The study site was situated in Dawei Mountain National Nature Reserve (DMNNR, 22°54’N, 103°42’E), Yunnan Province, China. The area is under a tropical monsoon climate, with a rainy season from May to October and a dry season from November to next April. The annual temperature is 15.2 ~ 22.9°C and the annual precipitation is 1,262 ~ 2,200 mm (Wang et al., 2018). The soil in the region is classified as yellow soil based on the Genetic Soil Classification of China. The arbor layer of C. pingbianense forest consists mainly of Lithocarpus. Three 30 m × 30 m sampling plots were established as replicates. Each plot contained five subplots, including one large plot of 8 m × 8 m and four small plots of 5 m × 5 m, and each subplot was separated by more than 3 m. A total of 10 clumps of vigorous bamboos were numbered and labeled in the 8 m × 8 m sample plot, and 30 bamboo clumps were marked in three replicates as sample clumps for the later investigation of emerged bamboo shoot number. These four small plots were prepared to collect soil samples in different seasons.

Soil samples were collected in July 2020 (summer), October 2020 (fall), January 2021 (winter) and April 2021 (spring). Our sampling in reserve was approved by the Pingbian Sub-bureau of DMNNR. For each sampling, five bamboo clumps in a small plot were randomly selected and dug out. After shaking off the loosely bound soil, the tightly adhered soils were collected and used as rhizosphere soil. The fresh soils were mixed and sieved (mesh size 2 mm) and then divided into two parts: one part was stored at −70°C for soil microbiome analysis, and the other part was air-dried for chemical property analysis. In addition, the emerged bamboo shoots of C. pingbianense were also counted. In July and October 2020, as well as January and April 2021, the number of bamboo shoots emerged from the sample clusters was surveyed every 10 days, with the shoot tip 1 cm ~ 2 cm above the ground as the standard for shoot emergence.

While the soil samples were being collected, soil temperature (ST) and soil moisture (SM) in the rhizome distribution area of C. pingbianense were measured by a portable soil temperature and moisture recorder (Tr-6, SHUNKEDA, Beijing). Soil moisture herein refers to soil volumetric water content, that is, the volume fraction of water per unit of total soil volume. A portion of the collected soil samples were dried, ground and sifted to determine soil PH, organic matter, nitrogen, phosphorus and potassium levels. Soil pH was determined with a glass electrode at a 2.5:1 water: soil ratio. Soil organic matter (OM) was determined using the potassium dichromate volumetric method. Soil total nitrogen (TN) was determined using the Kjeldahl nitrogen method. Soil total potassium (TK) and soil available potassium (AK) were determined using the atomic absorption photometry method. Soil hydrolyzable nitrogen (HN) was determined using the alkaline hydrolysis diffusion method. Soil available phosphorus (AP) was determined using the hydrochloric acid-sulfuric acid extraction method (Wang et al., 2022).

The microbial community DNA was extracted from 0.5 g of fresh soil for each sample by using the FastDNA™ SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, United States). DNA quality was examined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, United States). To identify the characteristics of soil bacterial communities, the V3-V4 variable region of the 16S rRNA gene was amplified with primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) (Zeng and An, 2021). The PCR reaction system (20 μL) for bacteria included 4 μL of 5 × FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of TransStart FastPfu DNA Polymerase (TransGen, Beijing, China), 0.2 μL of BSA and 10 ng of Template DNA. Meanwhile, to identify the characteristics of fungal communities, the internal transcribed spacer 1 (ITS1) region of the ribosomal RNA gene was amplified using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) (Tauro et al., 2021). The PCR reaction system (20 μL) for fungi included 2 μL of 10 × buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.2 μL of TaKaRa rTaq DNA Polymerase (Takara, Shiga, Japan), 0.2 μL of BSA and 10 ng of Template DNA. Bacterial and fungal PCR amplifications were performed using an ABI GeneAmp® 9,700 (Applied Biosystems, Foster City, CA, United States), and the following conditions were used: 95°C for 3 min, 35 cycles at 95°C for 30 s, 55°C for 30 s, 72°C for 45 s, and a final extension at 72°C for 10 min. The PCR product qualities were detected by 2% agarose gel electrophoresis, and qualified products were used for DNA library construction using the NEXTflex® Rapid DNA-Seq Kit (Bioo Scientific, Austin, Texas, United States). Then, sequencing was performed on an Illumina MiSeqPE300 Platform (Illumina, San Diego, CA, United States) by Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China). The raw sequencing data were deposited in the NCBI Sequence Read Archive (SRA) under the BioProject number PRJNA806496.

