Response of Bacterial and Fungal Soil Communities to Chinese Fir (Cunninghamia lanceolate) Long-Term Monoculture Plantations

Soil Analysis

Soil pH was measured with a pH meter using a soil-to-water ratio of 1:5. Soil moisture was determined gravimetrically after drying the soil at 105°C for 24 h, and all results were expressed on an oven-dry basis. Microbial biomass C (MBC) and N (MBN) were determined by the chloroform fumigation extraction method (Vance et al., 1987; Jenkinson, 1988). Soil mineral N (NH₄⁺–N, NO₃^––N) was extracted using 2 M KCl and measured by Westco Smart Chem Discrete Wet Chemistry Analyzer (Westco Scientific Instruments, United States). Total C (TC) and N (TN) were measured using an elemental analyzer (Vario MICRO cube, Elementar, Germany). Dissolved organic C (DOC) and N (DON) were extracted by 2 M KCl using a soil-to-water ratio of 1:5 and then measured by a TOC-VCPH/CPN analyzer fitted with a TN unit (Shimadzu Scientific Instruments, Japan). Soil available P (AP) was extracted by 0.5 M NaHCO₃ solution, colored by a molybdenum–antimony solution, and measured using the spectrophotometric method.

DNA Extraction and High-Throughput Sequencing

Genomic DNA was extracted from 0.3 g of each composite soil sample from each sample plot (n = 16), using the MoBio Powersoil^TM DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, United States) according to the manufacturer’s instructions, with the final elution step in deionized water instead of TE buffer. DNA quantity and concentration were determined using a DeNovix DS-11 spectrophotometer (DeNovix, Wilmington, DE, United States).

The V3–V4 region of the bacterial 16S rRNA gene and ITS1 region of the fungal internal transcribed spacer (ITS) were amplified using the bacterial-specific primer of 341F and 806R, eukaryotic-specific primers ITS5-1737F and ITS2-2043R, with unique barcodes added to the reverse primer, respectively. All PCR reactions were carried out in 30-μL reactions with 15 μL of Phusion^® High-Fidelity PCR Master Mix (New England Biolabs, United Kingdom), 0.2 μM of forward and reverse primers, and 10 ng of template DNA. The thermal cycling conditions consisted of initial denaturation at 98°C for 1 min, followed by 30 cycles of denaturation at 98°C for 10 s, annealing at 50°C for 30 s, and elongation at 72°C for 30 s, and then a final extension step at 72°C for 5 min. Amplified products were detected by electrophoresis in a 2% (w/v) agarose gel and purified with QIAquick PCR Purification Kit (QIAGEN, Germany). Sequencing libraries were generated using the Ion Plus Fragment Library Kit 48 rxns (Thermo Scientific) following the manufacturer’s recommendations. The library quality was assessed on the Qubit^® 2.0 Fluorometer (Invitrogen, United States). The high-throughput sequencing was conducted on the Ion S5^TM XL platform at Novogene Bioinformatics Technology Co., Ltd. (Beijing, China), and 400-bp/600-bp single-end reads were generated.

Bioinformatics Analysis

Single-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Quality filtering on the raw reads were performed under specific filtering conditions to obtain the high-quality clean reads according to the Cutadapt (Martin, 2011) (V1.9.1)¹ quality-controlled process. The reads were compared with the reference database using the UCHIME algorithm (UCHIME Algorithm)² to detect chimera sequences, and then the chimera sequences were removed (Edgar et al., 2011; Haas et al., 2011). Then the clean reads were finally obtained. Sequence analyses were performed by the Uparse software (Edgar, 2013)³. Sequences with ≥97% similarity were assigned to the same OTUs. The SILVA ribosomal RNA gene database was used for sequence/OTU classification. All the sequencing data have been deposited in the European Nucleotide Archive under accession number PRJEB29419. Observed OTU numbers, Chao1 richness, ACE evenness, as well as Shannon’s diversity indices, were calculated as metrics for alpha diversity. Beta diversity was calculated based on both weighted and unweighted UniFrac by the QIIME software.

Soil Microbial Community Diversity

After quality filtering and chimera check, a total of 2,509,952 and 2,565,913 high-quality 16S rRNA/ITS gene sequences were obtained with an average length of 413 and 237 across all samples, respectively. The number of 16S rRNA sequences obtained per sample varied from 55,856 to 92,288, and the number of ITS gene sequences per sample varied from 80,017 to 80,411 (Supplementary Table S2). These sequences were grouped into OTU (97% similarity), and the number of bacterial and fungal OTU ranged from 1,044 to 1,606, 1,109 to 1,544, respectively, depending on different soil samples (Supplementary Table S2).

