Characteristics and correlation of the microbial communities and physicochemical factors from pit mud of Strong-flavor Baijiu

Characteristics and correlation of the microbial communities and physicochemical factors from pit mud of Strong-flavor Baijiu | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characteristics and correlation of the microbial communities and physicochemical factors from pit mud of Strong-flavor Baijiu Kaixian Zhu, Qin Xiao, Siqi Yuan, Jun Liu, Hongwei Shang, Mingyi Guo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6186207/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract To gain a deeper understanding of the role microorganisms play in shaping the quality of baijiu, this study comprehensively examined the microbial communities and physicochemical characteristics of pit mud (PM) sourced from various ages (20, 30, 40, and 50 years) and distinct spatial layers, as well as explored the correlations between these factors. Fungal communities in PM had lower diversity and richness than prokaryotic microorganisms. Microbial diversity decreased with increasing PM age at the same spatial location. In the same aged PM, the middle layer had higher prokaryotic microbial diversity, while the upper and middle layers had higher eukaryotic microbial diversity. Firmicutes and Ascomycota are dominant prokaryotic and eukaryotic phyla, respectively. At the genus level, 19 prokaryotic and 16 eukaryotic dominant genera were detected. Lactobacillus was predominant in the upper and middle layers, and Caldicoprobacter was more abundant in 20- and 30-year-old PM. Among eukaryotes, Kazachstania dominated in younger PM, and Priceomyces was unique to 50-year-old PM. Physicochemical analysis showed that moisture and available phosphorus increased, while pH and ammonia nitrogen decreased with PM age. Significant differences in pH and ammonia nitrogen were observed among spatial locations. Spearman correlation analysis indicated that Methanobacterium and Thermoascus abundance were significantly correlated with pH, ammonia nitrogen, and available phosphorus. The research reveals notable spatial and temporal differences in microbial taxa and their influence on physicochemical indices. It offers valuable insights for the management of pit mud (PM) and the production of high-quality baijiu, underscoring the importance of considering spatial factors and optimizing fermentation performance. Biological sciences/Biological techniques Biological sciences/Microbiology pit mud high-throughput sequencing prokaryotic and eukaryotic microorganisms physicochemical factors correlation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Strong-flavor Baijiu (SFB), renowned for its strong aroma, sweet taste, and smooth finish, is a highly esteemed Chinese distilled liquor [ 1 ] . Its unique flavor profile arises from a distinctive fermentation mash called pit mud (PM) [ 2 ] . PM serves as a culture medium for the proliferation and metabolism of microbial communities, and conversely, these microbial communities and their metabolic products are the main reasons for altering not only the textural parameters but also physicochemical indicators of the mash. Therefore, studying the microbial communities and metabolism in the PM is crucial for revealing the aroma formation mechanisms of SFB [ 3 , 4 ] . Early research on PM microorganisms focused on identifying key microbial players involved in flavor development. The discovery of caproic acid-producing bacterium, Clostridium butyricum, in Maotai PM marked a significant milestone, establishing a link between specific microorganisms and the unique aroma profile of the liquor [ 5 , 6 ] . Due to the extended fermentation period of PM, there is a distinct succession of microbial communities across different stages of fermentation. However, studying the static distribution of microbial communities at a single stage does not reflect the entire process of baijiu fermentation. Therefore, it is particularly important to analyze the community structure of dominant microbial groups at various fermentation stages. For example, during the initial stages of fermentation, aerobic and facultatively anaerobic microorganisms prevail in PM. As fermentation progresses, the aerobic microbes gradually decline while anaerobic and facultatively anaerobic bacteria continue to proliferate. In the middle and later stages, lactic acid bacteria and anaerobic bacteria become dominant, leading to a reduction in microbial diversity in PM [ 7 – 9 ] . Meanwhile, PM is situated at varying depths and environmental conditions within the cellar. For instance, the base of the PM is perpetually submerged in yellow liquid, creating an anaerobic environment, while sections of the cellar wall are in contact with air and transition to an aerobic state after sealing. Consequently, the microbial composition of PM serves as a pivotal bioindicator for evaluating the quality and condition of PM, enabling the rapid and reliable differentiation and grading of PM maturity. However, the influence of PM location on microbial communities and their impact on the aroma of SFB has not been extensively studied. To clarify the differences and correlations between microbial diversity and the physicochemical properties of PM of varying ages and strata, this study was conducted to analyze the microbial community composition within the layers of 20-, 30-, 40-, and 50-year-old PM samples from a Luzhou distillery, encompassing the upper, middle, and bottom layers. The primary objectives were to investigate the disparities and interrelations between microbial diversity and the physicochemical characteristics of PM, providing insights into the effects of aging and stratification on the microbial ecology and quality of PM. 2. Materials and Methods 2.1 Sample collection PM samples were collected from four distinct fermentation cellars at Luzhou Kangqingfang Wine Industry, each with an age of 20, 30, 40, and 50 years. For each age group, three cellars were randomly selected to serve as parallel samples. Aseptic sampling was conducted from the upper, middle, and bottom layers of each cellar, with the specific sampling locations depicted in Fig. 1 . All samples were sealed in sterile bags and stored at -80℃ for subsequent analysis. 2.2 Determination of physicochemical factors The moisture content was detected using a gravimetric method after drying at 105 ± 5 ◦C drying to constant weight[10]. The pH was measured at a 1:3 (wt/vol) ratio in ultrapure water. The mixture was vortexed for 30 seconds and then centrifuged at 12,000 r/min for 10 minutes. T the supernatant was subsequently determined using a pH meter (PHS-3D, Shanghai San-Xin Instrumentation Inc, China)[11].Determination of available phosphorous (available P) was determined by ammonium fluoride (1 mol/L)-hydrochloric acid (0.5 mol/L) colorimetric method[12]. 1 mL of fresh PM filtrate was transferred into the colorimetric tube and diluted to 50 mL of volume with distilled water, followed by the addition of 1 ~ 2 drops of sodium potassium tartrate solution and 1 mL of Nessler’s reagent. After standing for 10min, the concentration of ammonium nitrogen (NH4 +-N) was detected by ultraviolet–visible spectrophotometry (T2602, Shanghai Yoke Instruments Meters Co.,ltd, China) at the wavelength of 425 nm [12]. 2.3 Microbiome sequencing The DNeasy® PowerSoil® Pro Kit was utilized for the extraction of total DNA from the samples, following the protocol provided in the kit documentation. The quality of the extracted genomic DNA was assessed using 1% agarose gel electrophoresis, while the concentration and purity were measured using a NanoDrop 2000 spectrophotometer. Samples meeting the sequencing requirements in both quality and concentration were stored at -80°C. High-throughput sequencing was conducted using the Illumina MiSeq platform at Shanghai Meiji Bio. For bacterial and archaeal 16S rRNA gene sequencing, the V4 variable region was amplified using the universal primers 515FmodF/806RmodR (5'-GTGYCAGCMGCCGCGGTAA-3'/5'-GGACTACNVGGGTWTCTAAT-3'). For fungal ITS1 region sequencing, the primers ITS1F/ITS2R (5'-CTTGGTCATTTAGAGGAAGTAA-3'/5'-GCTGCGTTCTTCATCGATGC-3') were employed. The amplification procedure involved an initial denaturation at 95°C for 3 minutes, followed by 35 cycles (27 cycles for eukaryotes) consisting of denaturation at 95°C for 30 seconds, annealing at 55°C for 30 seconds, and extension at 72°C for 30 seconds. This was followed by a final extension at 72°C for 10 minutes, and the reaction was stored at 10°C until completion. The PCR products were mixed with an equal volume of 1x TAE buffer and subjected to electrophoresis using a 2% agarose gel for band detection. PCR products were pooled at equidensity ratios. Subsequently, the PCR mixtures were purified using the Qiagen Gel Extraction Kit (Qiagen, Germany). Sequencing libraries were constructed using the TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) according to the manufacturer's instructions, with the addition of index codes. The library quality was evaluated using the Qubit® 2.0 Fluorometer (Thermo Scientific). Finally, the libraries were sequenced on the Illumina NovaSeq platform, yielding 250 bp paired-end reads. 2.4 Bioinformatics and statistical analysis Amplified sequence variants (ASVs) were classified using the QIIME2 naive Bayes classifier, which was trained on 99% operational taxonomic units (OTUs) against the SILVA database (version 138) [ 13 ] . The taxonomy was constructed by aligningASVs to a pre-trained GREENGENES 13_8 99% reference database using the QIIME2 feature-classifier plugin [ 13 ] . Microbial diversity was assessed using alpha-diversity metrics, specifically the Chao index. Community composition was analyzed using beta-diversity, as implemented in the q2-diversity pipeline within QIIME2. To identify taxa that exhibited significant differences (p < 0.05) among treatments, the linear discriminant analysis effect size (LEfSe) was employed. For principal coordinates analysis (PCoA), PERMANOVA (Adonis function with 999 permutations) was utilized to evaluate the bacterial community composition based on the Bray–Curtis dissimilarity metric. Physical and chemical indicators were processed and analyzed using IBM SPSS Statistics 27 and Excel 2019. The correlation between each indicator was compared, and the correlation heat map was drawn using ChiPlot ( https://www.chiplot.online/ ) (accessed on 26 January 2025). 3. Results 3.1 Analysis of differences in physicochemical factors As shown in Fig. 2, the moisture and available phosphorus content exhibited an increasing trend with advancing age, whereas the pH and ammonia nitrogen content demonstrated a corresponding decline. For moisture content, no statistically significant differences were observed among samples from different years in the upper and bottom layers. However, in the middle layer, moisture content was significantly higher in the 50-year-old samples compared to those from 20- and 30-year-old samples (P = 0.032 and P = 0.014, respectively). Across all samples with different durations, a consistent trend was observed in the vertical distribution of moisture content, with the highest values in the middle layer, followed by the bottom layer, and the lowest values in the upper layer. Moisture content not only influences other physicochemical properties, such as pH, but also affects microbial growth in PM [ 12 ] . Moreover, it serves as an indicator of PM maturity, with moisture content > 40% being associated with mature PM [ 12 ] . In this study, the moisture content in the upper layer of 20-year-old PM was lower than 40%, which may impact the compactness and anaerobic conditions of the PM (Fig. 2). For pH values, distinct temporal trends were observed across different layers. In the upper layer, pH significantly decreased with increasing age, with samples from 30-, 40-, and 50-year-old groups exhibiting significantly lower pH values compared to the 20-year-old group. In the middle layer, a similar decreasing trend was observed; however, no statistically significant differences were detected among samples from different years. In the bottom layer, the pH of the 50-year-old samples was significantly lower than that of the 20-year-old samples ( P = 0.016). Across all samples with different durations, a consistent vertical distribution of pH was observed, with the lowest pH values typically found in the middle layer, followed by the bottom layer, and the highest pH values in the upper layer (Fig. 2). The low pH value facilitates the enrichment of acid-tolerant or acidophilic bacteria [ 14 ] . Additionally, pH significantly influences the formation of ethanol and other flavor precursors [ 12 ] . Previous studies have reported that the pH in the bottom layer of PM is higher than that in the upper and middle layers. However, this trend was not observed in the current study. Instead, the pH in the middle and bottom layers of PM exhibited an increasing trend from 30 to 40 years of fermentation, followed by a decrease from 40 to 50 years. Compared to 10-year-old PM, the pH values of 30- and 50-year-old samples were significantly higher, with no significant difference between the 30- and 50-year-old samples [ 11 ] . These findings suggest that microbial metabolism varies distinctly across different fermentation systems, ultimately influencing the quality of the final product. Ammonium nitrogen is essential for microbial growth and for the synthesis of a variety of enzymes and proteins. The maintenance of optimal ammonium nitrogen concentrations is crucial for preserving the quality of PM and, consequently, for enhancing the overall quality of liquor [ 15 ] . In the present study, the ammonia nitrogen content exhibited a decline with increasing fermentation duration. In the upper and middle layers, the 20-year-old samples had significantly lower ammonia nitrogen content compared to those from the 30-, 40-, and 50-year-old samples (P < 0.05). A similar trend was observed in the bottom layer, where the 20-year-old samples had significantly lower ammonia nitrogen content than