Diversity and co-occurrence patterns of soil bacterial and fungal communities in two morels

doi:10.21203/rs.3.rs-4898036/v1

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Diversity and co-occurrence patterns of soil bacterial and fungal communities in two morels

https://doi.org/10.21203/rs.3.rs-4898036/v1

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This study investigates the influence of different morels growth on soil properties, microbial communities, and cultivation outcomes. Through comprehensive analysis, we found that M. sextelata and M. septimelata significantly affect soil physical and chemical properties, including pH and effective content of carbon (C), nitrogen (N), and potassium (K). While both species exhibit similar effects on soil nutrient enhancement, M. sextelata displays superior pH reduction and disease resistance. Microbial community analysis revealed significant differences between M. sextelataand M. septimelata, with M. sextelatashowing a higher abundance of certain bacterial taxa, indicating resilience to environmental stress. Co-occurrence network analysis demonstrates the complexity of microbial interactions, with M. sextelata exhibiting a more intricate network. Moreover, soil phosphorus levels play a crucial role in shaping fungal community structure. Understanding these intricate relationships is vital for effective morel cultivation and ecosystem management strategies.

Biological sciences/Microbiology

Biological sciences/Microbiology/Environmental microbiology

Biological sciences/Microbiology/Fungi

M. sextelata

M. septimelata

microbial community

niche width

co-occurrence network

In China, the cultivation of morel mushrooms has rapidly expanded, reaching an estimated 16,000 hectares during the 2021–2022 production season, with further growth anticipated ¹. However, the large-scale cultivation of morel has encountered significant challenges. Currently, morel cultivation is primarily conducted through two methods: field greenhouse cultivation and indoor layer cultivation ^2,3. Field greenhouse cultivation faces extreme weather conditions, such as strong winds, heavy rain, and heavy snow, which can destroy greenhouse facilities and significant financial losses for farmers ⁴. Additionally, due to environmental constraints, greenhouse cultivation is limited to once a year ². As a result, indoor cultivation is gaining popularity because it is not restricted by seasonal variations.

Morel predominantly exists as soil saprophytes, acquiring nutrients from the soil and the surrounding matrix ⁵. Therefore, morel cultivation will greatly affect the physical and chemical properties and microbial community structure of soil. Previous studies have demonstrated that the cultivation of morel can lead to a decline in soil pH, nutrient imbalances, and the secretion of autotoxic substances, contributing to continuous cropping obstacles ^6,7. Moreover, the changes in fungal and bacterial community structures in the soil caused by morel cultivation have garnered considerable attention ^5,8,9. However, there has been limited research on the effects of different morel varieties on soil physicochemical properties and microbial communities.

The relationship between soil physical and chemical properties and microorganisms in morel cultivation is complex and diverse. The growth and decomposition activities of morels positively affected the soil carbon and nitrogen cycles. Lohberger, et al. ¹⁰ observed a significant increase in organic carbon and total nitrogen content in the soil within morel-cultivated areas. The effects of soil pH, P, and K on the growth of morels have been the focus of several studies. Wassom ¹¹ found that morels were found in soils that were high in K and P, and near neutral pH. Additionally, Morchella esculenta cultivation in fallow paddy fields and drylands indicated that morel cultivation could enhance soil health and increase soil P and K ¹². Finally, Zhang, et al. ¹³ found that bed soils with low organic matter, N, and P contents but high K will likely support high production of morel. These studies collectively suggest that soil pH, N, P, and K play significant roles in morel growth and production. However, different morel varieties exhibit varying growth patterns, appearance, and soil nutrient requirements, leading to distinct effects on soil physical and chemical properties ^2,14–16.

The soil microbiome plays a pivotal role in morel cultivation during growth and development. Microorganisms have two functions. First, they facilitate the conversion of lignocellulosic raw materials into specialized nutrient-rich fertilizers essential for mushroom growth ⁵. Second, they can influence the induction of primordia and ascocarp formation ¹⁷. One study found evidence of bacterial farming by the fungus Morchella crassipes, indicating a symbiotic relationship between the fungus and bacteria ¹⁸. Research indicates significant differences in soil microbial diversity and composition among different morel varieties. For instance, Benucci, et al. ⁹ discovered a close association between the ascocarps of M. sextelata and microorganisms such as Pedobacter, Pseudomonas, Stenotrophomonas, and Flavobacterium. Conversely, the predominant bacteria in M. rufobrunnea soil are Proteobacteria and Bacteroidetes ¹⁹. Overall, the presence and interactions of bacteria with morels can significantly affect the growth and physiology of the fungus.

