDA2 explained 42.62% of the total variation, with pH, TN, AN, and TOC identified as the most influential factors (all r2 > 0.820, p < 0.001; Table S4). The RDA biplots revealed a clear separation among the systems for both communities, with pH and TOC consistently emerging as dominant factors. It is noteworthy that certain soil enzymes (BDG, ACP, and LAP) exhibited a significant correlation with fungal community structure but not with bacterial community structure. This suggests that these microbial groups may have differential responses to enzymatic activities. Mantel tests provided additional insights into the correlations between soil properties and microbial community compositions across the four intercropping systems (Figure S2; Table S5). In BG, bacterial composition was strongly correlated with ACP (r = 0.754, p < 0.1), while fungal composition was significantly correlated with both ACP (r = 0.774, p < 0.05) and pH (r = 0.507, p < 0.1). FG showed a strong correlation between fungal composition and TOC (r = 0.644, p < 0.1). In MG, bacterial composition was significantly correlated with pH (r = 0.556, p < 0.05), while fungal composition correlated with AN, AK, and pH (r > 0.520, p < 0.05). In GG, bacterial and fungal compositions exhibited strong correlations with TOC (r = 0.905, p < 0.1) and ACP (r = 0.763, p < 0.1), respectively.

3.5. Functional Analysis of Bacterial and Fungal Communities

The potential ecological roles of microorganisms in the studied intercropping systems were elucidated through the use of FAPROTAX and FUNGuild, respectively, with the objective of conducting functional predictions of bacterial and fungal communities. The relative abundances of the dominant functional groups demonstrated variation across systems (Figure 7; Table S6). In the bacterial communities, the predominant functional groups were identified as chemoheterotrophy (17.27%–18.84%) and aerobic chemoheterotrophy (12.46%–16.60%), which were observed across all systems. Furthermore, nitrogen cycling processes, including nitrate reduction (4.99%–5.94%), denitrification (4.00%–4.19%), and nitrogen fixation (2.39%–3.72%), were also well represented. It is worthy of note that cellulolysis exhibited a higher relative abundance in the BG (5.18%) and FG (4.64%) systems in comparison to the GG (2.34%) and MG (1.58%) systems. In the case of the fungal communities, 63.3% of the sequences could not be assigned to functional groups in the FUNGuild database. Among the fungal communities that could be classified, saprotrophs (27.50%–37.64%) and pathotroph–saprotrophs (15.87%–49.08%) were the most prevalent. The highest relative abundance of pathotroph–saprotroph–symbiotrophs was observed in the FG system (24.04%), while the MG system exhibited the highest proportion of symbiotrophic fungi (13.33%). The BG system demonstrated a markedly higher abundance of pathotroph–saprotrophs (49.08%) in comparison with the other systems. Significant correlations were observed between functional group abundances and major microbial phyla (Figure 7c; Table S7). The PICRUSt2 and BugBase analyses provided further insights into the functional capabilities of the bacterial community. The KEGG profiles indicated that Metabolism, Genetic Information Processing, and Environmental Information Processing were the primary level 1 categories (Table S8). At the second level of the KEGG hierarchy, “Others,” Amino Acid Metabolism, Carbohydrate Metabolism, and Replication and Repair were the most abundant categories. LEfSe analysis identified several differentially abundant KEGG level 2 categories among systems (LDA > 2.5, p < 0.05) (Figure S3; Table S9). The BG system demonstrated an enrichment in transporter-related functions, whereas the GG system exhibited an enrichment in ribosome and DNA-related functions. MG displayed a higher abundance of protein synthesis and secretion-related functions, whereas FG exhibited enrichment in categories pertaining to functions that remain unknown. BugBase predictions indicated that the bacterial phenotypes observed across all systems were predominantly aerobic (64.94%–74.27%), potentially pathogenic (50.48%–58.44%), and stress-tolerant (59.72%–74.40%) (Figure S4; Table S10). The Kruskal–Wallis test demonstrated that BG exhibited significantly higher abundances of aerobic, stress-tolerant, and gram-positive phenotypes, whereas MG displayed higher abundances of anaerobic, biofilm-forming, and gram-negative phenotypes (p < 0.05). Pearson correlation analysis revealed significant relationships between dominant microbial functional groups and soil properties (Figure S5). In the bacterial communities, cellulolysis showed positive correlations with soil pH (r = 0.809), AP (r = 0.864), and ACP activity (r = 0.871) (all p < 0.001). Nitrogen fixation was positively correlated with TOC and TN (both r = 0.762, p < 0.001). Among the fungal groups, saprotrophs exhibited a negative correlation with AN (r = −0.852, p < 0.001), while pathotrophs and pathotroph–symbiotrophs showed positive correlations (r = 0.899 and r = 0.856, respectively; p < 0.001).