the 50-year-old samples ( P = 0.039, Fig. 2). Across samples of different durations, a consistent vertical gradient was observed, with the highest ammonia nitrogen content in the bottom layer and the lowest in the upper layer. These findings are consistent with previous results [ 16 , 17 ] , who suggested that these factors contribute to the habitat preferences of specific microbial species, including Lactobacillus acetotolerans (LA), Anaeromassilibacillus senegalensis (AS), Clostridium kluyveri (CK), Clostridium luticellarii (CL), Proteiniphilum saccharofermentans (PRS), Petrimonas sulfuriphila (PS), and Clostridium limosum (CLL) [ 16 ] . The available phosphorus content exhibited substantial temporal and spatial variability. Initially, it decreased with time before subsequently increasing. In the upper layer, significant differences in available phosphorus content were observed across each decade ( P < 0.01). Intriguingly, spatial heterogeneity was also evident within the samples, with available phosphorus content increasing with proximity to the bottom and under more anaerobic conditions. For instance, in the 20-year-old samples, available phosphorus content in the upper, middle, and bottom layers was 73.37, 116.63, and 184.95 mg/kg, respectively, with highly significant differences among layers (P < 0.01). This trend was consistent across fermentation durations, with available phosphorus content consistently increasing from the upper to the bottom layers within the same fermentation age, and these differences remained statistically significant ( P < 0.01). Additionally, based on previous studies, the content of available phosphorus in PM ranging from 150 to 300 mg/100 g is considered to produce top-quality wine, whereas levels below 50 mg/100 g are associated with inferior quality [ 18 , 19 ] . In the present study, we observed that the effective phosphorus content in the bottom PM of different ages consistently met the criteria for high-quality PM. In contrast, the effective phosphorus content in the corresponding PM samples from the upper cellar level was relatively lower, indicating a lower quality of PM. Consistent with previous studies, the available phosphorus content increases with the depth of PM, particularly in 50-year-old PM, where a significant rise is observed (Fig. 2). This may be attributed to the death and deposition of disadvantageous microorganisms that are not conducive to liquor brewing, leading to an increased accumulation of elemental phosphorus [ 17 ] . Notably, while Zhao's study did not differentiate sampling locations, our findings revealed a distinct spatial distribution of phosphorus content within the PM. Phosphorus tends to accumulate in the bottom layer of the PM, whereas the upper and middle layers exhibited lower concentrations. This spatial variation suggests that the depletion of effective phosphorus in the upper and middle layers of PM may adversely affect microbial growth and community structure in these regions, thereby influencing the overall fermentation process and liquor quality. 3.2 Alpha-diversity analysis In this study, we employed the Illumina MiSeq platform to sequence and analyze the microbial communities in PM samples. After optimization, a total of 1,974,341 valid prokaryotic microbial sequences and 1,857,046 valid eukaryotic microbial sequences were obtained. To assess the sequencing depth and data coverage, we constructed rarefaction curves using the Sobs index for each sample (Figure S1 ). The curves plateaued with increasing sequencing depth, indicating that the sequencing volume was adequate and the data obtained were representative of the microbial communities within the samples. The α-diversity indices were employed to evaluate the community diversity within each sample, including the abundance, uniformity, and richness of microbial species. Specifically, we utilized the Sobs index, Chao1 index, Shannon index, Simpson index, and coverage of the α-diversity index based on amplicon sequence variants (ASVs) at a 100% similarity level (Tables 1 and 2 ). The results indicated that the coverage values for all PM samples, regardless of age or location, exceeded 99.95%. This high coverage suggests that the sequencing data comprehensively captured the prokaryotic and eukaryotic species within the samples and accurately reflected their richness and diversity. As shown in Table 1 , the number of ASVs and valid sequences of prokaryotic microorganisms exhibited a tendency to increase with the age of the samples. Within the same year, the diversity of prokaryotic microorganisms, as measured by the Chao1 and Shannon indices, initially increased and then decreased with the progression of the location. The Simpson index was lowest in the middle layer, indicating that prokaryotic microorganisms in this layer were most abundant and evenly distributed. Consequently, the microbial diversity of the middle layer was higher than that of the upper and bottom layers within the same year. The microbial community in PM tends to stabilize with increasing cellar age, accompanied by a decline in microbial diversity, a trend that was confirmed in this study. This phenomenon is likely attributable to the selective pressures exerted by the environment during long-term fermentation, which eliminates less adapted microorganisms and allows more resilient ones to form dominant populations [ 20 ] . As illustrated in Table 1 , the number of ASVs and valid sequences of prokaryotic microorganisms exhibited a tendency to increase with advancing age. In samples from the same fermentation year, the middle layer of PM demonstrated the highest prokaryotic microbial diversity, as evidenced by the Chao1 and Shannon indices, compared to the upper and bottom layers. Concurrently, the Simpson index was lower in the middle layer, suggesting that prokaryotic microorganisms in this layer were both more abundant and more evenly distributed. This pattern indicates that the microbial diversity of the middle layer was superior to that of the upper and bottom layers within the same year of fermentation. Moreover, the microbial diversity of PM at the same layer generally decreased with increasing age, which aligns with previous findings [ 21 ] . This trend is likely attributable to the selective pressures exerted by the winemaking environment over time. As fermentation progresses, microorganisms that are not well-adapted to the winemaking conditions are progressively eliminated through microbial dynamics and community turnover. In contrast, certain microorganisms that can adapt to the PM microenvironment gradually establish dominance, leading to the formation of a more stable microbial ecosystem. This stabilization process results in a gradual enrichment of prokaryotic microorganisms that are beneficial to winemaking within the microenvironment. Table 1 α-diversity indices of prokaryotic microorganisms Samples Number of ASV Number of valid sequences Sob Index Chao1 Index Shannon Index Simpson Index Coverage/% 20 years LS20 229.00 ± 17.66 b 44652.33 ± 2176.79 a 83.67 ± 8.38 a 83.67 ± 8.38 a 3.05 ± 0.48 ab 0.11 ± 0.04 ab 100.00 ± 0.00 a LZ20 269.67 ± 6.85 a 49090.33 ± 2508.22 a 96.33 ± 2.4 a 96.33 ± 2.49 a 3.74 ± 0.13 a 0.04 ± 0.01 b 100.00 ± 0.00 a LD20 204.67 ± 15.17 b 46204.00 ± 3115.69 a 88.33 ± 4.11 a 88.33 ± 4.11 a 2.85 ± 0.09 b 0.13 ± 0.02 a 100.00 ± 0.00 a 30 Year LS30 208.00 ± 47.59 a 51244.33 ± 6137.01 a 81.00 ± 17.45 a 81.00 ± 17.45 a 2.83 ± 0.86 a 0.18 ± 0.14 a 100.00 ± 0.00 a LZ30 229.33 ± 46.74 a 45312.67 ± 8723.5 a 88.00 ± 4.24 a 88.00 ± 4.24 a 3.06 ± 0.48 a 0.13 ± 0.08 a 100.00 ± 0.00 a LD30 211.00 ± 47.34 a 53192.00 ± 2659.16 a 83.00 ± 11.52 a 83.00 ± 11.52 a 2.94 ± 0.53 a 0.14 ± 0.09 a 100.00 ± 0.00 a 40 years LS40 223.33 ± 30.35 a 56062.00 ± 2347.34 a 81.67 ± 1.25 a 81.67 ± 1.25 a 2.65 ± 0.16 a 0.16 ± 0.04 a 100.00 ± 0.00 a LZ40 249.00 ± 35.11 a 54858.67 ± 4883.46 a 87.67 ± 4.99 a 88.00 ± 5.1 a 2.49 ± 0.79 a 0.25 ± 0.20 a 99.99 ± 0.00 a LD40 281.33 ± 119.44 a 55578.33 ± 5272.92 a 81.00 ± 9.42 a 82.00 ± 9.93 a 2.67 ± 0.18 a 0.14 ± 0.03 a 100.00 ± 0.00 a 50 years LS50 413.33 ± 22.45 a 67386.67 ± 4370.93 a 104.67 ± 4.03 a 104.67 ± 4.03 a 3.26 ± 0.11 ab 0.09 ± 0.01 ab 100.00 ± 0.00 a LZ50 315.33 ± 43.87 b 72136.00 ± 5231.33 a 96.67 ± 2.05 b 96.67 ± 2.05 b 3.59 ± 0.25 a 0.05 ± 0.02 b 100.00 ± 0.00 a LD50 196.00 ± 20.61 c 62396.33 ± 7844.17 a 76.67 ± 2.87 c 76.67 ± 2.87 c 3.01 ± 0.03 b 0.09 ± 0.01 a 100.00 ± 0.00 a As depicted in Table 2 , the fungal diversity and richness (as measured by Sob, Chao1, and Shannon indices) in the upper and middle layers of PM were significantly higher than those in the bottom layer of PM within the same-aged pits. The elevated oxygen levels in the pit mouth and the anaerobic conditions in the pit bottom, which is frequently inundated with yellow liquid, may be detrimental to the survival of aerobic fungi. Consequently, this environmental gradient likely contributes to the reduced diversity and abundance of fungal communities in the bottom PM. Furthermore, when comparing PM from pits of different ages, fungal diversity was found to be higher in younger pits, indicating that the physicochemical characteristics of aged pits are less conducive to fungal survival. Table 2 α-diversity indices of eukaryotic microorganisms. Samples Number of ASV Number of valid sequences Sob Index Chao1 Index Shannon Index Simpson Index Coverage/% 20 years LS20 155.67 ± 33.89a 43478.67 ± 12283.20a 62.00 ± 7.48a 62.33 ± 7.93a 3.15 ± 0.28a 0.08 ± 0.03a 99.99 ± 0.01a LZ20 195.67 ± 28.77a 52499.67 ± 1703.62a 70.33 ± 1.70a 70.33 ± 1.70a 3.19 ± 0.19a 0.09 ± 0.03a 100.00 ± 0.00a LD20 133.00 ± 41.14a 45318.33 ± 9429.03a 60.67 ± 11.56a 60.67 ± 11.56a 2.96 ± 0.69a 0.11 ± 0.09a 100.00 ± 0.00a 30 Year LS30 129.00 ± 4.90b 55135.00 ± 583.65a 57.67 ± 2.49a 57.67 ± 2.49a 2.11 ± 0.23a 0.28 ± 0.05a 100.00 ± 0.00a LZ30 122.33 ± 15.20b 60679.00 ± 5082.39a 55.33 ± 4.19a 55.83 ± 3.70a 2.06 ± 0.76a 0.26 ± 0.14a 99.99 ± 0.01a LD30 154.00.33 ± 4.50a 58408.00 ± 9392.16a 61.67 ± 4.11a 61.67 ± 4.11a 2.92 ± 0.26a 0.10 ± 0.04a 100.00 ± 0.00a 40 years LS40 87.33 ± 20.07a 43006.00 ± 3081.51a 44.33 ± 5.79a 44.50 ± 5.61a 1.61 ± 1.14a 0.51 ± 0.31a 99.99 ± 0.01a LZ40 134.67 ± 83.69a 44698.00 ± 6335.40a 53.00 ± 18.71a 53.33 ± 18.80a 2.10 ± 1.13a 0.36 ± 0.29a 100.00 ± 0.01a LD40 160.00 ± 18.02a 44413.67 ± 2728.08a 54.00 ± 13.14a 54.00 ± 13.14a 3.01 ± 0.12a 0.08 ± 0.02a 100.00 ± 0.00a 50 years LS50 228.33 ± 76.53a 61075.00 ± 8036.54a 61.00 ± 5.72a 61.00 ± 5.72a 3.11 ± 0.20a 0.08 ± 0.01a 100.00 ± 0.00a LZ50 190.00 ± 55.87a 58920.33 ± 5597.99a 58.33 ± 7.32a 58.33 ± 7.32a 2.99 ± 0.06a 0.09 ± 0.01a 100.00 ± 0.00a LD50 185.33 ± 60.40a 51383.67 ± 4317.53a 56.00 ± 8.83a 56.00 ± 8.83a 2.91 ± 0.15a 0.1 ± 0.01a 100.00 ± 0.00a 3.3 Analysis of microbial species composition Based on the taxonomic ASVs, the community structure of the samples was analyzed. A total of 29 phyla, 63 classes, 132 orders, 229 families, and 409 genera of prokaryotic microorganisms were identified across all samples through sequencing. Additionally, 10 phyla, 36 classes, 81 orders, 198 families, and 407 genera of eukaryotic microorganisms were detected. 3.3.1 Analysis of microbial community composition at the phylum level in PM A total of 29 phyla, comprising archaea and bacteria, were detected among prokaryotic microorganisms across all samples (Fig. 3 ). These included 25 bacterial phyla and 4 archaeal phyla. As illustrated in Fig. 3 , four dominant phyla (relative abundance ≥ 1%) were identified, with their relative abundances in all samples as follows: Firmicutes (75.94%) > Euryarchaeota (10.40%) > Bacteroidota (6.79%) > Halobacterota (5.02%). The absolute dominance of Firmicutes in each sample is consistent with the findings of Chen et al. [ 22 ] . The relative abundance of Firmicutes in all samples ranged from 60–89%. Notably, the relative abundance of Firmicutes in PM from the same location increased with increasing age. For samples from different locations within the same year, the trend was consistent for 20- and 50-year-old PM, with the abundance initially increasing and then decreasing as the location moved downward. In contrast, the abundance of Firmicutes in 30- and 40-year-old PM decreased gradually. Firmicutes are often used as a key indicator for evaluating PM quality, as they are crucial for the production of aroma and acid (Liang et al., 2015). The trend of Euryarchaeota was opposite to that of Firmicutes. For samples of the same year from different locations, the trend was consistent for 20- and 50-year-old PM, with the abundance of Euryarchaeota initially decreasing and then increasing as the location moved downward. In contrast, the abundance of Euryarchaeota in 30- and 40-year-old PM increased gradually. The relative abundances of Bacteroidota and Halobacterota did not exhibit clear patterns. The highest relative abundance of Bacteroidota (16.4%) was observed in the LS20 sample, while the highest relative abundance of Halobacterota (12.7%) was found in the LD40 sample (Fig. 3 ). A total of 10 phyla were identified among eukaryotic microorganisms across all samples, with three dominant phyla (relative abundance ≥ 1%) detected: Ascomycota (78%), Basidiomycota (19%), and unclassified_k__Fungi (1.2%) (Fig. 4 ). As depicted in Fig. 4 , Ascomycota was the most abundant phylum in each sample, consistent with previous findings [ 9 , 23 ] . The relative abundance of Ascomycota initially increased and then decreased with increasing sample age at the same location, while no clear trend was observed across different locations within the same year. Basidiomycota exhibited a contrasting pattern, with its abundance decreasing and then increasing in the upper and middle locations as sample age increased, while it increased and then decreased in the bottom location. Mortierellomycota exhibited significant variation across different ages, with higher abundance in 20- and 30-year-old PM samples, reaching a maximum of 4.5% in LS20, and lower abundance in 40- and 50-year-old PM. Notably, Mortierellomycota was undetectable in LS40 and LZ40. This finding aligns with Wang et al, who reported its presence in new PM but its absence in old PM [ 24 ] . Thus, Mortierellomycota may serve as a potential indicator for determining the age of PM. Glomeromycota was detected only in LZ40 and LD40 samples, with relative abundances of 0.02% and 1.17%, respectively. Although rarely detected in PM, Glomeromycota has been reported in Daqu microbes [ 25 ] . 