M. sextelata and M. septimelata are two popular morel species, each with distinct characteristics. M. sextelata is known for its high yield, while M. septimelata is recognized for its excellent disease resistance ^20,21. Therefore, this study selected these two species for indoor layer cultivation and compared the changes in soil physical and chemical properties and microbial communities after their cultivation. This research aims to enhance our understanding of how different morel varieties impact soil ecosystems and to provide insights for optimizing cultivation practices.

2.1 Changes in soil physicochemical properties

To better distinguish the changes in the soil physical and chemical properties after morel planting, we compared the soil before and after planting. Cultivation of M. sextelata and M. septimelata did not significantly affect the physical and chemical properties of the soil (Table 1). However, relatively higher soil OC, NN, AP, Ca, and K contents were observed after planting M. sextelata. Nevertheless, compared to the original soil (pH 6.46), the pH of the soil associated with both M. sextelata and M. septimelata exhibited a significant reduction of 15.3% and 13.5%, respectively.

Table 1

Alterations in soil physical and chemical properties.
Treatment	Yield	pH	OC (mg kg^− 1)	NN (mg kg^− 1)	AP (mg kg^− 1)	Elements
Treatment	(kg ha^− 1)	pH	OC (mg kg^− 1)	NN (mg kg^− 1)	AP (mg kg^− 1)	Ca (mg L^− 1)	K (mg L^− 1)	Mg (mg L^− 1)	Na (mg L^− 1)
Primitive soil	-	6.46 ± 0.14 a	12.1 ± 0.94 b	0.33 ± 0.16 b	11.2 ± 3.55 c	2.37 ± 0a	4.00 ± 0.98 c	35.9 ± 1.34 a	3.17 ± 1.24 a
M. sextelata	4045 ± 60.7 a	5.47 ± 0.07 b	21.2 ± 4.59 a	3.28 ± 0.71 a	26.0 ± 4.55 a	2.03 ± 0.90 a	27.7 ± 0.61 a	1.40 ± 0.09 b	2.63 ± 0.94 a
M. septimelata	3558 ± 44.1 b	5.59 ± 0.04 b	14.6 ± 3.72 ab	1.17 ± 0.59 b	18.7 ± 2.53 b	1.16 ± 0.44 a	25.7 ± 0.76 b	1.57 ± 0.00 b	2.74 ± 1.37 a
Note: OC, organic carbon; NN, nitrate nitrogen; AP, available phosphorus. The data presented arplicates ± SD. The letter indicates statistical significance statistically significant differences based on the LSD Fisher test with a $\:P<\:0.05$.

2.2 Soil microbial community diversity and structure

Following quality filtering, 494,834 high-quality 16S rDNA sequences and 560,802 high-quality ITS rDNA sequences were obtained from six soil samples. Each sample retained more than 20 sequences, resulting in the identification of 6,483 bacterial and 1,201 fungal OTUs. Three alpha diversity indices (Coverage, Chao, Ace, and Shannon) were employed to assess the coverage, richness, and diversity of the soil microbial communities.

The growth of morels influenced the alpha diversity of bacteria and fungi, with notable differences observed between M. sextelata and M. septimelata (Fig. 1A). Specifically, the Chao, Ace, and Shannon indices of fungi and bacteria associated with M. sextelata were higher compared to those of M. septimelata, suggesting greater microbial abundance in M. sextelata. Principal component analysis (PCA) of fungal genera indicated that samples from M. sextelata and M. septimelata tended to cluster together, signifying similarity (Fig. 1B). In contrast, the PCA results for the bacterial genera displayed considerable dissimilarity (Fig. 1C). These findings suggest divergent niches between M. sextelata and M. septimelata, with M. sextelata exhibiting a broader niche and more scattered sample distribution, while M. septimelata demonstrates a narrower niche with samples concentrated in closer proximity (Fig. 1D).