3.6. Symbiotic Network Analysis

The rhizosphere microbial co-occurrence networks of giant lily were analyzed across four intercropping systems. The bacterial network in the BG system was the most complex (964 nodes, 4660 edges), followed by FG (780 nodes, 3801 edges), MG (677 nodes, 4247 edges), and GG (612 nodes, 3576 edges). Fungal networks were less complex, with BG having 357 nodes and 932 edges, FG 297 nodes and 595 edges, GG 285 nodes and 645 edges, and MG 261 nodes and 527 edges. The bacterial-fungal co-occurrence networks showed BG with the highest complexity (1160 nodes, 7502 edges), followed by FG (984 nodes, 5835 edges), MG (862 nodes, 5947 edges), and GG (836 nodes, 6186 edges). Positive correlations slightly outnumbered negative ones in all systems. In BG, the bacterial phyla Proteobacteria (28.22%), Acidobacteriota (20.54%), and Planctomycetota (13.38%) were most abundant, while fungal phyla Ascomycota (54.06%) and Basidiomycota (27.17%) dominated. FG showed a similar pattern with Proteobacteria (33.97%), Acidobacteriota (21.03%), and Planctomycetota (8.72%) as dominant bacterial phyla and Ascomycota (57.58%) and Basidiomycota (23.57%) as main fungal phyla. In MG, predominant bacterial phyla were Proteobacteria (25.55%), Acidobacteriota (20.68%), and Planctomycetota (20.24%), with Ascomycota (54.02%) and Basidiomycota (30.27%) dominating fungi. GG exhibited Acidobacteriota (25%), Proteobacteria (24.35%), and Planctomycetota (15.69%) as main bacterial phyla, and Ascomycota (52.63%) and Basidiomycota (19.65%) as predominant fungal phyla. Keystone taxa analysis (Figure S6; Table S11) revealed BG had the most keystone taxa (67 bacterial, 19 fungal), followed by FG (29 bacterial, 11 fungal), MG (15 bacterial, 20 fungal), and GG (11 bacterial, 11 fungal). In BG, keystone bacterial taxa were mainly from Acidobacteriota, Proteobacteria, and Planctomycetota, while fungal keystone taxa were primarily Ascomycota. FG’s bacterial keystone taxa were predominantly Proteobacteria, with fungal keystone taxa mainly from Ascomycota. MG showed bacterial keystone taxa, primarily from Proteobacteria and Acidobacteriota, and fungal keystone taxa, mainly from Ascomycota and Basidiomycota. In GG, bacterial keystone taxa were predominantly Acidobacteriota and Proteobacteria, while fungal keystone taxa were primarily Ascomycota and Fungi_unclassified. Spearman correlation analysis revealed significant relationships between keystone taxa and soil properties across all systems. In the BG system, Acidobacteriota showed negative correlations with AN (r = −1.00, p < 0.001) and AK (r = −0.70, p = 0.188), while Proteobacteria exhibited positive correlations with TOC (r = 0.80, p = 0.104) and pH (r = 0.872, p = 0.054). In the MG, Acidobacteriota was strongly correlated with TOC (r = 0.90, p = 0.037), pH (r = −0.462, p = 0.434), and AP (r = −0.975, p = 0.005). Proteobacteria showed a positive correlation with pH (r = 0.667, p = 0.219) and a negative correlation with TP (r = −0.667, p = 0.219). In FG, fungal keystone taxa (Basidiomycota, Ascomycota, Glomeromycota) were positively correlated with TOC (r = 0.80, p = 0.104) and negatively correlated with pH, AP, and AK (all r = −0.50, p = 0.391). Bacterial keystone taxa were correlated with AP (r = −0.80, p = 0.104). In GG, Proteobacteria showed positive correlations with AP (r = 0.90, p = 0.037) and BDG (r = 0.80, p = 0.104), while Acidobacteriota exhibited negative correlations with TOC and AK (both r = −0.90, p = 0.037). Fungal keystone taxa negatively correlated with pH (r = −0.90, p = 0.037) and positively with ACP and NAG (both r = 0.90, p = 0.037) (Figure 8).

