Nitrates and Microbiome Components Engaged in Denitrification within Soil Regulate Morchella spp. Growth

Abstract

Morels (Morchella spp.) are a kind of rare and precious edible fungus and have been successfully cultivated in many places. Currently, the widespread cultivation of morels poses a significant challenge owing to their demanding environmental requirements. Soil properties and the soil microbiome are thought to play pivotal roles in morel growth. However, it remains unknown what factors exert a decisive influence on morel growth. In this study, soils with different morel yields were studied in nine sites from four locations. The basic soil physical and chemical properties were measured. In addition, the soil microbiome was analyzed using high-throughput metagenomic sequencing. We found that soil pH, nitrogen, carbon and conductivity were key indicators for the impact on microbial communities in soil for cultivating morels. Among these, nitrate was more positively associated with morel yield. The soil microbial diversity was more abundant in the soil with a high morel yield. Moreover, certain unknown archaea might be unfavorable to morel growth. The microbes that perform incomplete denitrification (no step of N₂O reduction to N₂) and nitrogen fixation were positively and negatively correlated with morel growth, respectively. In summary, morels prefer to live in nutrient-rich soils with a variety of microbes and are supported by nitrate and microbiome components involved in denitrification. The findings elucidate a pivotal mechanism in eliciting morel fructification and provide valuable insights for guiding production practices.

1. Introduction

Morels (Morchella spp.) are rare and precious edible fungi and are named as such because their pilei resemble a lamb tripe [1]. They are rich in amino acids, polysaccharides and trace elements, with high edible and medicinal value. There are few wild morels and their fruiting season is short, making them much more valuable. To meet demand, recently, morels have been successfully cultivated and commercially harvested in China [2], as well as in many places around the world [3]. Morels are planted by farmers as an important means to increase income. Sometimes, however, there are considerable differences the in yield of morels under the same management conditions between two adjacent plots [4]. This phenomenon has seriously restricted the promotion of morel cultivation at a large scale and the planting confidence of farmers. It is unknown why morels grown in consecutive zones and managed identically do not develop similarly and may exhibit significant differences in yield. Until now, no pertinent research has been able to explain this situation and identify the key drivers. It is generally believed that differences in soil properties are the main cause of this variation [5]. The soil environment has a significant impact on morels because they reside primarily in soil. Generally, nutrient bags are placed on the soil to support morel growth by providing a carbon and nitrogen source. Recently, a study found that the carbon used to promote morel growth initially came from exogenous nutrient bags to the soil, whereas the required nitrogen seemed to originate directly from the soil [6]. Unfortunately, the optimal carbon and nitrogen sources for the development of morels may vary due to differences in carbon and nitrogen concentrations, as well as morel strains [7]. Even so, the carbon and nitrogen in the soil play a vital role in morel growth. In addition, phosphorus and potassium are beneficial to the synthesis and accumulation of organic matter in morel cells, and a high content of mineral salts under normal growth conditions promote the formation of the sclerotium [8]. The optimal moisture of the soil should be controlled between 20 to 25% for general morel cultivation, showing medium, low and high levels in this moisture range, respectively, before spawning, during mycelium growth and in the fruiting body formation stage [8]. Morels are able to live under a broad range of soil pH and preferably grow in soil with a neutral pH [9]. All in all, it is crucial that the physical and chemical properties of soil determine the growth of morels.

On the other hand, the potential microorganisms in the soil may either promote or hinder the development of morels. In fact, as a kind of fungus, the developmental trajectory of morels is intricately intertwined with the indigenous microbiome residing in the soil [10,11]. The information from bacterial community structure within the pond soil of morels revealed that soil characterized by abundant bacterial populations and a sophisticated community structure fosters a propitious environment for mycelial proliferation and pseudo sclerotium formation [12]. Meanwhile, the bacterial abundance observed in a soil region where morels thrive was significantly greater compared to that in a soil region with poor morel growth [4]. However, the introduction of morel cultivation has been demonstrated to induce a discernible decline in the overall fungal diversity within the soil [13,14]. This might be due to a competitive relationship between morels and other fungi. Nevertheless, soil microbes may be a double-edged sword for morel cultivation, as certain soil microbes have the potential to facilitate the growth and development of morels, while others may substantially diminish the yield and quality of morels [15]. In summary, acquiring knowledge about the condition of soil microbes is of the utmost importance for the advancement of the morel industry.