Repeated measures ANOVA showed no interaction between planting rotations and sampling time on soil fungal and bacterial diversity indices such as OTUs, Chao1, and ACE (Supplementary Table S3). At both sampling months, the number of bacterial OTUs, ACE, Chao1, and Shannon index significantly decreased in TPR compared with those in NF, FRP, and SRP (Table 2) (P < 0.05). On the other hand, the number of fungal OTUs and Shannon index significantly increased in SRP compared with those in NF and TRP, while no significant change was observed between NF, FRP, and TRP, and between FRP and SRP at both sampling months (Table 3) (P < 0.05). The fungal ACE index was significantly lower in TRP than in SRP, while there was no significant difference among NF, FRP, and TRP at both sampling months (Table 3) (P < 0.05). The fungal Chao1 index did not change with planting rotations at both sampling months (Table 3) (P > 0.05).

Soil Microbial Community Composition

The fungal phyla with high relative abundance were Basidiomycota (12.9–76.9%) and Ascomycota (14.3–52.8%) in all treatments at both sampling months, followed by Mortierellomycota (1.6–12.5%) (Figure 1A and Supplementary Figure S1a). Different plantation rotations had various impacts on fungal and bacterial compositions at the phylum level in December and June, respectively. In both months, the relative abundance of Basidiomycota significantly decreased in FRP and SRP relative to NF, while no significant difference was observed between NF and TRP (Figure 1A and Supplementary Figure S1a) (P < 0.05). In December, the relative abundance of Ascomycota significantly increased in FRP and SRP compared with NF, while no significant difference was observed between NF and TRP, as well as FRP and TRP (Supplementary Figure S1a) (P < 0.05). There was no significant difference in the relative abundance of phyla Mortierellomycota, Glomeromycota, Rozellomycota, and Chytridiomycota among the different plantation rotations (Supplementary Figure S1a) (P > 0.05). In June, The relative abundance of Ascomycota significantly increased in FRP and SRP compared with that of NF, while no significant difference was observed between NF and TRP, and among FRP, SRP, and TRP (Figure 1A) (P < 0.05). There was no significant difference in the relative abundance of Mortierellomycota and Chytridiomycota among different plantation rotations (Figure 1A) (P > 0.05).

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FIGURE 1

Relative abundance of the most abundant fungal (A) and bacterial (B) groups in different plantations in June at the phyla level. Others represented unclassified groups. Data are means ± standard error (n = 4). Significant differences are indicated with distinct letters. NF, natural forest; FRP, first rotation plantation; SRP, second rotation plantation; TRP, third rotation plantation.

The bacterial phyla with a high relative abundance were Acidobacteria (39.1–57.7%) and Proteobacteria (21.2–39.5%) in all treatments at both sampling months, followed by Actinobacteria (6.1–19.2%), Chloroflexi (2.0–9.3%), and Verrucomicrobia (0.5–4.3%) (Figure 1B and Supplementary Figure S1b). Overall, the relative abundance of Acidobacteria, Proteobacteria, Actinobacteria, Chloroflexi, and Verrucomicrobia were not influenced by age of successive rotations (Figure 1B and Supplementary Figure S1b) (P > 0.05).

At the genus level, the most abundant species were largely unclassified, ranging from 65.8 to 76.3%, and 40.9 to 92.4% for bacteria and fungi, respectively (Supplementary Figure S2). The bacterial genus with a high relative abundance were Acidothermus, Acidobacter, Candidatus solibacter, and Bryobacter (Figure 2B and Supplementary Figure S2b), while the fungal genera with high relative abundance appeared to be inconsistent in different plantation rotations (Figure 2A and Supplementary Figure S2a).

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FIGURE 2

Relative abundance of the most abundant fungal (A) and bacterial (B) groups in different plantations in June at the genus level. Others represented unclassified groups. Data are means ± standard error (n = 4). Significant differences are indicated with distinct letters. NF, natural forest; FRP, first rotation plantation; SRP, second rotation plantation; TRP, third rotation plantation.

The NMDS clearly grouped the bacterial and fungal communities according to the four plantations and sampling months (Figures 3A,B). Particularly, the bacterial and fungal communities were clearly separated between NF and successive rotations (FRP, SRP, and TRP) across two sampling months, and the fungal communities appeared to be clustered more closely compared to their bacterial counterpart (Figures 3A,B).