The FLASH (v1.2.11) software was used for raw reads splicing (Magoč and Salzberg, 2011) and the fastp (v0.19.6) software was used for quality control (Chen et al., 2018). Operational taxonomic units (OTUs) were classified using the Uparse (v7.0) software based on the similarity of 97% (Edgar, 2013), and the taxonomic assignment of representative OTUs was performed by the RDP classifier (Wang et al., 2007). During this process, the 16S data were aligned with the Silva database (Release138)¹ and the ITS data were aligned with the Unite database (Release 8.0).²

Before calculating the diversity indices, bacterial and fungal OTU tables were rarified to the minimum reads per sample, respectively. Soil microbial community alpha diversity was determined using the Chao1 and Shannon indexes, and one-way analysis of variance (ANOVA) was used to determine whether there was a statistical difference in the diversity index among samples. Furthermore, ANOVA was also utilized to analyze the significance of soil properties as well as bamboo shoot numbers in different seasons. Based on the Bray-Curtis distance algorithm, principal co-ordinate analysis (PCoA) was used to analyze the beta diversity of microbial communities. An analysis of similarities (ANOSIM) test was used to compare within-and between-group similarity.

Venn diagrams were constructed to reveal the common or unique OTUs among samples of different seasons. According to the taxonomic information, bacterial and fungal community compositions were calculated at the phylum and genus levels. Linear discriminant analysis (LDA) effect size (LEfSe) was applied to determine the species with significant differences among samples (Segata et al., 2011), with the LDA score set to 3.5 and the taxa set from phylum to genus.

FAPROTAX was used to predict bacterial ecological functions (Louca et al., 2016) and BugBase was used to predict the bacterial phenotypes (Ward et al., 2017). The Kruskal-Wallis rank test was performed to analyze the difference in predicted results among groups. Functional profiling of soil fungal community was predicted based on FUNGuild (Nguyen et al., 2016). Redundancy analysis (RDA) was conducted to determine the relationship between microbial community structure and selected soil properties. The correlations of microbial taxa (genus level) with soil properties and bamboo shoot number were assessed by calculating Spearman’s rank correlation coefficient.

Soil physicochemical properties of C. pingbianense rhizosphere in four seasons were determined and analyzed. The rhizosphere soil was acidic and there was no significant difference in soil pH between seasons (Supplementary Figure S1). Soil TN and OM contents in summer and autumn were significantly lower than those in spring and winter (p < 0.01). In contrast, soil TK content did not differ significantly among samples of different seasons. Soil HN and AK contents were significantly lower in autumn than in other seasons (p < 0.01), and the AP content also showed the lowest value in the autumn sample. The ST and SM contents followed similar trends through the seasons, exhibiting higher levels in summer and autumn and lower levels in spring and winter (Figures 1A–H).

In addition, the numbers of emerged bamboo shoots in July, October, January and April of marked bamboo clumps were counted. Herein, these data were taken as the bamboo shooting productivity of C. pingbianense in four seasons. The result showed that there was no significant difference among the number of shoots produced in spring, summer and winter, while the shoot number in autumn was significantly higher than that in other seasons (p < 0.01) (Figure 1I; Supplementary Table S1).

After quality filtering, a total of 429,954 and 791,445 effective sequences were generated from all soil samples and were classified into 3,675 bacterial OTUs and 5,119 fungal OTUs subsequently (Supplementary Table S2). Venn diagrams (Supplementary Figure S2) showed that the soil samples of four seasons had more shared bacterial OTUs than fungi, while more unique fungal OTUs were presented in the sample of each season.

The bacterial Chao1 (Figure 2A) and Shannon indexes (Figure 2C) in soil samples of four seasons were in the descending order of winter, spring, autumn and summer, but the differences among samples were not significant, indicating the bacterial community richness and diversity in C. pingbianense rhizosphere soil did not fluctuate drastically at the seasonal scale. In contrast, the fungal Chao1 (Figure 2B) and Shannon (Figure 2D) indexes were relatively high in spring and autumn samples, and the fungal Chao1 index in spring soil showed significant differences from those in summer and winter soils (p < 0.05), suggesting that the fungal richness was more susceptible to seasonal changes compared to bacteria.

