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Core microbiota drive multi-functionality of the soil microbiome in the Cinnamomum camphora coppice planting

BMC Microbiology volume 24, Article number: 18 (2024) Cite this article

Abstract

Background

Cinnamomum camphora (L.) Presl (C. camphora) is an evergreen broad-leaved tree cultivated in subtropical China. The use of C. camphora as clonal cuttings for coppice management has become popular recently. However, little is known about the relationship between soil core microbiota and ecosystem multi-functionality under tree planting. Particularly, the effects of soil core microbiota on maintaining ecosystem multi-functionality under C. camphora coppice planting remained unclear.

Materials and methods

In this study, we collected soil samples from three points (i.e., the abandoned land, the root zone, and the transition zone) in the C. camphora coppice planting to investigate whether core microbiota influences ecosystem multi-functions.

Results

The result showed a significant difference in soil core microbiota community between the abandoned land (AL), root zone (RZ), and transition zone (TZ), and soil ecosystem multi-functionality of core microbiota in RZ had increased significantly (by 230.8%) compared to the AL. Soil core microbiota played a more significant influence on ecosystem multi-functionality than the non-core microbiota. Moreover, the co-occurrence network demonstrated that the soil ecosystem network consisted of five major ecological clusters. Soil core microbiota within cluster 1 were significantly higher than in cluster 4, and there is also a higher Copiotrophs/Oligotrophs ratio in cluster 1. Our results corroborated that soil core microbiota is crucial for maintaining ecosystem multi-functionality. Especially, the core taxa within the clusters of networks under tree planting, with the same ecological preferences, had a significant contribution to ecosystem multi-functionality.

Conclusion

Overall, our results provide further insight into the linkage between core taxa and ecosystem multi-functionality. This enables us to predict how ecosystem functions respond to the environmental changes in areas under the C. camphora coppice planting. Thus, conserving the soil microbiota, especially the core taxa, is essential to maintaining the multiple ecosystem functions under the C. camphora coppice planting.

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Introduction

Soils are living ecosystems forming the foundation of terrestrial ecosystems, with multiple ecological functions, including maintaining sustainable agricultural production and environmental well-being [1, 2]. However, global climate change and anthropogenic activities are exerting significant pressure on the natural environment surrounding the agricultural landscape, resulting in soil quality degradation and species loss [3]. As a part of broader restoration efforts and to improve soil health and functions, tree planting has been applied globally [4]. It is suggested that planting more trees can create more diverse niches for underground microbes to thrive [5, 6]. Indeed, an increasing number of studies support the argument that tree planting can significantly influence the diversity and composition of soil microbiota directly by modifying soil properties and resource utilization patterns [7, 8]. However, some studies suggest that tree planting may have adverse effects. Particularly, some tree species can alter soil structure and water distribution in the soil column, negatively influencing the stability and resilience of restored ecosystems [9, 10]. Therefore, understanding the variation in soil microbiota in agroecosystems under tree planting is of great significance for improving soil health and functions and achieving sustainable agricultural production [11].

Soil microbiomes are highly complex and diverse, and they are involved in multiple important ecological and physiological functions (hereafter, multi-functionality [12, 13]), including soil organic matter decomposition and cycling, influence the availability of mineral nutrients, nitrogen fixation, and primary productivity [14,15,16]. Recently, links between soil biodiversity and ecosystem multi-functionality have received increasing attention, and studies have reported positive relationships between higher levels of microbial abundance and ecosystem multi-functionality [17, 18]. Nevertheless, soil ecosystem multi-functionality among microorganisms may vary with microbial functional groups, with certain groups having greater effects than others [19, 20].

Microbiota are present in large numbers across all ecosystems and are regarded as “core taxa” due to their significant influence on soil community functioning [21, 22]. However, the role of the core microbiota in maintaining the ecosystem multi-functionality remains unexplored [23]. Recent studies also have shown that soil organisms tend to coexist and form distinct ecological clusters of specific taxa [24, 25]. These taxa within the clusters are anticipated to interact and perform multiple functions, such as maintaining soil quality and enhancing agriculture production [26, 27]. As a result, ecological clusters could potentially form based on the core soil taxa that contribute to multiple ecosystem functions. Unfortunately, to our knowledge, no previous studies have examined the correlation between the multiple ecosystem functions and the ecological clusters of soil core groups under tree plantation.

Cinnamomum camphora (L.) Presl is a broad-leaved evergreen tree from the Lauraceae family with a wide distribution in subtropical China [7]. The tree contains many secondary metabolites, and the whole plant has traditionally been felled to harvest the roots, stems, and leaves and extract essential oil [28,29,30]. In recent years, the C. camphora tree has been utilized for coppice management, particularly in southern China [31, 32]. Then, the aerial parts of the C. camphora coppice were cut down at a distance of 20 cm above the ground from July to September annually. Unlike traditional tree planting, the remaining part of the tree stump regenerates year over year, leading to the circular production of C. camphora coppice, parallel to managing perennial crops. Despite the popularity of coppice management, few studies have examined the impact of coppice management practice on soil core microbiota and ecosystem multi-functionality.

To enrich our cognition on how soil core microbiota and ecosystem multifunctionality respond to the C. camphora coppice plantation, we collected the top 15 cm soils from the five-year C. camphora coppice plantation land to make further analysis. Soil samples were collected from three different points, the abandoned land (AL), the root zone (RZ), and the transition zone (TZ). Particularly, we asked the following questions: (1) how C. camphora coppice planting affects soil core microbiota, (2) how C. camphora coppice planting impacts soil ecosystem multi-functionality, and (3) what is the relationship between soil microbiota and ecosystem multi-functionality under C. camphora coppice planting? This study emphasized the significance of soil core microbiota and ecosystem multi-functionality in agroecosystems, which can ultimately offer a theoretical basis for scientifically guiding the production and management of C. camphora coppice plantations in subtropical China.