3.3.2 Analysis of microbial community composition at the genus level in PM A total of 409 prokaryotic genera were identified, among which 19 were considered dominant genera (relative abundance ≥ 1%). The remaining unidentified and non-dominant genera were collectively classified as "Others," with their relative abundances depicted in Fig. 5 . Across all samples, the top 10 prokaryotic microbial genera by relative abundance were as follows: Lactobacillus (14.69%), Hydrogenispora (14.25%), Caproiciproducens (12.32%), Methanobacterium (9.48%), Clostridium_sensu_stricto_12 (7.29%), Caldicoprobacter (3.36%), Methanoculleus (3.23%), Sedimentibacter (2.80%), Syntrophomonas (2.29%), and Methanosarcina (2.20%). As shown in Fig. 5 , the relative abundance of unknown bacteria in the middle layer of PM was consistently higher across all age samples. Overall, the relative abundance of Lactobacillus was significantly higher in the upper and middle layers of PM, with a maximum value of 40.76% observed in LZ50 and a minimum of 0.64% in LD20 (Fig. 5 ). Recent studies have demonstrated that the genus Lactobacillus is one of the predominant microbial taxa in the PM of strong-flavor Chinese baijiu, and its relative abundance is closely associated with the quality of the PM [ 20 ] . In the current study, the abundance of Lactobacillus was notably higher in the middle and upper layers of PM samples aged 30, 40, and 50 years. When Lactobacillus is highly abundant in the PM, it can produce organic acids such as lactic acid through its metabolic activities, thereby maintaining the acidic environment of the PM [ 26 ] . This acidic condition effectively inhibits the growth of harmful microorganisms while promoting the proliferation of beneficial microbial populations. Moreover, the metabolic products of Lactobacillus can provide favorable growth conditions for other functional bacteria, such as Caproicibacterium (formerly known as Clostridium or Acetobacterium ), which play a key role in shaping the flavor and quality of baijiu [ 11 ] . Additionally, during the maturation process of PM, the relative abundance of Lactobacillus changes dynamically with the aging of the PM. In newly established PM, Lactobacillus typically dominates the microbial community. However, as the PM ages, the relative abundance of Lactobacillus gradually decreases, while the abundance of other functional bacteria, such as Caproicibacterium , increases. This succession pattern reflects the dynamic changes in the microbial community structure of PM and is closely related to the improvement of PM quality. The high abundance of Lactobacillus in the early stages of PM is essential for its initial stabilization and the formation of high-quality PM. Hydrogenispora , which ranked second in relative abundance, exhibited a decreasing trend followed by an increase with the downward movement of the PM position within the same aged PM. For the same position, its abundance gradually decreased with increasing age. In contrast, Caproiciproducens increased with the age of the sample and was more abundant in the middle and bottom layers of PM of the same aged PM. This phenomenon may be attributed to the inhibitory effect of caproic acid, produced by Caproiciproducens , on Lactobacillus. As a result, samples with a high relative abundance of Caproiciproducens tend to exhibit a lower relative abundance of Lactobacillus [ 27 ] . Methanobacterium was more abundant in the bottom layer, with the highest content in LD20 (18.27%). The symbiosis between Methanobacterium and Caproiciproducens is conducive to the production of caproic acid, a precursor to the main aromatic compound ethyl caproate in Nongxiangxing Baijiu [ 28 ] . Clostridium_sensu_stricto_12 was more abundant in the middle and upper layers of PM and generally increased with age [ 29 ] . This bacterium is an important group for the production of short-chain fatty acids in PM, such as butyric acid and hexanoic acid, and its abundance increased with age and PM quality [ 30 ] . Caldicoprobacter was more abundant in PM of younger ages (20 and 30 years) and its content increased and then decreased with the downward movement of the PM position, reaching the highest value of 6.84% in LZ30. Sedimentibacter was detected in all samples and was more abundant in PM of 20 and 30 years. Previous studies have shown that Sedimentibacter often dominates PM and can degrade amino acids to produce small molecules such as acetic acid and ammonia nitrogen through symbiosis with methanogens [ 31 ] . Aminobacterium was unique to 40- and 50-year-old PM samples, and some studies have shown that this type of bacterium is more abundant in older, higher-quality PM [ 17 ] . In this study, the eukaryotic microbial community structure was comprehensively analyzed, revealing a total of 407 genera. Among these, 16 dominant genera (relative abundance ≥ 1%) were identified, while the remaining genera, including those with unknown or non-dominant status, were collectively categorized as "Others." The relative abundances of these genera are presented in Fig. 6 . Across all samples, the top 10 eukaryotic microorganisms in terms of relative abundance were identified as Thermomyces (19.85%), Debaryomyces (15.63%), Trichosporon (8.53%), Wallemia (8.40%), unclassified_f_Dipodascaceae (7.00%), Thermoascus (6.51%), unclassified_f_Aaspergillaceae (5.88%), Aspergillus (5.34%), Penicillium (3.87%), and Cladosporium (2.27%)(Fig. 6 ). These high-abundance fungi were primarily composed of yeasts and molds, which have been shown to play a crucial role in the production of ethanol, organic acids, and multi-class esters during the fermentation process of Baijiu [ 32 ] . Thermomyces is one of the dominant fungi in Baijiu PM, exhibiting distinct distribution patterns across different ages and layers of PM. Studies have shown that Thermomyces has a higher relative abundance in PM from 5-year-old cellars, and its abundance is negatively correlated with physicochemical factors of PM, such as moisture, ammonia nitrogen, and available phosphorus content [ 33 ] . This distribution pattern suggests that Thermomyces plays a significant role in the fermentation process of Baijiu, potentially influencing the flavor and quality of the final product. In contrast, Trichosporon and Debaryomyces exhibited higher relative abundances in 50-year-old PM, while Wallemia was more abundant in the middle layer of PM across all vintage samples. The community composition exhibited significant interannual variability, with some fungi detected only in a few samples. For instance, Kazachstania was the dominant genus in 20-, 30-, and 40-year-old PM samples. However, this genus has been reported as the dominant fungus in Wuliangbaijiu grains [ 34 ] , and emerged as a unique dominant bacterium in some PM samples in this study, potentially influenced by the production process. Priceomyces was identified as the dominant genus in 50-year-old PM, with its abundance decreasing and then increasing from the upper to the lower layers of the PM. Mortierella , which was the dominant genus in LS20 PM, was not detected in the 50-year-old PM samples. 3.4 Analysis of the differential microorganisms in samples To elucidate the differences inPM microorganisms among various groups, we employed the Linear Discriminant Analysis Effect Size (LEfSe) method to identify differentially abundant microorganisms, and LDA histograms were generated to visualize the results. The LDA histograms highlight the microorganisms that exhibit significant differences among groups, with the length of the bars representing the magnitude of the effect sizes of these differentially abundant taxa. The results for prokaryotic differential microorganisms are presented in Fig. 7. As shown in the species hierarchical tree diagram (Fig. 7a) and the LDA discriminant histogram (Fig. 7b), a total of 38 differentially abundant prokaryotic microorganisms were detected across all samples. Among these, 13 differentially abundant taxa (LDA score > 4) were identified at the genus level. Specifically, the differential microbial species for each group were as follows: for LZ20, Oxobacter and Sedimentibacter ; for LD20, Syntrophaceticus ; for LS30, Anaerosporobacter ; for LD30, Methanobrevibacter ; for LS40, Clostridium sensu stricto 12; for LZ40, unclassified_f__ Clostridiaceae and norank_f__norank_o__norank_c__ Limnochordia ; for LD40, norank_f__norank_o__ Limnochordales and norank_f__SRB2; for LZ50, Lactobacillus ; and for LD50, Pelotomaculum and Caproiciproducens . Figure 8 presents the LEfSe multilevel species hierarchical tree (a) and the LDA discriminant bar graph (b) for fungal species across all samples. As shown in Fig. 8, a total of 11 differentially abundant eukaryotic microorganisms were identified, with 5 detected at the genus level (LDA score > 2). Specifically, the differentially abundant microbial species were as follows: for LS20, Trichosporon and Kazachstania ; for LZ30, Apiotrichum ; and for LD50, unclassified_p__Ascomycota and Priceomyces (Fig. 8 ) . The results of differential microbial analysis among samples revealed that the majority of differentially abundant prokaryotic and eukaryotic microorganisms are key biological groups involved in the Baijiu fermentation process, such as Clostridium sensu stricto 12 , Sedimentibacter , and Trichosporon . Given their functional significance, it is reasonable to hypothesize that the distinct compositions of prokaryotic and eukaryotic microorganisms in PM of varying ages may lead to the production of diverse metabolites during fermentation. These differences in microbial metabolite profiles are likely to influence the quality of the PM, thereby affecting the sensory and chemical characteristics of the resulting Baijiu. Previous studies have shown that Clostridium and Sedimentibacter play crucial roles in the fermentation process of Baijiu, contributing to the production of organic acids and alcohols [ 11 , 28 ] . The presence of these genera in different abundances across PM samples of varying ages suggests that they may have distinct metabolic activities depending on the environmental conditions of the PM. For instance, Clostridium sensu stricto 12 has been reported to be involved in the production of short-chain fatty acids, which are essential for the flavor profile of Baijiu [ 33 ] . Similarly, Trichosporon and other eukaryotic microorganisms contribute to the production of esters and other flavor compounds, which are critical for the sensory characteristics of Baijiu [ 35 ] . The distinct microbial compositions in PM of different ages may also reflect the long-term adaptation of these microorganisms to the specific conditions of the fermentation environment. Older PM samples, which have undergone prolonged periods of fermentation, may harbor more diverse and specialized microbial communities compared to younger PM samples. This diversity and specialization could lead to the production of a wider range of metabolites, enhancing the complexity and quality of the resulting Baijiu. 3.5 Correlation analysis of PM microorganisms and their physicochemical indexes To elucidate the contribution of functional microorganisms to the physicochemical properties of PM, a Spearman correlation heatmap analysis was performed, examining the relationships between physicochemical factors and the relative abundance of the top 15 most abundant microorganisms in PM. Figure 9(a) illustrates the correlation analysis between prokaryotic microorganisms and physicochemical factors in PM. As shown in Fig. 9, several genera exhibited notable correlations with key physicochemical parameters. Specifically, Lactobacillus , Clostridium sensu stricto 12 , Caldicoprobacter , and Proteiniphilum were negatively correlated with moisture, pH, ammonia nitrogen, and available phosphorus (Fig. 9a). Meanwhile, Hydrogenispora , Methanoculleus , Sedimentibacter , and Petrimonas displayed negative correlations with moisture and available phosphorus. In contrast, Methanobacterium was positively correlated with all four physicochemical factors. Additionally, Hydrogenispora , Methanoculleus , Sedimentibacter , and Petrimonas were positively correlated with pH and ammonia nitrogen. From Fig. 9(a), it is evident that pH, ammonia nitrogen, and available phosphorus were significantly correlated with several genera among the top 15 most abundant microorganisms. These findings are consistent with previous studies that have identified similar correlations between specific microorganisms and physicochemical factors in PM. For instance, Clostridium and Lactobacillus have been reported to play crucial roles in the fermentation process of Baijiu, contributing to the production of organic acids and alcohols [ 28 ] . The negative correlation of these genera with moisture and pH suggests that they thrive in relatively dry and acidic conditions, which are common in mature PM. Furthermore, the positive correlation of Methanobacterium with all four physicochemical factors indicates its significant role in methanogenesis, which is essential for the production of volatile organic compounds that contribute to the aroma of Baijiu [ 36 ] . Figure 9(b) presents the correlation analysis between eukaryotic microorganisms and physicochemical factors. As indicated in Fig. 9(b), Kazachstania , Apiotrichum , and Thermoascus exhibited significant positive correlations with pH and ammonia nitrogen. Conversely, Thermomyces and Thermoascus showed significant negative correlations with available phosphorus (Fig. 9b). Notably, moisture did not display significant correlations with these eukaryotic microorganisms. The significant correlations between eukaryotic microorganisms and specific physicochemical factors highlight their potential roles in the fermentation process. For example, Kazachstania and Apiotrichum are known to contribute to the production of esters and other flavor compounds, which are essential for the aroma profile of Baijiu [ 37 ] . The positive correlation of these genera with pH and ammonia nitrogen suggests that they may thrive in environments with higher alkalinity and nitrogen content, which are conducive to their metabolic activities. In contrast, the negative correlation of Thermomyces with available phosphorus indicates that this genus may prefer environments with lower phosphorus levels, which could be related to its specific metabolic pathways [ 37 ] . 