Overall, it is of vital importance to understand the proper soil physical and chemical properties and soil microbiome for the successful cultivation and healthy growth of morels. Identifying the key driving factors in soil is important to ensure the widespread adoption of large-scale, correct cultivation management for morels. In the present study, therefore, we aimed to determine the physical and chemical properties of soil and the structure and function of the soil microbiome that play a key role in the growth and development of morels. To find these vital factors, soils with different morel growth conditions at nine sites from four locations were studied. Subsequently, a comprehensive analysis was conducted to elucidate the key factors pertaining to the physical and chemical properties of the soil and the suitable microbial conditions necessary for the growth of morels.

2. Materials and Methods

2.1. Experiment Description and Soil Sampling

This study investigated soil from cultivated morels sourced from 9 sites across 4 locations in Sichuan Province, China (Figure 1). A total 4 locations, including Jingyan village (J), Siweigan village (S), Chawan village (C) and Lujiaba village (L), were selected, showing variations in the morel yield. At the same location, two or three adjacent sites with different morel yields were investigated. The morel yields were categorized into 4 levels: barren (B), low (L), medium (M) and high (H). Therefore, this study investigated 9 treatments as following: JL, JB, SM, SL, CH, CM, CB, LH and LB (

Table 1. The details of nine sampling sites and their morel yields.

Treatments	Sampling Location	Morel Yield a	Yield Classification	Classification
		kg hm−2		Standard b
JL	Jingyan village (J)	800 ± 50	Low-yield fields (L)
JB		250 ± 100	Barren fields (B)
SM	Siweigan village (S)	4000 ± 300	Medium-yield fields (M)
SL		1200 ± 200	Low-yield fields (L)
CH	Chawan village (C)	9000 ± 500	High-yield fields (H)
CM		5000 ± 400	Medium-yield fields (M)
CB		500 ± 150	Barren fields (B)
LH	Lujiaba village (L)	8000 ± 500	High-yield fields (H)
LB		200 ± 40	Barren fields (B)

^a The morel yields of nine sampling sites were provided by farmers, including the highest, middle and lowest measured or estimated values. ^b The classification standard of morel yield (kg hm⁻²) was an empirical value based on production practice.

). The morel strain grown in these locations was exclusively the seven-sister Morchella sextelata. Soil samples of all sites were collected within 1 to 2 weeks after the complete harvest of the morels in the spring of 2023. After removing the uppermost layer of about 2 cm of soil, the rhizosphere soil within 2 to 8 cm was collected from six points (six replicates) at the same site. For testing basic soil physical and chemical properties, a part of each sample was air-dried, and another part was transferred to a −20 °C freezer. At each site, in addition, the soil samples from three points were thoroughly blended to create a composite soil sample, and two such composite soil samples were rapidly frozen with dry ice and then transferred to a −80 °C freezer for subsequent microbial analysis.

2.2. Soil Physical and Chemical Properties

Soil water content was measured by the drying method at 105 °C for 24 h. Ammonium (NH₄⁺), nitrite (NO₂⁻) and nitrate (NO₃⁻) were extracted from fresh samples with a potassium chloride (KCl) solution and analyzed using a continuous-flow analyzer (AA3, SEAL Analytical, Norderstedt, Germany). The fresh samples were extracted with deionized water, and the extract was used to determine the pH and electrical conductivity (EC) with calibrated electrodes (model FE20 and SG3; Mettler Toledo, Greifensee, Switzerland). The total carbon (TC) and total nitrogen (TN) of air-dried samples were measured using an elemental analyzer (FlashEA 1112 series, Thermo Fisher Scientific, Berlin, Germany). The available nitrogen (AN) was determined by diffusion after melting with sodium hydroxide (NaOH). The total phosphorus (TP) and available phosphorus (AP) were determined by molybdenum–antimony resistance spectrophotometry after melting with NaOH and sodium bicarbonate (NaHCO₃), respectively. The total potassium (TK) and available potassium (AK) were determined by flame spectrophotometry after melting with NaOH and ammonium acetate (NH₄C₂H₃O₂). The soil organic matter (SOM) was quantified using the loss-on-ignition method [16].