Results of PCoA analysis showed that soil microbial communities could be distinctly separated in different seasons, and the fungal PCoA (Figure 2F) separated samples more clearly than that of bacteria (Figure 2E). ANOSIM analysis indicated that seasonal changes significantly affected the structures of soil bacterial (R = 0.6543, p = 0.001) and fungal (R = 0.6698, p = 0.001) communities (Supplementary Figure S3). Pairwise comparisons of bacterial or fungal community among four seasons further manifested differences between diverse groups, with the R values greater than 0.25 for all sample pairs (Supplementary Table S3).

Seasonal changes affect the composition of the rhizosphere microbial communities. At the phylum level, bacterial or fungal taxa with a relative abundance lower than 1% were grouped into “others.” Proteobacteria, Acidobacteriota and Actinobacteriota were the dominant soil bacterial phyla, and the three together accounted for more than 75% of bacterial communities in each season. Among them, the relative abundance of Proteobacteria decreased gradually from spring to winter. Acidobacteriota showed a higher relative abundance in summer and winter samples than that in spring and autumn, while an opposite trend was observed for Actinobacteriota (Figure 3A). At the genus level, Acidothermus, norank_f_Xanthobacteraceae, Roseiarcus, Bradyrhizobium, Bryobacter and Candidatus Solibacter were the dominant bacterial genera in soil samples of four seasons (Figure 3C).

As for fungi, Basidiomycota, Ascomycota, Mortierellomycota and Rozellomycota were the dominant soil fungal phyla in all seasons. Basidiomycota exhibited a downward tendency in relative abundance from spring to winter. Mortierellomycota had the highest relative abundance in the summer sample. Ascomycota and Rozellomycota showed the lowest relative abundance in the summer and spring sample, respectively (Figure 3B). At the genus level, Saitozyma, Mortierella, Solicoccozyma, Trichoderma and Microdochium had a high relative abundance in soil fungal communities in all seasons. What is noteworthy is that the genus Russula was especially enriched and dominant in spring and autumn samples (Figure 3D).

Functional predictions of the bacterial community (Figure 4A) showed that chemoheterotrophy, Aerobic chemoheterotrophy, cellulolysis and nitrogen fixation dominated the soil ecological functions in all seasons, indicating the soil bacterial community of C. pingbianense rhizosphere had a high capacity for carbohydrate conversion and biological nitrogen fixation. Furthermore, the low relative abundance of nitrate respiration, nitrite respiration and nitrous oxide denitrification suggested weak nitrification and denitrification in the bacterial community. The rhizosphere bacterial taxa were divided into nine phenotypes by the BugBase tool (Figure 4B). It was noted that the relative abundance of the same phenotypic bacteria did not differ significantly in samples of the four seasons (p > 0.05). In each season, however, the relative abundance varied widely across the different phenotypic bacteria. For instance, the relative abundance of Gram-negative and aerobic bacteria was much higher than that of Gram-positive and anaerobic bacteria.

Functional guild composition of the fungal community assigned by FUNGuild software was shown in Figure 4C. Saprotrophic fungi occupied a high proportion (more than 20%) in C. pingbianense rhizosphere soil of each season, including soil saprotroph (e.g., Apiotrichum), wood saprotroph (e.g., Trechispora), undefined saprotroph (e.g., Trichoderma) and so on. The relative abundances of symbiotrophic fungi were significantly higher in spring (25.63%) and autumn (22.70%) samples than those in summer (1.82%) and winter (2.88%), and the predominant species were ectomycorrhizal fungi, including Russula, Lactarius, Chloridium, etc. Pathotrophic fungi had the highest relative abundance (6.96%) in the winter sample, mainly including animal pathogens such as Metarhizium. Moreover, in all seasons, Saitozyma and Mortierella belonging to multiple trophic modes were dominant in soil fungal communities.