4. Conclusion This study comprehensively investigated the microbial communities and physicochemical properties of PM across different ages and spatial layers. The results revealed that fungal communities in PM exhibited lower species diversity and richness compared to prokaryotic microorganisms. Microbial diversity in PM generally decreased with increasing age at the same spatial location. Within the same aged PM, the middle layer harbored higher prokaryotic microbial diversity than the upper and bottom layers, whereas the upper and middle layers exhibited higher eukaryotic microbial diversity than the bottom layer. This spatial heterogeneity suggests that microbial composition and PM quality vary significantly across different positions within the same cellar. At the phylum level, Firmicutes and Ascomycota are dominant prokaryotic and eukaryotic phyla, respectively. At the genus level, 19 dominant genera were detected among prokaryotes, and 16 among eukaryotes. Notably, Lactobacillus was the predominant genus in the upper and middle layers of all PM samples, while Caldicoprobacter was more abundant in 20- and 30-year-old PM. Among eukaryotes, Kazachstania dominated in 20-, 30-, and 40-year-old PM, whereas Priceomyces was unique to 50-year-old PM. LEfSe analysis identified 13 different prokaryotic and five different eukaryotic microorganisms at the genus level across PM samples of varying ages and spatial locations. Physicochemical analysis showed that moisture and available phosphorus increased, while pH and ammonia nitrogen decreased with increasing PM age. Significant differences in pH and ammonia nitrogen were observed among spatial locations within the same cellar age (20- and 50-year-old PM). Spearman correlation analysis revealed that pH, ammonia nitrogen, and available phosphorus were significantly correlated with the top 15 most abundant prokaryotic and eukaryotic genera. These results highlight the critical influence of pH, ammonia nitrogen, and available phosphorus on microbial community structure. In summary, this study systematically characterized the physicochemical properties and microbial composition of PM across different locations and ages. Our findings demonstrate significant variations in dominant microbial taxa and physicochemical indices across spatial and temporal gradients. The high abundance of Lactobacillus and low available phosphorus in the upper PM layers of older cellars suggest potential degradation of PM quality in these regions. Therefore, spatial considerations, overall fermentation performance, and the quality of the resulting liquor must be integrated into the management of PM. This research enriches our understanding of the multidimensional microbiota of PM and provides valuable theoretical insights for guiding the maintenance of PM and the production of high-quality Baijiu. Future work should focus on elucidating the functional roles of key microbial taxa and optimizing PM management practices to enhance Baijiu fermentation efficiency and quality. Declarations Conflicts of Interest The authors confirm that they have no conflicts of interest with respect to the work described in this manuscript. Funding This research was financially supported by the Key Laboratory of Culinary Science of Sichuan Provincial Higher Education Institutions (PRKX2021Z03); the University-level Scientific Research Funded Project of Sichuan Tourism University (2024SCTUZZ01); and the Ministry of Education's Industry-University Cooperation Collaborative Education Project (241203242180007), Sichuan Science and Technology Program (No. 21ZYZFZDYF0019, No. 2021YFS0341) and Natural Science Basic Research Project of Sichuan Provincial Department of Science and Technology (No. 2022NSFSC1763). Author Contribution In this research, Jinsong Zhao and Kaixian Zhu were responsible for the conceptualization, while the methodology was jointly developed by Jinsong Zhao, Siqi Yuan, Kaixian Zhu and Qin Xiao. The validation work was carried out by Jinsong Zhao, Siqi Yuan, Qin Xiao. Jun Liu and Mingyi Guo conducted the investigation. Kaixian Zhu and Hongwei Shang provided the resources. Kaixian Zhu, Jinsong Zhao and Siqi Yuan prepared the original draft of the manuscript. The writing review and editing were undertaken by Jinsong Zhao and Siqi Yuan. Jinsong Zhao and Hongwei Shang supervised the entire project. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets generated and analyzed during the current study are available in the NCBI repository, https://dataview.ncbi.nlm.nih.gov/object/PRJNA1240440. References Yin X, Yoshizaki Y, Ikenaga M, et al. 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Supplementary Files SupplementaryFigure1.docx Cite Share Download PDF Status: Published Journal Publication published 12 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviewers agreed at journal 22 May, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 28 Mar, 2025 Editor invited by journal 25 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 08 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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microorganism.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/7a14ecf4f513f665f214b67d.jpg"},{"id":80835287,"identity":"3dba93c4-e5d4-4142-bb94-23b4ed216d65","added_by":"auto","created_at":"2025-04-17 14:43:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87752,"visible":true,"origin":"","legend":"\u003cp\u003eCircos diagram at the level of phylum in eukaryotic microorganism\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/de8667e192244ad66113d513.jpg"},{"id":80835870,"identity":"7d1743e4-14ff-4f92-87a7-4f221f63d71b","added_by":"auto","created_at":"2025-04-17 14:51:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94164,"visible":true,"origin":"","legend":"\u003cp\u003eCircos diagram of prokaryotic microorganisms at the genus level\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/c21759179749b29ce369b6d4.jpg"},{"id":80835872,"identity":"34cb84d0-ec46-434a-ab10-36ec4c722df8","added_by":"auto","created_at":"2025-04-17 14:51:37","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":108662,"visible":true,"origin":"","legend":"\u003cp\u003eCircos diagram at eukaryotic microorganisms at the genus level\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/58244e94f787b8116b0437f2.jpg"},{"id":80835299,"identity":"61654309-49be-4804-86da-257f9bbbc605","added_by":"auto","created_at":"2025-04-17 14:43:37","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119516,"visible":true,"origin":"","legend":"\u003cp\u003eLefSe multi-level species hierarchical tree diagram (a), and LDA discriminant bar chart (b) of prokaryotic microorganisms.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/7a0b77b85fd9a1d530167178.jpg"},{"id":80835292,"identity":"59412187-8858-4c04-947c-838c3f198267","added_by":"auto","created_at":"2025-04-17 14:43:37","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":90559,"visible":true,"origin":"","legend":"\u003cp\u003eLefSe multi-level species hierarchical tree diagram (a), and LDA discriminant bar chart (b) of eukaryotic microorganisms.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/d3cf2b73879132bb40addbee.jpg"},{"id":80835880,"identity":"ed4817db-44f6-432a-9690-cd0afb021571","added_by":"auto","created_at":"2025-04-17 14:51:37","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":71046,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of prokaryotic (a) and eukaryotic (b) microorganisms with physicochemical factors.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/2474d72776e6d58022c54471.jpg"},{"id":89310663,"identity":"19967d31-3560-43fe-acda-33c5e9b63c19","added_by":"auto","created_at":"2025-08-18 16:09:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1874958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/7985b6d1-5ed2-429a-aacd-671e8f18041e.pdf"},{"id":80835289,"identity":"ccf0f6c3-0e36-4e25-88b9-44f110c78ce9","added_by":"auto","created_at":"2025-04-17 14:43:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":133514,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6186207/v1/b970384e0f475b841eeb6033.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics and correlation of the microbial communities and physicochemical factors from pit mud of Strong-flavor Baijiu","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStrong-flavor Baijiu (SFB), renowned for its strong aroma, sweet taste, and smooth finish, is a highly esteemed Chinese distilled liquor \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Its unique flavor profile arises from a distinctive fermentation mash called pit mud (PM)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. PM serves as a culture medium for the proliferation and metabolism of microbial communities, and conversely, these microbial communities and their metabolic products are the main reasons for altering not only the textural parameters but also physicochemical indicators of the mash. Therefore, studying the microbial communities and metabolism in the PM is crucial for revealing the aroma formation mechanisms of SFB\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEarly research on PM microorganisms focused on identifying key microbial players involved in flavor development. The discovery of caproic acid-producing bacterium, Clostridium butyricum, in Maotai PM marked a significant milestone, establishing a link between specific microorganisms and the unique aroma profile of the liquor\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDue to the extended fermentation period of PM, there is a distinct succession of microbial communities across different stages of fermentation. However, studying the static distribution of microbial communities at a single stage does not reflect the entire process of baijiu fermentation. Therefore, it is particularly important to analyze the community structure of dominant microbial groups at various fermentation stages. For example, during the initial stages of fermentation, aerobic and facultatively anaerobic microorganisms prevail in PM. As fermentation progresses, the aerobic microbes gradually decline while anaerobic and facultatively anaerobic bacteria continue to proliferate. In the middle and later stages, lactic acid bacteria and anaerobic bacteria become dominant, leading to a reduction in microbial diversity in PM\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMeanwhile, PM is situated at varying depths and environmental conditions within the cellar. For instance, the base of the PM is perpetually submerged in yellow liquid, creating an anaerobic environment, while sections of the cellar wall are in contact with air and transition to an aerobic state after sealing. Consequently, the microbial composition of PM serves as a pivotal bioindicator for evaluating the quality and condition of PM, enabling the rapid and reliable differentiation and grading of PM maturity. However, the influence of PM location on microbial communities and their impact on the aroma of SFB has not been extensively studied.\u003c/p\u003e \u003cp\u003eTo clarify the differences and correlations between microbial diversity and the physicochemical properties of PM of varying ages and strata, this study was conducted to analyze the microbial community composition within the layers of 20-, 30-, 40-, and 50-year-old PM samples from a Luzhou distillery, encompassing the upper, middle, and bottom layers. The primary objectives were to investigate the disparities and interrelations between microbial diversity and the physicochemical characteristics of PM, providing insights into the effects of aging and stratification on the microbial ecology and quality of PM.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection\u003c/h2\u003e \u003cp\u003ePM samples were collected from four distinct fermentation cellars at Luzhou Kangqingfang Wine Industry, each with an age of 20, 30, 40, and 50 years. For each age group, three cellars were randomly selected to serve as parallel samples. Aseptic sampling was conducted from the upper, middle, and bottom layers of each cellar, with the specific sampling locations depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All samples were sealed in sterile bags and stored at -80℃ for subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Determination of physicochemical factors\u003c/h2\u003e \u003cp\u003eThe moisture content was detected using a gravimetric method after drying at 105\u0026thinsp;\u0026plusmn;\u0026thinsp;5 ◦C drying to constant weight[10]. The pH was measured at a 1:3 (wt/vol) ratio in ultrapure water. The mixture was vortexed for 30 seconds and then centrifuged at 12,000 r/min for 10 minutes. T the supernatant was subsequently determined using a pH meter (PHS-3D, Shanghai San-Xin Instrumentation Inc, China)[11].Determination of available phosphorous (available P) was determined by ammonium fluoride (1 mol/L)-hydrochloric acid (0.5 mol/L) colorimetric method[12]. 1 mL of fresh PM filtrate was transferred into the colorimetric tube and diluted to 50 mL of volume with distilled water, followed by the addition of 1\u0026thinsp;~\u0026thinsp;2 drops of sodium potassium tartrate solution and 1 mL of Nessler\u0026rsquo;s reagent. After standing for 10min, the concentration of ammonium nitrogen (NH4 +-N) was detected by ultraviolet\u0026ndash;visible spectrophotometry (T2602, Shanghai Yoke Instruments Meters Co.,ltd, China) at the wavelength of 425 nm [12].