4. Discussion

4.1. Intercropping Effects on Soil Properties and Enzyme Activities in the Giant Lily Rhizosphere

The physicochemical properties and enzyme activities of the rhizosphere soil exhibit notable discrepancies, underscoring the impact of intercropping on the nutrient and enzyme activity profiles of the giant lily rhizosphere soil. These differences can be attributed to a number of factors, including alterations in understory vegetation composition, litter input, root exudates, and microclimatic conditions, which in turn affect the nutrient cycling processes in the giant lily rhizosphere [45]. The elevated TOC and TN contents in the BG and FG systems compared with the GG control can be attributed to the presence of tree canopies, which promoted the accumulation of organic matter and nitrogen in forest soils through litter inputs and root exudation. Similar outcomes have been documented in investigations examining the influence of tree species composition on soil carbon and nitrogen dynamics [46,47]. However, the diminished TOC and TN in the MG system underscores the intricate interactions between diverse tree species in mixed forest ecosystems and their ramifications for nutrient storage and cycling dynamics. This finding aligns with that of a previous study [48], which demonstrated that the impact of mixed forests on soil properties can fluctuate depending on the specific tree species involved and their relative abundances. The higher AN, AP, and AK contents in the BG system suggest that bamboo understories play a key role in enhancing nutrient mineralization and availability in the giant lily rhizosphere. This corroborates previous findings on the ecological function of bamboo in forest ecosystems, which demonstrated that bamboo can enhance soil fertility and nutrient cycling through its rapid growth, high litter production, and extensive root systems [49,50]. The current study extends these findings by illustrating the effect of bamboo understories on nutrient availability in the giant lily rhizosphere, a threatened species in karst ecosystems. Soil enzymes are of critical importance for the cycling of nutrients and the decomposition of organic matter. BDG is involved in the breakdown of cellulose and the subsequent release of glucose and serves as a key energy source for soil microbes [51]. ACP catalyzes the hydrolysis of organic phosphorus compounds, thereby rendering phosphorus available for plant uptake [52]. LAP plays a role in the breakdown of peptides and the release of amino acids, which are important sources of nitrogen for both plants and microbes. In this study, soil enzyme activity exhibited distinct patterns across the intercropping systems. BG system resulted in elevated BDG and ACP activity, which corresponded to enhanced carbon and phosphorus availability. These findings are consistent with those of previous studies that have demonstrated a correlation between elevated soil enzyme activity and enhanced nutrient availability and soil fertility in forest ecosystems [50]. However, the divergent pattern of LAP activity, which was the highest in the GG control, underscores the complexity of nitrogen cycling processes in these intercropping systems [52]. The higher LAP activity in the GG control may indicate a greater reliance on organic nitrogen sources in this system, possibly because of the lower inorganic nitrogen availability compared with the other systems. This finding highlights the need for further research to elucidate the specific mechanisms driving nitrogen cycling in these intercropping systems, as emphasized by other researchers [51,52]. The disparate responses of soil pH to various intercropping practices illustrate the impact of overstory composition on rhizosphere acidity. The lower soil pH in the rhizosphere of giant lily in the GG system compared with the other systems may be attributed to the acidifying effect of Chinese fir litter decomposition [53], whereas previous studies have shown that bamboo forests help reduce soil acidification in Chinese fir plantations [54].