Materials and methods

Study area

The field experiment was conducted on C. camphora coppice plantation land in Guixi City, Jiangxi Province, southern China (28°17′46″ N, 117°13′28″E). The research site has a subtropical monsoon climate with a mean annual temperature of 18.8℃. The experimental site receives a mean annual precipitation of 1980.8 mm and 1611.5 h of sunlight, respectively. The experimental soils belong to Ultisols, which was abandoned land before. At the beginning of the experiment, the topsoil (0–15 cm) had a soil pH of 4.83, soil organic carbon (SOC) of 12.95 g·kg− 1, soil total nitrogen (TN) of 0.96 g·kg− 1, soil available nitrogen (AN) of 102.93 g·kg− 1 and soil available phosphorus (AP) of 5.21 g·kg− 1.

Experiment design and soil sampling

In this study, we selected a 5000 m2C. camphora coppice plantation land established in 2015. The initial plantation land is dominated by species Firmiana simplex, Broussonetia papyrifera, Phytolacca acinose, Humulus scandens, Eleusine indica, Duchesnea indica, and Erigeron canadensis. Before C. camphora coppice planting, vegetation growing on this land should be cleared first, and then manual weed control will be carried out regularly every year. And we don’t use any weeding measures on the abandoned land (AL). Every year since 2016, in early September, we cut down the C. camphora tree from 20 cm above the ground to extract the essential oil. The row and inter-plant spacings of the C. camphora tree was 1 m × 1 m, with a planting density of 10,000 trees·hm− 2. The coppice plantation was fertilized, with an application rate of 150 g tree− 1 year− 1 for N2O5, P2O5, and K2O. The fertilizers were applied in two phases, with 50% applied in early March and the remaining 50% after the C. camphora tree was felled in late September. The fertilization point (FP) was a 15 cm deep circle at a distance of 25 cm from the center of the tree (Fig. 1).

Fig. 1
figure 1

Experiment layout with sampling details

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In this study, we selected three distinct locations as our sampling points, each with four replicates: (1) a point in the abandoned land (AL); (2) a point in the root zone (RZ); and (3) a point in the transition zone (TZ). From each point, Soil samples were randomly collected using the S-shaped sampling method after the C. camphora tree felling on September 12th, 2021. Soil samples from the top 15 cm were taken using a 2 cm diameter soil core sampler. Multiple soil samples (n = 20) were taken from a 50 m2 area and composited. In the Lab, the composited soil samples were passed through the 2 mm sieve separately to remove the impurities. The sieved soils for each sample were divided into three parts: one was air-dried to measure soil fertility, the second was preserved at 4℃ to measure enzyme activity, and the remaining was kept at -80℃ for DNA extraction.

Soil properties and enzyme activity

After the soil was suspended with water at a ratio of 1:2.5 (weight:volume), the pH was measured with a pH meter (FE28-Standard, METTLER-TOLEDO, Switzerland). Soil organic carbon (SOC) was determined by dichromate oxidation and titration with ferrous sulfate. Soil total nitrogen (TN) was determined using the Kjeldahl digestion distillation method. Soil available nitrogen (AN) and phosphorus (AP) were determined using alkali hydrolyzation and the Bray method, respectively [33]. Soil enzyme activities of invertase (INV), urease (UE), acid phosphatase (ACP), catalase (CAT), polyphenol oxidase (PPO), and peroxidase (POD) were determined using kits from Beijing Solarbio Technology Co. Ltd according to the manufacturer’s instructions [34, 35].

Soil microbial DNA extraction and Illumina MiSeq sequencing

DNA was extracted from 0.5 g of fresh soil using the FastDNA® SPIN Kit for Soil (MP Bio-medicals, Santa Ana, CA) following the manufacturer’s instructions. The purification of DNA was tested by Power Clean DNA Clean-Up Kit (MOBIO Laboratories, Carlsbad, CA, USA), and the PCR inhibitors were removed. The eluted DNA was examined by 1% (m/v) agarose gel electrophoresis and quantified using the NanoDrop® 2000 spectrophotometer (Thermo Fisher Scientific, USA). Aliquots of the DNA were stored in a − 20 °C freezer for subsequent analyses. The V4-V5 highly variable regions of the bacterial 16 S rRNA genes were amplified with barcoded universal primers 515 F/907R [36]. Moreover, the ITS regions of the fungal rRNA genes were amplified using primer sets ITS1/ITS2 [37].

Bioinformatics analysis for raw sequences

Raw Illumina reads were analyzed using the QIIME v.1.91 pipeline [38]. The low-quality (quality score < 25) or short (length < 200 bp) sequences were removed before downstream analysis [39]. From the remaining high-quality sequences, Operational Taxonomic Units (OTUs) were identified from the high-quality sequences at a 3% dissimilarity level using the UNOISE algorithm [40]. The RDP classifier with the SILVA 132 database [41] was utilized to predict the taxonomic identity of the final stock with confidence estimates of 80% confidence estimate. The representative sequences of ITS were annotated for species using the blast method with the UNITE database [42]. A total of 4427 bacteria OTUs and 1690 fungal OTUs were then selected for subsequent downstream analysis.

Statistical analyses

Statistical tests were performed by the Wilcoxon test and the Kruskal-Wallis test. Wilcoxon tests were used for within-group comparisons, and Kruskal Wallis tests were used for between-group comparisons. The core microbiota present in bacterial and fungal communities were identified using the following criteria: (1) the highly abundant OTUs with relative abundance in the top 10% were selected from all the soil samples, and (2) the ubiquitous OTUs were presented in 95% of the soil samples were retained. Thus, the selected OTUs under the two criteria were abundant and ubiquitous across the 12 soil samples in this study [43]. Principal Coordinates Analysis (PCoA) was used in the R packages phyloseq (v.1.32.0) to explore the differences in soil core microbiota community between the sampling points based on Bray–Curtis distances [44]. Significant differences in the overall community composition between the sampling points were examined statistically using analysis of similarity (ANOSIM). Analysis of differential OTUs was detected by the R packages edgeR (v.4.1.2) to identify the enriched OTUs in different sampling points [45].