Understanding the interactions between morels and these microorganisms is crucial for the successful cultivation and preservation of morel populations. The composition of the soil microbial communities following the cultivation of M. sextelata and M. septimelata is depicted in Fig. 2. The predominant fungal phyla observed were Ascomycota, Mortierellomycota, and Basidiomycota, collectively representing > 98% of the total soil fungi (Fig. 2A). Moreover, both species belonged to Ascomycota, with M.sextelata displaying a higher abundance (89.7%) compared to M.septimelata (80.0%). The dominant bacterial phyla observed include Proteobacteria, Actinobacteriota, Chloroflexi, Bacteroidota, Acidobacteriota, Firmicutes, Gemmatimonadota, Myxococcota, Methylomirabilota, and Verrucomicrobiota (Fig. 2B), collectively representing over 75% of the total soil bacteria. Actinobacteriota was the most prominent phylum associated with M. sextelata, accounting for a relative abundance of 37.7%. Conversely, Proteobacteria emerged as the predominant phylum associated with M. septimelata, with a relative abundance of 42.8%. Furthermore, the distributions of other bacterial phyla associated with M. sextelata and M. septimelata exhibited notable differences. Specifically, Actinobacteriota, Chloroflexi, and Acidobacteriota were found to be more abundant in M. sextelata compared to M. septimelata. Conversely, the abundance of Proteobacteria displayed the opposite trend, being higher in M. septimelata than in M. sextelata. The soil fungal ITS1 region and bacterial 16S rRNA genes associated with M. sextelata and M. septimelata exhibited 425 and 2976 shared operational taxonomic units (OTUs), respectively. The shared OTUs represented species common to both M. sextelata and M. septimelata, accounting for 35.3% and 45.9% of the total 16S rRNA and ITS datasets, respectively (Fig. 2A and 2B).

2.3 Interactions between microbial taxa in the network

To elucidate connectivity within the bacterial and fungal communities, we computed all possible Pearson correlation coefficients among the top 200 abundant OTUs in both M. sextelata and M. septimelata, constructing a co-occurrence network. Comparing the bacterial and fungal network structures of M. sextelata and M. septimelata, the network associated with M. sextelata exhibited greater complexity and a higher number of links than that of M. septimelata (Fig. 3A and 3B). Specifically, the edges connecting soil bacteria and fungi in M. sextelata numbered 1,579 and 1,813, respectively, whereas in M. septimelata, they numbered 1,124 and 1,444, respectively.

Following OTU taxonomic analysis, we identified OTU1225 and OTU439 as being associated with M. sextelata and M. septimelata, respectively. Subsequently, we assessed the relationship between the top 50 and 100 bacterial and fungal OTUs and morels (Fig. 4). Compared to M. septimelata, M. sextelata has more connections with microorganisms. Among the identified OTUs, 7 bacterial and 3 fungal species were found to negatively affect M. sextelata. Notably, Devosia, Exophiala pisciphila, and Nectriaceae showed a significant negative correlation with the abundance of M. sextelata (|r| > 0.9, $\:P\:<\:0.05$). Conversely, four bacterial and four fungal species exhibited significant positive effects on M. sextelata (top 50 in abundance), including Marmoricola, Vicinamibacteraceae, Ascomycota, and Chordomyces antarcticus (|r| > 0.9, $\:P\:<\:0.05$). However, the top 50 bacteria seemed to not affect the growth of M. septimelata and were only associated with two fungi. Therefore, we expanded the scope and established a network diagram including the top 100 microorganisms. The connections between M. septimelata and microorganisms remain limited, with only one bacterium and three fungi having a negative impact, and one fungus having a positive impact. Pyrenochaeta and Pleosporales had a significant negative effect on M. septimelata (|r| > 0.9, $\:P\:<\:0.05$).

2.4 Functional Prediction Analysis

FUNGuild classified and analyzed the fungal community based on microecological guilds, functionally categorizing fungi using published literature or authoritative website data ²². The primary functions attributed to the soil of M. sextelata and M. septimelata were identified as saprophytic and pathogenic bacteria, collectively constituting over 70% of the total abundance in the predictive functional analysis (Fig. 5). Specifically, the contribution rates of saprophytic bacteria and pathogens associated with M. sextelata were 58.7% and 18.3%, respectively, whereas those associated with M. septimelata were 65.9% and 4.4%, respectively. M. septimelata demonstrates a positive impact on pathogen inhibition and organic matter decomposition.