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FIGURE 3

Non-metric multidimensional (NMDS) scaling plot of bacterial (A) and fungal (B) community structure across all soil samples in different plantations and sampling times (using combined December and June data). Stress value is indicated in the figure. Data points are from different plantations and sampling seasons (NF, natural forest; FRP, first rotation plantation; SRP, second rotation plantation; TRP, third rotation plantation; Dec, December; Jun, June).

Changes in the Composition of Soil Bacterial and Fungal Communities

In the present study, the results of deep sequencing demonstrated that Basidiomycota, Ascomycota, and Mortierellomycota were the predominant fungal phyla, while Acidobacteria, Proteobacteria, Actinobacteria, and Chloroflexi were the predominant bacterial phyla in the acidic forest soil, which was consistent with previous reports (Buée et al., 2009; Nie et al., 2018; Wang et al., 2018). Our results showed that the relative abundance of major fungal phyla (Basidiomycota, Ascomycota, Mucoromycota) was significantly changed by the successive rotations (Figure 1). Among them, Mucoromycota belong to the first colonizers followed by Ascomycota (Torres et al., 2005), which are considered as cellulose decomposers or sugar fungi with limited ability to degrade lignin (Osono, 2007). Basidiomycota, however, prevails in the later litter decomposition process as they are able to degrade recalcitrant lignin-containing material (Osono, 2007; Lundell et al., 2010).

The litterfall of Chinese fir plantation have unique morphological (toughness and needle-shaped) and chemical (high content of recalcitrant lignin) properties (Zhou et al., 2015), resulting in the slowness and difficulty in their decomposition and thus in nutrient return. In this case, fungi play an essential role in the organic matter decomposition and further C cycling in forest ecosystem for their ability to attack a wide spectrum of C, from labile to recalcitrant (Voříšková and Baldrian, 2013). The lowest relative abundance of Ascomycota and Mucoromycota, and the highest relative abundance of Basidiomycota were observed in TRP (Figure 1), indicating a possible shift in C composition at the different plantations with time. Specifically, with the increase in plantation generation, the more recalcitrant C was accumulated in soil, thus leading to the increase in lignin decomposer (Basidiomycota) and decrease in sugar fungi (Ascomycota and Mucoromycota). These results implied that successive planting of C. lanceolata plantations may gradually deplete the available C source in the soil, which was supported by the decline in soil DOC in TRP (Table 1). These data appeared to be in accordance with earlier findings that revealed a decrease in catabolic activity of soil microbial community in consecutive monoculture of Chinese fir plantations, following an order of FRP > SRP > TRP (Wu et al., 2017).

Previous studies have reported a high dominance of Acidobacteria (around 50%) in strongly acidic soil (4 < pH < 5) (Lauber et al., 2009), which was in good agreement with our results. The relative abundance of Acidobacteria ranked first among all bacterial species in our study, ranging from 45 to 57% (Figure 1). Different successive rotations had no significant effect on the relative abundance of major bacterial phyla (Acidobacteria, Proteobacteria, Actinobacteria), which is likely to be explained by the narrow range of pH change (approximately 0.3 unit) detected among different rotations (Table 1). This idea is supported by earlier research that demonstrated pH as the best predictor for bacterial community structure by surveying 88 soils across America (Lauber et al., 2009). No change in the relative abundance of Acidobacteria and Actinobacteria is unexpected because the former can degrade various polysaccharides including cellulose and xylan (Ward et al., 2009), while the latter are capable of utilizing lignin-derived compounds and, thus, are involved in the later stage of litter decomposition (Kirby, 2006). The possible explanation may be that fungal groups more actively participate in the organic matter decomposition, rather than the bacterial members, since the fungal community could better adapt to the nutrient-poor environment (Wu et al., 2017).

It is noteworthy that a large part of the sequenced ITS regions belonged to the unclassified fungi (12.6–31.6% and 40.6–87.1% at the phylum and genus level, respectively) due to incompletely annotated environmental samples. The inadequate taxonomic annotation and errors in taxonomic assignments of ITS sequences deposited in the international DNA database greatly limited our identification of the fungal species (Vilgalys, 2003). By contrast, more pertinent taxonomic information was provided for bacterial species at the phylum level (2.0–4.4%) but not at the genus level (69.1–73.5%).

The authors would like to thank Dajun Du and Peiheng Suo for their help with the field and laboratory work.