Soil ecosystem multifunctionality was accessed by measuring: (1) soil nutrients cycling: AN and AP; (2) soil fertility: SOC and TN; and (3) soil activity: UE, CAT, ACP, CAT, PPO, and POD (Table S2). First, the normality of relevant soil data was checked using the Shapiro-Wilk test, and whenever necessary, variables were logarithm or square root transformed to meet normality and homogeneity assumptions [46]. Then, the average index approach was used to calculate the soil ecosystem multifunctionality [17, 47]. In order to ensure consistency in the scaling of the data, each variable was standardized with a range of zero to one. The average multifunctional index was then calculated by taking the mean value of indexes in each sampling point [48, 49]. A random forest model was used to predict the main drivers of soil multifunctionality with the R packages rfPermute (version 2.5) [50]. The significance of variables was determined through the percentage of mean squared error (MSE%): higher MSE% represents more significant variables [51].

The Weighted Gene Co-Expression Network Analysis (WGCNA) was used in the R packages WGCNA (v.1.70-3) to determine soil hub taxa [52]. The soft threshold power was determined using the scale-free topological criterion with an R2 of 0.9. The adjacency matrix was constructed according to the most appropriate soft threshold power of 10, which was then transformed into a topological overlap matrix (TOM). Then, TOM-based dissimilarity and dynamic branch cutting were used to identify different ecological clusters. The OTUs with high module membership values (greater than 0.9) were called hub taxa. The relative abundance of OTUs included in the clusters was normalized and then averaged to obtain the relative abundance of the cluster [27].

Results

The composition and structure of soil core microbiota in the C. camphora coppice planting

Under the AL, RZ, and TZ, there were 651,537 and 748,374 soil bacterial and fungal sequences. After filtering and standardization, 4404 and 1409 OTUs in soil bacteria and fungi were clustered, respectively. The soil core microbiota were confirmed by selecting highly abundant (with relative abundance in the top 10%) and ubiquitous OTUs (presenting in 95% of all soil samples). The analysis of soil core taxa identified a total of 375 OTUs, consisting of 332 soil bacterial taxa and 43 soil fungal taxa. (Table S1). The soil core taxa were dominated by bacteria taxa, and the phylum Proteobacteria had the highest number (n = 110), followed by Acidobacteria (n = 68), Actinobacteria (n = 59), and Chloroflexi (n = 37). The fungal phylum mainly consisted of Ascomycota (n = 36), Basidiomycota (n = 4), and Zygomycota (n = 3). (Fig. 2A).

Fig. 2
figure 2

(A) The number of soil microbial taxa in each phylum under C. camphora planting were illustrated in the pie chart. (B) PCoA plot depicting the core microbiota in the C. camphora coppice planting based on Bray–Curtis distances. (C) Ternary plot of all OTUs detected in the dataset with a relative abundance > 5% in at least one soil sample in the C. camphora coppice planting, where each point corresponds to one out and the size of a point represents the average relative abundance, and the position depicted the contribution of the indicated compartments to the total relative abundance. Colored circles represent OTUs enriched in one compartment compared with the others; green in AL, orange in RZ, and red in TZ, whereas gray circles represent OTUs not significantly enriched in a specific compartment. Note: AL, abandoned land; RZ, root zone; and TZ, transition zone

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The PCoA analysis revealed that the soil core microbiota community was distinctly separated in ordination space according to the sampling points (Fig. 2B). The microbiota community in the AL was clustered separately from the RZ and TZ and along the first coordinate axis. Overall, PCoA explained 64.74% of the total variance of the soil core microbiota community composition in the C. camphora coppice planting, of which the first axis (PCoA 1) explained 41.04% of the variance, and the second constraint axis (PCoA 2) explained 23.70% of the variance (Fig. 2B). ANOSIM confirmed a significant difference in soil core microbiota community between the sampling points (p < 0.05, Table S2). The compositional difference was much more pronounced between AL with RZ (RANOSIM = 0.9272, p = 0.027), and AL and TZ (RANOSIM = 0.9271, p = 0.030) than between RZ and TZ (RANOSIM = 0.8438, p = 0.026). The ternary plot was used to illustrate OTUs that are specifically enriched between different sampling points, and results showed 28 OTUs enriched in AL, 33 OTUs enriched in RZ, and 6 OTUs enriched in TZ (Fig. 2C). Among the enriched OTUs in AL, Chloroflexi was the most dominant phylum, with 9 OTUs, accounting for 32.14% of the total OTUs. Proteobacteria was the most dominant phylum, with 15 OTUs in RZ, representing 45.45% of the total OTUs (Fig. 3).

Fig. 3
figure 3

Heatmap of the number of soil core microbiota at phylum level in the C. camphora coppice planting. Note: AL, abandoned land; RZ, root zone; and TZ, transition zone

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The effects of soil core microbiota on soil ecosystem multifunctionality in the C. camphora coppice planting

Compared to the AL, the RZ significantly increased soil TN, AN, and AP contents, and significantly improved soil UE, PPO, and POD activities but decreased soil ACP activity. Specifically, the ecosystem multi-functionality of the soil microbiota increased significantly (by 230.8%) in the RZ compared to the AL (p < 0.01). There were also significant differences (p < 0.05) between RZ and TZ, and the RZ increased soil ecosystem multi-functionality by 160.8% relative to the TZ. However, no significant difference in ecosystem multi-functionality was observed between AL and TZ (p > 0.05; Fig. 4A). RF analysis found the significant influences of core microbiota on soil ecosystem multi-functionality (p < 0.001); however, the contribution of the non-core microbiota was less obvious (Fig. 4B).