4.2. Microbial Community Structure and Diversity Across Intercropping Systems

The results demonstrate the differential responses of microbial groups to environmental alterations induced by intercropping practices and the particular ecological conditions prevailing in each system. Analyses of alpha diversity revealed significant differences in fungal diversity in the giant lily rhizosphere soil among the systems, whereas bacterial diversity exhibited minimal variation. These findings indicate that fungi may be more susceptible to environmental alterations than bacteria in the vicinity of giant lily roots. This finding is consistent with previous studies that have demonstrated the heightened responsiveness of fungal communities [55,56]. The rapid growth rates and diverse metabolic capabilities of the bacteria may make them more adaptable to the selective effects of giant lily root exudates, thereby maintaining consistent diversity across different environments [57]. Beta diversity analyses based on PCoA and ANOSIM tests further confirmed the distinct separation between intercropping systems for both bacterial and fungal communities in giant lily rhizosphere soil, indicating that forest management practices and the associated environmental conditions significantly influence the structure and composition of rhizosphere microbial communities [58,59]. The composition of microbial communities at the phylum level in the giant lily rhizosphere revealed the dominance of Proteobacteria, Acidobacteriota, Planctomycetota, and Verrucomicrobiota in the bacterial communities and Ascomycota and Basidiomycota in the fungal communities. These phyla are commonly found in forest soils and play essential roles in various ecological processes [60,61]. However, notable differences in the relative abundance of these phyla were observed among the intercropping systems, which may be related to differences in substrate availability, soil properties, and plant community composition in the giant lily rhizosphere. Prior research has demonstrated that alterations in vegetation and soil characteristics can markedly impact the relative abundance of dominant microbial phyla within forest ecosystems [62].
The LEfSe analysis provided a more detailed insight into the microbial taxa that were significantly enriched in each intercropping system at different taxonomic levels in the giant lily rhizosphere soil. In the bacterial communities, FG was enriched in Proteobacteria (especially Rhizobium leguminosarum and Burkholderia cepacia) and Acidobacteriota (mainly Candidatus Solibacter usitatus). The enrichment of Acidobacteriota in FG may be attributed to their adaptation to acidic soils, as evidenced by the significantly lower soil pH in the FG system [63]. Notably, the BG system resulted in a higher abundance of Actinobacteriota, including Acidothermus cellulolyticus (order Frankiales, family Acidothermaceae), in the giant lily rhizosphere soil. The higher organic matter content of the bamboo forest soil, as indicated by the significantly higher TOC and TN content, likely facilitated the proliferation of Actinobacteria, which are known for their capacity to degrade complex organic compounds [64]. The enrichment of Actinobacteria in the BG system may have contributed to the enhanced nutrient cycling and organic matter decomposition in the giant lily rhizosphere. The GG system resulted in a higher proportion of Verrucomicrobiota (predominantly Chthoniobacter flavus) and Methylomirabilota (specifically Methylomirabilis oxyfera) in the giant lily rhizosphere soil. These bacterial groups thrive in nutrient-limited environments and play roles in the degradation of polysaccharides, organic matter, and C1 compounds [65,66]. These findings are consistent with the unique soil conditions and resource availability in forest gaps [67]. The MG system exhibited pronounced enrichment of Planctomycetota (especially Gemmata obscuriglobus) in the giant lily rhizosphere soil. These organisms are known to play a role in the degradation of plant-derived polymers and the nitrogen cycle [68]. Heterogeneous litter inputs and nutrient dynamics present in the mixed forest environment may have contributed to the enrichment of Planctomycetota. In the fungal communities of the giant lily rhizosphere, GG exhibited a significantly higher relative abundance of Ascomycota, particularly unclassified Ascomycota taxa. This suggests the presence of novel or understudied fungal lineages adapted to the distinctive ecological conditions of forest gaps. The BG system exhibited a higher abundance of specific Ascomycota groups, including Exophiala dermatitidis (order Chaetothyriales) and Trichoderma harzianum (order Hypocreales), as well as Basidiomycota taxa, such as Mycena galopus (order Agaricales) and Auricularia auricula-judae (order Auriculariales). The enrichment of these fungal groups in the BG is consistent with their functional role in the degradation of complex organic compounds and their adaptation to specific ecological conditions of bamboo forests [69,70]. FG and MG exhibited higher proportions of Basidiomycota taxa; FG displayed a greater abundance of Rhizoctonia solani (order Cantharellales) and Cryptococcus neoformans (order Tremellales), and MG exhibited a higher proportion of Entoloma sinuatum (order Agaricales). Basidiomycota are the primary decomposers of lignin and play a critical role in the carbon cycle of forest ecosystems [71].