LEfSe was used to identify the classified microbial taxa with significant abundance differences among samples of four seasons. Here, 14 differentially abundant taxonomic clades were detected as bacterial biomarkers. The representative bacterial genera included Pedomicrobium (p = 0.044) enriched in the summer sample and Roseiarcus (p = 0.034) in the autumn sample (Supplementary Figure S4; Supplementary Table S4A). By contrast, more fungal biomarkers, namely 46 fungal clades with significant differences, were detected in this study. At the genus level, the fungal biomarkers included but were not limited to Tomentella (p = 0.015) enriched in the spring sample, Saitozyma (p = 0.033) in the summer sample, Trichoderma (p = 0.030) in the autumn sample and Metarhizium (p = 0.043) in the winter sample (Figure 5; Supplementary Table S4B).

By analyzing the correlations between selected soil properties and microbial alpha diversity indexes, we found that the bacterial Chao1 index was positively correlated with most soil properties such as OM, TN, HN, AP and AK, and negatively correlated with ST and SM. The bacterial Shannon index showed a significant positive correlation with OM and AP (p < 0.05), and a significant negative correlation with ST and SM (p < 0.05). These results suggested that OM, AP, ST and SM were important factors affecting bacterial diversity. The fungal Chao1 and Shannon indexes were positively correlated with OM and negatively correlated with AP and ST. Among examined soil properties, AP had the greatest effect on fungal diversity (Supplementary Table S5).

Redundancy analysis result revealed that OM had the most significant effect (r² = 0.601, p = 0.011) on bacterial community structure, followed by ST (r² = 0.4787, p = 0.043) and SM (r² = 0.4199, p = 0.048) (Figure 6A; Supplementary Table S6A). With regard to fungi, the community structure was also significantly influenced by ST (r² = 0.5484, p = 0.022) and SM (r² = 0.4972, p = 0.047). Although the correlation between OM and fungal community did not reach a significant level, it was the most influential soil factor on fungal community structure (r² = 0.4174, p = 0.071) after ST and SM (Figure 6B; Supplementary Table S6B).

Correlations of the 20 most abundant microbial genera with soil properties and bamboo shoot number were further analyzed. The abundances of bacterial genera norank_f_Xanthobacteraceae and Candidatus_Solibacter were found to have a significant positive correlation with ST and SM. Candidatus_Solibacter, Acidothermus, Conexibacter, and Roseiarcus were found to have a significant positive correlation with bamboo shoot number, and have a negative or significant negative correlation with OM, AP, AK, and HN (Figure 7A). With regard to the fungal community, the genera Trichoderma and Tolypocladium had a significant positive correlation with bamboo shoot number, and Russula had an apparent negative correlation with HN and AP. Soil temperature and moisture were positively correlated with Trichoderma, Saitozyma and Solicoccozyma, and negatively correlated with Exophiala. Additionally, the correlations between most fungal genera and OM showed an opposite pattern to those with ST and SM (Figure 7B).

In this study, the SM contents of C. pingbianense rhizosphere were higher in summer and autumn than those in spring and winter, significantly affected by seasonal rainfall in the study area. The soil OM contents in summer and autumn were significantly lower than those in spring and winter. On one hand, this was likely due to the enhanced leaching of organic matter with the higher precipitation in summer and autumn (Cleveland et al., 2006; Dong et al., 2019). On the other hand, it may be because of the low ST in spring and winter resulting in weakened mineralization and relatively increased accumulation of OM (Bai et al., 2013; Brienen et al., 2015). Plants need more nutrients from the soil during their growth spurt when the soil organic carbon mineralization rate is high (Buckeridge et al., 2013). Bamboo shoots of C. pingbianense emerge in summer and peak in autumn, during which the soil OM may release large amounts of nutrients through high mineralization to meet the growing needs of bamboo shoots. The variation trend of soil TN in the four seasons was similar to that of OM. It is understandable because more than 90% of soil TN is organic nitrogen stored in the soil, and the organic nitrogen mainly concentrates in humus (Nannipieri and Eldor, 2009; Knicker, 2011). Surprisingly, in autumn, the soil HN, AP and AK contents were lower than those in other seasons. We speculated that autumn was the peak stage of bamboo shoots, and the sprouting growth consumed large amounts of nutrients in the soil. In addition, there was no significant difference in soil pH value and soil TK content among the four seasons. It was probably because these two properties were basically influenced by the soil parent material of sampling site and less affected by seasonal variations.