2.3. DNA Extraction and Metagenomic Sequencing

To obtain microbial information, the total genomic DNA in the soil was extracted using a FastDNA Spin Kit for Soil (MPbio, Santa Ana, CA, USA) according to the manufacturer’s protocols. The genomic DNA quality was detected by 1% agarose gel electrophoresis. The purity and concentration of genomic DNA were assessed using a NanoDrop 2000 spectrophotometer and TBS-380, respectively. The extracted DNA was fragmented, reaching an average size of approximately 400 base pairs, using a Covaris M220 instrument (Gene Company Limited, Hong Kong, China), to facilitate the construction of the paired-end library. Paired-end sequencing was performed on an Illumina NovaSeq platform (Illumina Inc., San Diego, CA, USA) using a NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles). All the above work was completed by Shanghai Majorbio Co., Ltd., Shanghai, China. Before data analysis, the paired-end Illumina reads were trimmed of adaptors, and low-quality reads were removed using fastp. Raw sequencing data were uploaded to the NCBI database with accession number PRJNA1063857.

2.4. Statistical Analysis and Visualization

The data on soil physical and chemical properties were preprocessed, analyzed and plotted using Microsoft Excel 2016, SPSS 26.0 and Origin 2021, respectively. The significant differences in soil physical and chemical properties among different treatments were determined using one-way and two-way analyses of variance (ANOVAs) with Fisher’s least significant difference (LSD, p < 0.05) test. Significant correlations between different soil physical and chemical properties were indicated by Pearson’s correlation coefficient (r, p < 0.05). The interactive analyses and visualization of metagenomic data were carried out on the Majorbio Cloud Platform (www.majorbio.com). The top 1% phyla and top 50 genera were visualized using a bar graph and hierarchically clustered heatmap, respectively. The Chao1, Shannon’s and Simpson’s indexes were used to evaluate the alpha diversity. The beta diversity of microbiome structure was calculated by principal coordinate analysis (PCoA) based on Bray–Curtis distances. A linear discriminant analysis of effect size (LEFSe, p < 0.05) was conducted to identify characteristic biomarkers. A correlation analysis of environmental factors was performed using redundancy analysis (RDA) and a correlation heatmap based on Spearman’s correlation coefficients.

3. Results

3.1. Soil Physical and Chemical Properties

The SOM, TC, TN, TP, TK, AN, AP, AK, NH₄⁺, NO₃⁻, NO₂⁻, pH, EC and water content, as soil physical and chemical properties, were measured in this study (

Table 2. Physical and chemical properties of soil from nine sampling sites.