4.3. Functional Profiles and Environmental Drivers of Microbial Communities

Functional predictions based on FAPROTAX and FUNGuild analyses indicated that the relative abundances of the dominant functional groups varied among the intercropping systems in the giant lily rhizosphere soil. The bacterial communities were predominantly characterized by chemoheterotrophy, aerobic chemoheterotrophy, and nitrogen cycling processes, which underscores the significance of organic carbon decomposition and nitrogen transformation in the giant lily rhizosphere. PICRUSt2 analysis provided further insights into the potential functional capabilities of the bacterial communities, revealing distinct functional profiles among the intercropping systems. Notably, BG demonstrated greater potential for nutrient acquisition and metabolism in the giant lily rhizosphere soil, which correlated with the observed improvement in soil health and nutrient cycling in bamboo forests. In the fungal communities of the giant lily rhizosphere soil, saprotrophic and pathogenic fungi were the dominant functional groups, underscoring the significance of fungal decomposers and plant-fungal interactions in the giant lily rhizosphere. The FG and MG systems exhibited a greater diversity of fungal ecological strategies, including pathotrophy, saprotrophy, and symbiotrophy. This suggests the potential for intricate plant-fungal interactions within these intercropping systems. The RDA results offered further insights into the relationship between soil properties and microbial community structures in the giant lily rhizosphere. For the bacterial community, soil pH, TN, AN, and TOC were identified as the most influential factors shaping the community structure. These findings align with those of previous studies that have emphasized the significance of soil pH, nitrogen, and organic carbon in shaping bacterial communities in forest soils [49,72]. Similarly, for the fungal community, the primary drivers of community structure were TN, pH, AP, and TOC. The distinct separation of the systems in the RDA biplots, with pH and TOC emerging as the dominant factors, indicates that these soil properties play a pivotal role in shaping the disparate microbial communities associated with different intercropping practices in the giant lily rhizosphere. These findings align with those of prior studies examining the influence of land use and management practices on soil microbial communities in forest ecosystems [73,74].

4.4. Microbial Co-Occurrence Networks and Keystone Taxa in Giant Lily Rhizosphere

The results of the co-occurrence network analysis indicate that the structure and interactions of the giant lily rhizosphere soil microbial community were significantly influenced by the distinct intercropping systems. Bacterial co-occurrence networks exhibited a greater degree of complexity than fungal networks, with the BG system displaying the highest complexity in terms of nodes and edges. This finding is consistent with the results of previous studies that have demonstrated that bacterial communities are more diverse and interconnected than fungal communities in rhizosphere soils [19,75]. The increased complexity of bacterial networks may be attributed to their adaptable metabolic capabilities and expeditious responses to environmental alterations [76]. Notably, the bacterial–fungal co-occurrence networks exhibited the highest connectivity, particularly in the BG system. The slightly higher proportion of positive correlations compared with negative correlations in these networks suggests that mutualistic interactions may be more prevalent than competitive interactions in giant lily rhizosphere soil under intercropping conditions [49,77]. This finding supports the notion that microbial interactions in the rhizosphere are not solely characterized by competition but also encompass facilitative and cooperative relationships [49,78]. The prevalence of positive interactions might contribute to the stability and resilience of microbial communities in response to environmental perturbations [49]. The identification of keystone taxa, including DA101, Pilimelia, Ramlibacter, and several Ascomycota genera, highlighted their potential role in maintaining the structure and function of the microbial community in giant lily rhizosphere soils [79]. The noteworthy correlations between these keystone taxa and soil properties suggest their potential involvement in nutrient cycling and ecosystem functioning [49]. For example, the bacterial keystone taxa DA101 and Pilimelia have been shown to play roles in organic matter decomposition and nitrogen cycling [47]. The higher complexity and connectivity of the co-occurrence networks in the BG system compared with the other systems suggests that intercropping with bamboo may enhance microbial interactions and niche differentiation in the giant lily rhizosphere soil. Previous studies have demonstrated that the expansion of bamboo into native forests can alter soil physicochemical properties, microbial community composition, and ecosystem function [80]. Alterations in soil characteristics and root exudates associated with bamboo intercropping may be responsible for the observed discrepancies in microbial co-occurrence patterns, as previous studies have demonstrated that plant interactions can affect soil microbial communities and co-occurrence patterns through root exudates [6,81]. In conclusion, our study revealed the intricate interactions and assembly patterns of bacterial and fungal communities in the rhizosphere soil of giant lily under different intercropping systems. The application of co-occurrence network analysis facilitated the elucidation of the potential ecological roles of keystone taxa and the significance of positive interactions in maintaining the stability and functionality of the microbial community. The bamboo forest intercropping system resulted in the most intricate and interconnected microbial networks, indicating that the incorporation of bamboo into forest management strategies may facilitate enhanced microbial interactions and potentially contribute to improved rhizosphere ecosystem function in giant lily. These findings emphasize the need for further investigations to elucidate the specific mechanisms underlying microbial interactions in intercropping systems, particularly in the context of bamboo integration, and their implications for sustainable forest management and ecosystem functioning in karst regions.