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Microbiome sequencing\u003c/h2\u003e \u003cp\u003eThe DNeasy\u0026reg; PowerSoil\u0026reg; Pro Kit was utilized for the extraction of total DNA from the samples, following the protocol provided in the kit documentation. The quality of the extracted genomic DNA was assessed using 1% agarose gel electrophoresis, while the concentration and purity were measured using a NanoDrop 2000 spectrophotometer. Samples meeting the sequencing requirements in both quality and concentration were stored at -80\u0026deg;C. High-throughput sequencing was conducted using the Illumina MiSeq platform at Shanghai Meiji Bio. For bacterial and archaeal 16S rRNA gene sequencing, the V4 variable region was amplified using the universal primers 515FmodF/806RmodR (5'-GTGYCAGCMGCCGCGGTAA-3'/5'-GGACTACNVGGGTWTCTAAT-3'). For fungal ITS1 region sequencing, the primers ITS1F/ITS2R (5'-CTTGGTCATTTAGAGGAAGTAA-3'/5'-GCTGCGTTCTTCATCGATGC-3') were employed. The amplification procedure involved an initial denaturation at 95\u0026deg;C for 3 minutes, followed by 35 cycles (27 cycles for eukaryotes) consisting of denaturation at 95\u0026deg;C for 30 seconds, annealing at 55\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 30 seconds. This was followed by a final extension at 72\u0026deg;C for 10 minutes, and the reaction was stored at 10\u0026deg;C until completion. The PCR products were mixed with an equal volume of 1x TAE buffer and subjected to electrophoresis using a 2% agarose gel for band detection. PCR products were pooled at equidensity ratios. Subsequently, the PCR mixtures were purified using the Qiagen Gel Extraction Kit (Qiagen, Germany). Sequencing libraries were constructed using the TruSeq\u0026reg; DNA PCR-Free Sample Preparation Kit (Illumina, USA) according to the manufacturer's instructions, with the addition of index codes. The library quality was evaluated using the Qubit\u0026reg; 2.0 Fluorometer (Thermo Scientific). Finally, the libraries were sequenced on the Illumina NovaSeq platform, yielding 250 bp paired-end reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Bioinformatics and statistical analysis\u003c/h2\u003e \u003cp\u003eAmplified sequence variants (ASVs) were classified using the QIIME2 naive Bayes classifier, which was trained on 99% operational taxonomic units (OTUs) against the SILVA database (version 138)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The taxonomy was constructed by aligningASVs to a pre-trained GREENGENES 13_8 99% reference database using the QIIME2 feature-classifier plugin \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Microbial diversity was assessed using alpha-diversity metrics, specifically the Chao index. Community composition was analyzed using beta-diversity, as implemented in the q2-diversity pipeline within QIIME2. To identify taxa that exhibited significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among treatments, the linear discriminant analysis effect size (LEfSe) was employed. For principal coordinates analysis (PCoA), PERMANOVA (Adonis function with 999 permutations) was utilized to evaluate the bacterial community composition based on the Bray\u0026ndash;Curtis dissimilarity metric. Physical and chemical indicators were processed and analyzed using IBM SPSS Statistics 27 and Excel 2019. The correlation between each indicator was compared, and the correlation heat map was drawn using ChiPlot (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chiplot.online/\u003c/span\u003e\u003cspan address=\"https://www.chiplot.online/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 26 January 2025).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Analysis of differences in physicochemical factors\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;2, the moisture and available phosphorus content exhibited an increasing trend with advancing age, whereas the pH and ammonia nitrogen content demonstrated a corresponding decline.\u003c/p\u003e\n \u003cp\u003eFor moisture content, no statistically significant differences were observed among samples from different years in the upper and bottom layers. However, in the middle layer, moisture content was significantly higher in the 50-year-old samples compared to those from 20- and 30-year-old samples (P\u0026thinsp;=\u0026thinsp;0.032 and P\u0026thinsp;=\u0026thinsp;0.014, respectively). Across all samples with different durations, a consistent trend was observed in the vertical distribution of moisture content, with the highest values in the middle layer, followed by the bottom layer, and the lowest values in the upper layer. Moisture content not only influences other physicochemical properties, such as pH, but also affects microbial growth in PM \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Moreover, it serves as an indicator of PM maturity, with moisture content\u0026thinsp;\u0026gt;\u0026thinsp;40% being associated with mature PM\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In this study, the moisture content in the upper layer of 20-year-old PM was lower than 40%, which may impact the compactness and anaerobic conditions of the PM (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eFor pH values, distinct temporal trends were observed across different layers. In the upper layer, pH significantly decreased with increasing age, with samples from 30-, 40-, and 50-year-old groups exhibiting significantly lower pH values compared to the 20-year-old group. In the middle layer, a similar decreasing trend was observed; however, no statistically significant differences were detected among samples from different years. In the bottom layer, the pH of the 50-year-old samples was significantly lower than that of the 20-year-old samples (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). Across all samples with different durations, a consistent vertical distribution of pH was observed, with the lowest pH values typically found in the middle layer, followed by the bottom layer, and the highest pH values in the upper layer (Fig. 2). The low pH value facilitates the enrichment of acid-tolerant or acidophilic bacteria \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Additionally, pH significantly influences the formation of ethanol and other flavor precursors\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Previous studies have reported that the pH in the bottom layer of PM is higher than that in the upper and middle layers. However, this trend was not observed in the current study. Instead, the pH in the middle and bottom layers of PM exhibited an increasing trend from 30 to 40 years of fermentation, followed by a decrease from 40 to 50 years. Compared to 10-year-old PM, the pH values of 30- and 50-year-old samples were significantly higher, with no significant difference between the 30- and 50-year-old samples \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that microbial metabolism varies distinctly across different fermentation systems, ultimately influencing the quality of the final product.\u003c/p\u003e\n \u003cp\u003eAmmonium nitrogen is essential for microbial growth and for the synthesis of a variety of enzymes and proteins. The maintenance of optimal ammonium nitrogen concentrations is crucial for preserving the quality of PM and, consequently, for enhancing the overall quality of liquor \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In the present study, the ammonia nitrogen content exhibited a decline with increasing fermentation duration. In the upper and middle layers, the 20-year-old samples had significantly lower ammonia nitrogen content compared to those from the 30-, 40-, and 50-year-old samples (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A similar trend was observed in the bottom layer, where the 20-year-old samples had significantly lower ammonia nitrogen content than the 50-year-old samples (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, Fig. 2). Across samples of different durations, a consistent vertical gradient was observed, with the highest ammonia nitrogen content in the bottom layer and the lowest in the upper layer. These findings are consistent with previous results \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, who suggested that these factors contribute to the habitat preferences of specific microbial species, including \u003cem\u003eLactobacillus acetotolerans\u003c/em\u003e (LA), \u003cem\u003eAnaeromassilibacillus senegalensis\u003c/em\u003e (AS), \u003cem\u003eClostridium kluyveri\u003c/em\u003e (CK), \u003cem\u003eClostridium luticellarii\u003c/em\u003e (CL), \u003cem\u003eProteiniphilum saccharofermentans\u003c/em\u003e (PRS), \u003cem\u003ePetrimonas sulfuriphila\u003c/em\u003e (PS), and \u003cem\u003eClostridium limosum\u003c/em\u003e (CLL) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe available phosphorus content exhibited substantial temporal and spatial variability. Initially, it decreased with time before subsequently increasing. In the upper layer, significant differences in available phosphorus content were observed across each decade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Intriguingly, spatial heterogeneity was also evident within the samples, with available phosphorus content increasing with proximity to the bottom and under more anaerobic conditions. For instance, in the 20-year-old samples, available phosphorus content in the upper, middle, and bottom layers was 73.37, 116.63, and 184.95 mg/kg, respectively, with highly significant differences among layers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This trend was consistent across fermentation durations, with available phosphorus content consistently increasing from the upper to the bottom layers within the same fermentation age, and these differences remained statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, based on previous studies, the content of available phosphorus in PM ranging from 150 to 300 mg/100 g is considered to produce top-quality wine, whereas levels below 50 mg/100 g are associated with inferior quality\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In the present study, we observed that the effective phosphorus content in the bottom PM of different ages consistently met the criteria for high-quality PM. In contrast, the effective phosphorus content in the corresponding PM samples from the upper cellar level was relatively lower, indicating a lower quality of PM. Consistent with previous studies, the available phosphorus content increases with the depth of PM, particularly in 50-year-old PM, where a significant rise is observed (Fig. 2). This may be attributed to the death and deposition of disadvantageous microorganisms that are not conducive to liquor brewing, leading to an increased accumulation of elemental phosphorus \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Notably, while Zhao\u0026apos;s study did not differentiate sampling locations, our findings revealed a distinct spatial distribution of phosphorus content within the PM. Phosphorus tends to accumulate in the bottom layer of the PM, whereas the upper and middle layers exhibited lower concentrations. This spatial variation suggests that the depletion of effective phosphorus in the upper and middle layers of PM may adversely affect microbial growth and community structure in these regions, thereby influencing the overall fermentation process and liquor quality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Alpha-diversity analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, we employed the Illumina MiSeq platform to sequence and analyze the microbial communities in PM samples. After optimization, a total of 1,974,341 valid prokaryotic microbial sequences and 1,857,046 valid eukaryotic microbial sequences were obtained. To assess the sequencing depth and data coverage, we constructed rarefaction curves using the Sobs index for each sample (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The curves plateaued with increasing sequencing depth, indicating that the sequencing volume was adequate and the data obtained were representative of the microbial communities within the samples.\u003c/p\u003e\n \u003cp\u003eThe \u0026alpha;-diversity indices were employed to evaluate the community diversity within each sample, including the abundance, uniformity, and richness of microbial species. Specifically, we utilized the Sobs index, Chao1 index, Shannon index, Simpson index, and coverage of the \u0026alpha;-diversity index based on amplicon sequence variants (ASVs) at a 100% similarity level (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The results indicated that the coverage values for all PM samples, regardless of age or location, exceeded 99.95%. This high coverage suggests that the sequencing data comprehensively captured the prokaryotic and eukaryotic species within the samples and accurately reflected their richness and diversity.\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the number of ASVs and valid sequences of prokaryotic microorganisms exhibited a tendency to increase with the age of the samples. Within the same year, the diversity of prokaryotic microorganisms, as measured by the Chao1 and Shannon indices, initially increased and then decreased with the progression of the location. The Simpson index was lowest in the middle layer, indicating that prokaryotic microorganisms in this layer were most abundant and evenly distributed. Consequently, the microbial diversity of the middle layer was higher than that of the upper and bottom layers within the same year. The microbial community in PM tends to stabilize with increasing cellar age, accompanied by a decline in microbial diversity, a trend that was confirmed in this study. This phenomenon is likely attributable to the selective pressures exerted by the environment during long-term fermentation, which eliminates less adapted microorganisms and allows more resilient ones to form dominant populations \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eAs illustrated in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the number of ASVs and valid sequences of prokaryotic microorganisms exhibited a tendency to increase with advancing age. In samples from the same fermentation year, the middle layer of PM demonstrated the highest prokaryotic microbial diversity, as evidenced by the Chao1 and Shannon indices, compared to the upper and bottom layers. Concurrently, the Simpson index was lower in the middle layer, suggesting that prokaryotic microorganisms in this layer were both more abundant and more evenly distributed. This pattern indicates that the microbial diversity of the middle layer was superior to that of the upper and bottom layers within the same year of fermentation.