Treatments	SOM	TC	TN	TP	TK	AN	AP	AK	NH4+	NO3−	NO2−	pH	EC	Water
	g/kg	g/kg	g/kg	g/kg	g/kg	mg/kg	mg/kg	mg/kg	mg/kg	mg/kg	mg/kg		μg/ms	%
JL	27.5 ± 2.8 a	18.2 ± 1.3 b	1.8 ± 0.2 a	0.7 ± 0.06 b	17.3 ± 1.0 ab	150.0 ± 15.7 b	31.9 ± 5.5 b	127.8 ± 2.6 cd	12.8 ± 0.7 cd	14.9 ± 2.6 cde	0.1 ± 0.02 a	7.2 ± 0.3 d	62.5 ± 18.9 cd	20 ± 2 cd
JB	23.5 ± 3.3 ab	16.2 ± 0.6 b	1.6 ± 0.1 ab	0.54 ± 0.05 b	16.6 ± 0.7 ab	129.6 ± 15.3 bc	20.2 ± 2.1 cd	108.1 ± 7.2 cd	14.3 ± 1.4 bc	6.2 ± 1.7 e	0.07 ± 0.02 b	7.6 ± 0.4 bc	131.9 ± 67.0 bc	27 ± 2 a
SM	28.4 ± 1.6 a	18.7 ± 1.6 b	1.7 ± 0.1 ab	0.86 ± 0.07 b	12.5 ± 0.5 c	125 ± 3.12 bc	33.0 ± 7.2 b	301 ± 45.7 b	10.3 ± 1.0 de	11.3 ± 0.3 cde	0.1 ± 0.02 a	7.4 ± 0.7 cd	117.5 ± 22.4 bcd	24 ± 2 ab
SL	27.3 ± 1.1 a	16.2 ± 0.7 b	1.6 ± 0.2 ab	0.85 ± 0.09 b	12.4 ± 0.9 c	120.5 ± 6.3 c	27.1 ± 6.0 bc	270.2 ± 31.8 b	9.8 ± 0.1 e	11.8 ± 0.9 cde	0.1 ± 0.02 a	7.5 ± 0.1 cd	111.7 ± 12.5 bcd	22 ± 1 bc
CH	23.1 ± 2.5 ab	24.5 ± 0.9 a	1.8 ± 0.2 a	1.74 ± 0.64 a	15.9 ± 0.5 b	179.1 ± 30.4 a	83.5 ± 10.5 a	1189.5 ± 101.5 a	12.0 ± 0.8 cde	26.0 ± 11.5 ab	0.11 ± 0.02 a	7.9 ± 0.2 ab	310.8 ± 33.72 a	21 ± 1 bc
CM	24.4 ± 2.6 ab	23.1 ± 1.4 a	1.6 ± 0.2 ab	0.6 ± 0.02 b	16.6 ± 0.8 ab	133.7 ± 8.9 bc	22.9 ± 6.7 bcd	158.1 ± 46.8 c	12.8 ± 1.2 cd	17.5 ± 6.2 bc	0.11 ± 0.01 a	8.1 ± 0.2 a	308.3 ± 79.3 a	27 ± 3 a
CB	26.7 ± 3.5 a	25.5 ± 2.7 a	1.8 ± 0.2 a	0.67 ± 0.03 b	16.2 ± 0.4 ab	144.1 ± 10.8 bc	13.2 ± 0.6 d	133.0 ± 18.9 cd	15.8 ± 3.8 b	16.7 ± 8.1 bcd	0.11 ± 0 a	8.1 ± 0.1 a	175.3 ± 39.7 b	27 ± 1 a
LH	23.9 ± 0.5 ab	15.4 ± 0.7 b	1.4 ± 0.1 b	0.83 ± 0.01 b	17.9 ± 1.9 a	145.2 ± 14.7 bc	19.2 ± 5.9 cd	82.6 ± 8.7 cd	22.3 ± 1.1 a	31.4 ± 0.2 a	0.11 ± 0.01 a	8.0 ± 0.1 a	110.2 ± 8.7 bcd	20 ± 3 c
LB	19.9 ± 3.9 b	13.9 ± 3.1 b	0.8 ± 0.3 c	0.57 ± 0.23 b	16.3 ± 2 ab	79.3 ± 21.0 d	13.0 ± 1.5 d	60.4 ± 5.2 d	16.3 ± 0.5 b	7.0 ± 0.2 de	0.11 ± 0 a	8.0 ± 0.1 ab	50.9 ± 6.5 d	16 ± 1 d
Location	0.376	<0.01 **	<0.01 **	0.011 *	<0.01 **	<0.01 **	<0.01 **	<0.01 **	<0.01 **	0.198	0.076	0.01 *	<0.01 **	<0.01 **
Yield	0.802	0.925	0.034 *	<0.01 **	0.554	<0.01 **	<0.01 **	<0.01 **	0.308	<0.01 **	0.39	0.276	<0.01 **	<0.01 **
Location * Yield	0.089	0.091	0.011 *	0.014 *	0.33	0.142	<0.01 **	<0.01 **	<0.01 **	0.02 *	0.444	0.211	<0.01 **	<0.01 **

One-way and two-way ANOVAs were carried out in this study; Different letters (a to e) within a column indicated significantly difference (p < 0.05); * and ** indicate statistical significance at 5% and 1%, respectively. JL, JB, SM, SL, CH, CM, CB, LH and LB were named based on location names (Jingyan, Siweigan, Chawan and Lujiaba villages) and morel yield (high, middle, low and barren yields), respectively. SOM: soil organic matter; TC: total carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: available nitrogen; AP: available phosphorus; AK: available potassium; NH₄⁺: ammonium; NO₃⁻: nitrate; NO₂⁻: nitrite; EC: conductivity.