Fig. 4
figure 4

(A) Variation in the multi-functionality index in the C. camphora coppice planting. Significant differences were found after multiple comparison tests after the Kruskal–Wallis analysis (*p < 0.05; **p < 0.01). (B) Random Forest mean predictor importance of microbial taxa for multifunctionality in the C. camphora coppice planting. Percentage increases in the MSE (mean squared error) of variables were used to estimate the importance of these predictors, and higher MSE% values implied more important predictors. The boxplot showed differences between core taxa (“Core”) and other non-core taxa (“Others”; ***p < 0.001; Wilcoxon rank-sum test). Note: AL, abandoned land; RZ, root zone; and TZ, transition zone

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The relationship between soil core ecological clusters and soil ecosystem multifunctionality in the C. camphora coppice planting

We utilized a co-occurrence network through WGCNA to explore the soil ecosystem network structure in the C. camphora coppice planting. Our findings showed that the network comprised five major ecological clusters with a total of 385 nodes (Fig. 5A). Least square regression analysis showed that cluster 1 had a significantly positive correlation with soil ecosystem multifunctionality (R2 = 0.70, p < 0.001) with 99 nodes. In contrast, there was a significant negative correlation between cluster 4 and soil ecosystem multi-functionality (R2 = 0.55, p < 0.01) with 132 nodes. The remaining clusters had no significant effect on soil ecosystem multi-functionality (p > 0.05, Fig. 5B).

Fig. 5
figure 5

Correlation between the soil core ecological clusters and soil ecosystem multifunctionality. (A) Co-occurrence networks based on the Weighted Gene Co-Expression Network Analysis (WGCNA). The colors of the nodes represent different ecological clusters. (B) The regression relationships between soil ecological multifunctionality index and the relative abundance of core taxa within the main ecological clusters. Statistical analysis was performed using ordinary least squares linear regressions; p values were indicated by asterisks (**p < 0.01; ***p < 0.001)

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The features of soil core ecological clusters and their relationships

The Wilcoxon rank sum test determined the correlations between cluster 1 and cluster 4. We observed that the relevance of the soil core microbiota within cluster 1 was significantly higher than the cluster 4 (p < 0.001, Fig. 6A). In cluster 1, Copiotrophs/ Oligotrophs (74.50%) were more abundant than in cluster 4 (39.74%) (Fig. 6B). The hub taxa in cluster 1 were dominated by members of Actinobacteria and Proteobacteria, whereas the members of Actinobacteria and Chloroflexi dominated the cluster 4 (Fig. 6C).

Fig. 6
figure 6

(A) Correlations between soil core taxa of cluster 1 and cluster 4 within the ecological network (***p < 0.001). (B) Value of Oligotrophs/Copiotrophs in cluster 1 and cluster 4. (C) The number of soil core microbiota at the phylum level in cluster 1 and cluster 4

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Regarding the relative abundance of soil key ecological clusters under different sampling points, RZ significantly (p < 0.05) increased the relative abundance of cluster 1 compared with AL (by 77.34%) and TZ (by 61.50%) (Fig. 7A). In contrast, AL significantly decreased the relative abundance of cluster 4 by 162.3% compared with RZ and by 78.91% compared with TZ (p < 0.01) (Fig. 7B). There was no significant difference between AL and TZ in cluster 1 and RZ and TZ in cluster 4 (Fig. 7).

Fig. 7
figure 7

(A) The relative abundance of soil core microbiota of cluster 1. (B) The relative abundance of soil core microbiota of cluster 4. Note: AL, abandoned land; RZ, root zone; and TZ, transition zone

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Discussion

Anthropogenic pressures have caused ecological deterioration on a global scale, and agricultural ecosystems are experiencing increasing challenges (e.g., species loss) [53, 54]. As a result, it is undoubtedly worth in soil ecosystems’ response pattern to human disturbance and how microbiota dominates ecosystem functional diversity under these changes [55, 56]. Here, we present empirical proof that soil ecosystem multi-functions in our study have improved under C. camphora tree planting. Moreover, we found a high contribution of soil core microbiota in the ecological clusters based on the co-occurrence network analysis is of high contribution in maintaining soil multifunctionality. These findings highlight a close connection between soil core microbiota and ecosystem function in the context of C. camphora tree planting.

In this study, we found that soil fertility in RZ and TZ was comparatively higher than in the AL, this may be related to the fertilizer application during the C. camphora coppice planting. However, many studies have found that even fertilization may not avoid soil fertility decline after land use pattern changes [56,57,58]. A previous study revealed that the conversion of unfertilized natural forests into tree plantations results in a significant reduction in soil fertility [59]. There are several potential reasons accounting for this. Firstly, although fertilizer can increase soil nutrient input, it is uncertain whether it can compensate for nutrients taken away by crop harvesting [7], specifically the C. camphora coppice harvest in this study. Furthermore, soil fertility changes may also correlate with the types of vegetation or crops [4]. For example, tree plantation can provide a more lasting and extensive surface covering relative to abandoned or agricultural land. Thus, it can alleviate soil nutrient loss, especially in the hot and rainy subtropical red soil region [60, 61]. Additionally, different litterfall types and root distribution of plants also have distinct effects on soil fertility [62, 63]. The present study demonstrated that RZ and TZ improved soil fertility compared with abandoned land. This is not only caused by fertilization, but the comprehensive effect of C. camphora coppice planting under current planting management patterns.

Tree planting positively impacts the soil ecosystem by improving soil structure, regulating nutrient cycling [64]. Our results showed a significant increase in soil multi-functionality in the RZ under C. camphora coppice planting (p < 0.01) compared to the abandoned land (Fig. 4A), implying the significance of tree planting in regulating ecosystem multi-functionality. Consistent with our findings, Yan et al. [55] demonstrated that the Robinia pseudoacacia tree planting is conducive to maintaining soil ecosystem multi-functionality in northern China. These findings suggest that the nutrients accumulation from leaf litter and other dead materials overtime increases soil organic matter, which positively affects overall soil health and functions [63, 65]. Moreover, trees and the expansion of their root systems in the soil provide an important source of nutrients to microbiota through root exudates that create a diverse niche for a range of microbiota, supporting biodiverse and healthy soil [66]. Collectively, tree planting and healthy soils can improve ecosystem resilience to natural and anthropogenic environmental changes while sustaining their multifunctionality [67].