2.5 Correlation analysis

The Mantel test and correlation analysis revealed a relationship between morel production and soil physical and chemical properties (Fig. 6). Nine variables were associated with the growth of the morels. Specifically, the growth of M. sextelata was positively correlated with Na but negatively correlated with pH, OC, NN, AP, Ca, and Mg. Conversely, the growth of M. septimelata showed a significant positive correlation with Mg and P ($\:P\:<\:0.01$), as well as a positive correlation with AP. Furthermore, correlations among soil physical and chemical properties were observed, with NN significantly positively correlated with OC, and pH significantly positively correlated with Mg ($\:P\:<\:0.01)$. Additionally, NN was significantly positively correlated with AP and K, while Mg was significantly negatively correlated with K and P ($\:P\:<\:0.05$).

Using the Spearman-based co-occurrence network, a significant correlation was observed between bacterial and fungal genera (relative abundance of the top 50 OTUs) and soil properties (Fig. 7). In this analysis, positive interactions were observed in bacterial and fungal systems, accounting for 37.2% and 60.5%, respectively ($\:\left|r\right|\:>\:0.5$, $\:P\:<\:0.05$). Within the bacterial system, P accounted for 28.6%, with pH, Mg, Ca, and Na accounting for 16.7%, 16.7%, 14.3%, and 11.9%, respectively. Notably, Stenotrophomonas exhibited significant negative correlations with P (r < -0.9, $\:P\:<\:0.05$), while Terrabacter and Vicinamibacteraceae showed significant positive correlations with P (r < 0.9, P$\:\:<\:0.05$). Notably, Stenotrophomonas may contribute to an increase in soil pH, with Devosia and Rhizobium tibeticum demonstrating similar effects on pH elevation. In the fungal system, AP accounted for 26.3%, with OC, P, Mg, Na, and NN accounting for 13.2%, 13.2%, 10.5%, 10.5%, and 10.5%, respectively. The four pairs of relationships are significantly correlated with each other ($\:\left|r\right|\:>\:0.88$, $\:P\:<\:0.05$). Trichothecium roseum was significantly positively correlated with Ca. P was significantly positively correlated with Chordomyces antarcticus, Myrmecridium, and Ascomycota (r < 0.88, $\:P\:<\:0.05$).

The study found that morel growth affects soil physical and chemical properties, including pH, C, N, and K ^23,24. Compared with the original soil, the soil OC, NN, AP, and other nutrients were improved after planting morel (Table 1). Previous studies have indicated that morel cultivation enhances soil nutrient availability and fosters soil health ^25,26. However, there was no significant difference in the physical and chemical properties of M. sextelata and M. septimelata. The growth of morel requires substantial amounts of carbon and nitrogen, with the mycelium decomposing animal and plant residues in the soil to obtain nutrients for its development ^10,27. Consequently, the organic matter in the planting layer soil increases after morel cultivation, aligning with previous studies ²⁸. It is reported that during the transition from mycelium to fruiting body, morel secretes phenolic acids, which may contribute to the observed decrease in soil pH ⁷. This acidic environment enhances the solubility of toxic elements, degrades soil structure, shifts microbial communities, and alters nutrient availability. Ultimately, this can lead to continuous cropping obstacles. What’s more, M. sextelata demonstrates a more pronounced effect on soil nutrient enhancement and pH reduction compared to M. septimelata, potentially contributing to the observed disparities in microbial communities between the two species.

The evenness of the soil microbial community was positively correlated with the yield of morel. In our study, the Chao, Ace, and Shannon values for both fungi and bacteria were higher in M. sextelata than in M. septimelata. Moreover, a narrower niche breadth is more advantageous in the habitat ²⁹. The niche breadth of M. sextelata was narrower than that of M. septimelata, indicating that the former was more dominant in the environment. However, niche narrowing may have adverse effects on the soil itself ³⁰. Additionally, certain microorganisms can significantly affect the growth of morels. For instance, Tan, et al. ⁸ showed that Chloroflexi can increase the yield of morels, while Bacteroidetes seem to have a detrimental effect.