On the other hand, our results indicated that ST, SM, OM and AP were key factors affecting soil microbial communities of C. pingbianense rhizosphere. Soil microbial community structure can be jointly regulated by the environment and available resources (Zhao et al., 2022). The seasonal variation leads to changes in soil temperature and moisture, both of which are significant drivers of soil microbial communities (Kim et al., 2020; Shi et al., 2020b; Romero-Trigueros et al., 2021). Meanwhile, the reciprocal effect between temperature and precipitation could influence carbon cycling by regulating microbes (You et al., 2014; Taylor et al., 2017). Our results confirmed that ST, SM, and OM had the most important effects on soil microbial community structure in C. pingbianense rhizosphere. Furthermore, the soil bacterial alpha diversity was also affected by ST, SM, and OM, which was consistent with recent studies on soil microorganisms (Su et al., 2022; Zhang et al., 2022). The fungal Chao1 index was found to have a strong negative correlation with AP content. The possible explanation was that the low contents of soil-available phosphorus in spring and autumn samples may promote the symbiosis between plants and mycorrhizal fungi, thereby increasing phosphorus uptake by plants (Shi et al., 2021), while the enrichment of mycorrhizal fungi might have increased the diversity of the fungal community.

Seasonal changes affect microbial diversity (Shen et al., 2021). In our study, the bacteria diversity in the rhizosphere soil of C. pingbianense was relatively low in summer and autumn, which was possibly associated with the high rainfall during this period. Soil moisture conditions can affect the soil pores connectivity and the adsorption capacity of soil to bacteria (Banks et al., 2003). The wetter condition is expected to expand the migration ability of dominant bacteria so that the motile species can more easily cross pore space to seek optimal conditions, thus reducing the potential of bacterial biodiversity at a macroscopic level (Wang and Or, 2010). Regarding the fungal community, the enrichment of ectomycorrhizal fungi in spring and autumn samples may probably result in the high fungal community diversity in these seasons. Although both the soil bacterial and fungal diversities were affected by seasonal changes, our results indicated that fungi were more affected. It was reported that soil bacterial and fungal communities had different adaptations to environmental perturbations, with the former being more resistant and adaptable in terms of diversity, structure and biomass (Kaisermann et al., 2015; Wang et al., 2019). Bacteria have a wider range of life than fungi and often form biofilms to resist external disturbances through extracellular polymers (Or et al., 2007), which may explain their relative stability.

Based on the microbial community composition analysis, three of the most dominant bacterial phyla in four seasons were Proteobacteria, Acidobacteriota, and Actinobacteriota. These bacterial phyla were also dominant taxa in other woodland soils (e.g., Cheng et al., 2021; Liang et al., 2021), indicating that these phyla had a wide ecological niche and could adapt to extensive ecological environment types. At the genus level, Acidothermus, norank_f_Xanthobacteraceae, Roseiarcus, Candidatus Solibacter and Bradyrhizobium were dominant soil bacterial genera in all seasons, most of which functioned as carbohydrate decomposers or nitrogen fixers. Among them, Roseiarcus and Bradyrhizobium belonging to Rhizobiales have the ability to nitrogen fixation (Morvan et al., 2020), while Acidothermus belonging to Actinobacteria has the function of degrading cellulose (Talia et al., 2012). Most members of Xanthobacteraceae are known to interact with plants and are capable of fixing nitrogen, and some species of the family can also degrade alkenes and aromatic compounds (Oren, 2014). Candidatus Solibacter can decompose nitrate and nitrite, and has complete enzymatic pathways (polysaccharide lyases and pectin esterases) for the degradation of cell wall polysaccharides (Ward et al., 2009; Rime et al., 2015). The functional prediction results confirmed that cellulolysis and nitrogen fixation were abundant in the bacterial community of C. pingbianense rhizosphere soil. Moreover, functions of nitrification and denitrification showed low abundances in the bacterial community. Given those two processes generally occur in neutral to slightly alkaline soil conditions (Wang et al., 2020), the acidic rhizosphere soil of C. pingbianense may inhibit both processes. Weak nitrification reduced the degree of conversion from ammonium nitrogen to nitrate nitrogen, and in contrast, the nitrogen fixation function in soil was abundant. This condition may result in high levels of ammonium nitrogen in the soil and promote the growth of bamboo shoots, as plants prefer to utilize ammonium nitrogen in the early growth stage (Li, 2008).