Soil microbiota have been identified as the most active component of the soil ecosystem [68]. The diversity, composition, and activities of these microbiota interact with various factors in the soil to regulate multiple critical ecological functions [69]. Through empirical research, we discovered notable distinctions in the community composition of soil core microbiota and variations in their ecological preferences within the C. camphora coppice planting. For example, in the RZ, the most commonly found taxa were Proteobacteria, which are known to thrive in high-nutrient environments due to their copiotrophic nature and ability to grow rapidly [70]. However, in the AL, Chloroflexi was the most abundant taxa and the microorganisms in the taxa are oligotrophic that generate energy through photosynthesis and break down organic matter in low-nutrient conditions [71]. By studying the functional roles of various microbial taxa, we can gain valuable insights into their contributions to crucial ecological processes such as nutrient cycling and carbon sequestration. However, further experimental and modeling approaches exploring the variation in microbiota composition and structure and the underlying mechanisms of such variations are needed to understand the long-term impacts of microbiota on soil ecosystem dynamics.

Some important soil functions are primarily determined by specific soil microbial taxa, rather than the entire microbial community [19]. Recently, researchers have focused on soil microbiota (soil core taxa, dominant taxa, or rare taxa) and ecosystem multi-functionality [43, 72, 73]. These studies highlight the importance of soil core microbiota influencing the multiple functions in soil ecosystems [74]. For example, Jiao et al. [11] revealed positive relationships between soil core taxa and ecosystem functions (e.g., nutrient cycling). Consistently, we reported that the soil core microbiota under the C. camphora coppice planting maintained significantly higher multiple ecosystem functions than the non-core microbiota (p < 0.001; Fig. 4B). This underscores the crucial role of core microbiota in preserving the multifaceted functions of soil ecosystems [75]. Notably, core microbiota are more generalist in distribution, where they can utilize a broad range of niches and resources, enhancing the productivity and stability of the soil ecosystems [76, 77].

Our results showed that the core taxa across the sampling points formed distinct ecological clusters (Fig. 5A), suggesting that the connections among these core taxa in the network were tied to their resource preferences. Such clustering due to resource preference and interaction between taxa within the cluster enhances the ecosystem multi-functionality [26]. Indeed, our research demonstrated that there was a strong positive association between the relative abundance of ecological cluster 1 and ecosystem multifunctionality (Fig. 5B), suggesting that the particular soil microbiota with comparable resource preferences play a crucial role in supporting various soil functions. The observed relationships can be explained by the following two factors. First, the tightness of microbial networks can enhance nutrient uptake and utilization [78]. In fact, soil core taxa within cluster 1 showed a strong connection to ecological multi-functionality. Therefore, higher co-occurrence and tightness of soil core taxa in ecological cluster 1 may have enhanced soil functions, including improved nutrient cycling [79]. Second, soil core microbiota with different ecological strategies can also impact the links between ecological clusters and multi-functionality [80, 81]. Specifically, we found a higher Copiotrophs/Oligotrophs ratio and increased relative abundance of copiotrophic taxa in cluster 1 may coincide with the improvement of soil ecological functions, such as organic matter, as copiotrophic species are capable of utilizing a wide variety of nutrients [82, 83].

Conclusion

In conclusion, our results showed that the C. camphora coppice planting altered the community composition of soil core microbiota and improved the overall functionality of soil ecosystems. In addition, our findings highlight the significance of soil core microbiota in maintaining the functionality of agroecosystems, such as the C. camphora coppice planting. Notably, the relative abundance of soil core microbiota within key ecological clusters significantly impacted the soil’s ability to perform multiple functions, suggesting that specific microbiota are crucial to maintaining soil functions in the C. camphora coppice planting. Therefore, maintaining diverse soil microbiota through tree planting may be the key to conserving soil health and maintaining agricultural sustainability across agricultural landscapes undergoing rapid anthropogenic changes.

Data availability

The datasets for this study can be found in Sequence Read Archive (SRP390129).

Abbreviations

C. camphora :

Cinnamomum camphora (L.) Presl

SOC:

Soil organic carbon

TN:

Total nitrogen

AN:

Available nitrogen

AP:

Available phosphorus

INV:

Invertase

UE:

Urease

ACP:

Acid phosphatase

CAT:

Catalase

PPO:

Polyphenol oxidase

POD:

Peroxidase

References

  1. Zhang S, Wang Y, Sun L, Qiu C, Ding Y, Gu H, et al. Organic mulching positively regulates the soil microbial communities and ecosystem functions in tea plantation. BMC Microbio. 2020;20:103.

    Article  Google Scholar 

  2. Ciancio A, Gamboni M. Soil biodiversity and tree crops resilience. Soil biological communities and ecosystem resilience. Springer International Publishing; 2017. p.321–43.

  3. Halperin S, Castro AJ, Quintas-Soriano C, Brandt JS. Assessing high quality agricultural lands through the ecosystem services lens: insights from a rapidly urbanizing agricultural region in the western United States. Agri Ecosyst Environ. 2023;349:108435.

    Article  Google Scholar 

  4. Pereira APA, Durrer A, Gumiere T, Gonçalves JLM, Robin A, Bouillet J-P, et al. Mixed eucalyptus plantations induce changes in microbial communities and increase biological functions in the soil and litter layers. For Ecol Manag. 2019;433:332–42.

    Article  Google Scholar 

  5. Asigbaase M, Dawoe E, Sjogersten S, Lomax BH. Decomposition and nutrient mineralisation of leaf litter in smallholder cocoa agroforests: a comparison of organic and conventional farms in Ghana. J Soils Sediments. 2021;21:1010–23.

    Article  CAS  Google Scholar 

  6. Yu Z, Liu S, Wang J, Wei X, Schuler J, Sun P, et al. Natural forests exhibit higher carbon sequestration and lower water consumption than planted forests in China. Glob Change Biol. 2019;25:68–77.

    Article  Google Scholar 

  7. Sun L, Zhang J, Zhao J, Lu X, Xiao C, Xiao Z, et al. Effects of Cinnamomum camphora coppice planting on soil fertility, microbial community structure and enzyme activity in subtropical China. Front Microbiol. 2023;14:1104077.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Kong W, Wei X, Wu Y, Shao M, Zhang Q, Sadowsky MJ, et al. Afforestation can lower microbial diversity and functionality in deep soil layers in a semiarid region. Global Change Biol. 2022;28:6086–101.