). Across various research sites and different morel yields, there were significant differences in TN, TP, AP, AK, EC and water content (p < 0.05), but there were no significant changes in SOM or NO₂⁻. TC, TK and pH showed significant differences only among different research sites (p < 0.05). The AN showed significant differences across various research locations or different morel yields (p < 0.05), rather than their interaction. NH₄⁺ showed significant differences across various research locations and interactions (p < 0.05), whereas NO₃⁻ showed significant differences among morel yield and interactions (p < 0.05). The correlation analysis (Figure 2) indicated that the core indicators of significant correlation were EC (11), TC (10), NO₃⁻ (10), TP (9), AP (9), TN (8), AN (8), AK (8), pH (8) and morel yield (8). In addition, the morel yield was positively correlated with TP, AN, AP, AK, NO₃⁻, NO₂⁻, pH and EC.

3.2. Soil Microbiome Community Structure and α Diversity from Different Sites

The soil microbiomes under Morchella sextelata cultivation were obtained from nine sites, from which five domains, 12 kingdoms, 228 phyla, 434 classes, 828 orders, 1598 families, 4813 genera and 26,918 species were identified. Bacteria and archaea were the main microorganisms in soils where morels were grown (Supplementary Table S1). For all research sites, Proteobacteria, Actinobacteria and Acidobacteria were the most dominant phyla, with abundances exceeding 10%, followed by Chloroflexi, Candidatus, Rokubacteria, Thaumarchaeota, Gemmatimonadetes, Nitrospirae and Planctomycetota, with abundances exceeding 1% (Figure 3a). It can be seen, however, that there were differences in soil microbiome structure across various research sites. This difference was more obvious among the different locations. Likewise, α diversity varied from site to site (Figure 3b). The Shannon and Simpson diversities were positively correlated with morel yield, whereas the Chao1 estimator was not. Furthermore, lower values of the Chao1 estimator were observed in the LH (10,400) and LB (10,116) samples. However, LH had the highest Shannon values (5.55) and the lowest Simpson values (0.020), and LB had the lowest Shannon values (5.11) and the highest Simpson values (0.038). In addition, PCoA analysis based on Bray–Curtis distances revealed that microbial community structure exhibited more similarities among JL, JB, SM and SL; among CH, CM and CB; and between LH and LB (Figure 3c).

3.3. Key Microbial Biomarkers Associated with Different Growth Conditions of Morels

An LEFSe analysis was performed to discern the characteristic biomarkers of soil microbiomes associated with four morel yield levels (H, M, L and B groups). A total of 26 bacterial, 6 archaeal and 37 fungal biomarkers (LDA > 3) were identified (Figure 4). The bacterial biomarkers for the H, M, L and B groups were mainly from Proteobacteria, Actinobacteria, Acidobacteria and Chloroflexi, respectively. The archaeal biomarkers all came from the B group, whereas the fungal biomarkers were complex. Better biomarkers (LDA > 4) were found only in bacteria.

3.4. Relationship between Soil Physical and Chemical Properties and Microbiome Community

An RDA analysis was performed to uncover the relationship between soil physical and chemical properties and soil microbial communities from nine research sites (Figure 5a). There was a total of 66.4% explanation for the variation by two main dimensionalities. The indicators of physical and chemical properties that significantly affected the microbial communities were pH (R², 76.3%), TN (R², 71.6%), C/N (R², 71.4%), TC (R², 66.8%) and EC (R², 66.1%). It also can be seen that there was the strongest positive correlation between morel yield and NO₃⁻ content. In addition, a correlation heatmap based on Spearman’s correlation coefficients (p < 0.05) was created to further elucidate the correlation between soil microbial communities (top 30 phyla) and soil physical and chemical properties (Figure 5b). Most of the soil microbiome at the phylum level was correlated with the TC indicator. The effects of soil NO₃⁻ concentration on morel yield and soil microbiome were consistent. They were positively correlated with Actinobacteria, Candidatus_Tectomicrobia and Firmicutes and negatively correlated with Acidobacteria, Candidatus_Eisenbacteria and candidate_division_NC10.