Similarly, in the soil fungal community of C. pingbianense rhizosphere, some fungal taxa associated with carbon metabolisms, such as Tremella yeast Saitozyma and Solicoccozyma belonging to Basidiomycota, and Trichoderma belonging to Ascomycota, were in high abundance in samples of various seasons. Yeast can act as a pioneer in cooperating with mold and bacteria to degrade organic matter, and promote the mineralization of soil organic carbon (Fang et al., 2021). Saitozyma and Solicoccozyma are usually isolated from habitat soil. Saitozyma can produce lipase (extracellular enzyme) to degrade fats, while Solicoccozyma has a pronounced ability to assimilate aromatic acids and low-weight aromatic compounds (Liu et al., 2015). Genus Trichoderma can not only secrete cellulase to degrade cellulose (Chen et al., 2021) but also participate in the dissolution of insoluble soil phosphorus, improving the ability of plants to acquire phosphorus (Bononi et al., 2020). Genus Mortierella and Tolypocladium also occupied a prominent position in the soil fungal communities. It was reported that Mortierella could effectively decompose organic matter and enhance the stress resistance of host plants by synthesizing arachidonic acid (Murase et al., 2012; Wani et al., 2017). Some species of Tolypocladium have strong antagonistic and inhibitory effects on various plant pathogenic fungi (Yu et al., 2021). The enrichment of these fungal taxa in the rhizosphere soil of C. pingbianense may promote bamboo forest growth and play a positive role in the emergence of bamboo shoots. Functional profiling of the fungal community revealed a high proportion of saprotrophic fungi in soil samples of four seasons, which may be more conducive to carbon decomposition, as carbon in rhizosphere soil is first used by saprophytic fungi (Ballhausen and de Boer, 2016). It was observed that the roots of C. pingbianense were free of mycorrhizae, and the ectomycorrhizal fungi detected probably came from the Fagaceae plants in the habitat (Toju et al., 2014). There are well-developed common mycelial networks in forest soil, which can connect host and non-host plants for carbon, nitrogen and phosphorus transfer and signal communication through shared mycelial networks (He et al., 2003; Wilson et al., 2006; Klein et al., 2016; Wang et al., 2021). As we have mentioned above, the enrichment of ectomycorrhizal fungi in spring and autumn samples may facilitate the adaptation of C. pingbianense to low phosphorus condition and enhance its access to soil phosphorus. Additionally, the fungal genus Trichoderma and bacterial genus Roseiarcus were identified as biomarkers in the autumn sample through LEfSe analysis. Although these two genera were dominant in soils of all seasons, they possessed higher abundances in the autumn sample, which may be one reason for the higher amounts of bamboo shoots in autumn compared to other seasons.

Soil microbes reflect soil type, environmental conditions and woodland management to some extent, and play a crucial role in soil ecosystems, especially in the regulation of carbon and nutrient cycles (Coskun et al., 2017; Prommer et al., 2020). In this research, bacterial genera Candidatus_Solibacter, Acidothermus and Roseiarcus, as well as fungal genera Trichoderma and Tolypocladium, were found to have a significant positive correlation with bamboo shoot productivity. At the same time, these taxa were also dominant microbes in the rhizosphere soil of C. pingbianense, and their significant roles in soil carbon and nitrogen cycles have been described in detail in the above discussion. Woody bamboos are considered the representative plant with the fastest growth speed in the world. The growth and development of shoots and young culms depend on the attached mature bamboo culms and the soil to provide adequate nutrients (Sun et al., 2019), and several studies have investigated that the stress of nutrient conditions in bamboo forests can lead to the reduction and degradation of bamboo shoots (Zhong et al., 2014; Chen et al., 2019). As C. pingbianense can produce bamboo shoots throughout the year, the sprouting of bamboo shoots and growth of young culms theoretically require a large supply of nutrients. The possible mechanism is that the humid evergreen broad-leaved forest in the upper layer provides rich litters, and the enrichments of microbes associated with carbohydrate degradation and nitrogen fixation in C. pingbianense rhizosphere accelerate the mineralization of organic matter and the fixation of available nitrogen, providing continuous and sufficient nutrients for the germination of rhizome buds and the growth of bamboo shoots. To sum up, these specific microbes may be one important ecological factor for the full-year shooting characteristic of C. pingbianense.