    Article  CAS  Google Scholar 

  9. Wu Z, Haack SE, Lin W, Li B, Wu L, Fang C, et al. Soil microbial community structure and metabolic activity of Pinus elliottii plantations across different stand ages in a subtropical area. PLoS ONE. 2015;10:e0135354.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Chen H, Shao M, Li Y. Soil desiccation in the Loess Plateau of China. Geoderma. 2008;143:91–100.

    Article  Google Scholar 

  11. Jiao S, Chen W, Wei G. Core Microbiota drive functional stability of soil microbiome in reforestation ecosystems. Global Change Biol. 2022;28:1038–47.

    Article  CAS  Google Scholar 

  12. Fan K, Chu H, Eldridge DJ, Gaitan JJ, Liu Y-R, Sokoya B, et al. Soil biodiversity supports the delivery of multiple ecosystem functions in urban greenspaces. Nat Ecol Evol. 2023;7:113–26.

    Article  PubMed  Google Scholar 

  13. Hu W, Ran J, Dong L, Du Q, Ji M, Yao S, et al. Aridity-driven shift in biodiversity–soil multifunctionality relationships. Nat Commun. 2021;12:5350.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Chen Q, Ding J, Zhu D, Hu H, Delgado-Baquerizo M, Ma Y, et al. Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils. Soil Biol Biochem. 2020;141:107686.

    Article  CAS  Google Scholar 

  15. de Vries FT, Wallenstein MD. Below-ground connections underlying above-ground food production: a framework for optimising ecological connections in the rhizosphere. J Ecol. 2017;105:913–20.

    Article  Google Scholar 

  16. Wagg C. Soil biodiversity and soil community composition determine ecosystem multifunctionality. PNAS. 2014;111:5266–70.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Delgado-Baquerizo M, Reich PB, Trivedi C, Eldridge DJ, Abades S, Alfaro FD, et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat Ecol Evol. 2020;4:210–20.

    Article  PubMed  Google Scholar 

  18. Wagg C, Schlaeppi K, Banerjee S, Kuramae EE, van der Heijden MGA. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat Commun. 2019;10:4841.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Zhu D, Lu L, Zhang Z, Qi D, Zhang M, O’Connor P, et al. Insights into the roles of fungi and protist in the giant panda gut microbiome and antibiotic resistome. Environ Int. 2021;155:106703.

    Article  CAS  PubMed  Google Scholar 

  20. Li H, Huo D, Wang W, Chen Y, Cheng X, Yu G, et al. Multifunctionality of biocrusts is positively predicted by network topologies consistent with interspecies facilitation. Mol Ecol. 2020;29:1560–73.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Luo J, Gu S, Guo X, Liu Y, Tao Q, Zhao H, et al. Core microbiota in the rhizosphere of heavy metal accumulators and its contribution to plant performance. Environ Sci Technol. 2022;56:12975–87.

    Article  CAS  PubMed  Google Scholar 

  22. Zhang L, Zhang M, Huang S, Li L, Gao Q, Wang Y, et al. A highly conserved core bacterial microbiota with nitrogen-fixation capacity inhabits the xylem sap in maize plants. Nat Commun. 2022;13:3361.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Shade A, Handelsman J. Beyond the Venn diagram: the hunt for a core microbiome. Environ Int. 2012;14:4–12.

    CAS  Google Scholar 

  24. Wu Y, Wu J, Saleem M, Wang B, Hu S, Bai Y, et al. Ecological clusters based on responses of soil microbial phylotypes to precipitation explain ecosystem functions. Soil Biol Biochem. 2020;142:107717.

    Article  CAS  Google Scholar 

  25. de Menezes AB, Prendergast-Miller MT, Richardson AE, Toscas P, Farrell M, Macdonald LM, et al. Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters. Environ Microbiol. 2015;17:2677–89.

    Article  PubMed  Google Scholar 

  26. Wang Y, Chen P, Wang F, Han W, Qiao M, Dong W, et al. The ecological clusters of soil organisms drive the ecosystem multifunctionality under long-term fertilization. Environ Int. 2022;161:107133.

    Article  CAS  PubMed  Google Scholar 

  27. Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.

    Article  CAS  PubMed  Google Scholar 

  28. Liu Z, Li H, Zhu Z, Huang D, Qi Y, Ma C, et al. Cinnamomum camphora fruit peel as a source of essential oil extracted using the solvent-free microwave-assisted method compared with conventional hydrodistillation. LWT. 2022;153:112549.

    Article  CAS  Google Scholar 

  29. Hou J, Zhang J, Zhang B, Jin X, Zhang H, Jin Z. Transcriptional analysis of metabolic pathways and regulatory mechanisms of essential oil biosynthesis in the leaves of Cinnamomum camphora (L.) Presl. Front Genet. 2020;11:598714.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Jiang H, Wang J, Song L, Cao X, Yao X, Tang F, et al. GC×GC-TOFMS analysis of essential oils composition from leaves, twigs and seeds of Cinnamomum camphora L. Presl and their insecticidal and repellent activities. Molecules. 2016;21:423.

    Article  PubMed Central  PubMed  Google Scholar 

  31. Xiao Z, Zhang B, Wang Y, Li F, Jin Z, Lü X, et al. Transcriptomic analysis reveals that exogenous indole-3-butyric acid affects the rooting process during stem segment culturing of Cinnamomum camphora Linalool type. Plant Mol Biol Rep. 2022;40:661–73.

    Article  CAS  Google Scholar 

  32. Zhao J. Dynamic changes of the contents of essential oil and nutrients of Cinnamomum camphora var. Linaloolifera and their correlation. Scientia Silvae Sinicae. 2021;57:57–67.

    CAS  Google Scholar 

  33. Akhtar K, Wang W, Ren G, Khan A, Feng Y, Yang G. Changes in soil enzymes, soil properties, and maize crop productivity under wheat straw mulching in Guanzhong, China. Soil till Res. 2018;182:94–102.