Co-occurrence network analysis revealed that M. sextelata exhibited more connections with microorganisms compared to M. septimelata. For example, Devosia, a bacterium known to promote plant growth and enhance crop productivity, showed a negative correlation with M. sextelata in our study ³¹. This negative correlation might be attributed to competitive interactions between Devosia and M. sextelata. Marmoricola and Vicinamibacteraceae, on the other hand, demonstrated significant potential in environmental remediation applications. Marmoricola is a salt-tolerant bacterium, while Vicinamibacteraceae can passivate heavy metals and degrade harmful substances in the environment ^32–35. These microorganisms may play a crucial role in supporting the growth of M. sextelata by improving soil conditions and mitigating environmental stressors. Additionally, Chordomyces antarcticus had a positive effect on the growth of morels and is well adapted to alkaline environments, potentially aiding M. sextelata in better adapting to soil conditions ^36,37. Myrmecridium and Ascomycota, being close relatives, can promote morel growth ³⁸. The fewer connections observed between M. septimelata and microorganisms suggest that this species might rely more heavily on the physical and chemical properties of the soil rather than on direct microbial interactions. This is further supported by the FUNGuild analysis, which indicated a higher pathogen contribution rate in M. septimelata compared to M. sextelata. The higher pathogen presence could imply that M. septimelata has lower disease resistance, making it more vulnerable to adverse soil conditions.

The Mantel test and correlation analysis revealed significant links between morel growth and soil physical and chemical properties (Fig. 6), despite no significant differences in these properties between soils for the two morel species ($\:P\:<\:0.05$). These soil properties, however, play a substantial role in shaping microbial community structure and diversity, which in turn affects morel growth. For example, alterations in soil P levels can stimulate the growth of specific fungal groups with specialized phosphorus acquisition traits, thereby reshaping the microbial community ³⁹. Our analysis showed that Streptomyces, typically a pathogen, demonstrated a positive correlation with soil phosphorus. This suggests that while Streptomyces can be a pathogen, its presence is also associated with beneficial nutrient cycling in the soil ^12,40. The co-occurrence network based on Spearman correlation further highlighted these complex relationships. Within the bacterial system, Stenotrophomonas exhibited significant negative correlations with phosphorus (r < -0.9, $\:P\:<\:0.05$), despite its known plant growth-promoting properties ^41,42. This negative correlation might be due to competitive dynamics influenced by phosphorus availability. Conversely, Terrabacter and Vicinamibacteraceae showed significant positive correlations with phosphorus (r > 0.9, $\:P\:<\:0.05$), highlighting their roles in phosphorus cycling and availability. In the fungal system, Trichothecium roseum had a notable positive correlation with Ca, indicating a preference for calcium-rich environments.

4.1 Experiment description and sampling

Between May and August 2023, in Chengjiang City, Yunnan Province, China (102°90 E, 24°67 N, elevation 1740 m), an intelligent mushroom house was used to cultivate morels. To investigate changes in the physical, chemical, and microbial properties of different morel varieties after cultivation, we conducted indoor experiments using two widely planted morel varieties from Yunnan Province. Each treatment consisted of three biological replicates. The strains of edible fungi and exogenous nutrition bags were sourced from the edible fungi production base of Ningzhou Street, Huaning County, Yunnan Province. The culture medium comprised 85% wheat, 5% rice husk, 8% humus, 1% lime, and 1% gypsum with a water content of 60%. Similarly, the components of the exogenous nutrient bag consisted of 89% wheat, 10% rice husk, and 1% lime with a water content of 60%. Morel was cultivated using a layered frame, and the cultivation method followed that of previous studies ^43,44. Notably, the indoor temperature during the growth period was maintained below 20°C. Humidity was maintained between 60% and 65% during the mycelial growth stage and increased to 80–90% during the fruiting stage ⁴⁵. The mycelium period is maintained in complete darkness, while the light intensity during the fruiting body growth period is set at 500 lx ⁴⁶.

Additionally, the test soil was collected from nearby farmland consisting of 8.5% sand, 25.9% silt, and 35.6% clay. The soil was thoroughly mixed with gravel and plant residue for later use. Harvested soil was collected from the shelves using a five-point sampling method, with soil taken from a depth of approximately 10 cm. All samples were meticulously collected in sterile plastic bags and subsequently divided into at least two portions based on research requirements. One portion was carefully preserved at -80°C for DNA high-throughput sequencing analysis, while the other was air-dried for the detection and analysis of physical and chemical indices.

4.2 Soil property analysis

To determine the soil pH, a soil and water suspension at a ratio of 1:5 (w/v) was prepared. The nitrate nitrogen (NN) content in the soil was extracted using a potassium chloride solution and quantified by colorimetry ⁴⁷. Soil-available phosphorus (AP) was extracted using a sodium bicarbonate solution and its content was assessed using molybdenum antimony colorimetry ⁴⁸. The organic carbon (OC) content of the soil was determined using a total carbon analyzer (Multi N/C 2100S, Analytik Jena, Germany). Additionally, the contents of four elements (Na, Mg, Ca, and K) were extracted from the effective state using ammonium acetate and determined using an inductively coupled plasma emission spectrometer (ICPE-9820, Shimadzu).