\u003c/p\u003e\n \u003cp\u003eMoreover, the microbial diversity of PM at the same layer generally decreased with increasing age, which aligns with previous findings\u0026nbsp;\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. This trend is likely attributable to the selective pressures exerted by the winemaking environment over time. As fermentation progresses, microorganisms that are not well-adapted to the winemaking conditions are progressively eliminated through microbial dynamics and community turnover. In contrast, certain microorganisms that can adapt to the PM microenvironment gradually establish dominance, leading to the formation of a more stable microbial ecosystem. This stabilization process results in a gradual enrichment of prokaryotic microorganisms that are beneficial to winemaking within the microenvironment.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026alpha;-diversity indices of prokaryotic microorganisms\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSamples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of ASV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of valid sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSob Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChao1 Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShannon Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimpson Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage/%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.00\u0026thinsp;\u0026plusmn;\u0026thinsp;17.66\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44652.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2176.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49090.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2508.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204.67\u0026thinsp;\u0026plusmn;\u0026thinsp;15.17\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46204.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3115.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208.00\u0026thinsp;\u0026plusmn;\u0026thinsp;47.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51244.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6137.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.00\u0026thinsp;\u0026plusmn;\u0026thinsp;17.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.00\u0026thinsp;\u0026plusmn;\u0026thinsp;17.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.33\u0026thinsp;\u0026plusmn;\u0026thinsp;46.74\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45312.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8723.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211.00\u0026thinsp;\u0026plusmn;\u0026thinsp;47.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53192.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2659.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.33\u0026thinsp;\u0026plusmn;\u0026thinsp;30.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56062.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2347.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.00\u0026thinsp;\u0026plusmn;\u0026thinsp;35.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54858.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4883.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281.33\u0026thinsp;\u0026plusmn;\u0026thinsp;119.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55578.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5272.92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e413.33\u0026thinsp;\u0026plusmn;\u0026thinsp;22.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67386.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4370.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315.33\u0026thinsp;\u0026plusmn;\u0026thinsp;43.87\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72136.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5231.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196.00\u0026thinsp;\u0026plusmn;\u0026thinsp;20.61\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62396.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7844.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the fungal diversity and richness (as measured by Sob, Chao1, and Shannon indices) in the upper and middle layers of PM were significantly higher than those in the bottom layer of PM within the same-aged pits. The elevated oxygen levels in the pit mouth and the anaerobic conditions in the pit bottom, which is frequently inundated with yellow liquid, may be detrimental to the survival of aerobic fungi. Consequently, this environmental gradient likely contributes to the reduced diversity and abundance of fungal communities in the bottom PM. Furthermore, when comparing PM from pits of different ages, fungal diversity was found to be higher in younger pits, indicating that the physicochemical characteristics of aged pits are less conducive to fungal survival.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u0026alpha;-diversity indices of eukaryotic microorganisms.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSamples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of ASV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of valid sequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSob Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChao1 Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShannon Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimpson Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage/%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155.67\u0026thinsp;\u0026plusmn;\u0026thinsp;33.89a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43478.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12283.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.93a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195.67\u0026thinsp;\u0026plusmn;\u0026thinsp;28.77a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52499.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1703.62a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.00\u0026thinsp;\u0026plusmn;\u0026thinsp;41.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45318.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9429.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.90b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55135.00\u0026thinsp;\u0026plusmn;\u0026thinsp;583.65a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.20b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60679.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5082.39a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.00.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58408.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9392.16a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.33\u0026thinsp;\u0026plusmn;\u0026thinsp;20.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43006.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3081.51a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.61a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.67\u0026thinsp;\u0026plusmn;\u0026thinsp;83.69a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44698.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6335.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.00\u0026thinsp;\u0026plusmn;\u0026thinsp;18.71a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.33\u0026thinsp;\u0026plusmn;\u0026thinsp;18.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.00\u0026thinsp;\u0026plusmn;\u0026thinsp;18.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44413.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2728.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.00\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.00\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228.33\u0026thinsp;\u0026plusmn;\u0026thinsp;76.53a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61075.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8036.54a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLZ50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.00\u0026thinsp;\u0026plusmn;\u0026thinsp;55.87a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58920.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5597.99a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185.33\u0026thinsp;\u0026plusmn;\u0026thinsp;60.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51383.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4317.53a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Analysis of microbial species composition\u003c/h2\u003e\n \u003cp\u003eBased on the taxonomic ASVs, the community structure of the samples was analyzed. A total of 29 phyla, 63 classes, 132 orders, 229 families, and 409 genera of prokaryotic microorganisms were identified across all samples through sequencing. Additionally, 10 phyla, 36 classes, 81 orders, 198 families, and 407 genera of eukaryotic microorganisms were detected.\u003c/p\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Analysis of microbial community composition at the phylum level in PM\u003c/h2\u003e\n \u003cp\u003eA total of 29 phyla, comprising archaea and bacteria, were detected among prokaryotic microorganisms across all samples (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These included 25 bacterial phyla and 4 archaeal phyla. As illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, four dominant phyla (relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;1%) were identified, with their relative abundances in all samples as follows: \u003cem\u003eFirmicutes\u003c/em\u003e (75.94%)\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eEuryarchaeota\u003c/em\u003e (10.40%)\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eBacteroidota\u003c/em\u003e (6.79%)\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eHalobacterota\u003c/em\u003e (5.02%). The absolute dominance of Firmicutes in each sample is consistent with the findings of Chen et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The relative abundance of Firmicutes in all samples ranged from 60\u0026ndash;89%. Notably, the relative abundance of Firmicutes in PM from the same location increased with increasing age. For samples from different locations within the same year, the trend was consistent for 20- and 50-year-old PM, with the abundance initially increasing and then decreasing as the location moved downward. In contrast, the abundance of Firmicutes in 30- and 40-year-old PM decreased gradually. Firmicutes are often used as a key indicator for evaluating PM quality, as they are crucial for the production of aroma and acid (Liang et al., 2015).\u003c/p\u003e\n \u003cp\u003eThe trend of \u003cem\u003eEuryarchaeota\u003c/em\u003e was opposite to that of Firmicutes. For samples of the same year from different locations, the trend was consistent for 20- and 50-year-old PM, with the abundance of \u003cem\u003eEuryarchaeota\u003c/em\u003e initially decreasing and then increasing as the location moved downward. In contrast, the abundance of \u003cem\u003eEuryarchaeota\u003c/em\u003e in 30- and 40-year-old PM increased gradually. The relative abundances of \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003eHalobacterota\u003c/em\u003e did not exhibit clear patterns. The highest relative abundance of \u003cem\u003eBacteroidota\u003c/em\u003e (16.4%) was observed in the LS20 sample, while the highest relative abundance of \u003cem\u003eHalobacterota\u003c/em\u003e (12.7%) was found in the LD40 sample (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eA total of 10 phyla were identified among eukaryotic microorganisms across all samples, with three dominant phyla (relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;1%) detected: \u003cem\u003eAscomycota\u003c/em\u003e (78%), \u003cem\u003eBasidiomycota\u003c/em\u003e (19%), and unclassified_k__Fungi (1.2%) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). As depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cem\u003eAscomycota\u003c/em\u003e was the most abundant phylum in each sample, consistent with previous findings \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The relative abundance of \u003cem\u003eAscomycota\u003c/em\u003e initially increased and then decreased with increasing sample age at the same location, while no clear trend was observed across different locations within the same year. Basidiomycota exhibited a contrasting pattern, with its abundance decreasing and then increasing in the upper and middle locations as sample age increased, while it increased and then decreased in the bottom location.