    Article  Google Scholar 

  34. Saiya-Cork KR, Sinsabaugh RL, Zak DR. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biol Biochem. 2002;34:1309–15.

    Article  CAS  Google Scholar 

  35. Tabatabai MA. Methods of Soil Analysis, Part 2.

  36. Biddle JF, Fitz-Gibbon S, Schuster SC, Brenchley JE, House CH. Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. PNAS. 2008;105:10583–8.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Adams RI, Miletto M, Taylor JW, Bruns TD. Dispersal in microbes: fungi in indoor air are dominated by outdoor air and show dispersal limitation at short distances. ISME J. 2013;7:1460–0.

    Article  PubMed Central  Google Scholar 

  38. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27:2957–63.

    Article  PubMed Central  PubMed  Google Scholar 

  40. Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. preprint. Bioinformatics; 2016.

  41. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, et al. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2013;22:5271–7.

    Article  PubMed  Google Scholar 

  43. Jiao S, Qi J, Jin C, Liu Y, Wang Y, Pan H, et al. Core phylotypes enhance the resistance of soil microbiome to environmental changes to maintain multifunctionality in agricultural ecosystems. Global Change Biol. 2022;28:6653–64.

    Article  CAS  Google Scholar 

  44. McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    Article  CAS  PubMed  Google Scholar 

  46. Hanusz Z, Tarasinska J, Zielinski W. Shapiro–Wilk test with known mean. REVSTAT-Stat J. 2016;14:89–100.

    Google Scholar 

  47. Lefcheck JS, Byrnes JEK, Isbell F, Gamfeldt L, Griffin JN, Eisenhauer N, et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat Commun. 2015;6:6936.

    Article  CAS  PubMed  Google Scholar 

  48. Schuldt A, Assmann T, Brezzi M, Buscot F, Eichenberg D, Gutknecht J, et al. Biodiversity across trophic levels drives multifunctionality in highly diverse forests. Nat Commun. 2018;9:2989.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Fanin N, Gundale MJ, Farrell M, Ciobanu M, Baldock JA, Nilsson M-C, et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat Ecol Evol. 2017;2:269–78.

    Article  PubMed  Google Scholar 

  50. Archer E. Package ‘rfPermute.’ 2016.

  51. Breiman L. Forests. Kluwer Academic Publishers; 2001.

  52. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

    Article  PubMed Central  PubMed  Google Scholar 

  53. Rillig MC, Ryo M, Lehmann A. Classifying human influences on terrestrial ecosystems. Glob Change Biol. 2021;27:2273–8.

    Article  CAS  Google Scholar 

  54. Wei X, Fu T, He G, Zhong Z, Yang M, Lou F, et al. Types of vegetables shape composition, diversity, and co-occurrence networks of soil bacteria and fungi in karst areas of southwest China. BMC Microbiol. 2023;23:194.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Yan Y, Wang J, Ding J, Zhang S, Zhao W. Afforestation promotes ecosystem multifunctionality in a hilly area of the Loess Plateau. CATENA. 2023;223:106905.

    Article  CAS  Google Scholar 

  56. Monkai J, Goldberg SD, Hyde KD, Harrison RD, Mortimer PE, Xu JC. Natural forests maintain a greater soil microbial diversity than that in rubber plantations in Southwest China. Agr Ecosyst Environ. 2018;265:190–7.

    Article  Google Scholar 

  57. Rosinger C, Rousk J, Sandén H. Can enzymatic stoichiometry be used to determine growth-limiting nutrients for microorganisms?-A critical assessment in two subtropical soils. Soil Biol Biochem. 2019;128:115–26.

    Article  CAS  Google Scholar 

  58. Wang J, Zou Y, Di Gioia D, Singh BK, Li Q. Conversion to agroforestry and monoculture plantation is detrimental to the soil carbon and nitrogen cycles and microbial communities of a rainforest. Soil Biol Biochem. 2020;147:107849.

    Article  CAS  Google Scholar 

  59. Liu T, Wu X, Li H, Alharbi H, Wang J, Dang P, et al. Soil organic matter, nitrogen and pH driven change in bacterial community following forest conversion. For Ecol Manag. 2020;477:118473.

    Article  Google Scholar 

  60. Hinojosa MB, Albert-Belda E, Gomez-Munoz B, Moreno JM. High Fire frequency reduces soil fertility underneath woody plant canopies of Mediterranean ecosystems. Sci Total Environ. 2021;752:141877.

    Article  CAS  PubMed  Google Scholar 

  61. Tang C, Liu Y, Li Z, Guo L, Xu A, Zhao J. Effectiveness of vegetation cover pattern on regulating soil erosion and runoff generation in red soil environment, southern China. Ecol Indic. 2021;129:107956.

    Article  Google Scholar 

  62. Li X, Wang H, Luan J, Chang S, Gao B, Wang Y, et al. Functional diversity dominates positive species mixture effects on ecosystem multifunctionality in subtropical plantations. For Ecosyst. 2022;9:100039.

    Article  Google Scholar 

  63. Männistö M, Ganzert L, Tiirola M, Häggblom MM, Stark S. Do shifts in life strategies explain microbial community responses to increasing nitrogen in tundra soil? Soil Biol Biochem. 2016;96:216–28.

    Article  Google Scholar 

  64. Garland G, Edlinger A, Banerjee S, Degrune F, García-Palacios P, Pescador DS, et al. Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems. Nat Food. 2021;2:28–37.

    Article  PubMed  Google Scholar 

  65. López-Sánchez A, Bareth G, Bolten A, Rose LE, Mansfeldt T, Sapp M, et al. Effects of declining oak vitality on ecosystem multifunctionality: lessons from a Spanish oak woodland. For Ecol Manag. 2021;484:118927.

    Article  Google Scholar 

  66. Zeng Y, Wu H, Ouyang S, Chen L, Fang X, Peng C, et al. Ecosystem service multifunctionality of Chinese fir plantations differing in stand age and implications for sustainable management. Sci Total Environ. 2021;788:147791.