5. Conclusions

The present study investigated the effects of different intercropping systems on rhizosphere microbial community structure, soil properties, and enzyme activities associated with the giant lily in a mountainous karst area. According to the results, overstory vegetation influences the composition and diversity of the giant lily rhizosphere microbiome significantly. The bamboo–giant lily system notably enhanced soil health and microbial diversity compared with other intercropping systems. The bamboo–giant lily system significantly enhanced soil health, increasing total organic carbon by 26.9% and available phosphorus by 4.41 while fostering a more diverse and interconnected microbial community. This system promoted positive microbial interactions (54.53% of all interactions) and enriched key functional groups, particularly cellulolytic bacteria and pathotrophic–saprotrophic fungi. Soil pH, total nitrogen, available nitrogen, and total organic carbon emerged as the primary drivers of microbial community structure, explaining over 42% of the observed variation. The identification of keystone taxa from Acidobacteriota, Proteobacteria, and Ascomycota provided new targets for rhizosphere engineering. Our findings provide valuable insights into the complex interactions among intercropping systems, soil properties, and microbial communities in the giant lily rhizosphere, laying a foundation for the understanding of the underlying ecological dynamics and informing sustainable management strategies in karst ecosystems. Future research could build upon the results to further explore the mechanisms underlying microbial interactions and their ecosystem-level impacts across diverse sites and over longer time scales.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f15112004/s1, Figure S1: Pearson correlation analysis between the top 20 genera and soil physicochemical properties in the giant lily rhizosphere under different intercropping systems; Figure S2: Mantel test results showing correlations between soil properties and microbial community compositions across the four intercropping systems; Figure S3: Linear discriminant analysis effect size (LEfSe) of differentially abundant KEGG level 2 categories among systems in the giant lily rhizosphere; Figure S4: BugBase predictions of bacterial phenotypes in the giant lily rhizosphere under different intercropping systems; Figure S5: Pearson correlation analysis between dominant microbial functional groups and soil properties in the giant lily rhizosphere under different intercropping systems; Figure S6: Keystone taxa analysis of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems; Table S1: Composition of the understory vegetation among the forest types in the study area; Table S2: Relative abundance of bacterial and fungal phyla in the giant lily rhizosphere under different intercropping systems; Table S3: Pearson correlation analysis between the top 20 bacterial and fungal genera and soil physicochemical properties in the giant lily rhizosphere; Table S4: RDA analysis of bacteria and fungi; Table S5: Mantel test results showing correlations between soil properties and microbial community compositions across the four intercropping systems; Table S6: Relative abundances of the dominant functional groups in the giant lily rhizosphere under different intercropping systems based on FAPROTAX and FUNGuild analyses; Table S7: Correlation analysis between dominant functional groups of soil bacteria and fungi and dominant bacterial phyla in the giant lily rhizosphere; Table S8: KEGG profiles of bacterial communities in the giant lily rhizosphere under different intercropping systems based on PICRUSt2 analysis; Table S9: Linear discriminant analysis effect size (LEfSe) results of differentially abundant KEGG level 2 categories among systems in the giant lily rhizosphere; Table S10: BugBase predictions of bacterial phenotypes in the giant lily rhizosphere under different intercropping systems; Table S11: Keystone taxa analysis of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems.