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMortierellomycota\u003c/em\u003e exhibited significant variation across different ages, with higher abundance in 20- and 30-year-old PM samples, reaching a maximum of 4.5% in LS20, and lower abundance in 40- and 50-year-old PM. Notably, \u003cem\u003eMortierellomycota\u003c/em\u003e was undetectable in LS40 and LZ40. This finding aligns with Wang et al, who reported its presence in new PM but its absence in old PM\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Thus, \u003cem\u003eMortierellomycota\u003c/em\u003e may serve as a potential indicator for determining the age of PM. \u003cem\u003eGlomeromycota\u003c/em\u003e was detected only in LZ40 and LD40 samples, with relative abundances of 0.02% and 1.17%, respectively. Although rarely detected in PM, \u003cem\u003eGlomeromycota\u003c/em\u003e has been reported in Daqu microbes \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Analysis of microbial community composition at the genus level in PM\u003c/h2\u003e\n \u003cp\u003eA total of 409 prokaryotic genera were identified, among which 19 were considered dominant genera (relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;1%). The remaining unidentified and non-dominant genera were collectively classified as \u0026quot;Others,\u0026quot; with their relative abundances depicted in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Across all samples, the top 10 prokaryotic microbial genera by relative abundance were as follows: \u003cem\u003eLactobacillus\u003c/em\u003e (14.69%), \u003cem\u003eHydrogenispora\u003c/em\u003e (14.25%), \u003cem\u003eCaproiciproducens\u003c/em\u003e (12.32%), \u003cem\u003eMethanobacterium\u003c/em\u003e (9.48%), \u003cem\u003eClostridium_sensu_stricto_12\u003c/em\u003e (7.29%), \u003cem\u003eCaldicoprobacter\u003c/em\u003e (3.36%), \u003cem\u003eMethanoculleus\u003c/em\u003e (3.23%), \u003cem\u003eSedimentibacter\u003c/em\u003e (2.80%), \u003cem\u003eSyntrophomonas\u003c/em\u003e (2.29%), and \u003cem\u003eMethanosarcina\u003c/em\u003e (2.20%).\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the relative abundance of unknown bacteria in the middle layer of PM was consistently higher across all age samples. Overall, the relative abundance of \u003cem\u003eLactobacillus\u003c/em\u003e was significantly higher in the upper and middle layers of PM, with a maximum value of 40.76% observed in LZ50 and a minimum of 0.64% in LD20 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRecent studies have demonstrated that the genus Lactobacillus is one of the predominant microbial taxa in the PM of strong-flavor Chinese baijiu, and its relative abundance is closely associated with the quality of the PM\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In the current study, the abundance of \u003cem\u003eLactobacillus\u003c/em\u003e was notably higher in the middle and upper layers of PM samples aged 30, 40, and 50 years. When Lactobacillus is highly abundant in the PM, it can produce organic acids such as lactic acid through its metabolic activities, thereby maintaining the acidic environment of the PM \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This acidic condition effectively inhibits the growth of harmful microorganisms while promoting the proliferation of beneficial microbial populations. Moreover, the metabolic products of Lactobacillus can provide favorable growth conditions for other functional bacteria, such as \u003cem\u003eCaproicibacterium\u003c/em\u003e (formerly known as \u003cem\u003eClostridium\u003c/em\u003e or \u003cem\u003eAcetobacterium\u003c/em\u003e), which play a key role in shaping the flavor and quality of baijiu \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eAdditionally, during the maturation process of PM, the relative abundance of \u003cem\u003eLactobacillus\u003c/em\u003e changes dynamically with the aging of the PM. In newly established PM, \u003cem\u003eLactobacillus\u003c/em\u003e typically dominates the microbial community. However, as the PM ages, the relative abundance of \u003cem\u003eLactobacillus\u003c/em\u003e gradually decreases, while the abundance of other functional bacteria, such as \u003cem\u003eCaproicibacterium\u003c/em\u003e, increases. This succession pattern reflects the dynamic changes in the microbial community structure of PM and is closely related to the improvement of PM quality. The high abundance of \u003cem\u003eLactobacillus\u003c/em\u003e in the early stages of PM is essential for its initial stabilization and the formation of high-quality PM.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHydrogenispora\u003c/em\u003e, which ranked second in relative abundance, exhibited a decreasing trend followed by an increase with the downward movement of the PM position within the same aged PM. For the same position, its abundance gradually decreased with increasing age. In contrast, \u003cem\u003eCaproiciproducens\u003c/em\u003e increased with the age of the sample and was more abundant in the middle and bottom layers of PM of the same aged PM. This phenomenon may be attributed to the inhibitory effect of caproic acid, produced by \u003cem\u003eCaproiciproducens\u003c/em\u003e, on Lactobacillus. As a result, samples with a high relative abundance of \u003cem\u003eCaproiciproducens\u003c/em\u003e tend to exhibit a lower relative abundance of Lactobacillus \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMethanobacterium\u003c/em\u003e was more abundant in the bottom layer, with the highest content in LD20 (18.27%). The symbiosis between \u003cem\u003eMethanobacterium\u003c/em\u003e and \u003cem\u003eCaproiciproducens\u003c/em\u003e is conducive to the production of caproic acid, a precursor to the main aromatic compound ethyl caproate in Nongxiangxing Baijiu \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eClostridium_sensu_stricto_12\u003c/em\u003e was more abundant in the middle and upper layers of PM and generally increased with age \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This bacterium is an important group for the production of short-chain fatty acids in PM, such as butyric acid and hexanoic acid, and its abundance increased with age and PM quality\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCaldicoprobacter\u003c/em\u003e was more abundant in PM of younger ages (20 and 30 years) and its content increased and then decreased with the downward movement of the PM position, reaching the highest value of 6.84% in LZ30. \u003cem\u003eSedimentibacter\u003c/em\u003e was detected in all samples and was more abundant in PM of 20 and 30 years. Previous studies have shown that \u003cem\u003eSedimentibacter\u003c/em\u003e often dominates PM and can degrade amino acids to produce small molecules such as acetic acid and ammonia nitrogen through symbiosis with methanogens \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eAminobacterium\u003c/em\u003e was unique to 40- and 50-year-old PM samples, and some studies have shown that this type of bacterium is more abundant in older, higher-quality PM\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eIn this study, the eukaryotic microbial community structure was comprehensively analyzed, revealing a total of 407 genera. Among these, 16 dominant genera (relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;1%) were identified, while the remaining genera, including those with unknown or non-dominant status, were collectively categorized as \u0026quot;Others.\u0026quot; The relative abundances of these genera are presented in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Across all samples, the top 10 eukaryotic microorganisms in terms of relative abundance were identified as \u003cem\u003eThermomyces\u003c/em\u003e (19.85%), \u003cem\u003eDebaryomyces\u003c/em\u003e (15.63%), \u003cem\u003eTrichosporon\u003c/em\u003e (8.53%), \u003cem\u003eWallemia\u003c/em\u003e (8.40%), \u003cem\u003eunclassified_f_Dipodascaceae\u003c/em\u003e (7.00%), \u003cem\u003eThermoascus\u003c/em\u003e (6.51%), \u003cem\u003eunclassified_f_Aaspergillaceae\u003c/em\u003e (5.88%), \u003cem\u003eAspergillus\u003c/em\u003e (5.34%), \u003cem\u003ePenicillium\u003c/em\u003e (3.87%), and \u003cem\u003eCladosporium\u003c/em\u003e (2.27%)(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). These high-abundance fungi were primarily composed of yeasts and molds, which have been shown to play a crucial role in the production of ethanol, organic acids, and multi-class esters during the fermentation process of Baijiu \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eThermomyces\u003c/em\u003e is one of the dominant fungi in Baijiu PM, exhibiting distinct distribution patterns across different ages and layers of PM. Studies have shown that \u003cem\u003eThermomyces\u003c/em\u003e has a higher relative abundance in PM from 5-year-old cellars, and its abundance is negatively correlated with physicochemical factors of PM, such as moisture, ammonia nitrogen, and available phosphorus content\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This distribution pattern suggests that \u003cem\u003eThermomyces\u003c/em\u003e plays a significant role in the fermentation process of Baijiu, potentially influencing the flavor and quality of the final product.\u003c/p\u003e\n \u003cp\u003eIn contrast, \u003cem\u003eTrichosporon\u003c/em\u003e and \u003cem\u003eDebaryomyces\u003c/em\u003e exhibited higher relative abundances in 50-year-old PM, while \u003cem\u003eWallemia\u003c/em\u003e was more abundant in the middle layer of PM across all vintage samples. The community composition exhibited significant interannual variability, with some fungi detected only in a few samples. For instance, \u003cem\u003eKazachstania\u003c/em\u003e was the dominant genus in 20-, 30-, and 40-year-old PM samples. However, this genus has been reported as the dominant fungus in Wuliangbaijiu grains\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, and emerged as a unique dominant bacterium in some PM samples in this study, potentially influenced by the production process. \u003cem\u003ePriceomyces\u003c/em\u003e was identified as the dominant genus in 50-year-old PM, with its abundance decreasing and then increasing from the upper to the lower layers of the PM. \u003cem\u003eMortierella\u003c/em\u003e, which was the dominant genus in LS20 PM, was not detected in the 50-year-old PM samples.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Analysis of the differential microorganisms in samples\u003c/h2\u003e\n \u003cp\u003eTo elucidate the differences inPM microorganisms among various groups, we employed the Linear Discriminant Analysis Effect Size (LEfSe) method to identify differentially abundant microorganisms, and LDA histograms were generated to visualize the results. The LDA histograms highlight the microorganisms that exhibit significant differences among groups, with the length of the bars representing the magnitude of the effect sizes of these differentially abundant taxa. The results for prokaryotic differential microorganisms are presented in Fig.\u0026nbsp;7.\u003c/p\u003e\n \u003cp\u003eAs shown in the species hierarchical tree diagram (Fig. 7a) and the LDA discriminant histogram (Fig. 7b), a total of 38 differentially abundant prokaryotic microorganisms were detected across all samples. Among these, 13 differentially abundant taxa (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;4) were identified at the genus level. Specifically, the differential microbial species for each group were as follows: for LZ20, \u003cem\u003eOxobacter\u003c/em\u003e and \u003cem\u003eSedimentibacter\u003c/em\u003e; for LD20, \u003cem\u003eSyntrophaceticus\u003c/em\u003e; for LS30, \u003cem\u003eAnaerosporobacter\u003c/em\u003e; for LD30, \u003cem\u003eMethanobrevibacter\u003c/em\u003e; for LS40, Clostridium sensu stricto 12; for LZ40, unclassified_f__\u003cem\u003eClostridiaceae\u003c/em\u003e and norank_f__norank_o__norank_c__\u003cem\u003eLimnochordia\u003c/em\u003e; for LD40, norank_f__norank_o__\u003cem\u003eLimnochordales\u003c/em\u003e and norank_f__SRB2; for LZ50, \u003cem\u003eLactobacillus\u003c/em\u003e; and for LD50, \u003cem\u003ePelotomaculum\u003c/em\u003e and \u003cem\u003eCaproiciproducens\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eFigure 8 presents the LEfSe multilevel species hierarchical tree (a) and the LDA discriminant bar graph (b) for fungal species across all samples. As shown in Fig. 8, a total of 11 differentially abundant eukaryotic microorganisms were identified, with 5 detected at the genus level (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2). Specifically, the differentially abundant microbial species were as follows: for LS20, \u003cem\u003eTrichosporon\u003c/em\u003e and \u003cem\u003eKazachstania\u003c/em\u003e; for LZ30, \u003cem\u003eApiotrichum\u003c/em\u003e; and for LD50, \u003cem\u003eunclassified_p__Ascomycota\u003c/em\u003e and \u003cem\u003ePriceomyces\u003c/em\u003e (Fig. 8\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eThe results of differential microbial analysis among samples revealed that the majority of differentially abundant prokaryotic and eukaryotic microorganisms are key biological groups involved in the Baijiu fermentation process, such as \u003cem\u003eClostridium sensu stricto 12\u003c/em\u003e, \u003cem\u003eSedimentibacter\u003c/em\u003e, and \u003cem\u003eTrichosporon\u003c/em\u003e. Given their functional significance, it is reasonable to hypothesize that the distinct compositions of prokaryotic and eukaryotic microorganisms in PM of varying ages may lead to the production of diverse metabolites during fermentation. These differences in microbial metabolite profiles are likely to influence the quality of the PM, thereby affecting the sensory and chemical characteristics of the resulting Baijiu. Previous studies have shown that \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eSedimentibacter\u003c/em\u003e play crucial roles in the fermentation process of Baijiu, contributing to the production of organic acids and alcohols\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The presence of these genera in different abundances across PM samples of varying ages suggests that they may have distinct metabolic activities depending on the environmental conditions of the PM. For instance, \u003cem\u003eClostridium sensu stricto 12\u003c/em\u003e has been reported to be involved in the production of short-chain fatty acids, which are essential for the flavor profile of Baijiu \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Similarly, \u003cem\u003eTrichosporon\u003c/em\u003e and other eukaryotic microorganisms contribute to the production of esters and other flavor compounds, which are critical for the sensory characteristics of Baijiu \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. The distinct microbial compositions in PM of different ages may also reflect the long-term adaptation of these microorganisms to the specific conditions of the fermentation environment. Older PM samples, which have undergone prolonged periods of fermentation, may harbor more diverse and specialized microbial communities compared to younger PM samples. This diversity and specialization could lead to the production of a wider range of metabolites, enhancing the complexity and quality of the resulting Baijiu.