    Article  CAS  PubMed  Google Scholar 

  67. Xie H, Wang G, Yu M. Ecosystem multifunctionality is highly related to the shelterbelt structure and plant species diversity in mixed shelterbelts of eastern China. Glob Ecol and Conserv. 2018;16:e00470.

    Google Scholar 

  68. Hararuk O, Smith MJ, Luo Y. Microbial models with data-driven parameters predict stronger soil carbon responses to climate change. Glob Change Biol. 2015;21:2439–53.

    Article  Google Scholar 

  69. Gamfeldt L, Hillebrand H, Jonsson PR. Multiple functions increase the importance of biodiversity for overall ecosystem functioning. Ecology. 2008;89:1223–31.

    Article  PubMed  Google Scholar 

  70. Jiang S, Xing Y, Liu G, Hu C, Wang X, Yan G, et al. Changes in soil bacterial and fungal community composition and functional groups during the succession of boreal forests. Soil Biol Biochem. 2021;161:108393.

    Article  CAS  Google Scholar 

  71. Zhu L, Wang X, Chen F, Li C, Wu L. Effects of the successive planting of Eucalyptus urophylla on soil bacterial and fungal community structure, diversity, microbial biomass, and enzyme activity. Land Degrad Dev. 2019;30:636–46.

    Article  Google Scholar 

  72. Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.

    Article  PubMed Central  PubMed  Google Scholar 

  73. Martinson VG, Douglas AE, Jaenike J. Community structure of the gut microbiota in sympatric species of wild Drosophila. Ecol Lett. 2017;20:629–39.

    Article  PubMed  Google Scholar 

  74. Du S, Li X, Feng J, Huang Q, Liu Y. Soil core microbiota drive community resistance to mercury stress and maintain functional stability. Sci Total Environ. 2023;894:165056.

    Article  CAS  PubMed  Google Scholar 

  75. Wan W, He D, Li X, Xing Y, Liu S, Ye L, et al. Linking rare and abundant phod-harboring bacteria with ecosystem multifunctionality in subtropical forests: from community diversity to environmental adaptation. Sci Total Environ. 2021;796:148943.

    Article  CAS  PubMed  Google Scholar 

  76. Jia X, Dini-Andreote F, Salles JF. Community assembly processes of the microbial rare biosphere. Trends Microbiol. 2018;26:738–47.

    Article  CAS  PubMed  Google Scholar 

  77. Barberán A, Ramirez KS, Leff JW, Bradford MA, Wall DH, Fierer N. Why are some microbes more ubiquitous than others? Predicting the habitat breadth of soil bacteria. Ecol Lett. 2014;17:794–802.

    Article  PubMed  Google Scholar 

  78. Morriën E, Hannula SE, Snoek LB, Helmsing NR, Zweers H, de Hollander M, et al. Soil networks become more connected and take up more carbon as nature restoration progresses. Nat Commun. 2017;8:14349.

    Article  PubMed Central  PubMed  Google Scholar 

  79. Crowther TW, Stanton DWG, Thomas SM, A’Bear AD, Hiscox J, Jones TH, et al. Top-down control of soil fungal community composition by a globally distributed keystone consumer. Ecology. 2013;94:2518–28.

    Article  PubMed  Google Scholar 

  80. Yang C, Zhang X, Ni H, Gai X, Huang Z, Du X, et al. Soil carbon and associated bacterial community shifts driven by fine root traits along a chronosequence of Moso bamboo (Phyllostachys edulis) plantations in subtropical China. Sci Total Environ. 2021;752:142333.

    Article  CAS  PubMed  Google Scholar 

  81. Qu Z, Liu B, Ma Y, Xu J, Sun H. The response of the soil bacterial community and function to forest succession caused by forest Disease. Funct Ecol. 2020;34:2548–59.

    Article  Google Scholar 

  82. Han Z, Xu P, Li Z, Lin H, Zhu C, Wang J, et al. Microbial diversity and the abundance of keystone species drive the response of soil multifunctionality to organic substitution and biochar amendment in a tea plantation. GCB Bioenergy. 2022;14:481–95.

    Article  CAS  Google Scholar 

  83. Trivedi P, Anderson IC, Singh BK. Microbial modulators of soil carbon storage: integrating genomic and metabolic knowledge for global prediction. Trends Microbiol. 2013;21:11.s.

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Acknowledgements

We thank the anonymous reviewers and editors for their helpful comments regarding the manuscript.

Funding

This study is supported by the National Natural Science Foundation of China (32060333, 31660599), the Jiangxi Provincial Science and Technology Program (20204BCJL23046, 20181ACF60002), and the National Key Research and Development Program of China (No. 2021YFD1901201-05).

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  1. Luyuan Sun and Guilong Li are co-first authors and contributed equally to this work.

Authors and Affiliations

  1. Jiangxi Provincial Engineering Research Center for Seed- breeding and Utilization of Camphor Trees, Nanchang Institute of Technology, Nanchang, 330099, China

    Luyuan Sun, Jiao Zhao & Jie Zhang

  2. Soil and Fertilizer & Resources and Environment Institute, Jiangxi Academy of Agricultural Sciences, Nanchang, 330200, China

    Luyuan Sun, Guilong Li & Jia Liu

  3. Jiangxi Academy of Forestry, Nanchang, 330032, China

    Ting Zhang

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Contributions

LYS, GLL, JieZ, and JL contributed to the conception and design of the study. JieZ, JiaoZ, and TZ completed the field sampling. GLL performed the statistical analysis. LYS and GLL wrote the manuscript. LYS, GLL, and JL contributed to the revision of the manuscript. All authors contributed to the article and approved the submitted version.

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Sun, L., Li, G., Zhao, J. et al. Core microbiota drive multi-functionality of the soil microbiome in the Cinnamomum camphora coppice planting. BMC Microbiol 24, 18 (2024). https://0-doi-org.brum.beds.ac.uk/10.1186/s12866-023-03170-8

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BMC Microbiology

ISSN: 1471-2180

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