Author Contributions

Conceptualization, J.Z. and Z.W.; methodology, J.Z., H.W., G.G. and Z.W.; software, J.Z., H.W., G.G., Y.P. and Z.W.; validation, J.Z., H.W., G.G., Y.P., Y.N., Z.H., Z.C. and Z.W.; formal analysis, J.Z., H.W., G.G. and Y.P.; investigation, J.Z., H.W., G.G., Y.P., Y.N., Z.H. and Z.C.; resources, J.Z. and Z.W.; data curation, J.Z. and H.W.; writing—original draft preparation, J.Z., H.W., G.G., Y.P. and Y.N.; writing—review and editing, J.Z., Z.H. and Z.W.; visualization, J.Z., H.W., G.G. and Y.P.; supervision, J.Z., X.X. and Z.W.; project administration, J.Z. and Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Fundamental Research Funds of CAF: CAFYBB2022XE002, and the People’s Government of Zhejiang Province−Chinese Academy of Forestry cooperative project: 2023B04.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author/s.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Soil physicochemical properties in the giant lily rhizosphere under different intercropping systems: (a) total organic carbon (TOC), (b) total nitrogen (TN), (c) total phosphorus (TP), (d) available nitrogen (AN), (e) available phosphorus (AP), (f) available potassium (AK), (g) pH, (h) β-D-glucosidase (BDG), (i) acid phosphatase (ACP), (j) N-acetyl-β-D-glucosaminidase (NAG), and (k) leucine aminopeptidase (LAP). Error bars represent standard deviations (n = 5). Different lowercase letters indicate significant differences among systems (LSD post hoc test, p ≤ 0.05).
Figure 1. Soil physicochemical properties in the giant lily rhizosphere under different intercropping systems: (a) total organic carbon (TOC), (b) total nitrogen (TN), (c) total phosphorus (TP), (d) available nitrogen (AN), (e) available phosphorus (AP), (f) available potassium (AK), (g) pH, (h) β-D-glucosidase (BDG), (i) acid phosphatase (ACP), (j) N-acetyl-β-D-glucosaminidase (NAG), and (k) leucine aminopeptidase (LAP). Error bars represent standard deviations (n = 5). Different lowercase letters indicate significant differences among systems (LSD post hoc test, p ≤ 0.05).
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Figure 2. Amplicon Sequence Variant (ASV) richness of (a) bacteria and (b) fungi in the giant lily rhizosphere under different intercropping systems; blue circles indicate shared taxa across systems; grey circles indicate non-shared ASVs; black bars indicate the number of shared taxa.
Figure 2. Amplicon Sequence Variant (ASV) richness of (a) bacteria and (b) fungi in the giant lily rhizosphere under different intercropping systems; blue circles indicate shared taxa across systems; grey circles indicate non-shared ASVs; black bars indicate the number of shared taxa.
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Figure 3. Alpha diversity indices of (a–c) bacterial and (d–f) fungal communities in the giant lily rhizosphere under different intercropping systems. Lowercase letters indicate significant differences among systems (p = 0.05).
Figure 3. Alpha diversity indices of (a–c) bacterial and (d–f) fungal communities in the giant lily rhizosphere under different intercropping systems. Lowercase letters indicate significant differences among systems (p = 0.05).
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Figure 4. Principal coordinate analysis (PCoA) and analysis of similarities (ANOSIM) tests of (a,b) bacterial and (c,d) fungal communities in the giant lily rhizosphere under different intercropping systems.
Figure 4. Principal coordinate analysis (PCoA) and analysis of similarities (ANOSIM) tests of (a,b) bacterial and (c,d) fungal communities in the giant lily rhizosphere under different intercropping systems.