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Correlation analysis of PM microorganisms and their physicochemical indexes\u003c/h2\u003e\n \u003cp\u003eTo elucidate the contribution of functional microorganisms to the physicochemical properties of PM, a Spearman correlation heatmap analysis was performed, examining the relationships between physicochemical factors and the relative abundance of the top 15 most abundant microorganisms in PM.\u003c/p\u003e\n \u003cp\u003eFigure 9(a) illustrates the correlation analysis between prokaryotic microorganisms and physicochemical factors in PM. As shown in Fig. 9, several genera exhibited notable correlations with key physicochemical parameters. Specifically, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eClostridium sensu stricto 12\u003c/em\u003e, \u003cem\u003eCaldicoprobacter\u003c/em\u003e, and \u003cem\u003eProteiniphilum\u003c/em\u003e were negatively correlated with moisture, pH, ammonia nitrogen, and available phosphorus (Fig. 9a). Meanwhile, \u003cem\u003eHydrogenispora\u003c/em\u003e, \u003cem\u003eMethanoculleus\u003c/em\u003e, \u003cem\u003eSedimentibacter\u003c/em\u003e, and \u003cem\u003ePetrimonas\u003c/em\u003e displayed negative correlations with moisture and available phosphorus. In contrast, \u003cem\u003eMethanobacterium\u003c/em\u003e was positively correlated with all four physicochemical factors. Additionally, \u003cem\u003eHydrogenispora\u003c/em\u003e, \u003cem\u003eMethanoculleus\u003c/em\u003e, \u003cem\u003eSedimentibacter\u003c/em\u003e, and \u003cem\u003ePetrimonas\u003c/em\u003e were positively correlated with pH and ammonia nitrogen. From Fig. 9(a), it is evident that pH, ammonia nitrogen, and available phosphorus were significantly correlated with several genera among the top 15 most abundant microorganisms. These findings are consistent with previous studies that have identified similar correlations between specific microorganisms and physicochemical factors in PM. For instance, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e have been reported to play crucial roles in the fermentation process of Baijiu, contributing to the production of organic acids and alcohols \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The negative correlation of these genera with moisture and pH suggests that they thrive in relatively dry and acidic conditions, which are common in mature PM. Furthermore, the positive correlation of \u003cem\u003eMethanobacterium\u003c/em\u003e with all four physicochemical factors indicates its significant role in methanogenesis, which is essential for the production of volatile organic compounds that contribute to the aroma of Baijiu \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eFigure 9(b) presents the correlation analysis between eukaryotic microorganisms and physicochemical factors. As indicated in Fig. 9(b), \u003cem\u003eKazachstania\u003c/em\u003e, \u003cem\u003eApiotrichum\u003c/em\u003e, and \u003cem\u003eThermoascus\u003c/em\u003e exhibited significant positive correlations with pH and ammonia nitrogen. Conversely, \u003cem\u003eThermomyces\u003c/em\u003e and \u003cem\u003eThermoascus\u003c/em\u003e showed significant negative correlations with available phosphorus (Fig. 9b). Notably, moisture did not display significant correlations with these eukaryotic microorganisms.\u003c/p\u003e\n \u003cp\u003eThe significant correlations between eukaryotic microorganisms and specific physicochemical factors highlight their potential roles in the fermentation process. For example, \u003cem\u003eKazachstania\u003c/em\u003e and \u003cem\u003eApiotrichum\u003c/em\u003e are known to contribute to the production of esters and other flavor compounds, which are essential for the aroma profile of Baijiu\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The positive correlation of these genera with pH and ammonia nitrogen suggests that they may thrive in environments with higher alkalinity and nitrogen content, which are conducive to their metabolic activities. In contrast, the negative correlation of \u003cem\u003eThermomyces\u003c/em\u003e with available phosphorus indicates that this genus may prefer environments with lower phosphorus levels, which could be related to its specific metabolic pathways\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study comprehensively investigated the microbial communities and physicochemical properties of PM across different ages and spatial layers. The results revealed that fungal communities in PM exhibited lower species diversity and richness compared to prokaryotic microorganisms. Microbial diversity in PM generally decreased with increasing age at the same spatial location. Within the same aged PM, the middle layer harbored higher prokaryotic microbial diversity than the upper and bottom layers, whereas the upper and middle layers exhibited higher eukaryotic microbial diversity than the bottom layer. This spatial heterogeneity suggests that microbial composition and PM quality vary significantly across different positions within the same cellar. At the phylum level, \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eAscomycota\u003c/em\u003e are dominant prokaryotic and eukaryotic phyla, respectively. At the genus level, 19 dominant genera were detected among prokaryotes, and 16 among eukaryotes. Notably, \u003cem\u003eLactobacillus\u003c/em\u003e was the predominant genus in the upper and middle layers of all PM samples, while \u003cem\u003eCaldicoprobacter\u003c/em\u003e was more abundant in 20- and 30-year-old PM. Among eukaryotes, \u003cem\u003eKazachstania\u003c/em\u003e dominated in 20-, 30-, and 40-year-old PM, whereas \u003cem\u003ePriceomyces\u003c/em\u003e was unique to 50-year-old PM. LEfSe analysis identified 13 different prokaryotic and five different eukaryotic microorganisms at the genus level across PM samples of varying ages and spatial locations. Physicochemical analysis showed that moisture and available phosphorus increased, while pH and ammonia nitrogen decreased with increasing PM age. Significant differences in pH and ammonia nitrogen were observed among spatial locations within the same cellar age (20- and 50-year-old PM). Spearman correlation analysis revealed that pH, ammonia nitrogen, and available phosphorus were significantly correlated with the top 15 most abundant prokaryotic and eukaryotic genera. These results highlight the critical influence of pH, ammonia nitrogen, and available phosphorus on microbial community structure. In summary, this study systematically characterized the physicochemical properties and microbial composition of PM across different locations and ages. Our findings demonstrate significant variations in dominant microbial taxa and physicochemical indices across spatial and temporal gradients. The high abundance of \u003cem\u003eLactobacillus\u003c/em\u003e and low available phosphorus in the upper PM layers of older cellars suggest potential degradation of PM quality in these regions. Therefore, spatial considerations, overall fermentation performance, and the quality of the resulting liquor must be integrated into the management of PM. This research enriches our understanding of the multidimensional microbiota of PM and provides valuable theoretical insights for guiding the maintenance of PM and the production of high-quality Baijiu. Future work should focus on elucidating the functional roles of key microbial taxa and optimizing PM management practices to enhance Baijiu fermentation efficiency and quality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors confirm that they have no conflicts of interest with respect to the work described in this manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was financially supported by the Key Laboratory of Culinary Science of Sichuan Provincial Higher Education Institutions (PRKX2021Z03); the University-level Scientific Research Funded Project of Sichuan Tourism University (2024SCTUZZ01); and the Ministry of Education's Industry-University Cooperation Collaborative Education Project (241203242180007), Sichuan Science and Technology Program (No. 21ZYZFZDYF0019, No. 2021YFS0341) and Natural Science Basic Research Project of Sichuan Provincial Department of Science and Technology (No. 2022NSFSC1763).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eIn this research, Jinsong Zhao and Kaixian Zhu were responsible for the conceptualization, while the methodology was jointly developed by Jinsong Zhao, Siqi Yuan, Kaixian Zhu and Qin Xiao. The validation work was carried out by Jinsong Zhao, Siqi Yuan, Qin Xiao. Jun Liu and Mingyi Guo conducted the investigation. Kaixian Zhu and Hongwei Shang provided the resources. Kaixian Zhu, Jinsong Zhao and Siqi Yuan prepared the original draft of the manuscript. The writing review and editing were undertaken by Jinsong Zhao and Siqi Yuan. Jinsong Zhao and Hongwei Shang supervised the entire project. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available in the NCBI repository, https://dataview.ncbi.nlm.nih.gov/object/PRJNA1240440.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYin X, Yoshizaki Y, Ikenaga M, et al. Manufactural impact of the solid-state saccharification process in rice-flavor baijiu production[J]. 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Process Biochemistry, 2024, 146: 433-450.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pit mud, high-throughput sequencing, prokaryotic and eukaryotic microorganisms, physicochemical factors, correlation analysis","lastPublishedDoi":"10.21203/rs.3.rs-6186207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6186207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo gain a deeper understanding of the role microorganisms play in shaping the quality of baijiu, this study comprehensively examined the microbial communities and physicochemical characteristics of pit mud (PM) sourced from various ages (20, 30, 40, and 50 years) and distinct spatial layers, as well as explored the correlations between these factors. Fungal communities in PM had lower diversity and richness than prokaryotic microorganisms. Microbial diversity decreased with increasing PM age at the same spatial location. In the same aged PM, the middle layer had higher prokaryotic microbial diversity, while the upper and middle layers had higher eukaryotic microbial diversity. \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eAscomycota\u003c/em\u003e are dominant prokaryotic and eukaryotic phyla, respectively. At the genus level, 19 prokaryotic and 16 eukaryotic dominant genera were detected. \u003cem\u003eLactobacillus\u003c/em\u003e was predominant in the upper and middle layers, and \u003cem\u003eCaldicoprobacter\u003c/em\u003e was more abundant in 20- and 30-year-old PM. Among eukaryotes, \u003cem\u003eKazachstania\u003c/em\u003e dominated in younger PM, and \u003cem\u003ePriceomyces\u003c/em\u003e was unique to 50-year-old PM. Physicochemical analysis showed that moisture and available phosphorus increased, while pH and ammonia nitrogen decreased with PM age. Significant differences in pH and ammonia nitrogen were observed among spatial locations. Spearman correlation analysis indicated that \u003cem\u003eMethanobacterium\u003c/em\u003e and \u003cem\u003eThermoascus\u003c/em\u003e abundance were significantly correlated with pH, ammonia nitrogen, and available phosphorus. The research reveals notable spatial and temporal differences in microbial taxa and their influence on physicochemical indices. It offers valuable insights for the management of pit mud (PM) and the production of high-quality baijiu, underscoring the importance of considering spatial factors and optimizing fermentation performance.\u003c/p\u003e","manuscriptTitle":"Characteristics and correlation of the microbial communities and physicochemical factors from pit mud of Strong-flavor Baijiu","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 14:43:32","doi":"10.21203/rs.3.rs-6186207/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-18T05:50:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T14:06:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23692098888096314847085264118351717920","date":"2025-05-22T14:39:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T13:43:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184414622430238697575387775092936700029","date":"2025-04-18T11:02:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308946818789455831702286803157082884169","date":"2025-04-08T19:48:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-29T02:37:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-29T02:32:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-25T07:11:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T08:18:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-09T00:22:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c150f92-d9af-4a41-a1b6-b849d094e79b","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47248740,"name":"Biological sciences/Biological techniques"},{"id":47248741,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-08-18T16:05:16+00:00","versionOfRecord":{"articleIdentity":"rs-6186207","link":"https://doi.org/10.1038/s41598-025-15108-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-12 15:57:03","publishedOnDateReadable":"August 12th, 2025"},"versionCreatedAt":"2025-04-17 14:43:32","video":"","vorDoi":"10.1038/s41598-025-15108-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-15108-3","workflowStages":[]},"version":"v1","identity":"rs-6186207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6186207","identity":"rs-6186207","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}