4.3 Sample DNA extraction, sequencing, and analysis

The samples were processed and analyzed by Shanghai Majorbio Biomedical Technology Co., Ltd. The sequencing experiment followed these steps: DNA extraction, primer joint design and synthesis, PCR amplification and product purification, product quantification and homogenization, PE library construction, and Illumina sequencing ⁴⁹. First, genomic DNA was extracted from the samples and analyzed using 1% agarose gel electrophoresis to verify the extraction quality. DNA concentration and purity were then measured with a NanoDrop2000 spectrophotometer (Thermo Scientific, USA). PCR products were recovered using 2% agarose gel electrophoresis. Each sample underwent the formal experimental procedures in triplicate to ensure reproducibility. Finally, the purified PCR products were assessed and quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA) to ensure adequate DNA quantity and quality for sequencing. This comprehensive approach ensured the accuracy and reliability of the sequencing data obtained for subsequent analysis.

4.4 Statistical analysis and network construction

The analysis of variance (one-way ANOVA) and determination of the least significant difference (LSD) $\:(P\:<\:0.05$) for soil physical and chemical properties were conducted using the Excel plug-in XLSTAT (Version 2019.2.2).

Alpha diversity metrics, such as Chao 1 and Shannon indices, were calculated using Mothur software (http://www.mothur.org/wiki/Calculators) ⁵⁰. Differences between Alpha diversity groups were analyzed using the Wilcoxon rank-sum test. Principal coordinate analysis (PCoA) based on the Bray-Curtis distance algorithm was employed to assess the similarity of the microbial community structures among the samples.

Niche breadth encompasses an array of resources utilized by a population or other biological units within a community ⁵¹. A narrower niche for morels suggests a specialization species that is potentially disadvantaged in resource competition ^52,53, which is commonly employed to quantify the niche breadth of species. The specific calculation formula is as follows ⁵⁴:

$$\:{B}_{j}=\frac{1}{\sum\:_{i=1}^{N}\:{P}_{ij}^{2}}$$

Bj represents the niche breadth of OTUj within the community, where N is the total number of species present in the community and Pij denotes the relative abundance of OTUj within community i.

The Mantel test was performed using Qiime (2020.2.0) software to assess the correlation between morels and soil properties. Considering species abundance, morel’s data were analyzed using the Bray-Curtis algorithm, whereas environmental factors were evaluated using the Euclidean algorithm.

Network analysis was performed in Cytoscape (version 3.10.1) to explore the correlations between microorganisms at the phylum level and soil properties, utilizing Spearman correlations with a significance threshold of $\:P\:<\:0.05$ and an absolute correlation coefficient greater than |0.5|. Additionally, network analysis was employed to examine co-occurrence patterns between bacterial and fungal community members at the genus level, considering only those with average relative abundances exceeding 0.1%. Positive and negative associations between these community members were identified and visualized using network analysis ⁵⁵.

C: Carbon

N: Nitrogen

K: Potassium

NN: Nitrate nitrogen

AP: Available phosphorus

OC: Organic carbon

Acknowledgements

Not applicable.

Authors' contributions

X.L.: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. T.F.: Data curation, Formal analysis, Investigation, Writing – review & editing. Y.W.: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. W.L.: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – review & editing. L.T.: Investigation, Methodology, Validation, Writing – review & editing. B.C.: Validation, Visualization, Writing – review & editing. R.Y.: Methodology, Validation, Writing – review & editing. X.W.: Conceptualization, Supervision, Validation, Writing – review & editing. Y.Z.: Conceptualization, Validation, Writing – review & editing. B.Z.: Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing.

Data availability statement

The raw sequencing reads generated in this study have been deposited in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject) under accession code PRJNA1147851. The data shown in this study are provided in the Source Data file.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This research was funded by the Guizhou Provincial Major Scientific and Technological Program: YQK [2023] 024, Qian Agricultural Science Doctoral Fund (2024) No. 09, Qiankehe support [2022] 083, Qiankehe support [2023] 054, China Agriculture Research System CARS-20.

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No competing interests reported.

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Diversity and co-occurrence patterns of soil bacterial and fungal communities in two morels

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2 Results and discussions

3 Discussion

4 Materials and methods

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