3.5. Nitrogen Cycling in Soil Microbiomes from Different Sites

All the functional microbes involved in the nitrogen cycle were selected from metagenomic data and classified as nitrification, denitrification, dissimilatory nitrate reduction, assimilation nitrate reduction and nitrogen fixation microbes (Figure 6). The microbes associated with denitrification were the most abundant at almost all research sites according to the RPKM, followed by dissimilatory nitrate reduction, assimilation nitrate reduction, nitrification and nitrogen fixation. However, there were some differences in the abundance of these functional microbes among different research sites, especially among different research locations. Overall, the abundances of narH/Y/G/Z- and nxrA/B-type microbes that performed the conversion between nitrate and nitrite were the highest. However, the morel yield was only positively correlated with nirK-type microbes. Of course, the abundance of nirK-type microbes was also relatively high. On the contrary, the morel yield was negatively correlated with the nrfA-, norC-, nosZ- and nifK/H/D-type microbes. Here, the functional microbes of the nirK type and nosZ type and the functional microbes of the nifK/H/D type can perform denitrification and nitrogen fixation, respectively. In addition, they were closely associated with morel yield. However, only nirK- and nosZ-type microbes were more abundant.

4. Discussion

4.1. Potential Mechanisms of Soil Microbiome Impact on Morel Growth

There is a complex relationship between soil microbiome and morel growth on whether natural or artificial conditions [3]. In this study, the differences in the diversity, community structure and composition of soil microbiomes between different research locations were more pronounced than those between different sites in the same location. This is because the soil niche formed by climate, topography, soil type and other factors determines the distribution and diversity of microbiome in soil [17,18]. Within a given soil habitat, furthermore, there are discrepancies in biogeochemistry that determine the type of microbiome that is present [19]. Therefore, there was a greater degree of similarity in microbial community structure among JL, JB, SM and SL (Tianfu New District, Chengdu); among CH, CM and CB (Jintang District, Chengdu); and between LH and LB (Qingzhou District, Guangyuan). Previous studies have observed changes in the structure and composition of the microbiome due to the presence of morels [4,15]. The correlation analysis indicated that there was a positive correlation between α diversity and morel yield. Furthermore, higher morel yields corresponded to greater soil microbial diversity at different sites in the same location. These results suggested that a rich soil microbial community may contribute to the morel growth. In addition, Proteobacteria, Actinobacteria and Acidobacteria were the main constituents of the soil microbiome in all research locations, accounting for more than 70%. They are abundant in the soil, regardless of whether morels are grown [4,20]. At the phylum level, therefore, it is difficult to directly identify a harmful or beneficial soil microbiome for morel growth. However, a total of 26 bacterial, 6 archaeal and 37 fungal biomarkers (LDA > 3) associated with morel yields were identified through LEFSe analysis at the genus level. In particular, six archaeal biomarkers were found only in soil with a barren morel yield (Figure 4c). In addition, there was a better biomarker (LDA > 4) belonging to bacterial Acidobacteria in the low-morel-yield soil. Unfortunately, these biomarkers have not been classified due to the presence of numerous unknown microbiomes in soil [21]. In summary, the soil microbial diversity is higher in the soils with high morel yields. However, certain unknown archaea might be associated with barren morel yields.

4.2. Relationship between Soil Microbiomes, Soil Physical and Chemical Properties and Morel Growth

The soil physical and chemical properties varied among the different research sites in this study and are intrinsically linked to the soil microbiome [22,23], as well as morel growth [10]. These differences can be attributed to the fact that the physical and chemical properties of soil at different research sites have high spatial variability, which leads to microorganisms developing appropriate strategies to deal with them [11]. According to RDA analysis, the soil pH, TN, C/N, TC and EC were the main indicators of impact on microbial communities. In addition to EC, these indicators have proven to be the most important factors in numerous studies aimed at elucidating the inherent interplay between the soil microbiome and its environment [22,23]. At a global scale, topsoil pH plays a key role in interpreting soil bacterial communities based on metabarcoding analysis [20]. Likewise, soil microorganisms are involved in the conversion of carbon and nitrogen to shape their habitat [24]. However, EC, as an indicator of inorganic nutrients, is less studied and requires attention. These factors were also the main indicators of correlation with other physical and chemical properties of the soil and morel yield in this study. Furthermore, TC, TN, TP, AN, AP, AK, NO₃⁻, EC and pH were significantly correlated with morel yield (Figure 2). Notably, only NO₃⁻ was significantly associated with morel yield and did not significantly change between research sites (Table 1). The RDA analysis also revealed that NO₃⁻ content and morel yield exerted a consistent influence on the soil microbiome (Figure 5a), suggesting that NO₃⁻ and morels have similar functions or requirements in shaping soil habitats. Previous reports also showed that NO₃⁻ has a positive influence on morel fructification, whereas NH₄⁺ has a negative influence on mycelial growth [25,26]. In another study on various nitrogen sources, sodium nitrate was also identified as an optimal nitrogen source for morel cultivation [27]. Therefore, we speculate that soil NO₃⁻ content is a crucial indicator of morel growth. Notably, soil moisture, a key indicator of morel growth, did not show a significant correlation with morel yield. This may be related to the fact that the soil moisture was measured after morel harvest, and the optimal soil moisture requirements are different at different stages of morel growth. Nevertheless, soil moisture was adequate, even in high-yield fields, at around 20% (Table 1, approx. 60% WFPS). We speculate that soil moisture will be higher during the growth period due to water management measures, tending to create anaerobic conditions that favor denitrification [28]. Therefore, creating suitable conditions for denitrification is crucial for morel growth.