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Figure 5. Composition and linear discriminant analysis effect size (LEfSe) analysis of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems. (a,b) Relative abundance at the phylum level; (c,d) LEfSe results (phylum to genus level).
Figure 5. Composition and linear discriminant analysis effect size (LEfSe) analysis of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems. (a,b) Relative abundance at the phylum level; (c,d) LEfSe results (phylum to genus level).
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Figure 6. Redundancy analysis (RDA) of (a) bacterial and (b) fungal communities in the giant lily rhizosphere under different intercropping systems.
Figure 6. Redundancy analysis (RDA) of (a) bacterial and (b) fungal communities in the giant lily rhizosphere under different intercropping systems.
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Figure 7. Functional predictions and correlations with dominant phyla for (a) bacterial functional annotation of prokaryotic taxa (FAPROTAX) and (b) fungal functional guilds (FUNGuild) in the giant lily rhizosphere under different intercropping systems. (c) Correlation analysis between dominant functional groups and dominant bacterial phyla. Asterisks indicate significance levels: * (0.01< p ≤ 0.05), ** (0.001< p ≤ 0.01).
Figure 7. Functional predictions and correlations with dominant phyla for (a) bacterial functional annotation of prokaryotic taxa (FAPROTAX) and (b) fungal functional guilds (FUNGuild) in the giant lily rhizosphere under different intercropping systems. (c) Correlation analysis between dominant functional groups and dominant bacterial phyla. Asterisks indicate significance levels: * (0.01< p ≤ 0.05), ** (0.001< p ≤ 0.01).
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Figure 8. Co-occurrence networks of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems: (a,e) bamboo–giant lily, (b,f) Chinese fir–giant lily, (c,g) Moso bamboo–giant lily, and (d,h) forest gap–giant lily intercropping.
Figure 8. Co-occurrence networks of bacterial and fungal communities in the giant lily rhizosphere under different intercropping systems: (a,e) bamboo–giant lily, (b,f) Chinese fir–giant lily, (c,g) Moso bamboo–giant lily, and (d,h) forest gap–giant lily intercropping.
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Table 1. Vegetation characteristics of the studied intercropping systems.
Table 1. Vegetation characteristics of the studied intercropping systems.
Sample PlotAltitude/mAspect and SlopeBambooChina Fir
DBH (cm)Height (m)Density Stem (hm2)DBH (cm)Height (m)Density Stem (hm2)
BG830SE 29°10.43 ± 1.1822.5 ± 1.52100---
FG840SW 25°---29.28 ± 3.1217.5 ± 2.5350
MG840S 25°12.43 ± 0.8722.75 ± 1.593331.48 ± 2.5718.65 ± 3.35300
GG835S 23°------
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Zhang, J.; Wu, H.; Gao, G.; Peng, Y.; Ning, Y.; Huang, Z.; Chen, Z.; Xu, X.; Wu, Z. Karst Ecosystem: Moso Bamboo Intercropping Enhances Soil Fertility and Microbial Diversity in the Rhizosphere of Giant Lily (Cardiocrinum giganteum). Forests 2024, 15, 2004. https://doi.org/10.3390/f15112004

AMA Style

Zhang J, Wu H, Gao G, Peng Y, Ning Y, Huang Z, Chen Z, Xu X, Wu Z. Karst Ecosystem: Moso Bamboo Intercropping Enhances Soil Fertility and Microbial Diversity in the Rhizosphere of Giant Lily (Cardiocrinum giganteum). Forests. 2024; 15(11):2004. https://doi.org/10.3390/f15112004

Chicago/Turabian Style

Zhang, Jie, Haoyu Wu, Guibin Gao, Yuwen Peng, Yilin Ning, Zhiyuan Huang, Zedong Chen, Xiangyang Xu, and Zhizhuang Wu. 2024. "Karst Ecosystem: Moso Bamboo Intercropping Enhances Soil Fertility and Microbial Diversity in the Rhizosphere of Giant Lily (Cardiocrinum giganteum)" Forests 15, no. 11: 2004. https://doi.org/10.3390/f15112004

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