4.3. Microbial Mechanisms of Nitrogen Cycling Associated with Morel Growth

As mentioned above, numerous studies have demonstrated the pivotal role of nitrogen nutrients and various nitrogenous forms in the morel fructification. Studying the soil microbiome components engaged in nitrogen cycling represents a potent avenue to unravel the underlying mechanism of this phenomenon, but its potential remains largely untapped. Yu et al. [26] performed some work on this aspect, revealing that microorganisms engaged in nitrogen fixation and nitrification were associated with morel production. In our study, however, we found that morel yield was closely related to the microorganisms involved in denitrification and nitrogen fixation (Figure 6). The main reason for this difference in nitrification and denitrification is that the assumed functions of certain microorganisms were used by Yu et al. [26] and the actual expression functions of these microorganisms were used in our study. For instance, Nitrospira were rich in the work of Yu et al. [26] and in our study, and they were reported by Yu et al. [26] to be nitrifying microorganisms but in reality exhibited the capacity for both nitrification carried out by amoA-type microbes and denitrification carried out by nirK-type microbes [29,30]. The present investigation revealed a rich abundance of nirK-type microbes, showcasing a positive correlation with NO₃⁻ content and morel yield, thus indicating the pivotal role of microbial denitrification in morel growth [31]. Notably, the nosZ-type microbes that perform N₂O reduction to N₂ were abundant and negatively correlated with morel yield, further suggesting that denitrifying microorganisms that perform incomplete denitrification (no last step of N₂O reduction to N₂) are more conducive to morel growth. In addition, nitrogen fixation carried out by nifK/H/D-type microbes was poor and negatively corelated with morel yield in our study. However, the bacterial microbes involved in nitrogen fixation were found in soils with high morel yields [26]. In fact, microorganisms capable of nitrogen fixation tend to thrive as pioneers in poor soils rather than rich soils [10]. Therefore, we speculate that morels thrive in nutrient-rich soils and that nitrogen fixation carried out by microorganisms has a negative impact on morel growth but is essential [32]. Overall, for morels to thrive, it is essential to increase microbes that perform incomplete denitrification and decrease microbes involved in nitrogen fixation.

5. Conclusions

In this study, the physical and chemical properties of soil were inherently linked to the soil microbiome, including morels. pH, TN, TC, C/N and EC were the primary soil properties in shaping soil microbial habitats. In addition, nutrients such as TP, AN, AP, AK and NO₃⁻ were crucial for morel growth or habitat development, with NO₃⁻ levels being the most important factor influencing morel yield. Meanwhile, the microbiome components that perform incomplete denitrification served to further encourage morel mushrooms to thrive. Furthermore, the soil microbiome components involved in nitrogen fixation were sparse, but their presence still had an impact on morel development. In addition, rich microbial diversity promoted morel proliferation, but certain archaea species might hinder their growth. In summary, morels seem to prefer to live in nutrient-rich soils with a variety of microorganisms and be supported by NO₃⁻ and microorganisms involved in incomplete denitrification. In the future, we should further confirm this conclusion through controlled experiments and seek the best conditions to provide support for the healthy growth of morels.